Risk Management Methods in the Aviation Enterprise (Springer Aerospace Technology) 981336016X, 9789813360167

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Springer Aerospace Technology

Valery Dmitryevich Sharov Vadim Vadimovich Vorobyov Dmitry Alexandrovich Zatuchny

Risk Management Methods in the Aviation Enterprise

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Valery Dmitryevich Sharov · Vadim Vadimovich Vorobyov · Dmitry Alexandrovich Zatuchny

Risk Management Methods in the Aviation Enterprise Interpreted by Anna Kudriashova

Valery Dmitryevich Sharov Doctor of Technical Sciences Moscow, Russia

Vadim Vadimovich Vorobyov Doctor of Technical Sciences Moscow, Russia

Dmitry Alexandrovich Zatuchny Doctor of Technical Sciences Moscow, Russia

ISSN 1869-1730 ISSN 1869-1749 (electronic) Springer Aerospace Technology ISBN 978-981-33-6016-7 ISBN 978-981-33-6017-4 (eBook) https://doi.org/10.1007/978-981-33-6017-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Currently, against the background of the constantly changing market situation and unstable development of air traffic volumes are noted not the highest values of safety indicators. Nevertheless, ensuring safety is a central requirement of society in relation to the aviation industry. Safety is based on a competent combination of three levels of interaction: the human factor, the technical equipment of a particular airline, and external factors. Failure to account for any of these levels can lead to unpredictable consequences and ultimately lead to inefficiency or failure of the entire Safety Management System (SMS) when managing safety is necessary to predict the corresponding threats. These risks can arise from the shortcomings of the relevant airfield structure and the lack of automation in the risk management system so that responsibility for risk management is shifted to specific people. This book offers specifically developed methods of safety management, some of which have already been tested in the practice of such airlines as Aeroflot Cargo, Sibir, Volga-Dnepr, and others. These methods allow us to predict the risks of an airline and automate their management. Recently, there have been aviation accidents, and even plane crashes, associated with rolling the aircraft off the runway. In Chap. 2 of this book, a method for managing the risk of rolling an aircraft off the runway and its software implementation are proposed. Methods for dealing with external factors affecting safety are discussed in Chap. 3 of this monograph. At the same time, it should be noted that the current development of civil aviation indicates that it is necessary to take into account not only the primary risk associated with safety but also the economic factors associated with the regularity of flights. The regularity of flights is the most critical characteristic of the quality of the product offered by airlines and is related to safety.

v

vi

Preface

The method of managing the risk associated with the regularity of flights and tested in the practice of one of the airlines is proposed in Chap. 3 of this book. Moscow, Russia

Valery Dmitryevich Sharov Vadim Vadimovich Vorobyov Dmitry Alexandrovich Zatuchny

Contents

1 Methods of Safety Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Methods Based on the Model of Aviation “Event Tree” Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Main Provisions. Methodology of Event Type List Forming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 “Tree” Formation and Principles of Basic Probabilities Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Basic Probabilities Evaluation for the Group “Environment” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Method of a Posteriori Probabilities Adjustment . . . . . . . . . . 1.1.5 Implementation of the Method in the Automated System of Forecasting and Prevention of Aviation Accidents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Method of Safety Risk Management Using Conditional Factors and Fuzzy Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Formulation of the Problem and Approach to the Solution. Content and Structure of the Source Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Severity Factor and Method of Its Calculation . . . . . . . . . . . . 1.2.3 Admissibility Factor and Method of Its Calculation . . . . . . . 1.2.4 Implementation of the Method in Automated Risk Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Method of Safety Risk Management Based on the Three-Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Formulation of the Problem. Algorithm for Estimating the Deviation and Event Risk Coefficient (DERC) . . . . . . . . 1.3.2 Hazard Risk Assessment Algorithm (HRA) . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Aircraft Overrun Risk-Reducing Methods . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Description of Aircraft Runway Movement After Landing . . . . . . . .

1 1 1 4 11 27

32 35

35 38 42 55 59 59 65 68 73 73

vii

viii

Contents

2.2 Analysis of the Problem of Information Supply on Runway Surface Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 2.3 Methods of the Crew Situational Raise Awareness About Runway Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 2.3.1 Institutional Arrangements and Improvement of Interpretation Methods for Descriptive Information . . . . . 79 2.3.2 Experimental Detection of Correlation Dependence Between Canadian and Russian Runway Friction Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 2.4 Overrun Risk Management Method Based on Statistical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 2.4.1 Development of a Mathematical Model for Aircraft Runway Movement and Its Implementation in the Program “Overrun Probability” . . . . . . . . . . . . . . . . . . 87 2.4.2 Joint Application of the Program “Overrun Probability” and Multidimensional Statistical Methods of Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 2.5 Overrun Prognostication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 2.5.1 Modification of “Overrun Probability” Program Mathematical Model for Prognostication Task . . . . . . . . . . . . 99 2.5.2 Overrun Prognosticating Algorithm Development for Aborted Takeoff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 3 Practices to Combat External Impact on the Aircraft Navigation Systems in Civil Aviation and Flight Regulatory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Implementation of Methods for Struggle with Unauthorized Actions on Aircraft Navigation Systems . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Main Hardware Methods of Protection from Unauthorized Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Principal Methods of Aircraft Navigation Systems Stability Enhancement Under Unauthorized Actions . . . . . . 3.1.3 Application of Unauthorized Activities Detection Aids for Operation Stability Improvement of the Aircraft Navigation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Measurement of Weather Radiosonde Altitude by the Method of Relative Navigation Sightings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Method of Flight Regularity Management in Aviation Enterprise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

113 113 113 114

120 123 131 144

Annex A: Operational Forecasting and Risk Assessment Upcoming Flight Program Aspfaa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Annex B: Automated Risk Management System Arms . . . . . . . . . . . . . . . . 153

Contents

ix

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Annex E: Using the “Overrun Prognosis” Program as a Module Asfpaa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

Abbreviations

AA AC RF ACFT AE AEI AFSS AIP AL AP ARMS ARMS AS ASFPA ATC ATIS BC AFM BPHF CA CAME CAP CAR CAST CATS CFIT CRFI CS CST DB DBMS DERC DMA DMM

Aircraft accident Air Code of the Russian Federation Aircraft Aviation event Aviation equipment inventory Automated flight safety system Aeronautical Information Publication Airline Antenna pattern Airline Risk Management Solution Group Automated risk management system Aviation security Automated system of forecasting and prevention of aircraft accidents Air traffic control Automatic Terminal Information Service Aircraft Flight Manual Basic probability of a hazard factor Civil aviation Continuing Airworthiness Management Exposition Civil Aviation Publication (UKCAA) Canadian Aviation Rules Commercial Aviation Safety Team Casual Model for Air Transport Safety Controlled Flight Into Terrain Canadian Runway Friction Index Control station Clear sky turbulence Database Database management system Deviation and event risk coefficient The decision-making altitude Decision-making manager xi

xii

EASA ECAST FAA FAR FAR FATA RF FCR FRM FST GLONASS GNSS GPS HF HRA IATA ICAO IDE INS LOC-I LV MD METAR MF NOTAM NS NTSB PIC QFE QFF QNH RE SAFA SARPs SID SMM SRNS SSP STAR TAF TLC UAV

Abbreviations

European Aviation Safety Agency European Commercial Aviation Safety Team Federal Aviation Agency Federal aviation regulations Federal Aviation Rules Federal Air Transport Agency of the Russian Federation (Rosaviatsiya) Flight Control Room Flight regulation manual Fuzzy set theory Global navigation satellite system of Russia Global Navigation Satellite System Global Positioning System Hazard factor Hazard risk assessment International Air Transport Association International Civil Aviation Organization An indicator of damage events Inertial navigation system Lost of Control in Flight Linguistic variables Manager decision Meteorological Aerodrome Report Membership functions Notice to Airmen Navigation spacecraft National Transport Safety Bureau Pilot in command Atmospheric pressure at the airfield, corresponding to the level of the runway threshold of the working landing course Station pressure reduced to sea level based on actual weather conditions Air pressure at the airfield, reduced to sea level under the conditions of the standard atmosphere Runway Exertion Safety Assessment of Foreign Aircraft Standard and Recommended Practices Standard Instrument Departure Safety management manual The satellite radio navigation system Safety state program Standard Terminal Approach Rout Terminal Aerodrome Forecast Takeoff and landing characteristics Unmanned Air Vehicle

Chapter 1

Methods of Safety Risk Management

1.1 Methods Based on the Model of Aviation “Event Tree” Development 1.1.1 Main Provisions. Methodology of Event Type List Forming The method refers to logical-probabilistic quantitative methods and is based on a “technocratic concept”. In the notation used in this book, the formulas for calculating the risk of each type of aviation event (AE) Rj and the total risk are as follows: R j = Pj S j ; R =

m 

Rj;

(1.1)

j=1

where Pj —the probability of AE type j; Sj —average damage when a j-type AE occurs; m—number of AE types. Three levels of risk management (operative, tactical, and strategic) are interpreted in the predictive model as an assessment of the risk value on three forecasting horizons or three levels of detail: – the risk of the upcoming flight (operative forecast), let’s call it “operative risk”; – the average risk of flights performed as a characteristic of the airline for 1–6 months within the medium-term forecast—“medium-term risk”;

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4_1

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– long-term risk (a year or more), taking into account the planned changes in activity—“long-term risk”. Despite all the apparent differences in the volume of initial data, the complexity of calculations, and the specifics of responding to these risks, a single method for their assessment using the AE probabilistic model was proposed. To implement the method, it was necessary to solve the following problems: – select the most likely and significant scenarios (event types); – construct the corresponding «trees»; – develop an algorithm for risk assessment for each type of event based on the forecast of manifestations of hazard factors (HF) according to the airline’s operational activities; – to develop a method a posteriori refinement of the risk if there is any evidence of the manifestation of HF, expressed in the initiating or intermediate event; – develop a methodology for forming management decisions based on the completed risk analysis. First, to solve risk assessment problems following the accepted method of calculation, it is necessary to develop a list of events or negative scenarios (outcomes) of the flight. There is no unified classification of events in the world of civil aviation (CA). Several classifications differ in the construction principle and the number of event types (categories). The classifier [1], developed by specialists of the CAST Group (USA) and ICAO, contains 34 categories. The classification by category of accidents (ACCID), adopted in IATA, includes 13 categories, and in 2013, the percentage distribution of events by category was as follows [2]: – – – – – – – – – – – – –

collision of a serviceable aircraft with the ground—11%; landing outside the airport/landing on the water—3%; impact of the tail section of the fuselage—8%; loss of control in-flight—9%; landing with the landing gear retracted/damage to the landing gear—18%; deviation of the aircraft when moving on the runway—19%; rough landing—10%; damage in flight—5%; ground damage—16%; the undershoot to the threshold of the runway—1%; collision on the runway—0%; mid-air collision—0%; other—0%.

Table 1.1 shows data from Boeing [3] for the period 2002–2011 about those who died in aircraft accidents with Western-made jet aircraft in events classified as six common types.

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

3

Table 1.1 Number of deaths in six types of aviation accidents Category

LOC

CFIT

RE + ARC

MAC

SCF-NP

WSTRW

Died on board

1439

1078

919

156

225

96

Died on land

80

0

54

69

0

1

Total

1519

1078

973

225

225

97

In % of the total quantity (%)

31,9

22,6

20,4

4,7

4,7

2,0

A total of 4,547 people have died on board, and 214 people have died on the ground in aircraft accidents. Thus, more than 86% of the total number of deaths occurred in six types of aviation accidents (ACCID). The data provided that a limited number of the most common events can be reasonably identified. This output can be used when developing a classifier. It is not only advisable to take the ICAO classifier as a basis but also take into account the classifiers used in the automated safety system (FS) of the Russian Federation [4] and in the rules for investigating accidents and incidents in CA of the Russian Federation [5]. It should be noted that all these classifiers were developed to collect data and classify events that occurred. It is necessary for forecasting purposes to develop a list of end events (tree’s vertexes), taking into account that many list events will be initiating or intermediate tree events. Additionally, each type of event must be considered at a specific operation stage, as shown below. 1. 2. 3. 4. 5. 6. 7. 8.

Parking—from the beginning of ground and technical maintenance of the aircraft to the beginning of the aircraft’s movement for flight. Taxiing to takeoff (towing)—of the aircraft motion to perform a flight before the start of the takeoff (run-up). Takeoff—from the start of the takeoff until reaching an altitude of 450 m or to the point where the transition from the takeoff configuration to the route ends. Climb—from the endpoint of the takeoff stage to the exit point of the airfield departure scheme (SID). Enroute flight—from the exit point of the sid scheme of the departure aerodrome to the entry point of the STAR aerodrome’s arrival scheme of landing. Descent and approach—from the exit point of the STAR scheme of the arrival airfield to an altitude of 50 feet (15 m). Landing—from a height of 50 feet (15 m) to the end of the run. Taxiing after landing (towing)—from the end of the run to the stop of the aircraft in the parking.

The principle of classifier construction is developed in [6, 7]. An example of a specific list of events is shown in Table 1.2.

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1 Methods of Safety Risk Management

Table 1.2 Example of a list of events No. Code

Type of event

Operation stage

1

ARC

Unsafe touch of the runway

Takeoff and landing

2

BIRD

Birdstrike

All stages of operation, except parking

3

CFIT

Collision of a serviceable aircraft with Takeoff, climb, en route flight, the ground in flight descent, approach, and landing

4

FIRE

Fire

5

GCOL

Collision with an object on the ground Parking, taxiing before takeoff, takeoff, landing, taxiing after landing

6

LOC-I

Loss of control in the air

Takeoff, climb, en route flight, descent and approach, landing

7

MAC

Aircraft collision in the air

Takeoff, climb, en route flight, descent and approach, landing

8

RE

Rolling off the runway

Taxiing before takeoff, takeoff, landing, taxiing after landing

9

SEC

Accidents related to aviation security

All stages of operation

10

SCF-PP

Failure/shutdown of the power plant

All stages of operation

11

DECOM Depressurization of the aircraft

Climb, en route flight, descent, and approach

12

ADES

All stages of operation, in addition to towing and parking

Destruction of the airframe

All stages of operation

1.1.2 “Tree” Formation and Principles of Basic Probabilities Evaluation 1.1.2.1

Methodology of “Tree” Design

Considering the accepted model of scenarios for the development of an aviation event. The assumed structure of the “tree” is shown in Fig. 1.1. Following the accepted approach to risk assessment, it is necessary for each type of event from the classifier to build an “aviation event development tree”. To build a “tree”, the author proposed using three technologies together: “fault tree” (FTA), “analysis of types and consequences of failures” (FMEA), and partially—“event tree” (ETA) [7]. All three methods according to GOST [8] are among the most common risk analysis methods. It is advisable to use the technology of the FTA method as a basis. Information on its use for risk assessment and instructions are available in GOST [9]. The method is widely used for analyzing the reliability of technical systems when calculating the probability of a final event as a “system downtime coefficient”. Examples of fault or failure tree analysis (FTA) are given in [10–14]. However, the method has the following main disadvantages [15]:

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

5

Aviation event of a certain type

Intermediate event

Safety barrier

Hazard factors

Fig. 1.1 Schematic diagram of the “aviation event development tree”

– it is difficult to determine whether all critical paths to the final event are taken into account, and in this situation, probability analysis using the FTA method is not possible; – the fault tree is a static model in which the time dependency factor is not taken into account; – the fault tree can only be applied to binary states (functional/non-functional); – human errors can be taken into account in the fault tree diagram at a qualitative level, but it is difficult to quantify them. These disadvantages can be partially compensated by using the ETA [16] and FMEA [17] methods. Using ETA, we can plot scenarios after the initial event occurs, analyze the operational state or failure of auxiliary systems or functions designed to reduce the consequences of failure, and evaluate their impact. Adding the FMEA method makes it possible to identify the types of component failures, causes of failures, and consequences for the system. It is essential that when developing a model for the development of an aviation event, the method allows us to identify the types of HF impacts and suggest options for strengthening existing or installing new safety barriers. The combination of FTA and ETA has long been used in the nuclear power industry [11, 14]. In fact, [14] describes using the FMEA/FMECA method for types of failures associated with personnel errors and equipment malfunction. There are known examples of using a combination of aerospace industry methods, particularly in NASA [18]. The general order of building a “tree” is as follows. 1.

2.

The first stage is determined and formulated in the final event. For the “tree”, this is one of the 12 events in the list, for example, ARC—an unsafe touch of the runway. This event is placed at the top. Next, we need to restore the top-down sequence of events leading to the final event. Symbols are used for building transitions (in GOST [9] they are called “filters”). Table 1.3 shows the main symbols used when building “trees”.

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1 Methods of Safety Risk Management

Table 1.3 Logical signs and symbols Symbol

Function

Description

AND

An output event occurs if all input events coincide

OR

An output event occurs if any input events occur (either one or in any combination)

Triggering event

The symbol is used to indicate a hazard factor

AND OR

Simultaneously, with the construction of the “tree” from top to bottom, a list of initiating events that determine the development of as types is compiled, primarily based on experts’ opinions and statistical data on the as type. In this case, an ascending FMEA method is implemented—from the HF to the AE types. Traditionally, the HF is divided into three groups “Human–MachineEnvironment”. The HF of each of these groups can influence development. For example, the HF for “Rolling out of the runway” is and the state of the runway (environment), and the failure of the thrust reverse (machine), and the late start of braking of the aircraft (human). The manifestation of each HF can be considered as a random initiating event. Therefore, for the sake of brevity and convenience of building a “tree”, the term “hazard factor” (HF) is used to refer to the initiating event. HF characteristics can be numerical values, random parameters of stochastic nature with known and unknown distributions, parameters of non-numeric nature that can be calculated based on expert estimates. The concept of “basic probability of a hazard factor” (BPHF) is introduced as the probability of HF manifestation. The main methods for evaluating it are shown in Table 1.4. A schematic diagram of the construction of the “tree” taking into account the designations of initiating (H), intermediate (E), and final (I) events accepted in this work is shown in Fig. 1.2. The upper index for H, E, and I denote the tree levels. Level 1 corresponds to one of the 12 types’ final events, and then each subsequent level increases by one in the downward direction. The lower double index is the ordinal number of the event at this level and the event’s number at the next top-level affected by this event. The number of levels and the number of events for each level are different in each tree. The effectiveness of barriers can be taken into account by using the conditional probability of transition from an event at this level to the next top-level event. For convenience, we will call these conditional probabilities transfer coefficients, denoting them as K. Their upper indexes correspond to the event level they belong to, and their lower indexes differ for the “And” and “Or” signs. For K at sign “Or” index is the number of one event to which this K applies, and the sign “And” index includes the numbers of all events that affect this event and combined with the sign “And”.

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

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Table 1.4 Data sources and methods for estimating basic HF probabilities Group

Type of hazard factor

Data source

BPHF evaluation method

Human

Decision error

Aviation event statistics

Probability estimation by frequency*

The output parameters of the piloting restrictions

Transcripts of flight information records of airline crews

Parametric and nonparametric methods for estimating the probability of going beyond the limits*

Operator error

Published research on human operator errors

The average values of the probabilities in similar operator errors*

Failures of aircraft systems and devices

A database of failures of similar systems and units of the ACFT

Probability estimation by frequency

Machine

Technical descriptions Set reliability indicators of aircraft systems and for systems and devises devises Environment

Deterioration of weather conditions

TAF aviation forecast

Weather hazards

Getting into the satellite track of another aircraft

Violations during ground maintenance of aircraft

The developed method for calculating the probability of critical deterioration of weather conditions A special method for estimating the probability of another aircraft entering the satellite track

Database of aviation A particular method for events, airport features, assessing the probability expert assessments of violations during ground handling

Errors of traffic controllers Database of aviation A particular method for of the ATS service events, airport features, estimating the probability expert assessments of ATS errors Birdstrike

Database of aviation events, regional features, expert assessments

A unique technique for assessing the probability of a collision with birds

Acts of unlawful interference

Database of aviation events, ornithological features of regions, seasonally adjusted expert assessments

A special method for assessing the likelihood of illegal interference

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1 Methods of Safety Risk Management

Fig. 1.2 The scheme of building a “tree of development of the aviation event”

Transfer coefficients can be calculated based on event statistics or based on an expert survey. For ease of writing general calculation formulas, enter the following notation: q—the level of the tree where the event in question is located; i = 1, n—the ordinal number of the event in question at level q, where n is the number of level q events that affect a specific event at the next level (q–1); j = 1, m—the ordinal number of the event at the level (q + 1), where m is the number of events at the level (q + 1) that affect the event in question; k = 1, l—ordinal level events (q–1), which is affected by the event in question, where l is the number of events (q–1), which are part of the same group as the event affected by the considered. Based on the entered notation, a q-level event, regardless of whether it is an q initiator, intermediate, or final event, can be designated as Aik . Calculation formulas, in general, can be represented as follows: with the “And” sign: m  q q+1 P Aik = K i

j=1

with the “Or” sign

q+1

P(A ji ),

(1.2)

1.1 Methods Based on the Model of Aviation “Event Tree” Development. q

P(Aik ) = 1 −

  m  q+1 q+1 1 − K 12..m P A ji . j=1

9

(1.3)

Appendix 1 shows a fragment of the CFIT tree with a list of HF.

1.1.2.2

Methods for Evaluation of Emergency Factors Basic Probabilities

Calculations of BPHW in “trees” for the group of factors “Human” are carried out using different methods, depending on the types of HF (Table 1.5). Consider the probability estimation at step 1 for each type of HF. Stage 1 for type 1 HF “Error of information perception and decision making”. The estimation of BPHF (Hi) for this type of HF can be performed using a simplified method based on the frequency of such events from the AFSS database [19]: P(Hi ) =

N Hi , n

(1.4)

where N Hi —the number of manifestations of HF, n is the number of flights. Stage 1 for type 2 HF “Getting out of the pilot parameters limits”. This type of HF includes going beyond the limits of all piloting parameters that are fixed using objective control (speed, altitude, roll, pitch, vertical overload, etc.). The proposal to use the probability of pilot parameters exceeding flight information limits to assess hazard factors was formulated in [20]. Let X—be a random variable—a deviation from the nominal value of the estimated parameter, for example, the deviation of the actual approach speed Va from the set one, Vapp , i.e. X = Va − V app ; x1 and x2 are the maximum allowed deviations. Then the probability of the random variable X exceeding the allowed limits is calculated using the well-known formula from [21] Table 1.5 Types of HF and stages of calculating the BPHW Type of HF

Stage 1

Stage 2

1. Error of information perception and decision making

Estimation of the average probability of HF for an airline or industry (if there is insufficient sample capacity for an AL)

Adjustment based on the “basic indicators of the pilot’s personality” and “situational indicators”

2. Getting out of the pilot parameters limits

Probability estimation based Adjustment based only on on the results of processing the “situational indicators” PI of this pilot for 50 flights

10

1 Methods of Safety Risk Management

P = 1 − [F(x2 ) − F(x1 )],

(1.5)

where F(x)—probability distribution function. If the distribution law is known, then (1.5) gets a particular expression, for example, for a normal distribution:

  x2 − m x1 − m − F∗ , P = 1 − F∗ σ σ where m—mathematical expectation, σ —standard deviation of a random variable X. F ∗ (x)—known normal distribution function. The probability estimation can be performed in two ways: for the hypothesis that the deviation distribution belongs to one of the parametric distributions, and for the general case of a nonparametric distribution. The nonparametric method gives a more reliable estimate [22]. Specific options for calculating the probabilities of this type of HF are given in [23]. Stage 2—Correction using indicators. The correction considers the pilot’s personality’s general psychophysiological characteristics (fundamental indicators) and its abilities, depending on a particular flight (situational indicators). The calculation of indicators is a separate task that specialists must solve in the field of human factors. For example, within the ASFPAA project framework, two research projects were carried out on this topic: under Chuntul [24] and Plotnikov [25]. The adjustment of probability estimates obtained at stage 1 using these indicators is described for one of the trees in Appendix 1. Estimation of basic probabilities for the “Machine” group. The group of factors “Machine” is the most numerous in “trees”. Implementing this method in the project ASFPAA in total in all trees contains almost 2000 sites in this group. The description of calculation algorithms for the “Machine” group in the project is contained in the report [26]. It is essential to use complete and reliable databases of the airworthiness maintenance system from information and analytical systems for monitoring the life cycle of aviation equipment. Requirements for such databases are given in specialists of the State research institute of CA and MSTU CA [27]. The results obtained in the development of algorithms for evaluating the BPHF for the “Environment” group are given in articles [28–32] and are briefly described in Sect. 1.1.3.

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

11

1.1.3 Basic Probabilities Evaluation for the Group “Environment” 1.1.3.1

Structure of Group Accountable Factors

All HF the group “Environment” can be divided into four groups: 1. 2. 3. 4.

The impact of the natural environment (meteorological and geophysical factors). Disadvantages of airfield and air navigation infrastructure. Factors related to aviation safety (AS). Shortcomings of regulatory documentation.

Risk assessment clauses 3 and 4 are usually considered separately. Thus, AS factors can be allocated to a separate SEC tree, and a particular assessment methodology and computer program are used for regulatory documentation. The exception is the HF “Disadvantages in the airport’s AS system”, which considers the disadvantages of the airfield fence, which increases the risk of animals entering and, consequently, collisions with aircraft. When developing an AEDT, the number of HZ for the “Environment” group can be huge. For example, in the automated system for forecasting and preventing aviation accidents (ASFPAA), which implements the principle of building “trees for the development of aviation events”(see Sect. 1.1.5), there are 167 of them. For example, Table 1.6 below shows the HF for the “Controlled flight into terrain” (CFIT) tree, divided according to the above classification.

1.1.3.2

Basic Probabilities Evaluation for the Natural Environment Factors

The estimate is based on using the actual METAR weather and TAF forecast. There are 10 such HF in the CFIT tree in the LOC-17 tree and in the ARC-6 tree. Separate task predicts the coupling coefficient at the destination airport to calculate the probability of rolling out. Forecasting for an extended period (more than two weeks) with sufficient accuracy is not yet possible [33, 34]. For operational risk management, it is necessary to solve the following tasks: 1. 2.

3.

Set the time frame for operational forecasting and, accordingly, determine which forecasts will be used. Develop a procedure for obtaining data on the airfield’s operational minimum (it is assumed that the minimum of the aircraft and the minimum of the PIC performing this flight are known). Develop methods for predicting HF based on various weather forecasts and airfield data.

The time frame for operational forecasting and meteorological forecast selection.

CFIT_E_7

The appearance 3 of visual illusions in the crew during takeoff

2

CFIT_E_5

CFIT_E_3

15

Erroneous 16 indication of the a/d takeoff dispatcher to the crew about the height below the safe one

Incorrect information about the safe height in the departure scheme

Name of the HF

(continued)

CFIT_E_25 Disadvantages of the approach scheme that make it difficult for the crew to maintain it

CFIT_E_23 The failure of terrestrial communication facilities on the route of flight

CFIT_E_22 Terrain features on the flight route that cause radio interference

Code

3

Dangerous weather during takeoff

14

CFIT_E_4

The presence of obstacles in the path of the aircraft when deviating from the safe flight path during takeoff

Name of the HF

2

CFIT_E_1

Code

№

1

№

CFIT_E_2

1

Name of the HF

Kod

No.

Getting into a downstream or a bump during takeoff

Disadvantages of airfield and air navigation infrastructure

Disadvantages of airfield and air navigation infrastructure

Environmental impacts

Table 1.6 Hazard factors of the “Environment” group of the CFIT tree

12 1 Methods of Safety Risk Management

CFIT_E_16 Getting into a 5 downstream or a bump on the route

CFIT_E_17 Dangerous 6 weather when flying on the route

6

19

Blinding the 18 crew with a laser during takeoff

CFIT_E_10 Lack of radar control in the departure area

CFIT_E_8

Name of the HF

(continued)

CFIT_E_29 Incorrect information about the safe height in the landing scheme

CFIT_E_27 The lack of an accurate schema approach

CFIT_E_26 Erroneous deviation of the dispatcher of airfield landing beyond the limits of accounting for obstacles

Code

5

Erroneous 17 information from the dispatcher about the pressure of the takeoff airfield

Name of the HF

№

CFIT_E_6

Code

№

CFIT_E_9

4

Name of the HF

Kod

No.

Weather below 4 the minimum of PIC at the takeoff airfield

Disadvantages of airfield and air navigation infrastructure

Disadvantages of airfield and air navigation infrastructure

Environmental impacts

Table 1.6 (continued)

1.1 Methods Based on the Model of Aviation “Event Tree” Development. 13

CFIT_E_30 Dangerous weather during descent and landing

CFIT_E_33 The appearance 9 of visual illusions in the crew during descent and landing

8

9

8

CFIT_E_14 Failure of 22 ground communications at the takeoff airfield

CFIT_E_13 Terrain features 21 in the area of the takeoff airfield that cause radio interference

Name of the HF

(continued)

CFIT_E_36 Arrival airfield radar is not working

CFIT_E_34 Blinding the crew with a laser during descent and landing

CFIT_E_31 Erroneous indication of the landing airfield dispatcher to the crew about taking the height below the safe height

Code

№

Name of the HF

CFIT_E_12 Disadvantages 20 of radio communication by the ATC dispatcher of the takeoff airfield

Code

CFIT_E_28 Getting into a downstream or a bump during descent and landing

7

7

№

Kod

No.

Name of the HF

Disadvantages of airfield and air navigation infrastructure

Disadvantages of airfield and air navigation infrastructure

Environmental impacts

Table 1.6 (continued)

14 1 Methods of Safety Risk Management

CFIT_E_19 Radar stations on the route do not work

12

25

CFIT_E_18 Erroneous 24 instruction of the dispatcher to the crew about taking the height below the safe height

11

Name of the HF

(continued)

CFIT_E_39 Terrain features in the area of the landing airfield that cause radio interference

CFIT_E_38 Disadvantages of conducting radio communication by the ATC dispatcher of the landing airfield

CFIT_E_37 The dispatcher of the landing airfield set the wrong radio frequency

Code

№

Name of the HF

CFIT_E_15 The presence of 23 obstacles in the way of the aircraft when violating the safe flight altitude along the route

CFIT_E_35 Weather below 10 the minimum of PIC at the airfield landing

10

Code

№

Kod

No.

Name of the HF

Disadvantages of airfield and air navigation infrastructure

Disadvantages of airfield and air navigation infrastructure

Environmental impacts

Table 1.6 (continued)

1.1 Methods Based on the Model of Aviation “Event Tree” Development. 15

Name of the HF

CFIT_E_40 Failure of ground communications at the landing airfield

Code

№

CFIT_E_21 Disadvantages 26 of radio communication by the ATC dispatcher of the takeoff airfield

13

Name of the HF

Code

№

Name of the HF

No.

Kod

Disadvantages of airfield and air navigation infrastructure

Disadvantages of airfield and air navigation infrastructure

Environmental impacts

Table 1.6 (continued)

16 1 Methods of Safety Risk Management

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

17

The official TAF (Terminal Aerodrome Forecast) aircraft code is used. As established in the FAR of the Russian Federation [35], the TAF validity period is 6, 9 h (“short”) or 24, 30 h (“long” forecasts). TAF is issued at least one hour before starting this forecast period every 3 h starting from 24.00 UTC, i.e. at 00.00, 03.00, etc. However, it is known from practice that 30-h forecasts are rare, i.e. we need to focus on the maximum duration of the forecast in 24 h. If the forecast time is approaching when the next TAF forecast is released (for example, 13.50), the TAF forecast depth will be about 22 h. (at 13.50, the forecast is valid from 12.00 of this day to 12.00 of the next, and the forecast from 15.00 has not yet appeared). With a maximum flight duration of 12 h, taking into account the reserve for leaving for a spare and 30 min of additional stock, we get that it is guaranteed that we can perform operational forecasting with TAF not earlier than 7 h before departure. Full information about each flight is known 6 h before departure. It is proposed not to change the airline’s technological schedule, and the time 6 h before the flight departure was fixed as the official time of forecasting. You can implement the idea of performing calculations at a depth of 72 h using other forecasts. They are not official and are less accurate than TAF, but they are linked to airports, which is their crucial advantage over other existing forecasts. The most “deep” is the global forecast based on the NAFES (North American Ensemble Forecast System) model. The forecast is developed jointly by the United States and Canada’s weather centers for a period of 10 days for airfields around the world. Forecasts are updated twice a day, at 01.30; 13.30 UTC and are available in Russian on the website https://meteocenter.asia/. The forecast structure and volume of information differ significantly from TAF. It is linked to four points: 4, 10, 16, and 22 h of local time. The forecast at the time of arrival of the aircraft is calculated by interpolation. The forecast does not have visibility and height of the lower border of clouds HLBC, which does not allow us to directly use it in the calculation algorithm focused on TAF. However, weather elements are predicted that allow us to make approximate estimates of the situation at the airport at the time of arrival, namely: – temperature, dew point, precipitation and cloud patterns, – forecast of precipitation for 6 h allows us to predict the deterioration of visibility, icing of the aircraft, the coefficient of adhesion on the runway; – potential energy of convective instability as an indicator of the development of cumulonimbus clouds, windshift, and wind shear. The same site provides an American-Canadian forecasting model for aviation based on two mathematical models GFS (Global Forecast System) and CMC (Canadian Meteorological Centre). Forecasts for this model are updated four times a day, at 00.30, 06.30, 12.30, 18.30 UTC. For calculations using this forecast, we can use the algorithm developed for the TAF forecast. The forecast structure and volume of information differ significantly from TAF. It is linked to four points: 4, 10, 16, and 22 h of local time. The forecast

18

1 Methods of Safety Risk Management

at the time of arrival of the aircraft is calculated by interpolation. The forecast does not have visibility and height of the cloud ceiling (HCC), which does not allow you to use it in the calculation algorithm focused on TAF directly. However, weather elements are predicted that allow us to make approximate estimates of the situation at the airport at the time of arrival, namely: – temperature, dew point, precipitation, and cloud patterns, – forecast of precipitation for 6 h allows us to predict the deterioration of visibility, icing of the aircraft, the coefficient of adhesion on the runway; The potential energy of convective instability is an indicator of cumulonimbus clouds’ development, windshift, and wind shear. The same site provides an American-Canadian forecasting model for aviation based on two mathematical models GFS (Global Forecast System) and CMC (Canadian Meteorological Centre). Forecasts for this model are updated four times a day, at 00.30, 06.30, 12.30, 18.30 UTC. For calculations using this forecast, we can use the algorithm developed for the TAF forecast. To calculate several HF in several trees, we need to have data about the actual minimum weather that the crew is guided by during the flight. For example, from the number of HF listed in Table 1.13, this is CFIT_E_35 “Weather below the minimum PIC at the landing airfield”. In the ARC tree, unsafe touch of the runway is “Difficult weather conditions that reduce visibility and create illusions”, similar to the HF is in other trees. The minimum for landing is the minimum values in meters of two parameters—the range (L) of meteorological visibility (or visibility on the runway by high-intensity lights) and the height of the lower cloud boundary (vertical visibility H). These values are set for the airfield, aircraft, and PIC. A combination of the worst (highest) values of both parameters is used to make a lending decision. The actual values of L and H are compared with the minimum. If at least one of the actual values is less than the minimum, landing is prohibited. The lows of the aircraft are constant. The minimum PIC is recorded in the flight task, i.e. it is known. Thus, calculating the minimum for landing is reduced to obtaining the current operating minimum of this airfield. The minimum aerodromes for each runway depend on the equipment of groundbased radio aids, the possibility of using GNSS, obstacles in the scheme, etc. Following FAR-128 [36], the tables of operating minimums of airfields are included in part C of the flight operations manual (FOM) of airlines. In any case, 6 h before departure, i.e. the minimum of each runway of the landing airfield is known at the time of the forecast. Obtaining minimums will be associated with the need for changes in the air navigation support service schedule to realize operational forecasting with a higher degree of advance. Weather conditions worse than the minimum can occur in any flight. Following the FAR-128PIC can decide to take off at almost any values of the weather forecast. The probabilities of “worsening weather conditions” can be calculated in the same

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

19

Table 1.7 Forecast accuracy Predicted element Accuracy of forecasts desirable from the operator’s point of Resources view Wind direction

± 20°

80% of cases

Wind speed

± 10 km/h (5 kn.)

80% of cases

Visibility

± 200 m to 800 m ± 30% from 800 m to 10 km

80% of cases

Rainfall

The presence or absence

80% of cases

Cloud amount

One category below 450 m (1500 ft) 70% of cases Presence or absence of BKN or OVC between 450 m (1500 ft) and 3000 m (10,000 ft)

The cloud ceiling

± 30 m (100 ft) to 300 m (1000 ft) ± 30% from 300 m (1000 ft) to 3000 m (10,000 ft)

70% of cases

way if we assume that “difficult weather conditions” are conditions under which the actual visibility values (La) and/or vertical visibility (Na) are worse than at least plus 30%, i.e. L a ≤ 1, 3L ,

Ha ≤ 1, 3H,

where {L,H}—minimum for landing. Let’s consider the calculation for CFIT_E_35 “Weather below the minimum of PIC at the landing airfield”. By Annex 4 of the FAR of the Russian Federation [35] and Annex 3 of the ICAO [37], CA has established requirements for the accuracy of forecasts, shown in Table 1.7. If we assume the normal distribution of the visibility range L and cloud height H, then the predicted value of these values acquires the meaning of mathematical expectation (let’s denote L), and “security”—the meaning of the probability of hitting a random error value in the specified confidence interval. The standard deviation of the distribution can be calculated using the well-known formula from [21]. For example, for visibility, the probability of falling into the “security” zone:

  L + 200 − L L − 200 − L P L − 200 < L F < L + 200 = F ∗ − F∗ = 0, 8 σ σ

x

e− 2 dt—normal distribution function. −∞   After transformation, F ∗ 200 = 0, 9. σ 200 Applying the table for F* from [21], we find σ = 1,68 = 119m.

where F ∗ =

√1 2π

t

(1.7)

20

1 Methods of Safety Risk Management

Thus, for the predicted visibility values L ≤ 800 m, we have a normal distribution with parameters (MO = L, CKO = 119), which makes it easy to calculate the probability of visibility below the minimum for predicted values less than 800 m. For example, visibility is predicted to be 500 m. Let’s calculate how likely it is to expect that the actual visibility will be worse than the minimum for landing the Il-76, i.e. less than 550 m. By the formula (1.7),we have P(0 < L F < 550) = F ∗

550 − 500 119



−F∗

0 − 500 119

= F ∗ (0, 42) − 1 + F ∗ (4, 2) = 0, 51.

As we can see, even if the forecast is slightly lower than the minimum, there are still severe chances of landing at the destination airport. However, if 400 m is forecast, the probability of visibility below 550 is almost 0.9. Similarly, we can calculate probabilities for a forecast of more than 800 m, as well as for the height of the lower cloud boundary (vertical visibility). This technique is used in ASFPAA (see paragraph 1.1.5) to calculate BPHF worsening conditions for several types of events.

1.1.3.3

Methodology for the Runway Performance Treatment

Rolling out of the runway during landing and aborted takeoff is one of the most frequent aviation events [38]. When developing the appropriate “tree” (RE), the problem arises of developing a simple method for accounting for runway characteristics, the main of which are. – available takeoff distance ATD (TORA) = RUNWAY; – the available distance of the aborted takeoff ADAT (ASDA) = RUNWAY + PBC; – available landing distance ALD (LDA) = runway. The following simplified approach is proposed to assess the impact of runway characteristics. Before landing, the crew calculates the maximum allowable Gm landing mass. The calculation’s input data is the runway length, temperature, runway slope, pressure, and wind. Let’s assume that the actual mass of Gf was equal to the maximum allowed, i.e. Gf = Gm . This means that under these conditions, the minimum level of safety is provided according to the “stop within the runway” criterion, i.e. the probability of rolling out of the runway is equal to the maximum permissible Padd . In all cases, with Gf < Gm , the probability of rolling out P < Radd . Thus, the BPHF “Threat of rolling out of the runway during landing” can be considered as a function of the difference between the maximum and actual takeoff weight G = G m − G  ; P = f (G).

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

21

In order to evaluate the type of this function, consider another function—the supply for the available landing distance over the minimum (LD) of the same difference between the actual landing mass and the maximum allowed: L D = ϕ(G)

(1.8)

The methodology will consider specific data, for example, for the An-124–100 aircraft. Using the program for calculating takeoff and landing characteristics (TLC) for standard atmosphere conditions (h = 0, t = + 15 oC) and taking the runway angle to zero, we calculate the required landing distance LD for different G, starting from the maximum (330t) to the minimum practically (210t). We get for these conditions for Gm = 330t the required LD = 2800 m. Further, for each Gi, we calculate the difference between LD = 2800 m and the required LDi for this Gi. (i = 1…7) as LDi = 2800-LDi (Table 1.8). The dependence LD = ϕ(G) constructed from Table 1.8 is shown in Fig. 1.3. If GF = Gm on this runway for the conditions under consideration, the supply LD = 0. In this case, the minimum acceptable level of security is provided, and Table 1.8 The supply of LD depending on the supply at the landing mass i

Gi

Gi

LDi

LDi

1

330

0

2800

0

2

310

20

2565

235

3

290

40

2378

422

4

270

60

2233

567

5

250

80

2106

694

6

230

100

1978

822

7

210

120

1868

932

Fig. 1.3 Supply for available landing distance depending on the supply for landing weight

1200

1000

∆ LD, m

800

600

∆LD dLD Linear Linear∆LD (dLD)

400

200

0

00

2020 40 40 60 60 80 80 100 100120 120

∆G, t

22

1 Methods of Safety Risk Management

the probability of rolling out is equal to the Padd . If the actual weight is less than the maximum allowed for these conditions, an additional margin appears. The graph shows that this stock increases nonlinearly when the stock increases by G. Hence, we can assume that the probability of rolling out over the Padd depends significantly nonlinearly on G. In this case, this probability changes from the Padd at G = 0 to a particular value of Pmin at G = 120t. Knowing the values of Padd and Pmin , one could construct the dependence P = f (G). The Padd probability is the maximum allowable rollout probability embedded in the AFM. Recall that the AFM already has a safety factor of 1.67 for the main airfield and 1.43 for the reserve, but the specific probabilities in the AFM, of course, are not specified. These probabilities can be estimated from indirect data. For example, in the Canadian source [39], when assessing the risks of rolling out, exceeding the actual landing distance concerning the calculated one is allowed with a probability of 0.001. However, this probability already takes into account the influence of random factors (crew errors, equipment failures, errors in determining the state of the runway, etc.), and these are the HF that are taken into account in the AEDT. Therefore, the probability of 0, 001 as a BPHF will be deliberately overestimated. It is known that the AFM graph of the ACFT has a supply for the probability of rolling out [40]. It is proposed to consider rolling out at Gf = Gm as a complicated situation with a probability of 10–4 , and rolling out with a maximum supply of G = 120t is considered as an unlikely event according to [40] with a probability of 10–6 . Assuming the nonlinear exponential nature of the dependence (1.8), with the vertical axis touching at G = 0, we obtain: P=

0, 0001  √ ex p 0, 5772 G 

(1.9)

Then, with a mass supply of 64 tons, the probability will decrease by 100 times, and then there will be a statistically insignificant decrease in probability. The dependency graph (1.9) is shown in Fig. 1.4.

1.1.3.4

Methodology for Aerodrome Infrastructure Drawbacks Treatment

The methodology for assessing experts’ consistency and objectivity with an example of solving a practical problem is given in Annex 2. Basic HF this group, also shown in Table 1.13, column 2, are HF in other “trees” as “Flawed marking of the airfield”, “Disadvantages of ATS at the airport”, “violation of the traffic patterns of equipment and staff movement at the airport”, etc. Consider, for example, the HF “Shortcomings of ATS at the airport”.

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

23

Fig. 1.4 Dependence of the rollout probability on the supply by landing mass

Direct surveys of experts on the likelihood of deficiencies at airfields did not yield acceptable results. Therefore, it was proposed to assess the shortcomings of ATS on a 3-point scale: 1 2

3

point—no significant drawbacks (type 1 airfield); points—insufficient informing of the crew, late delivery of navigation warnings, ignorance of visual control of the aircraft according to the technology, and other average shortcomings (type 2 airfield); points—serious shortcomings, errors in the air situation assessment, incorrect maintenance of the traffic schedule, etc. (type 3 airfield).

Let’s denote the BPHF HF estimate for an airfield of maximum complexity as P_3. As a result, n experts give estimates for k airfields X_i (j), where i = 1, 2,…, n are expert numbers, j = 1, 2,…, k are airfield numbers, i.e. X_i (j) ∈ {1,2,3}. Average estimates of the complexity of airfields for this HF are as follows: β(j) =

1 n X(j), j = 1 . . . .k. i=1 n

(1.10)

If the total number of HF for all trees is M, we consider the mth (1 ≤ m ≤ M) HF, for which there is an AFSS statistics. Let r ∈ [1;3] be the rating of the airfield evaluated by experts. We introduce the function y = P(r), as the BPHF for an airfield with a rating r ∈ [1;3] (see Fig. 1.5).

24

1 Methods of Safety Risk Management

Fig. 1.5 Basic view of the graph of the probability function P(x)

Let’s denote P1 , P2 , P3 —BPHF for an airfield with ratings 1, 2, 3. in this case, the probability ratio P3 = dm . P1

(1.11)

For the m-th HF, we introduce a fictitious discrete random variable γm in the following distribution series (the second line contains the values ∝(i) m —the relative contribution of i category airfields to the AE in connection with the m-th HF, these values should be obtained from a survey of experts and refined by the statistics of the AE). γm

1

2

3

(i) ∝m

(1) ∝m

(2) ∝m

∝m

(3)

Calculate the mathematical expectation of a random variable γm (2) (3) ∗ M(γm ) = ∝(1) m +2 ∝m +3 ∝m = xm .

(1.12)

 ∗ ∗ m = Pm = Nnfligh , is assumed to be equal to the In this case, the probability P rm relative frequency of the AE according to the DB AFSS statistics due to this HF with the number m. (Nfligh = 9,531,243—the number of flights over about 20 years). If we assume that the derivative of the function at x = 1 is zero and is a quadratic polynomial, then P(r) =

(0, 25(dm − 1)(x − 1)2 + 1) P3 dm

Using (1.13), we get the BPHF estimate for an airfield of complexity 3:

(1.13)

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

P3 =

4P∗m · dm . (dm − 1)(rm − 1)2 + 4

25

(1.14)

To complete the method, the range of variation of the BPHF is calculated. Let M be the total number of different HF at the lower level of the “tree”. Let’s denote n-th number of manifestations of m-th HF, m = 1,2 … M, then the average number of manifestations of each HF in the tree: n=

1 M nm . m=1 M

Calculate the M1 = max(nm − n), M2 = max|nm − n|parameters, then the range of variation in the probability of AE dm:  dm =

 30 + 20·(nMm1−n) , if nm ≥ n , 30 + 20·(nMm2−n) , if nm < n

(1.15)

In the formula (1.15), the average value of the range is 30 based on a survey of experts and AE statistics, and the range [3, 25] can be adjusted by changing the coefficients of the left and right “scattering shoulders”, which are now equal to 20 in the ratio (1.15). There is freedom in choosing the parameter value, which can be used when configuring the model. Finally, we obtain an estimate of the BPHF for the airfield j = 1, 2,…, k, taking into account the nonlinear dependence of the BPHF on the complexity of the airfield λ(j): Pj =

⎡ ⎤ ⎡⎡ ⎤ ⎤2 n 1 (0, 25(dm − 1)(λ(j) − 1)2 + 1) P ⎢ 1 ⎥ P3 = 3 ⎣ (dm − 1)⎣⎣ ri (j)⎦ − 1⎦ + 1⎦ dm dm 40 n

(1.16)

i=1

The final calculations use the airfield’s median estimate to the nearest integer value of 1, 2, 3. In real work, we receive expert estimates ri (j) calculated using the formula (1.10), airfield complexity parameters β(j), and probability estimates calculated using the formula (1.16) for each airfield Pj (j = 1, 2,…, k). To reduce the influence of “extreme estimates” and obtain uniform probabilities on a scale of 1-2-3, the following method is used. All airfields are divided into three clusters: 1—minimum complexity, 2—average complexity, and 3—maximum complexity. Within each cluster, the calculated BPHF Pj , j = 1…k, are averaged (as a geometric mean), and then all airfields within the cluster are assigned the probability average for the cluster. In this case, the probability is also recalculated using the algorithm for calculating the average probabilities P1 and P2 and, generally speaking, takes a lower value than the original base probability P3 for the maximum complexity airfield. Finally, the system receives an assessment of the airfield’s complexity with the number (1, 2, 3) and BPHF estimates for each type of airfield P1 , P2 , P3 .

26

1 Methods of Safety Risk Management

Table 1.9 shows a fragment of a table containing intermediate parameters for calculating the BPHF full set of HF. Table 1.10 shows the final BPHF “Erroneous deviation beyond the obstacle registration zone” for several airfields, calculated using this method. A detailed description of the method and all calculations are given in [26]. Table 1.9 Intermediate parameters for calculating the BPHF for the “Environment” group (a fragment of table 4.9 from the report [26]) The hazard factor (HF)

the Deviation Number of from the AE average numbers as 70,78

dn

Deficiencies in the marking of the airfield of departure

17

-53,78

Deficiencies in the marking of the airfield of arrival

29

The average estimate of the relative assistance of the airfield to the AE

The average value of the airfield category for the HF

an(1)

an(2)

an(3)

14,6

0,1

0,3

0,6

2,50

-41,78

18,0

0,1

0,3

0,6

2,50

Failure of 3 departure airfield lighting equipment

-67,78

10,6

0,167

0,333

0,5

2,33

Failure of arrival airfield lighting equipment

73

2,22

30.1

0,1

0,267

0,567

2,47

Unsatisfactory condition of the runway departure airfield

1

-69.78

10,0

0,1

0,267

0,633

2,53

Unsatisfactory condition of the runway arrival airfield

10

-60,78

12,6

0,187

0,333

0,633

2,33

92,22

34,2

0,133

0,3

0,5

2,33

Unsatisfactory 163 condition of the departure airfield taxiway

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

27

Table 1.10 Examples of clustered BPHF values (from [26]) Kod IATA

Airfield

Type

Clustered base probabilities for the HF “Incorrect approach by the dispatcher of the landing airfield outside the obstacle tracking zone”

YQX

Gander, Canada

1

4,43·10–7

KBK

Kuwait, Kuwait

1

4,43·10–7

IIR

Irkutsk, Russia

1

4,43·10–7

AIX

Bagram, Afghanistan

3

3,85·10–6

MMS

Minsk-2, Belarus

1

4,43·10–7

UEE

Sheremetyevo, Russia

1

7,50·10–7

UOT

Tambov, Russia

2

7,50·10–7

RBI

Baghdad, Iraq

2

7,50·10–7

1.1.4 Method of a Posteriori Probabilities Adjustment 1.1.4.1

Formulation of the Problem. Implementation of Fuzzy Estimations for a Priori Event Probability Distribution Forming

The received BPHF does not fully take into account the current activities of the airline. The model is updated periodically, but it is vital that the probability model also responds promptly to insignificant events and deviations in the airline. We will consider the probability of HF obtained using one of the procedures described above as an a priori estimate. The additional information obtained is used to refine it and obtain a posteriori evaluation. Let’s consider the solution to the example of the HF “Getting into the satellite track of another aircraft”. The method for estimating the probability of this HF is described in [26]. The method is based on the analysis of statistics of these events and expert assessment of the airfield on two indicators: the intensity of flights and ATC’s quality on a 3-point scale. According to the intensity of flights, airfields are divided into low-, medium-, and high-intensity airfields—the quality of ATC on airfields with good, satisfactory, and unsatisfactory quality ATC. The model is designed for the An-124–100 aircraft. It is assumed that the aircraft flying ahead has a category (a) for the satellite track (weight more than 136 t). The values of HF probabilities for each of the combinations of values of these parameters from [26] are summarized in Table 1.11. Let’s assume that for airfield X in the upcoming flight, the probability estimate P is obtained. This estimate will be used in the tree to predict the probability of an “Unsafe runway touch” event. At the same time, it became known that last week, a message was received from the crew landing at the same airfield, about getting into the satellite track from the aircraft that was landing in front of it. The P score needs to be adjusted.

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1 Methods of Safety Risk Management

Table 1.11 Estimation of the probability of getting into a satellite trail Quality indicator air traffic control Flight intensity indicator

Good

Satisfied

Unsatisfied

Small

0,000,006

0,00,009

0,00,049

Average

0,00,037

0,00,584

0,025

High

0,00,158

0,0213

0,0777

For the solution, we use the Bayes formula P( Ai |B) =

P(Ai )P( B|Ai ) k 

,

(1.13)

P(Ai )P( B|Ai )

i=1

where Ai —hypotheses about the conditions under which the event of interest to us can occur B; Ai —pairwise incompatible random events; P(Ai )—known a priori probabilities of events Ai ; P(B | Ai )—the probability of events B, provided that the event Ai occurs (the validity of the hypothesis Ai ). The formula allows us to calculate the conditional probabilities P (Ai | B) of events Ai (or the probability of validity of hypotheses Ai ) based on the fact that event B occurred (or did not occur). In the problem under consideration, event B is a hit in a vague trace. To avoid confusion in the future, we will call the probability estimate of this event “frequency”. To simplify further calculations, the range of possible probabilities from 10–1 to –6 10 is divided into intervals with discreteness of 10–1 . The Ai event is when the frequency of event B falls in one of the intervals. All Ai ’s form a complete event group. The “pessimistic” version of the estimate is accepted: each probability value is rounded to the left (greater) border of the corresponding interval of the logarithmic scale (see Table 1.12). An expert evaluation is conducted with 10 experts to obtain the probability distribution for a particular airfield, the results are summarized in Table 1.13. We consider the survey results as two fuzzy sets of corresponding parameter values and write them as belonging functions A1 and A2 . The values of fuzzy sets Table 1.12 “Pessimistic” version of the probability assessment Air traffic control quality Flight intensity

Good

Satisfied

Unsatisfied

Small

10–5

10–4

10–3

Average

10–3

10–2

10–1

High

10–2

10–1

10–1

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

29

Table 1.13 Expert survey for airfield X Flight intensity (X)

Air traffic control quality (X) Small Aver High

E-1

1

E-2 E-3

1 1

Good Satisf Unsatis E-1

1

E-2

1

E-3

1 1

E-4

1

E-4

E-5

1

E-5

E-6

1

E-6

E-7

1

E-7

E-8

1

E-8

1

E-9

1

E-9

1

E-10 Result

1 0,2

0,8

1 1 1

E-10 0

1

Result

0,5

0,4

0,1

Table 1.14 Membership function of a fuzzy set A = A1 •A2 Quality of air traffic control The intensity of flights

Good

Satisfied

Non-satisfied

Small

0.10

0.08

0.02

Average

0.40

0.32

0.08

High

0

0

0

are the first letters of the corresponding characteristics: A1 = M/0.2 + C/0.8 + B / 0.0; A2 = X/0.5 + Y/0.4 + H/0.1. The product A = A1 •A2 is written as a matrix, a set of values of the membership function of the fuzzy set “Flight intensity/The quality of the internal affairs” of airfield X(Table 1.14). Based on Tables 1.13 and 1.14, we construct a priori probability distribution of hypotheses. Let’s explain the construction using an example. According to Table 1.14, for the intensity “Small” and the ATC indicator “Good”, the left border of event B’s frequency interval is 10–5 , and the right border is 10–6 . According to Table 1.17, we have the degree of membership of the element “Small/Good” for airfield X is equal to 0.1. We assume that the degree of membership is equal to the probability of hitting the event B’s frequency in the interval (10–5 ; 10–6 ),—low intensity and good quality of ATC—according to Table 1.16. Two elements of a fuzzy set fall into the interval B (10–3 , 10–4 ): “Small/Non-satisfactory” and “Average/Good”. Therefore, the probability of an event falling within this interval is assumed to equal to the sum of 0.4 + 0.02 = 0.42. Similarly, calculated probabilities for other frequency intervals (hypotheses) are summarized in Table 1.15. The graph is shown in Fig. 1.6.

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1 Methods of Safety Risk Management

Table 1.15 The a priori probabilities of the events Ai at the airfield X Hypotheses

A1

Left border of the interval

10–1 10–2 10–3 10–4 10–5

A2

A3

A4

A5

Probability of occurrence of the B event frequency in the interval 0.08 0.32 0.42 0.08 0.1

Probability of hitting the interval

Fig. 1.6 A priori probability distribution of Ai events

0.5

0.42

0.4

0.32

0.3 0.2

0.1

0.08

0.08

0.1 0

1.0E-01 1.0E-02 1.0E-03 1.0E-04 1.0E-05 Frequency of events B (left border of the interval)

Table 1.16 Conditional probabilities of an event B when executing Ai hypotheses Hypotheses Left border of the interval P (B|Ai )

A1

A2

A3

A4

A5

0.1

0.01

0.001

0.0001

0.00001

(I)

0.00030

0.36973

0.09057

0.00990

0.0010

(II)

0.1

0.01

0.001

0.0001

0.00001

The average probability can be calculated as P = M( f ) =

n 

f ic Pi ( A|B)

(1.14)

i=1

where M(f)—is the mathematical expectation of the frequency distribution; fi c —left frequency limit for the i-th interval; Pi (A | C)—probability for the i-th interval. We have an average a priori “pessimistic” probability of getting into the satellite track at the airfield X: P = 0.0124.

Forming a Posteriori Probability Distribution of an Event At airfield X there was a hit of an aircraft in the satellite track of another aircraft on landing. Let’s consider two options.

Fig. 1.7 A posteriori distribution, option I

Probability of hitting the interval

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

31

0.968

1.0 0.8 0.6 0.4 0.2 0.008

0.024

0.000

0.000

1.0E-03

1.0E-04

1.0E-05

0.0 1.0E-01

1.0E-02

Frequency of events B (left border of the interval)

(I) (II)

There have been 100 previous landings at this airfield, and this is the first case of satellite tracking. The landing in which the satellite track was detected was the first landing of our aircraft at this airfield.

We need to calculate conditional probabilities P (B | Ai ). According to the binomial distribution formula, the probability of occurrence of events a in k tests:

k P(B = a) = (1.15) pa (1 − p)k−a , a where p is the probability of occurrence of the B event in a single test; k k! = a!(k−a)! —the number of combinations of k elements by a. a Calculation example. For option (I)—one event per 100 flights—we have k = 100, a = 1. We assume that if the event hypothesis A1 is fulfilled, event B’s probability is equal to the value of the left boundary of the interval or Fl; so, for hypothesis A1 , the probability p = Fl = 0.08. If the frequency was in the range from 0.1 to 0.01, the probability of hitting the satellite track would be equal to P(B|A1 ) =

100! 0, 081 (1 − 0, 08)100−1 = 100 × 0, 08 × 0, 9299 = 0, 0003. 1!(100 − 1)!

The results of the calculation using the formula (1.15) for variants (I) and (II) are summarized in Table 1.16. Next, we make calculations using the formula (1.13). The a posteriori distribution for variants I and II is shown in Figs. 1.11 and 1.12. As shown in Figs. 1.7 and 1.8, the probability distribution for event B’s frequency has changed significantly. The calculation of the refined point value of the event frequency (BPHF estimation in the upcoming flight) using the formula (1.15) gives the following results:

1 Methods of Safety Risk Management

Fig. 1.8 A posteriori distribution, option II

Probability of hitting the interval

32

1.0

0.990

0.8 0.6 0.4 0.2 0.010

0.000

0.000

0.000

0.0 1.0E-01 1.0E-02 1.0E-03 1.0E-04 1.0E-05 Frequency of events B (left border of the interval)

– for an option I (one message about hitting the satellite track per 100 flights), P1 (B) = 0.01. – for an option II (the message about hitting the satellite track was received during the first flight to this airfield) P2 (B) = 0.099. As we can see, the additional information reduced the a priori probability P = 0.0124 for option (I) and increased it for option (II). The described approach can be applied to all HF that can be specified based on information about production activities. Obviously, along with the mathematical expectation of frequency, other estimates can be used, for example, the upper limits of confidence intervals. A separate study will be required to take into account the severity of the event and the degree of confidence in additional information. For example, we can count reliable messages as a full-fledged event and consider them in calculations as 1, less reliable ones as a fraction of 1, 0.5, 0.3, 0.1, and so on. It is proposed to implement the following procedure for performing the correction of a priori probabilities. 1. 2.

if there is no clarifying information—correct every 50 flights to this airfield (k = 50n, n = 1, 2,…; a = 0). if the information is received, the correction is performed immediately. But in this case, if the a posteriori probability is less than the a priori probability at the time of receipt of the information, this a posteriori probability is discarded and the a priori probability for the next 50 flights is used to calculate the event (in this case, the “loss of control” event).

1.1.5 Implementation of the Method in the Automated System of Forecasting and Prevention of Aviation Accidents The practical implementation of the method requires significant resources and highly qualified specialists in various fields. The development of an SMS based on this method can only be performed in a large airline. This work was carried out in the

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

33

Volga-Dnepr Group of companies (GrC), a major air cargo carrier of the Russian Federation and controls more than 60% of the world market for air transportation of oversized cargo. In 2010, GrC together with Ulyanovsk State University initiated an innovative project to develop an automated system for predicting and preventing accidents in the organization and production of air transport (ASFPAA), which was supported by the government of the Russian Federation as part of the implementation of Resolution No. 218 of April 9 2010. To work as consultants were involved to the leading scientists of the Russian Federation in the management of FS, risk management, and the “human factor”. Scientists were from MSTU CA, the St. Petersburg State Aviation Administration University, the Interstate aviation Committee, and the Mil design Bureau, and other organizations. An expert Council under the President of the GrC was formed under the leadership of a corresponding member of the Russian Academy of Sciences of Machutov to ensure a highly professional expert assessment of the project implementation. The project’s goal is to increase the efficiency of air transport by switching to a preventive system for managing safety risks based on their quantitative assessment using software and mathematical modeling in the GrC airlines (and later in other airlines). One of the authors of this book, V. D. [30]participated in the development of the ASFPAA as a Deputy Head of the Volga-Dnepr airlines project team. The results of the project development are reflected in [7, 30–32, 41–43]. The developed system is aimed at solving the following tasks: (1)

(2)

(3) (4)

(5)

operational forecast of the probability of an aviation event in the upcoming flight, indicating the hazard factors (threats) and their combinations, and the possibility of adjusting the forecast to take into account the proposed options for management decisions; long-term forecast of periods of the critical probability of an accident with an indication of hazard factors (threats) and their combinations and the possibility of adjusting the forecast to take into account the management decisions taken; quantitative assessment of safety risks in value and kind based on the analysis of information about the airline’s operational activities; monitoring of the airline’s accepted indicators of the safety and prevention of accidents (PA) with the provision of an automated procedure for calculating current and directive levels. formation of projects of management decisions on safety and PA, with an assessment of their effectiveness and creation of an information system for their accounting and control.

The problems of developing ASFPAA were discussed at several international and all-Russian scientific conferences. The General schematic diagram of the ASFPAA is shown in Fig. 1.9. A brief description of an example of an upcoming flight risk assessment is provided in Annex 1.

34

1 Methods of Safety Risk Management

Fig. 1.9 Schematic diagram of the functioning ASFPAA

The software of the ASFPAA system has passed acceptance tests conducted by the joint committee of the Customer LLC (Volga-Dnepr Airlines) and the Contractor (Ulyanovsk State University), which is confirmed by The Commission’s act of 26.12.2012. The Commission of the Ministry of education and science confirmed that the project conditions were wholly realized by an act dated May 13, 2012. Currently, the system is used by Volga-Dnepr airlines in a test mode. We will highlight some areas of further development of the ASFPAA. 1.

2.

3.

4.

A variety of statistical methods of data analysis are used in the ASFPAA. It is advisable to proceed from the modern paradigm of applied statistics [22] and use (develop) appropriate nonparametric methods. For similar reasons, when analyzing the reliability of technical devices (the “Machine” group), it is necessary to get rid of the assumption of exponential distributions of random variables. The rejection of parametric models is also necessary when studying time-series trends for forecasting—from a linear trend model and a model in the form of a Fourier series segment to a trend of any kind. In long-term forecasting, evaluate the achievement of given boundary value and the intervals of increased probability of AE implementation, i.e. not only the distribution function but also the intensity. It is necessary to study the possibility of considering HF as independent events and variables. When detecting dependencies between HF, it is necessary to develop adequate methods for calculating the joint action of HF. To develop a consolidated hierarchical system of risk factors (RF), aggregating the individual district in the group, and generalized RF.

1.1 Methods Based on the Model of Aviation “Event Tree” Development.

5.

6.

7.

35

Due to the small number of as compared to the total number of flights, as frequencies are small numbers with a large relative spread. Statistical methods cannot provide reliable results. That justifies the widespread use of expert procedures. It is necessary to develop and include methods for confidence estimation of probabilities in the case of small numbers. In all ASFPAA algorithms, the stability of conclusions concerning the permissible deviations of input variables and model assumptions should be studied following the “General stability scheme”, and recommendations should be made for using the results in making managerial decisions. It is necessary to compare the results of expert assessments with the results of data analysis using statistical methods, develop new expert methods to solve several project tasks, particularly identify the impact of the number and composition of expert commissions on the accuracy of conclusions.

1.2 Method of Safety Risk Management Using Conditional Factors and Fuzzy Evaluations 1.2.1 Formulation of the Problem and Approach to the Solution. Content and Structure of the Source Data As already mentioned, the ICAO SMM [44] offers a model for calculating risk-based on expert analysis of information. In the examples given in Annexes 1, 2, and 3 to Chap. 5 in the second edition of the SMM 2009, the risk is assessed for the airport’s planned work, for flights on converging runways, and the specifics of flights at the new mountain airfield. Thus, [44] offers a qualitative assessment of the risk of future events related to the HF and changes in the AE based on expert assessments. In real life, this usually looks like discussing a specific issue at the Security Council or the risk commission with a general decision. The proposed risk matrix is quite suitable for this task. However, operators are also interested in solving another class’s problems, namely, systematic quantitative assessment of operational risks. The task is set as follows: based on information about deviations (inconsistencies), quantify the risks that occurred in the reporting period and, taking these estimates as a forecast, if necessary, to develop and implement corrective measures. At the same time, it is required that the matrix from the ICAO SMM be used in one form or another [24] since the authorized bodies require this in the field of the RF CA.

36

1 Methods of Safety Risk Management

The general approach to the solution. Following the general risk management methodology, actions on the most critical and dangerous events are taken directly based on the investigation results, i.e. an operational risk management loop is implemented. For tactical and strategic management within the framework of this approach includes the following method: – risk is considered within the framework of the traditional “technocratic concept” as a measure of quantitative measurement of danger, including the amount of damage caused by the impact of hazard factors (HF) and the probability (possibility) of HF occurrence; – both risk components are expressed numerically by “capability indicators” P* and “severity indicators” S*. The term “degree of possibility” is borrowed from the famous work of Dubois and Prad [45]. The vulnerability of the system (the effectiveness of security barriers) is taken into account when assessing the severity indicator S*; the separation of tactical and strategic risk management is implemented by setting periods for accounting for inconsistencies. For tactical management, this is usually one month, and for strategic management—6 months or a year. To build a model that reflects an airline’s activities, we need to determine the amount of information that is supposed to be processed. Composition and structure of source information. If we represent the information used in the safety management system (SMS) as an “iceberg of events”, then its upper part is aviation events in the sense of [5], which are subject to investigation. However, the central part of the information about insignificant events/deviations (after this referred to as events) remains hidden. This information can be obtained from various sources [46]: flight data collection systems [47], data on equipment failures and malfunctions, from inspections and audits, voluntary employee reports, etc. If we consider everything, even small fixed deviations, the volume of data for a large airline will be very large. For example, for “Sibir” airlines, the number of recorded events during the year is more than 12,000 [48], starting with aviation incidents and ending with comments about malfunctions in passenger seat belts and incorrectly issued baggage receipts. Therefore, the evaluation algorithm should be such that it can be implemented programmatically. We need a data collection and encoding system, a database, and a computer program. One of the options for structuring information can be the division of all recorded events (inconsistencies) in the airline’s areas of activity. For example, we can identify eight areas of activity; let’s call them sectors, following the IATA classification [49]. The list of sectors is shown in Table 1.17. In each sector, we can distinguish groups of events; let’s call them categories, based on standard features of HF manifestations.

1.2 Method of Safety Risk Management Using Conditional Factors …

37

Table 1.17 Areas of activity (sectors) for the top-level structuring of information about nonconformities (events, deviations) No

Code

Suffix

Title

1

ORG

O

General inconsistencies, document flow, organizational structure

2

FLT

F

Inconsistencies in the work of the flight crew

3

DSP

D

Inconsistencies in organizational and air navigation support for flights

4

MNT

M

Inconsistencies in the technical condition and maintenance of aircraft

5

CAB

C

Inconsistencies in the work of the cabin crew

6

GRH

G

Inconsistencies in the organization of ground handling

7

CGO

B

Inconsistencies in the organization of cargo transportation

8

SEC

S

Inconsistencies in the aviation security

The number of event categories (HF manifestations) will be different for different sectors, and their total number depends on the volume of work of the airline. The main requirement is that each recorded deviation in the activity of the AL finds its place in one of the categories. For example, in the “Flight operations” (FLT) sector, the following categories can be distinguished: – – – – – – – – – – – –

deviations in the organization of flight work; deviations when preparing the crew for flight; impact of weather conditions on flight performance; deviations when starting, taxiing, and towing; deviations on takeoff; deviations in the climb; deviations in horizontal flight; deviations on descent and approach; deviations during landing; disadvantages in informing passengers; disadvantages of staff training and qualification; disadvantages of maintaining documentation.

Lists of categories for each sector should be formed with the mandatory participation of experienced specialists by type of activity. At the same time, the lists for different airlines may differ significantly; it depends on the specifics of their operational activities. An example of a complete category classifier for the system developed by Sibir airlines (see Sect. 1.2.4) is given in Appendix 3. The following two paragraphs describe how to evaluate the “severity indicator” and “capability indicators”.

38

1 Methods of Safety Risk Management

1.2.2 Severity Factor and Method of Its Calculation It is assumed that events in each j-th category are manifestations of the HF associated with this category. Consider, for example, the FLT sector—“Inconsistencies in the work of the flight crew”. One of the categories of the sector “Irregularities in preparation for the flight”. We believe that every discrepancy (event, deviation) is a manifestation of the HF associated with shortcomings in the organization of flight preparation, documents, and procedures, in the training of airline personnel, in the work of external service providers, etc., which are associated with this technological operation—“Flight preparation”. Nonconformities may have different severity levels and should be evaluated by different Sij indicators. For example, there may be minor deviations (the pilot did not sign for the forecast form) and serious ones—a critical warning (NOTAM) for the landing airfield was not studied. As a result, there was a departure to the alternate airfield. Risk assessment in tactical and strategic management aims to identify systemic weaknesses and assess their dynamics. Therefore, the severity should be calculated based on the totality of events over the period. The severity indicator is S ∗j is calculated for the category. The initial data are expert assessments of each event’s severity (KSij) for different aspects of the activity on a 5-point scale. Experts develop evaluation criteria for the sector based on a standard table (see Table 1.18). The classification of severity by flight operation corresponds to [40]. Violations of the rules for the use of airspace are specific. They usually do not lead to losses, but they are potentially very dangerous. The approach to criteria from the UK document SAR-760 is used [50]. For the adequacy of calculations, it is necessary to translate expert assessments of KSi events into the Si severity indicator. We can use insurance statistics, data on the severity ratio of events from [51], and the severity ratio of comments in inspection manuals. For example, Table 1.19 shows such ratios, where the Si values for KSi = 1; 2; 3 are taken according to the SAFA verification method, and the values for KS = 4; 5 are obtained by doubling the previous indicators. The result is an approximately exponential relationship (Fig. 1.10), which makes programming more accessible. Each value of the Si event severity level for the j-th category is considered to implement a random variable Sj -the severity of the manifestation of the HF group of this category. With this approach, the upper confidence probability P can be calculated for any deviation of Sj values from the mathematical expectation using the Chebyshev inequality:   σ 2 (S j )  , P  S j − M(S j ) ≥ α ≤ α2 where

(1.17)

The complication of flight conditions

Difficult situation

Emergency

Catastrophic situation

2

3

4

5

Fatal case

Disability

Serious injuries

Minor injuries

Without consequences The increased load

1

People

Flight operation

Ks

Catastrophic damage

Large damage

Medium damage

Minor damage

Without prejudice

The property

Table 1.18 Severity assessment criteria for business aspects Regularity

–

–

More than 6 h

2 h–6 h

15 min–2 h

AL’s reputation

–

Serious harm

Substantial harm

Minor damage

Without consequences

–

Serious harm

Substantial harm

Minor damage

Without consequences

Environment

The collision almost happened

Risk of collision

A substantial level of complication

The slight complication of the situation

Without consequences

Air traffic

1.2 Method of Safety Risk Management Using Conditional Factors … 39

40

1 Methods of Safety Risk Management

Table 1.19 The ratio of expert assessments of severity and their levels KSij

1

2

3

4

5

Sij

1

4

8

16

32

32

Si

20 2 5

30

35

y = 0.5743e0.8318x R² = 0.973

Severity level

15

16

10

8

Exponential

4

(Severity level)

5

1

1

2

3

4

Ks

5

Fig. 1.10 Dependence of the Si event severity level on the Ks score

Membership function

F

Pacificator

~ F

The module fuzzy inference

~ P

Degasification

P*

Fuzzy knowledge base Fig. 1.11 Fuzzy logic inference system for the degree of capability

M (Sj ) and σ2 (Sj ) are the expectation and variance of the random variable Sj , α—deviation of Sj from the mathematical expectation. By setting α = Cσ (S j ), C ≥ 1, and assuming that Sj -M (Sj ) ≥ 0, we have: P[S j ≥ M(S j ) + Cσ (S j )] ≤

1 , C2

where is the maximum Pmax probability that Sj will not be less than the sum of M (Sj ) + C$ (Sj ):

1.2 Method of Safety Risk Management Using Conditional Factors …

41

Fig. 1.12 Raw MF (subnormal fuzzy sets)

Pmax [S j ≥ M(S j ) + Cσ (S j )] =

1 . C2

Accordingly, the minimum probability Pmin that Sj will be no more than the sum of M (Sj ) + C$ (Sj ): Pmin [S j ≤ M(S j ) + Cσ (S j )] = 1 −

1 C2

(1.18)

The formula (1.18) is calculated to estimate the confidence probability Pup = Pmin of a random variable S not going beyond the upper limit of the (M(S j ) ± Cσ (S j )) interval (M(S j ) ± Cσ (S j )). Table 1.20 shows the results of the calculation using the formula (1.18). Table 1.20 allows us to solve the inverse problem: by setting the probability of Pup , we get the value Sj , which can be taken as an estimate S ∗j of the severity of the category. However, if the sum M(S j )+Cσ (S j ) exceeds the largest of the observed Sij ∗ max values (let’s denote it as S max j ), then we recommend accepting the estimate S j = S j . For example, for the confidence probability Pup = 0.75, we have the rule: Table 1.20 Confidence probabilities of failure to go beyond the upper limit of the “severity index” interval for a category under various C C

1.7

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

Pup

0.65

0.69

0.72

0.75

0.77

0.79

0.81

0.83

0.84

42

1 Methods of Safety Risk Management



         S ∗j = M S j + 2σ S j when M S j + 2σ S j ≤ S max j     . S ∗j = S max when M S j + 2σ S j > S max j j

(1.19)

Rule (1.19) is implemented in the ASUR computer program of Sibir airlines (see clause 1.2.4). The resulting estimate is considered as a forecast of the severity of category events for the next period.

1.2.3 Admissibility Factor and Method of Its Calculation 1.2.3.1

Rationale for Fuzzy Evaluation Applicability

The p* capability score for each category is based on the relative number of HF manifestations (per 1,000 flights or flight hours) in that category. Therefore, to automate calculations, we need to have an algorithm for translating the frequency of events into an indicator of the degree of possibility. In SMM 2009 and 2013, there is no indication of a quantitative interpretation of the estimates, but in SMM 2006, there were such instructions, and they are also in some other documents (see Table 1.21). It can be seen that the wording of definitions and recommendations for assessing the capability indicator differ from document to document. There can be no clear instructions. Any term such as “often” or “sometimes” about operational events has different meanings for different experts and different airlines, i.e. such estimates are unclear. Therefore, when developing rules for their formation, the approaches of fuzzy set theory (FST) are used. Here are some basic definitions from FST based on [52, 53].  on a universal set U is a set of pairs (μ A (u), u), where μ A (u) is the A fuzzy set A  degree to which an element belongs to u ∈ U the fuzzy set A. The membership function expresses the degree of membership of an element, a definition first formulated by Zahde, we will give on work [52]:  universal set U is characterized by an affiliation function “A fuzzy subset of a A that matches μ A : U → [0, 1] each element u ∈ U with a number μ A (u) from the segment [1] that characterizes the degree of membership of the element to u the  subset A”. The introduction of fuzzy concepts allows us to set the values of variables not in numbers, but in words, which is more familiar to humans. Variables that take values from a set of words or phrases in a natural language are called linguistic variables (LV). The set of all possible LV values forms a term-set, an element of the term-set is called a term. Each LV must have a name, a set of terms, and rules that define the membership functions (MF) of ambiguous terms.

Document

Doc.9859 ICAO-2006

№

1

Extremely unlikely

10–9– 10–7

2

Russian

Kpane malovepot-noe

Unlikely to occur when considering several systems of the same type, but nevertheless has to be considered as being possible,

(continued)

Hactyplenie cobyti malovepotno, ecli paccmatpivat neckolko cictem togo e tipa, no vmecte c tem neobxodimo dopyckat taky vozmonoct

Ppaktiqecki nevozmonoe Should virtually Faktiqecki ne never occur in the dolno ppoizoti whole fleet life za vec cpok clyby papka BC

Russian

English

Extremely improbable

English < 10–9

Description

Determining the probability (degree of possibility)

1

Indicator of the Frequency for probability 1 h of flight

Table 1.21 Recommendations for evaluating the “capability indicator” in various documents

1.2 Method of Safety Risk Management Using Conditional Factors … 43

№

Document

Table 1.21 (continued)

Logically possible

Frequent

10–5– 10–3

10–3

4

5 Qactoe

Logiqecki vozmonoe

Malovepot-noe

Russian

May occur once or several times during operational life,

May occur once during total operational life of one system

Unlikely to occur during each system’s total operational life, it may occur several times when considering several systems of the same type

English

Unlikely

English 10–7– 10–5

Description

Determining the probability (degree of possibility)

3

Indicator of the Frequency for probability 1 h of flight

(continued)

Moet ppoizoti odin ili neckolko paz v teqenie cpoka kcplyatacii

Moet ppoizoti odin paz v teqenie vcego cpoka kcplyatacii odno cictemy

Hactyplenie cobyti malovepotno v teqenie vcego cpoka kcplyatacii kado cictemy, no ono moet ppoizoti neckolko paz, ecli paccmatpivat cely pd cictem togo e tipa

Russian

44 1 Methods of Safety Risk Management

Document

Doc.9859 ICAO-2009,2012-

№

2

Table 1.21 (continued)

–

–

–

–

–

1

2

3

4

5

Indicator of the Frequency for probability 1 h of flight

Frequent

Occasional

Remote

Unlikely

Qactoe

Ppoicxodwee vpem ot vpemeni

Claba

Malovepotnoe

Bozmonoct nactypleni cobyti poqti icklqena

Russian

Likely to occur many times (has occurred frequently)/

Likely to occur sometimes times (has occurred infrequently)/

(continued)

Moet ppoizoti mnogokpatno (ppoicxodilo qacto)

Moet ppoicxodit vpem ot vpemeni

Unlikely to occur, Malovepotno, no but possible (has vozmono, qto occurred rarely) ppoizodet

Very unlikely to Becma mala occur (not known vepotnoct, qto to have occurred)/ ppoizodet (net cvedeni o tom, qto ppoizoxlo)

Almost inconceivable that the event will occur

English

Kpane malovepot

Russian

English Extremely unlikely

Description

Determining the probability (degree of possibility)

1.2 Method of Safety Risk Management Using Conditional Factors … 45

Document

AC No: 120–92 FAA (transfer of the FS Foundation)

CAP-712

№

3

4

Table 1.21 (continued)

Extremely improbable

– –

< 10–9

4

5

1

Frequent

–

3 Occasional

Remote

Improbable

–

2

Ppaktiqeckinevepotny

Qacto

Inogda

Claby

Hevepotny

Russian

Bepotnoct mnogokpatnogo povtopeni

Bepotnoct pedkogo povtopeni

Malovepotny, no vozmony

Oqen malovepotny

(continued)

Should virtually Ppaktiqecki ne never occur in the dolen ppoizoti whole fleet life v teqenie vcego pepioda kcplyatacii

Likely to occur many times

Likely to occur sometimes

Unlikely, but possible to occur

Very unlikely to occur

Almost Poqti inconceivable that nevepotny the event will occur

English

Qpezvyqano malovepotn

Russian

English Extremely improbable

–

Description

Determining the probability (degree of possibility)

1

Indicator of the Frequency for probability 1 h of flight

46 1 Methods of Safety Risk Management

№

Document

Table 1.21 (continued)

Unlikely

10–7 –10–5

3

Malovepotnoe

Qpezvyqano malovepotnoe

Russian

Unlikely to occur during total operational life of each system but may occur several times when considering several systems of the same type

Unlikely to occur when considering systems of the same type, but nevertheless, has to be considered as being possible

English

Extremely unlikely

English 10–9 –10–7

Description

Determining the probability (degree of possibility)

2

Indicator of the Frequency for probability 1 h of flight

(continued)

Malovepotno, qto za pepiod kcplyatacii ppoizodet c kado iz cictem, no moet clyqitc neckolko paz, ecli paccmatpivat neckolko cictem dannogo tipa

Malovepotno, qto moet ppoizoti dae c odno iz cictem dannogo tipa, no, tem ni menee, dolen paccmatpivatc kak vozmony

Russian

1.2 Method of Safety Risk Management Using Conditional Factors … 47

Document

EASA guide, 2008

AP-25

№

5

6

Table 1.21 (continued)

Improbable Remote

Occasional

Frequent

–

10–9 –10–7 10–7 –10–5

10–5 –10–3

1–10−3

and logical operations on fuzzy sets. Various fuzzy inference algorithms are based on the compositional rule l. A. Zahde, which is given from [53, p. 39]:  between the fuzzy variables x and y, then for “If a fuzzy relationship is known R  the fuzzy value of the output variable is a fuzzy value of the input variable x = A, defined as:  ◦ R,  y=A where ◦ is the sign of the maximin composition”. In this task, the input frequency value and the value of the output’s coefficient are crisp numbers. The FST device allows us to solve the problem in this setting. The most suitable tool is a Sugeno-type fuzzy inference system implemented in MATLAB in the Fuzzy Logic Toolbox software package. In this case, the input variable is the frequency F (for example, the number of events per 1000 flights), and the output variable is the value of the indicator P*. The scheme of the fuzzy inference system for the problem under consideration is shown in Fig. 1.11. The scheme is based on the General approach from [53].

1.2 Method of Safety Risk Management Using Conditional Factors …

51

The system contains the following modules and source data: – MF used to represent linguistic terms;  – a pacificator that converts the input exact frequency F value to the fuzzy set F required for fuzzy output; – a fuzzy knowledge base that stores information about the dependency P = f(F) as a set of rules < If–then > ;  based – a fuzzy inference module that calculates the output variable as a fuzzy set F  on fuzzy values based on rules from the knowledge base P;  into a crisp number R*. – degasification that converts a fuzzy set P To solve this problem, we must: (a) (b) (c)

construct the MF of fuzzy terms of the linguistic variable “Frequency”; develop a system of rules for building a knowledge base; by setting different input values of F, get the output values of the coefficient P*.

Construction is performed using the Fuzzy Logic Toolbox software package in the MATLAB environment. For detailed instructions, see [53]. (a)

Building membership functions based on expert evaluations.

These terms’ membership functions are based on the expert survey method described in [54, 55]. N experts are involved in the work. Each expert fills out a questionnaire in which they indicate their opinion on whether an element belongs to u i (i = 1, n) a fuzzy set j ( j = 1, m) in the form of a binary estimate: 1—the element belongs to the (term) A set, 0—does not belong, where n—the total number of elements (intervals), m—the total number of terms. Let’s denote ci,k j the opinion of the k-th expert on whether the i-th element (interval) j-th fuzzy set. The degree to which the ui element belongs to the fuzzy set Aj is calculated using the formula: μ A j (u i ) =

N 1  k c . N k=1 j,i

A survey of eight experts was conducted at Sibir airlines to construct the membership functions of the terms “Very often”, “Often”, “Sometimes”, “Rarely”, and “Extremely rarely”. The experts were: flight Director, chief engineer of the IAS, Deputy General Director for quality and safety, Deputy head of the MCC, heads of transportation management services, flight attendants, cargo transportation, and aviation security services. After a discussion with experts and considering that the risk assessment was supposed to be carried out quarterly, the question was formulated as follows: how many events during the quarter do you think that events occur? “Extremely rarely”,

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1 Methods of Safety Risk Management

Fig. 1.13 Processed MF (normalized fuzzy sets)

“Rarely”, etc. The survey results are summarized in a table, and the procedure for processing expert assessments is described in more detail in Appendix 3. Graphs of essential membership functions are shown in Fig. 1.12. MF normalization is performed using the formula:  = nor m( A)  ⇔ μ A (u) = A

μ A (u) ,  hight ( A)

 is the maximum MF value? where hight ( A)− Normalized MF is shown in Fig. 1.13. To further use the MF, they must be reduced to one of the types specified in parametric form. Triangular and trapezoidal functions approximated MF terms of the input linguistic variable “Frequency”. The output variable “Indicator” is an exact number represented by a singleton function. Here are the characteristics from [53] for the MF types that are used in this problem (Table 1.22). The types and parameters of standard MF obtained from the MF shown in Fig. 1.18 are shown in Table 1.23. The membership functions (MF) as they are entered into the Sugeno fuzzy inference system of the MATLAB environment are shown in Fig. 1.14. (b)

Developing a system of rules for the knowledge base.

The knowledgebase is a collection of fuzzy rules of the type IF , then . A rule premise (antecedent) is a term defined by a fuzzy set on. 0 2 4 6 8 10 12

1.2 Method of Safety Risk Management Using Conditional Factors …

53

Table 1.22 Characteristics of the MF types used Function name Triangular

Analytic expression ⎧ ⎪ ⎪ ⎨ 0, u ≤ a or u > a μ(u) = u−a b−a , a < u ≤ b ⎪ ⎪ ⎩ c−a , b < u < c c−u

Trapezoidal μ(u) =

Singleton

μ(u) =

Table 1.23 Types and parameters of accessory functions (MF)

Extremely Rarely rare

1

⎧ ⎪ 0, u ≤ a or u > a ⎪ ⎪ ⎪ ⎨ u−a , a ≤ u ≤ b b−a

⎪ 1, b ≤ u ≤ c ⎪ ⎪ ⎪ ⎩ d−a d−c , c ≤ u ≤ d  1, u = a

Interpretation of the parameter (a, c)—carrier of a fuzzy set, a pessimistic estimate of a fuzzy number b is the maximum coordinate, an optimistic estimate of a fuzzy number (a, c)—carrier of a fuzzy set, a pessimistic estimate of a fuzzy number [b, c]—the kernel of a fuzzy set, an optimistic estimate of a fuzzy number

a is a clear number represented as a fuzzy set

0, u = a

Name of the MF

Type

Parameters

Extremely rare

Trapezoidal

a = 0; b = 0; c = 0,21; d =1

Rarely

Triangular

a = 0,21; b = 1,04; c = 3,13

Likely to occur sometimes

Trapezoidal

a = 0,63; b = 1,71; c = 3,54; d = 8,54

Frequent

Trapezoidal

a = 1,46; b = 4,38; c = 5,21; d = 12,29

Very often

Trapezoidal

a = 5,21; b = 12,29; c = 15; d = 15

Likely to Frequent occur sometimes

Very often

0.5

0 0

2

4

6

8

10

Number of events per 1,000 flights

Fig. 1.14 MF LP “frequency” at the entrance to the MATLAB environment program

12

54

1 Methods of Safety Risk Management

the universal set of the linguistic variable x. In the problem under consideration, this is a statement of the type “If the event happens rarely”. The conclusion of a rule (consequent) is a fact of the “y is b” type. In this problem, the conclusion has the form “the Indicator of the degree of possibility is equal to 1 (2, 3, 4 or 5)”. In this problem, the knowledge base consists of five simple rules from the ICAO matrix. For example, if the frequency of events is “extremely rare”, then the probability indicator is 1; if the frequency is rare, then the indicator is –2, and so on. These rules are shown in Fig. 1.15, as they are introduced into the Sugeno system. (c)

Calculation of an exact number of the capability indicator.

The calculation of an exact number of the capability indicator using fuzzy output in the Sugeno system is performed by merely setting the input frequency value in the fuzzy output visualization window. Figure 1.16 shows, as an example, the calculation of the degree of possibility for the frequency of 7 events per 1000 flights. The indicator of the degree of possibility turned out to be 3.96. Figure 1.17 shows the input–output relationship built by MATLAB.

Fig. 1.15 Creating the Sugeno system knowledge base in MATLAB

1.2 Method of Safety Risk Management Using Conditional Factors …

55

Fig. 1.16 Calculation of P for F = 7 with visualization of fuzzy output

For the problem under consideration, this is a flat curve. In general, a complex surface is obtained (with multiple MF and a fuzzy output function). For practical use of the obtained result, the curve is approximated by a logarithmic function to simplify programming, as shown in Fig. 1.18.

1.2.4 Implementation of the Method in Automated Risk Management System The developed method is implemented in the automated risk management system (ARMS) of the S7 Group of companies. Figure 1.19 shows the system operation’s general scheme, and below is a brief explanation of its operation. To implement the above algorithm, the S7 group of companies developed a computer program called Risk Manager, which combines data entry modules, a database management system (DBMS), and programs for calculating risk indicators.

56

1 Methods of Safety Risk Management

Fig. 1.17 Graph P* = f (F) based on Sugeno’s fuzzy output in MATLAB

The initial data of the ARMS are all recorded deviations in the airline’s production activities, including flight information data, information about failures and malfunctions from the airworthiness maintenance database. As well as data from sources not shown in the diagram: mandatory and voluntary reporting systems, reports on the results of external and internal audits, inspection checks, passenger complaints collection systems, etc. To calculate the degree of possibility (see Sect. 1.2.3) and to “link” the deviation to the flight, data on completed flights is used, which is received promptly from the flight control Center. It also automatically receives information about deviations in piloting according to data and about failures and malfunctions of aircraft systems and units from the airworthiness maintenance database. Designated specialists for each activity that make up the data entry group, linked to the category, enter all other information into the program via the input interface. Assessment of the severity of each deviation is performed by experts in the areas of activity-experienced specialists who hold the positions of heads of services and departments, pilots-instructors for types of aircraft.

1.2 Method of Safety Risk Management Using Conditional Factors …

57

Fig. 1.18 Approximation of the dependence P* = f(F)

The capability, severity, and risk indicators for the category are calculated automatically. The risk acceptance levels were determined empirically, and a decision-making procedure based on the traffic light principle was introduced (Fig. 1.20). The output of the program generates risk charts for each sector and a risk register for any period. Examples of the diagram and registry for the FLT sector are shown in Figs. 1.21 and 1.22. The program allows us to assess the risks of aircraft types and airfields. For example, Fig. 1.23 shows a risk diagram for airfields that “Sibir airlines” flew to in December 2009. Since 2010, ARMS has been an approved program for managing safety risks in the leading enterprises of the S7 group of companies: “Sibir airlines”, Globus airlines, and S7 ENGINEERING, which is fixed in the FSMM of these airlines.

Analysis of each deviaon and assessment of its severity

Risk calculaon, generang reports, charts, and a risk register

DBMS and soware for calculaon

Risk indicators, reports, charts

Entering all recorded deviaons in the producon acvity into the Risk Manager DB program

Data entry group

Production activities of the airline

Fig. 1.19 Scheme of functioning of the Automated risk management system (ASMS) of the S7 group of companies

Operaonal inf. MCC

Flight informaon database.

The database system of FR

Expert group

Preventive and corrective measures Assessment of risk acceptability, development of correcve and prevenve measures

Top managers of the company

58 1 Methods of Safety Risk Management

1.3 Method of Safety Risk Management Based on the Three-Component Model

59

Fig. 1.20 Risk levels and the “traffic light” decision-making principle of “Sibir airlines”

Fig. 1.21 Flight operations sector risk chart (FLT)

1.3 Method of Safety Risk Management Based on the Three-Component Model 1.3.1 Formulation of the Problem. Algorithm for Estimating the Deviation and Event Risk Coefficient (DERC) This method was developed based on the experience of the airline risk management group (ARMS) working under the European commercial aviation FS group (ECAST) program, which is described in [56, 57]. The main difference between this method and others is as follows.

Fig. 1.22 “Sibir airlines” FLT sector risk register

60 1 Methods of Safety Risk Management

1.3 Method of Safety Risk Management Based on the Three-Component Model

61

Fig. 1.23 Risks of the “Ground handling” sector, December 2009

1. 2.

The procedures for assessing the “risk of events that occurred” and the actual risk assessment as a forecast of negative consequences in the future are separated. The ICAO model is not two-dimensional, but three-dimensional, taking into account the system’s vulnerability by evaluating the effectiveness of security barriers.

The terms have the following meanings: Event is everything that happened that had (could have) affected the FS. The deviation and event risk coefficient (DERC) is a conditional quantitative indicator of the risk at the time of the event (deviation). Hazard is the manifestation of a hazard factor (HF) or a combination of them. Hazard risk assessment—(HRA) is a procedure for assessing the risk of hazards, taking into account the impact of safety barriers. The starting point is the data (events) because of identifying the HF. For urgent actions, there is a procedure adopted by the airline. However, at the same time, the initial classification of events is also performed by evaluating the DERC. All data is sent to the database, and statistical analysis is performed to identify hazards. It also performs the current “risk monitoring” and, if desired, all indicators of the FS level. The critical step is to identify hazards, which then become the subject of a detailed risk assessment. Corrective/preventive actions are developed based on both operational, urgent decisions and the DERC procedure, as well as statistical analysis and monitoring of risk and, of course, the HRA procedure. Actions are recorded in the database. Feedback is provided by adjusting activities and expected changes.

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1 Methods of Safety Risk Management

Dividing the process of risk assessment and management into quite natural and understandable components allows us to offer a new method for assessing the “event risk” and forecasting risks. By the doctrine of organizational accidents developed by Rizon [58] and adopted in the ICAO SMM [44], the analysis of security barriers plays a crucial role in preventing such events. Considering the experience of investigating aviation events, the introduction of the concept of an intermediate event, and the division of barriers into two groups seems justified. “Prevention barriers” counter most manifestations of HF. These are the correct decisions and actions of the crew, crosschecking procedures, good cabin ergonomics, and actions of the air traffic control manager (ATC), ground personnel, etc. However, these barriers may not work, and then there is an “Intermediate event” (IE). The IE is the point at which the course of an event begins to get out of control, the boundary between “prevention” and “parry”. “Parry barriers” prevent the IE from moving to the final event with significant damage. That is the correct response of the crew to failures, correcting errors—their own and others, and reserving the aircraft’s central systems. Each event analyzed had a specific outcome, but the event’s possible outcome could be much more difficult. For example, loss of communication or take off without permission may result in a mid-air collision. Thus, the damage is a random variable, and it could take on other values depending on the effectiveness of our prevention barriers and random factors. The DERC methodology is because when evaluating an event, we are concerned about two main issues: (1) (2)

What is the likely negative outcome of the IE in terms of possible damage? To what extent is the IE not developing into a damage event due to “Parry barriers”, and to what extent is it an accident (how lucky are we)? The operator sets efficiency. Possible option: “Missing”—barriers do not work at almost every IE. “Insignificant”— do not work one time for 10 IE. “Average”—does not work one time per 100 IE. “High”—not triggered one time per 1000 IE.

Based on these questions’ answers, the event is evaluated quantitatively in conditional units according to the matrix Fig. 1.24. The numerical values of the DERC correspond to the estimates obtained from the insurance database and given in [57]. The graphical interpretation (Fig. 1.25) shows the exponential nature of the dependence, which can be used to automate the calculation. The lower row of the matrix contains only an estimate equal to 1 because if the event causes minor damage, it makes no sense to estimate the FS stock. The event’s DERC index is evaluated based on the “traffic light” principle (Fig. 1.26).

1.3 Method of Safety Risk Management Based on the Three-Component Model Question 1 What damage could the most likely negative development of the event lead to? Catastrophic damage Emergency damage Medium damage Minor damage

63

Question 2 What is the effectiveness of the remaining barriers between the intermediate and likely pessimistic scenarios of a dangerous situation? Absent Minor Average High 2500 500 100

500 100 20

100 20 4

50 10 2

1

Fig. 1.24 Matrix for assessing the risk factor of events and deviations

Fig. 1.25 The dependence of the DERC from harm for the two performance barriers

Immediately investigate and take action Use for further analysis Just put it in the database Fig. 1.26 Scheme of actions depending on the value of the DERC

As an example of an assessment, let’s consider a real event that occurred in 2012 with an An-124–100 of Volga-Dnepr airlines. When landing at anchorage airport, the plane hit a severe turbulence zone, which led to injuries to people on board and damage to cargo. Here, the IE is a hit in a turbulence; the actual final event is injuries to people and damage to cargo. The prevention barriers (measures that could have prevented the aircraft from entering the turbulence zone) did not work. In this case, there could be an accident (emergency damage). Parry barriers are the crew’s actions, the use of seat belts, and the correct placement and attachment

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1 Methods of Safety Risk Management

of cargo. These barriers worked partially (the crew managed to control, but the people and cargo on board were injured). Can say that the effectiveness of barriers is negligible. So, possible answers to the questions: “Accident” and “Minor”, DERC = 100. We can add up the received DERC indices for any period, calculate relative values, and perform monitoring. DERC is calculated using expert estimates. Experts were asked to evaluate the event based on the available information by answering two questions. Treatment evaluations are performed using the fuzzy set theory (FST). The severity and effectiveness of barriers are considered as linguistic variables (LV). Each LV has four terms (Table 1.24). Based on verbal (fuzzy) expert assessments, several calculations are performed using membership functions. 3.

The degree of belonging of each event to each category of the severity of consequences is calculated using the formula: n 

Ai−m =

i=1

j

i−m .

N

(1.20)

where Ai−m —is the degree of belonging of the i-th event to the m-th category of severity, j E i−m —binary expert assessment (0 or 1) of the i-th event belonging to the m-th severity category; i—event number; j—expert number; N—total number of experts; m—severity category number (1—accident; 2—accident, etc.). 4.

The degree of parry barriers belonging to each event to each type of barrier is calculated using the formula: n 

Bi−k =

i=1

j

E i−k (1.21)

N

where Bi−k —the grade of membership (0 to 1) barriers of the i-th event for the k-th j type effectiveness; E i−m —assessment of the j-th expert (0 or 1) affiliation of the barriers of the i-th moment to k-th type effectiveness; j—number of the expert; N is Table 1.24 Terms of LV “level of damage” and “effectiveness of barriers” LV terms damage level Catastrophic

Emergency

LV termes efficiency of barriers Average

Minor damage

High

Average

Minor

Absent

1.3 Method of Safety Risk Management Based on the Three-Component Model

65

Table 1.25 Simplified assessment of “event risk” Risk category

Title

Description

C

Critical risk

There is a real prerequisite for the AE, a long stop of the aircraft, suspension of the operator’s certificate, license. Barriers are ineffective

B

Significant risk

Hidden HF may lead to AE, comments, or flight delays. Barriers are ineffective

A

Negligible risk

The situation is under control but should be taken into account when monitoring the DERC. The effectiveness of barriers is good

the total number of experts;k is the efficiency of the barriers (1—absent; 2—minor, etc.). 5.

The DERC coefficient of each event is calculated using the formula: D E RCi =

4  4 

Cmk Ai−m Bi−k ,

(1.22)

m=1 k=1

where Ckm is the value of the DERC coefficient in the matrix cell corresponding to the severity category m and the barrier efficiency type k. An example of calculation using the formulas (1.20)–(1.22) and monitoring DERC using the simple moving average method is given in Appendix 4. A simplified DERC method has been developed, where the risk associated with a past event is assessed on a three-level scale (see Table 1.25). Experienced experts in the areas of activity carried out the assessment. Simplified calculation of the DERC * index for the period is performed using the formula: D E RC ∗ =

0, 25N A + N B + 2N V , n

where NB and NC—the number of events by category; n—the number of flights for the period.

1.3.2 Hazard Risk Assessment Algorithm (HRA) The hazard risk assessment (HRA) procedure is the implementation of a strategic risk management method. Hazards are no longer isolated events, but well-defined items that are identified based on the study of a particular set of events expected changes in the activities of the AL, and become the subject of serious analysis.

66 1. Frequency of manifestations of hazard

1 Methods of Safety Risk Management 5. Assessment of the level of risk

Fig. 1.27 Risk assessment matrices DERC procedures

In essence, the already mentioned examples of using the ICAO risk matrix, which were in the second edition of the 2009 ICAO SMM, but were not included in the third edition, represent a hazard assessment. The HRA matrix has apparent advantages over the ICAO matrix. First, it directly takes into account two types of security barriers: prevention and parry barriers. The scheme was developed based on the ARMS group [56, 57], taking into account the authors’ own experience in the development of FS control systems in AL. The first matrix assesses the frequency of occurrence HF and barriers to prevention. The HF frequency is calculated based on statistical data. The values of the frequency ranges in Fig. 1.27 are increased by order of magnitude compared to those in [57] since airlines do not operate with probabilities less than 10–6 . Barriers are filters, some part of events gets through them, and experts evaluate their effectiveness. The second matrix uses the same scale for parry barriers, combines them with the severity level of the outcome, and the likely end States of the system. The result of two matrices in the form of an alphanumeric indicator is the input information for the third matrix, which outputs risk levels according to the “traffic light” principle. Compared to the DERC matrix, two new colors have been added to differentiate management decisions (MD). Risk acceptance assessment and development of measures are carried out according to the procedures adopted by the airline. An example of the MD scheme is shown in Fig. 1.28.

1.3 Method of Safety Risk Management Based on the Three-Component Model

67

Levels of risk of hazard

Fig. 1.28 Scheme of MD acceptance at different risk levels

Let’s look at an example of calculating risk. Let us assume that the Hazard is rolling the aircraft out of the runway after landing due to a decrease in the braking system (maintenance errors). It is calculated that this error occurs approximately once per 1000 flights, i.e. the frequency is 10–3 . An intermediate event is landing on the runway, where it is necessary for the brakes’ full effectiveness. Prevention barriers are actions that would allow us to detect an error before landing. Nevertheless, this defect appears only where full braking is necessary, so there are no barriers to prevention, i.e. the frequency of their failure is 1. We enter this data in the first matrix and get the figure of the indicator “5”. Parry barriers are actions to ensure a safe landing despite a defect. In this case, it is using the reverse until it stops completely. The efficiency is estimated as nine out of ten, i.e. the barrier failure rate is 10–1 . The most likely outcome is rolling the aircraft out of the runway with severe damage without losing life—an accident. According to these data from the second matrix, we have the letter of the risk indicator “C”. Entering the third matrix with the obtained risk indicator “5C”, we have a “red” risk, which is unacceptable and requires severe MD. The main difference between the HRA procedure and the DERC procedure is that the DERC analyzes the events that occurred, which could or could not reach the PS level or develop further. Therefore, some barriers were “broken” we cannot influence the past, and we only take into account the barriers that remain. The conditional “risk” that was there and then is calculated. They can be folded, monitored, and identified as hazards. At the same time, the picture of possible dangers will continuously change. In HRA, we assess the risk of danger as a forecast, assuming that all barriers are in place and not broken. Therefore, it is necessary to discuss the HF, barriers, and severity of the event by calculating all four-risk components. In practice, a particular program “HRA Tool” is used to calculate the risk of danger.

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1 Methods of Safety Risk Management

Table 1.26 Normalizing probabilities for assessing the risk of informing MD Actions

Minor

Average

Emergency

Catastrophic

Not required (risk is acceptable)

≤ 1 • 10−2

≤ 1 • 10−10

≤ 1 • 10−10

≤ 1 • 10−10

Monitoring the situation

[1 • 10−2 − 1 • 10−1 )

[1 • 10−10 − 1 • 10−9 )

[1 • 10−10 − 1 • 10−9 )

[1 • 10−10 − 1 • 10−9 )

Corrective actions

> 1 • 10−1

[1 • 10−9 − 1 • 10−8 )

[1 • 10−9 − 1 • 10−8 )

[1 • 10−9 − 1 • 10−8 )

Urgent events

–

[1 • 10−8 − 1 • 10−7 )

[1 • 10−8 − 1 • 10−7 )

[1 • 10−8 − 1 • 10−7 )

Cessation of activity

–

> 1 • 10−7

> 1 • 10−7

> 1 • 10−7

In contrast to the ICAO matrix, HRA performs a quantitative risk assessment using an algorithm consisting of two operations listed below. 1.

Calculation of the probability of P occurrence of an event with damage using the formula: P = PI E PP R PP A , where P IE is the probability of an initiating event; R PR is the probability of failure of prevention barriers; RPA is the probability of failure of parry barriers.

2.

Comparison of the obtained P-value with the normalized values of the probability of occurrence of events with damage according to Table 1.26, which are close to the probabilities of special situations established by the airworthiness standards of the aircraft [40], and making the appropriate management decision.

The DERC method was used to assess and monitor the FS level in “Volga-Dnepr” and “Air Bridge Cargo” airlines. The DERC and HRA procedures implemented by “Meridian” airlines were positively evaluated by the international business aviation organization’s auditors when conducting an audit for the second level of the IS-BAO standard. See Appendix 3 for usage examples.

References 1. Aviation Occurrence Categories Definitions and Usage Notes CAST-ICAO (2008) 2. Safety Report (2013) IATA, Montreal-Geneva (2014) 3. Etzold B (2013) BCA aviation safety: flight operations safety data source development. In: Boeing safety seminar-Moscow 18–20 June 2013.

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4. LLC Aeronautical consulting Agency (2002) Manual on the automated system’s information support for ensuring safety of civil aviation of the Russian Federation. LLC, M., p 192 5. Russian Federation (1998) Rules for the investigation of aviation accidents and incidents involving civil aircraft in the Russian Federation (PRAPI-98). Approved by decree of the government of the Russian Federation No. 609 of June 18, 1998. Aviaizdat, Moscow, p 140 6. Butov AA (2012) Automated system of forecasting and prevention of aviation accidents in the organization and production of air transportation. In Butov AA, Volkov MA, Makarov VP, Orlov AI, Sharov VD (eds) Izvestia of the Samara scientific center of the Russian Academy of Sciences, vol. 14, 4(2), pp 380–385 7. Sharov VD (2011) Methodology for applying the combined FMEA-FTA method for analyzing the risk of an aviation event. In: Sharov VD, Makarov VP (eds) Scientific Bulletin of MSTU GA, No. 174, pp 18–24 8. GOST R 51901.1-2002 (2002) Risk Management. In: Risk analysis of technological systems. Publishing House of Standards, Moscow, p 40 9. GOST R 51901.13-2005 (IEC 61025:1990) (2002) The management of risk. In: Analysis of the fault tree. Publishing House of Standards, Moscow, p 15 10. Makhutov NA (2009) Ensuring critical objects’ security by reducing their vulnerability. In: Makhutov NA, Petrov VP, Reznikov DO (eds) Problems of security and emergencies, No. 2. VINITI, Moscow, pp 50–69 11. Alexandrovskaya LN et al (2001) Statistical methods for analyzing the security of complex technical systems: textbook. Logos, Moscow, p 231 12. Ryabinin IA (2000) Reliability and safety of structurally complex systems. Saint Petersburg, Politechnika, p 248 13. Kravets VA (1980) Method of “failure tree” in the analysis of safety of oil and gas industry systems Infomneftegazstroy. Kravets, , Moscow, p 40 14. Shvyryaev YV (1992) Probabilistic analysis of nuclear power plant safety. In: Method of execution I. V. IAE Kurchatov, Moscow, p 266 15. GOST R ISO/IEC 31010-2011 (2002) Risk management. In: Risk assessment methods. Publishing House of Standards, Moscow, p 15 16. GOST R 54142-2010 (2002) Risk Management. Guidelines for the application of organizational security measures and risk assessment. In: Methodology for building a universal event tree. Publishing House of Standards, Moscow, p 15 17. GOST R 51901.12-2007 (2002) Risk Management. In: Method for analyzing the types and consequences of failures. Publishing House of Standards, Moscow, p 41 18. Fault tree handbook with aerospace applications prepared for NASA Office of Safety and Mission Assurance NASA Headquarters Washington, DC 20546 August 2002 19. Rukhlinskiy VM (2008) A new criterion to quantify the level of safety. Scientific Bulletin of MSTU GA, No. 135 (11), pp 202–204 20. Zubkov BV (2010) Theory, and practice of determining risks in airlines in the development of safety management system. In: Zubkov BV, Sharov VD (eds) MSTU GA, Moscow, p 196 21. Wentzel ES (1998) Probability theory: studies for universities, 5th edn. (Wentzel ES, ster.) The higher school of Economics, Moscow, p 576 22. Orlov AI (2012) A new paradigm of applied statistics. In: Orlov AI (eds) Factory laboratory. Diagnostics of materials, vol. 78, no. 1, part I., pp 87–93 23. Makarov VP (2013) Method of forecasting and prevention of aviation accidents based on the analysis of the tree of hazard factors: dis.... candidate of technical Sciences: 05.22.14 / Valery Petrovich Makarov, Moscow, p 144 24. Chuntul AV (2012) Development and justification of the methodology for forming psychophysiological indicators of a multi-seat pilot, and development of proposals for the algorithm for accounting for the impact of fatigue on flight activities: research report/head of the topic A.V. Chuntul. Corporation Russian ergonomics and intelligent systems, Moscow, p 52 25. Plotnikov NI (2012) Method of risk assessment for airline safety based on management and forecast of pilot resources: research report/head N. I. Plotnikov. Ulyanovsk, ZAO CPI is “air ticket,” p 64

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26. Butov AA (2012) Automated system of forecasting and prevention of aviation accidents in the organization and production of air transportation. In: Ulyanovsk AA (eds) Stage 4 Adaptation of the developed algorithms and software tools as: research report/head of the topic A. A. Butov. UlSU, Ulyanovsk, p 317 27. Kirpichev IG (2008) Fundamentals of construction and functional development of information and analytical system for monitoring the life cycle of aircraft components. In: Kirpichev IG, Kuleshov AA, Shapkin VS (eds) SRI of CA, Moscow, p 287 28. Sharov VD (2007) Methodology for assessing the probability of rolling out aircraft beyond the runway during landing. Scientific Bulletin of MSTU CA, No. 122 (12), pp 61–66 29. Sharov VD (2013) Assessment of the impact of the environment on safety. Scientific Bulletin of MSTU CA, No. 192, pp 47–54 30. Sharov VD (2007) General approaches to the identification and assessment of the risk of an aviation accident by the group of factors “environment”. Problems of safety, No. 2, pp 21–29 31. Sharov VD (2007) Synthesis of the function of a priori assessment of the level of safety of upcoming flights by the group of factors “Environment”. Problems of safety, No. 11, pp 13–23 32. Sharov VD (2013) About the automation of safety management process in the airline. In: Sharov VD (ed) Proceedings of the II international scientific conference on production organization, second Charnov readings. Collection of works, 7–8 Dec 2012. NP Association of controllers, Moscow, pp 164–175 33. Polyak II (1989) Multidimensional statistical climate models. Gidrometizdat, L., p 183 34. Filatova TV (1989) To assess the validity of aviation weather forecasts. In: Filatova TV (ed) Physical processes in the atmosphere and safety of aircraft: Interuniversity thematic collection (Silvestrova PV). OLAGA, L., pp 41–47 35. Ministry of Transport of the Russian Federation (2012) Federal aviation regulations Provision of meteorological information for aircraft flights. Ministry of Transport of the Russian Federation, Moscow, p 28 36. Federal aviation regulations “Preparation and execution of flights in civil aviation of the Russian Federation.” Order of the Ministry of Transport of the Russian Federation No. 128 on 31.07.09 37. ICAO (2007) Appendix 3 to the Convention on international civil aviation. Meteorological support for international civil aviation. ICAO, p 322 38. Sharov VD (2013) Forecasting, and prevention of aircraft rollouts beyond the runway. In: Sharov VD (ed) Lambert, p 112 39. TP 14082E (2003) Benefit-cost analysis of procedures for accounting for RW friction on landing, TP 14082E, Transport Canada 40. AVIAIZDAT (2004) Aviation rules: Part 25 Standards of airworthiness of transport category aircraft, 2nd ed. IAC, JSC, AVIAIZDAT, p 240 41. Sharov VD (2012) Forecasting, and prevention of aviation accidents in the organization and production of air transport. In: Sharov VD, Makarov VP, Orlov AI (eds) Aircraft Construction In Russia. Problems and prospects: materials of the Symposium with international participation. SSAU, Samara, pp 430–431 42. Sharov VD (2012) Controlling in-safety management. In: Sharov VD, Makarov VP et al (eds) Proceedings of the II international congress on controlling, 2nd edn (Falco SG). NP Association of Controllers, Moscow, pp 222–232 43. Sharov VD (2013) Identification of deviations in the controlling system (on the example of safety monitoring). In: Sharov VD, Orlov AI (eds) Proceedings of the III International congress on controlling green controlling (Falco SG). NP Association of controllers, Moscow, pp 277– 292 44. ICAO (2013) Safety management manual (SMM) Doc. 9859, 3rd edn. ICAO, p 300 45. Dubois D (1990) Theory of possibilities. In: Dubois D, Prad A Per. with FR (eds) Applications to knowledge representation in computer science. Radio and communication, M., p 288 46. Elisov LN (2003) Information and analytical support of flight safety: a Textbook, Part 1. In: Yelisov LN, Baranov VV (eds) MSTU GA, Moscow, p 134 47. ICAO (2001) Guide to the organization of collection, processing, and use of flight information in aviation enterprises of the CA of the Russian Federation. Air transport, Moscow, p 80

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48. Kulavskiy VG (2011) Management of operational safety of the airline. In: Kulavskiy VG, Sharov VD, Kudryavtsev AG (eds) Problems of flight safety, No. 3, pp 2–48 49. IATA (2013) The IOSA Standards Manual (ISM), 7th edn. IATA 50. CAP 760 Guidance on the Conduct of Hazard Identification (2006) Risk Assessment and the prediction of Safety Cases for aerodrome Operators and Air Traffic Controllers. CAA UK 51. Plotnikov NI (2012) The design of transport systems. Air transport: monograph. Ulyanovsk, ZAO CPI is “Air Manager”, p 64 52. OrlovAI (1980) Optimization problems and fuzzy variables. Znanie, Moscow, p 64 53. Shtovba SD (2007) Designing fuzzy systems using MATLAB. Hotline-Telecom, Moscow, p 288 54. Borisov AN (1990) Decision-making based on fuzzy models. In: Borisov AN, Kromberg OA, Fedorov IP (eds) Examples of use. Zinatne, Riga, p 184 55. Orlov AI (2011) Organizational and economic modeling: textbook: in 3 parts, Part 2 Expert assessments. Publishing House of Bauman Moscow State Technical University, Moscow, p 486 56. Guidance on Hazard Identification ECAST (2009) [Electronic resource] access Access mode: https://www.easa.europa.eu/. 57. Nisula J. Operational Risk Assessment. Next Generation Methodology (2009) [Electronic resource] Access mode:easa.europa.eu›essi/documents/ARMS.pdf 58. Reason JT (1997) Managing the risks of organizational accidents. AP Company, Brookfield Vermont USA

Chapter 2

Aircraft Overrun Risk-Reducing Methods

Risk management in an airline involves in-depth analysis and development of unique methods to reduce the risk of events that pose the greatest threat to safety. Rolling an aircraft off the runway is one of the most frequent events that result in accidents [1]. For the period from 2009 to 2013, according to IATA data, on average, about 20 AE related to aircraft overrun occurred annually in the world.

2.1 Description of Aircraft Runway Movement After Landing A differential equation describes the movement of the aircraft on the runway. Let’s give it in the following form: dV R ( f C y − C x)Sρ(V + w) 2 = − fT P g + − g sin θ. dt G 2G

(2.1)

where V—the aircraft’s airspeed; t—the current time; R—total engine thrust (reverse thrust—with a minus sign); G—the weight of the aircraft; fTP —coefficient of friction; g—acceleration of free fall; Cy, Cx—coefficients of aerodynamic forces of the aircraft; ρ—the air density; w—longitudinal component of wind speed; S—wing area of the aircraft; —slope of the runway relative to the horizon plane. Equation (2.1) is not suitable for calculations for the following reasons. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4_2

73

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2 Aircraft Overrun Risk-Reducing Methods

1.

In formula (2.1), fTP is the instantaneous coefficient of friction “wheel - runway surface”, but it is continuously changing due to the operation of anti-skid automation. When driving on a runway covered with a layer of water or slush, there is a phenomenon of hydroglissation, which complicates the calculation. The fTP coefficient for different runway states and different aircraft cannot be measured in advance with sufficient calculation accuracy. The forces acting on an aircraft are complex functions of speed and external conditions.

2. 3.

Modern aircraft have anti-skid automation, and the wheel condition is characterized in terms of the ICAO airport services manual [2] by the slip coefficient: KC =

V AC F T − V pneum × 100%, V AC F T

(2.2)

where Vacft is the speed of the aircraft; Vpneum is a linear velocity of the point on the lower surface of the wheel pneumatics. When the wheel is fully locked, KC = 100%, and when the wheel is free, KC = 0. It is known [3, 4] that the maximum braking efficiency is achieved at KC = 10–30%, and this efficiency is 20–40% higher than for a locked wheel (KC = 100%). Accordingly, we can only talk about the coefficient of friction, taking into account the KC . That is why the famous work of W. Horne i R. Dreber [5] mentions five types of friction coefficient (friction coefficient). In the Russian translation, friction coefficient became the «kofficientom ccepleni» and defined in [2] as «otnoxenie kacatelno coctavlwe cily, tpebywec dl coxpaneni pavnomepnogo otnocitelnogo dvieni medy coppikacawimic povepxnoctmi (povepxnoct pnevmatiki camoleta i ickycctvennogo pokpyti) k peppendikylpno coctavlwe cily, ydepivawe ti povepxnocti (ppiloenie cily pacppedelennogo veca k ppotektopy pnevmatiki BC)» This definition is given without taking into account the sliding coefficient and is sometimes misleading. They talk about two coefficients—friction and adhesion. Table 4 of the State Research Institute of CA No. 294-309-76 shows the relationship between these coefficients. For example, for a wet runway, μ = 0.5–0.7, and f = 0.15–0.25. It can be assumed that in this table μ is an analog of the standard coefficient of adhesion currently accepted in the Russian Federation [6], and fTP is an instantaneous value that can be used in equations of type (2.1). In the textbook [7], the coefficient of friction for a concrete runway is 0.02–0.03. In [3], the values of the maximum coefficients measured by ground vehicles are used as a quantitative indicator of adhesion. However, as shown, these measurement tools are very different, and not all measure the maximum coefficient. Other terms are also used in the literature, often without explanation. For example, the 2012 State Research Institute of CA [8] provides graphs of the “relative coefficient

2.1 Description of Aircraft Runway Movement After Landing

75

of adhesion”, and the well-known development of Aeroflot and Airbus [4] uses the term “coefficient of braking”. To avoid confusion, we will, as is customary in the ICAO airport services manual [2], understand adhesion’s coefficient as a quantitative assessment of braking conditions obtained using ground-based measurement tools designate it as µ. The standard coefficient of coupling of the Russian Federation, we will denote as Kadh . The condition of the runway surface and the effect of hydroplaning. The following runway states are defined. Dry—the runway is neither wet nor covered with precipitation. Damp—moisture does not form a shiny surface. Wet—the runway is covered with a layer of water or other precipitation less than 3 mm, or moisture forms a shiny surface without puddles. Covered with precipitation—more than 25% of the runway surface is covered with a layer of water, or slush more than 3 mm deep, or a layer of rolled snow, or a layer of ice. The presence of precipitation (“pollution”) reduces the effectiveness of braking. If the wheel can not squeeze out the entire layer of liquid between the Pneumatics and the runway surface, the phenomenon of hydroplaning occurs. The resulting hydrodynamic lift can balance the wheel’s share of the difference between weight and lift, and the wheel rises and stops rotating. The speed of the beginning of hydroplaning in the nodes is calculated by the classic formula of Horn [5]: √ V P = 9 P,

(2.3)

where P is the pneumatic pressure in pounds per square inch. According to the results of later research √ by Dutch scientists, a different formula is proposed for modern pneumatics: V P = 6 P. Hydroglissing is dangerous not only by a sharp drop in the friction coefficient but also by the loss of control from the front wheel. That dramatically reduces track handling and increases the risk of rolling off the runway. In detail and an accessible form, the conditions for the occurrence of this effect and its consequences are described in the textbook [7], general methods for recognizing the situation and parrying—in [9], recommendations for specific types of aircraft are contained in [4] and in aircraft flight manual (AFM) type of aircraft, for example, in [10]. Braking devices are used for aircraft braking: – aerodynamic braking equipment (spoilers, flaps, slats); – brakes (including automatic braking and release system); – reverse thrust of the engines.

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Force of braking

Fig. 2.1 Distribution of braking forces during automatic braking in low mode (based on [4])

The forces arising from each of the devices are elements of Eq. (2.1). The distribution of each element’s contribution to the total braking force depending on the speed on the run is shown in Fig. 2.1, based on data from [4]. The figure shows the situation for automatic braking in low mode, in which the wheel braking starts with an unavoidable delay to make the most of the reverse.

2.2 Analysis of the Problem of Information Supply on Runway Surface Conditions There are two primary forms of representation of the friction properties of the runway. The manuals (FCOM, AOM) of foreign-made aircraft use the runway condition and/or braking performance (braking action): bad, average, sound, etc. The Russian-made aircraft AFM uses numerical values of the standard coefficient of coupling of the Kadh following the Manual [6]. Numerical characteristics used in some other countries besides the Russian Federation, such as Canada, in the form of μ or Kadh , look preferable and should provide greater accuracy of calculation and reliability of the problem’s solution. However, this advantage cannot be realized due to the imperfection of measurement methods and the lack of unified approaches to their implementation. It should also be noted that the recommendations for translating the description of the runway condition and verbal braking characteristics into μ values are contradictory, which is especially crucial for Russian-made aircraft. As noted above, for any measurement method, μ will differ significantly from fTP . The difference is explained by the inability to simulate the vehicle braking conditions

2.2 Analysis of the Problem of Information Supply …

77

of the aircraft due to differences in processes at different speeds for different weights and pressure in pneumatics. As noted by the developers of the ATR aircraft [11, p. 6.2] “… in order to get a good braking rating for the ATR-72, performing a landing with a mass of 15,000 kg at a speed of 95 knots with a pressure in the pneumatics of 144 PSI, the airport must have the same free ATR-72”. The coupling and the ratio of coefficients fTP and μ differ for different runway states and speeds. In the manuals, there is a mixture of the concepts of coefficients and indices of friction, braking, adhesion, etc. That is partly due to translation inaccuracies. That makes it difficult to understand and use the recommendations in practice. Table 2.1 shows calculations based on the schedule of SRI of CA #. 294-309-76 and the graph in Fig. 5 from the research report of the safety state center in 2012 [8]. It can be seen that the ratio between fTP and μ is complex, and currently, there are no explicit algorithms for their recalculation. The situation is complicated by the fact that μ is measured differently in different countries. Figure 2.2, based on [2], shows different instruments and their μ values on the artificially moistened runway surface. Currently, the world’s civil aviation does not have uniform devices and methods for measuring runway surface characteristics. This problem is discussed in detail in [12]. NASA scientists are addressing the problem of establishing accordance between these measurements. They developed the ASTME-2666-09 Standard and proposed the International friction index (IFI). Work on the unification of runway measurements is also being carried out under the auspices of EASA. In 2008, the RuFAB project [13] was launched to study the runway characteristics and the braking process of the aircraft. In the EASACS-25 certification requirements, changes were made to classify runway conditions and the list of pollutants. It is proposed to modify the ICAO SNOWTAM format. All initiatives remain at the level of recommendations, and ICAO SIG 329 of 2012 states that “there is no relationship between the measured coefficient of adhesion and the system response to aircraft”. If the crew does not receive a response, they must evaluate it based on verbal information. In [12] are excerpts from two documents of the Russian Federation: Guidelines of the Russian Federation [6] and the collection of aeronautical information (AIP of the Russian Federation), which contain different values of the Kadh for the same braking performance. These translation tables are often misinterpreted. Table 2.1 Calculation of fTP by μ, V = 140 km/h

The condition of the runway

μ

fTP /μ

fFP

Dry

0.7

0.42

0.315

Layer of water

0.35

0.12

0.042

Snow

0.3

0.25

0.075

Slush

0.3

0.1

0.03

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2 Aircraft Overrun Risk-Reducing Methods

Fig. 2.2 Readings from different instruments on the same surface

They are developed based on the snow warning codes (SNOWTAM) from Appendix 15 and are duplicated in [2] as Table 4.1. In the same place, paragraph 4.5.4 says that this table and descriptive terms are developed only based on data obtained on a surface covered with snow and ice and should not be taken as a model for all conditions of runway surface contamination. Some confusion is caused by the fact that at some airfields of the Russian Federation, the ATIS report includes not a normative, but a measured Kadh . So, the factors of low awareness about the state of the runway are. 1. 2.

3.

4.

providing outdated information to the crew; non-compliance in several states with the ICAO Standard for measuring and coupling on each third of the runway, rather than the average value along its entire length; misrepresentations in the reception and interpretation of the information received by the crew, partly due to insufficient knowledge of the English language; lack of reasonable methods for calculating the coupling coefficients obtained using various devices and methods.

2.3 Methods of the Crew Situational Raise Awareness …

79

2.3 Methods of the Crew Situational Raise Awareness About Runway Conditions 2.3.1 Institutional Arrangements and Improvement of Interpretation Methods for Descriptive Information To reduce the risk of rolling out, airlines are developing preventive measures. For Volga-Dnepr airlines, events were relevant when flying to Canada, especially to the gander airfield, where two An-124 overruns took place in 1998 and 2007. Note that at the gander airfield in 1977–2007, there were 15 overruns, five of them with Russianmade aircraft [14]. Executive agreements were reached on factors 1 and 2 (see clause 2.2), and additional training was conducted on factor 3. Factor 4 proved to be the most difficult in terms of developing activities. The crews of Russian-built aircraft are required to convert information about the state of the runway in the parameters adopted in CA RF, which the User [6] are (1) frictional properties of the coatings; (2) precipitation; (3) the thickness of the sediment layer; (4) is the fraction of the area covered with dirt. These AFM parameters are used by the crew to calculate the takeoff and landing characteristics and make decisions. For parameters (2)–(4), there are correspondences between English and Russian terms. Difficulties arise when interpreting information (1). In Canada, the runway surface condition message (AMSCR) can contain either only descriptive information (RSC—Runway Surface Conditions), or this information and add a numerical expression for the adhesion index (CRFI—Canadian Runway Friction Index). The development of recommendations to crews on the use of RSC information is described in this section, and the work on establishing compliance between the CRFI and the Kadh is covered in a separate Sect. 2.3.2. To obtain numerical Kadh , we recommend first calculating the CRFI based on a table from AIP Canada (see Table 2.2). In this table, 95% crfi confidence intervals are set for runway states. For practical use, the table has been adjusted to increase the CRFI. Taking the hypothesis of a normal CRFI distribution, we calculate the left boundaries of the CRFI intervals for each runway state using the following formula:  P(a < x < b) = F ∗

b−M σ



 −F∗

 a−M , σ

where a is the left border of the interval; b—right border is equal to +∞; F*—normal distribution function; M—average value of each interval from the Canadian Table-4a; $—root-mean-square (standard) deviation, For F* = 0.05, we find the argument of the function F*:

(2.4)

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2 Aircraft Overrun Risk-Reducing Methods

Table 2.2 Translated into Russian Table-4 from AIP Canada

a−M = −1,645 σ a = M − 1,645 × σ

(2.5)

The new left borders are calculated using the formula (2.5) (see Table 2.3).

2.3.2 Experimental Detection of Correlation Dependence Between Canadian and Russian Runway Friction Indices Statement of the problem and methods of measurement There are no CRFI conversion tables in the Kadh , and air carriers develop them themselves. In [12], there are tables of conversion of some AL of the Russian Federation. For the same CRFI, the differences in the Kadh in the tables reach 0.25. A unique methodology was developed by the order of Volga-Dnepr airlines to obtain a reliable relationship between CRFI and Kadh at the Flight Research Institute named after M. M. Gromov, and joint research was carried out with the assistance of specialists from Gander airport, Canada. The work is described in the article [15] and the report [16]. The regulation from [6] was used: if there are no measuring instruments at the airport, the braking assessment can be performed based on braking results before stopping a car with locked wheels. The braking performance of [6] can also be estimated using a pendulum decelerometer. When braking the car, the maximum

2.3 Methods of the Crew Situational Raise Awareness …

81

Table 2.3 Calculation of recommended CRFI values for use by crews based on descriptive runway condition characteristics M

$

CRFI recomm.

0.7

0.650

0.025

0.61

0.3

0.6

0.450

0.075

0.33

Wet, concrete, water layer 0.25–0.76 mm—Wet, concrete

0.4

0.55

0.475

0.038

0.41

Heavy rain 0.76–2.54 mm, heavy rain

0.27

0.3

0.285

0.007

0.27

Loose snow 3–25 mm on Packed snow—loose snow 3–25 mm on Packed snow

0.19

0.37

0.280

0.045

0.21

Loose snow 3 mm or less on Packed snow—loose snow 3 mm or less on packed snow

0.12

0.31

0.215

0.048

0.14

Loose snow 3–25 mm on ice—loose snow on ice 3–25 mm

0.12

0.25

0.185

0.033

0.13

Loose snow on ice 3 mm or 0.08 less—loose snow on ice 3 mm or less

0.27

0.175

0.048

0.10

Loose snow on pavement 3–25 mm—loose snow on pavement 3–25 mm

0.21

0.39

0.300

0.045

0.23

Loose snow on pavement 3 mm or 0.16 less—loose snow on pavement 3 mm or less

0.76

0.460

0.150

0.21

Compacked snow covered with 0.23 sand—compackted and sanded snow

0.47

0.350

0.060

0.25

Packed snow—bare compackted snow

0.12

0.31

0.215

0.048

0.14

Sanded ice—sanded ice

0.19

0.35

0.270

0.040

0.20

Pure melting ice—wet ice

0.07

0.22

0.145

0.038

0.08

The condition of the runway

CRFI table 4 Left

Right

Damp, water layer less than 0.25 mm—damp

0.6

Wet, asphalt, water layer 0.25–0.76 mm—Wet, asphalt

deviation of the pendulum is fixed. The product of the acceleration value by 0.1 is equal to the standard coefficient of adhesion. The CRFI index is the decelerometer reading when a vehicle is braking on a runway with locked wheels at 50 km/h. The vehicle requirements are listed in the Canadian aerodromes operating manual. The CRFI measurement technology practically corresponds to the recommendations of the RF manual [6] on the definition of Kadh .

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2 Aircraft Overrun Risk-Reducing Methods

Fig. 2.3 The vehicle used for measurements at Gander airport

All measurements were made at Gander airport on March 11–14, 2008. The experiment used a standard vehicle and equipment used to measure CRFI at Gander airfield (Fig. 2.3). The car was retrofitted with a pendulum decelerometer made on FRI. The braking time was recorded using two stopwatches (Fig. 2.4). The measurements were carried out at different runway conditions (Fig. 2.5). Before starting work on the runway, a working area with a dry surface was selected. The car was decelerated until it stopped altogether. At the same time, the brake pedal was fixed in various positions. The method used is conventionally called the method of imitation of polluted. The essence of it is to use the braking of the wheels of the car with different intensities. Weak braking—with a slight compression of the pedal—corresponds to a slippery runway. The brake pedal’s full compression—in the “floor” with the wheels swinging on a dry runway corresponds to the absence of precipitation. Conditionally, the brake pedal positions were divided into five equally distributed positions—from minimum braking (slippery runway) to braking with locked wheels (dry runway), see Fig. 2.6. For each position, from 10 to 20 test measurements were made, depending on their stability, the total number of complete decelerations performed on a dry runway is more than 100, and the total number of test measurements is 72. The experiments recorded: runway index CRFI; pendulum decelerometer readings; time from the start of braking to stop. On a snow-covered runway, the measurements were performed under full braking.

2.3 Methods of the Crew Situational Raise Awareness …

83

Fig. 2.4 A set of measurement tools

The current atmospheric conditions were also recorded, the start and end times of measurements were noted, and the runway surface was photographed. Based on the measurements’ results, the protocols given in the report were drawn up [16]. The pendulum decelerometer made in FRI (Fig. 2.7) is similar to 1155 M in the principle of operation. Its scale is marked so that the pendulum’s vertical position corresponds to 0°, the horizontal −90°. With the help of the mobile ground laboratory (MGL) used in the FRI to evaluate the takeoff and landing characteristics and adhesion on the runway (Fig. 2.7), the calibration dependence of the Kadh on the angle of deflection of the pendulum was obtained. The dependence of the standard coefficient of adhesion on the car’s braking performance was also obtained, which is shown in the research report [16]. Experimental results and their use in the airline industry In total, ten series of experiments were performed with braking the car to a complete stop on dry (six series) and snow-covered (four series) Runway (see Fig. 2.13). Kadh corresponding to CRFI was calculated from two data sets:

84

2 Aircraft Overrun Risk-Reducing Methods

Fig. 2.5 Measurements were performed at different runway conditions

Fig. 2.6 Diagram explaining how to simulate a polluted runway

Fig. 2.7 Mobile ground laboratory, ATT-2, and pendulum decelerometer

2.3 Methods of the Crew Situational Raise Awareness …

85

Fig. 2.8 Processing of measurements on a runway covered with precipitation

– from the records of the deflection of the pendulum decelerometer—using the calibration dependence; – according to the calculated values of braking efficiency—using a correlation relationship. A separate table (Fig. 2.8) summarizes the adhesion assessment on a snow-covered runway. A layer of snow up to 40…70 mm. The Kadh values are within the spread of CRFI values. In [16], the graphs of the movement of the MGL when measuring the Kadh under the same conditions are given. The Kadh values for these records almost coincide with the results of experiments in Gander airport, which confirms the reliability of the obtained values. The correlation matrix obtained using the STATISTICA-7 package based on all results has the form:

Here, the Kr is the Kadh according to the measurement data of the pendulum decelerometer; Kx is the Kadh for the calculation of braking efficiency. Below are the results of the regression analysis in the STATISTICA-7 software package.

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2 Aircraft Overrun Risk-Reducing Methods

Kadh normative

Fig. 2.9 CRFI and Kadh dependency based on all data

Figure 2.9 shows the results of processing all data. There is a significant correlation between CRFI and Kadh with a correlation coefficient r 1 = 0.933. The coefficient of determination r- 2 = 0.8693 shows that the constructed regression explains the central part of the total spread. A small standard error of 0.059 indicates a slight scattering of the observed values relative to the regression scattering line. The obtained results allowed us to draw the following conclusions: 1.

2.

3.

The developed method provides an experimental determination of the correlation between the Kadh of the Russian Federation and the Canadian CRFI index. The CRFI measurement Technology meets the recommendations of Airfield operations manual RF-94 for determining the Kadh using a pendulum decelerometer. The CRFI value corresponds to the standard runway-coupling coefficient with sufficient accuracy for practical use.

2.3 Methods of the Crew Situational Raise Awareness …

87

The corresponding changes were made to The Flight Manuals of Volga-Dnepr and AirBridgeCargo airlines. The Russian aviation authorities, IAC, and the Ministry of Transport of Canada were informed about the work results.

2.4 Overrun Risk Management Method Based on Statistical Modeling 2.4.1 Development of a Mathematical Model for Aircraft Runway Movement and Its Implementation in the Program “Overrun Probability” Traditional flight information processing (FI) [17] does not allow us to determine how close the crew was to overrun on a particular flight. The facts of flight, long alignment, late braking of the wheels, early switching off (not using) the reverse, increased taxiing speed from the runway do not yet indicate the overrun risk. With a long and dry runway, such actions may be justified by the pilot’s desire to comfort passengers and release the runway faster. According to the Kadh and on foreign airfields, the crew evaluates the effectiveness of braking on the runway by other coefficients. Different instruments measure them, but experience [4, 14] and research [18] show that the coupling on the runway at the same Kadh has a stochastic character. Several random parameters must be taken into account to detect dangerous landings. If the risk of the minimal overrun is considered unacceptable and informative risk characteristic is the probability of event A P( A : L f > Ll) > Pmin ,

(2.6)

where L f —factual landing distance; Ll—available landing distance; Pmin —some acceptable probability of overrun; L f it can be divided into three sections (see Fig. 2.10): Lf = L1 + L2 + L3 ; where L 1 —is the air section from the runway threshold to the touchpoint; L 2 the section from the touchpoint to the beginning of full braking of the wheels; L 3 —braking section. The diagram of the main sections of the landing distance is shown in Fig. 2.10. It is assumed that the aircraft is working correctly, the reverse is enabled at the touchpoint, and braking is started at a distance L 2 from the touchpoint. The values L 1 and L 2 are assumed to be known from the interpretation of FI.

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2 Aircraft Overrun Risk-Reducing Methods

Fig. 2.10 The main sections of the landing distance of the aircraft

Approximate mileage formulas can be used to calculate the braking area. For an-124-100,1 the formula has the form: L2 + L3 =

(V m ± W )2  , 2g GR + μ ± 

(2.7)

where V m—instrument speed of the beginning of braking; W —component of wind speed in the direction of the runway; R—the average value of the thrust reverse; μ—coefficient of friction on the runway; —the slope of the runway; g—acceleration of free fall; G—landing weight. Under the accepted assumptions (see clause 2.1), as can be seen from the formula (2.7), L 3 it is a function of one random argument—the coefficient μ, which in turn depends on the measured K cy and some coefficient of efficiency of the brake system K m , taking into account random deviations of the system operation: μ = f (K adh , K m ).

(2.8)

The other parameters are not random, but even with this approach, it is analytically tricky to calculate the probability (2.6). Therefore, we use statistical modeling (Monte Carlo method—[19]). When developing a model for each type of aircraft, we can use formulas similar to (2.7), or computer programs for calculating takeoff and landing characteristics. The approach given in the work of the group of authors of the ICS RAS (A. M. Shevchenko et al.) [20] was also considered, where the results of research on the 1 Tolmachev V. I. et al. Aerodynamics of the An-124 aircraft (1 edition). – The company p. A-3395,

1987.

2.4 Overrun Risk Management Method Based on Statistical Modeling

89

assessment of the energy state of the aircraft at characteristic points of the trajectory and their application to improve the quality of control at critical stages of flight are presented. Ensuring the safety of takeoff and landing within the runway, clear runway zone. Air approach lanes, respectively, is considered a “terminal task with a difficult to predict or unguaranteed outcome”. The main obstacle to practical use is the lack of data in domestic sources for the function (2.8) and the inability to obtain a distribution F(μ) for each K adh . Therefore, for the implementation of the methodology, an option was chosen for Canadian airfields. In Canada in 1996–98, during test flights of various aircraft types, dependencies on μ the Canadian CRFI were established [14, 21]. As a result, the AIP Canada landing distance tables recommended for all types of aircraft were modified. That allows us to use the formulas from [14] to construct a mathematical model published in [22]. In the formulas below (2.9)–(2.12), speeds are in knots, distances are in feet, and weights are in pounds. A site L 3 based on accepted signs:   L3 =

2  V  m × 1,688 × RF 64,348 × (−ACC)

(2.9)

where V  m = Vm ± W —the ground speed of the start of full braking; W —oncoming (passing) component of the wind. R F—the reverse factor, which is calculated as follows: F R = 0.65 + 0.6 ×

μ − 0.25 × μD



μ μD

2 ,

(2.10)

where μ D —is the coefficient of friction on a dry clean runway (with CRFI = 0.8). Additional reduction μ due to suboptimal braking μ = μ M (1 − K m /100),

(2.11)

where μ M —coefficient of friction at maximum braking performance; K m —braking efficiency in%, (K m = 0.—“ideal” braking). Characteristics of braking on the ACC run: −4.62 × V  m 600 −μ+ √ W GT W GT × 2 (−0.1813 + 0.2087 × C R F I ) × (V  m)2 + , W GT × 2

ACC =

(2.12)

where WGT is the aircraft’s landing weight, pounds. Distribution functions F(μ) and F(K m ) are obtained by processing graphs [18, 21] for CRFI = 0.2; 0.3 and 0.4. One of the histograms is shown in Fig. 2.11.

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2 Aircraft Overrun Risk-Reducing Methods

0.25 f(μ) 0.20

Normal distribution нормальное

probability density

распределение 0.15

0.10

0.05 M

0.00 0.07 0.08 0.09 0.11 0.12 0.13 0.14 0.16 0.17 0.18 0.19 0.20 0.22 Fig. 2.11 Distribution of the probability density of the friction coefficient at the maximum wheel braking efficiency (K m = 0) for CRFI = 0.2

It turned out that the distribution μ for all CRFI is close to normal, which allowed us to use the “method of six random numbers” [19]. The dependences of mathematical expectation estimates M(μ) and standard S D(μ) deviation from C R F I are linearly approximated by the OLS: M ∗ (μ) = 0.491 × C R F I + 0.0380; S D ∗ (μ) = 0.129 × C R F I − 0.0035.

(2.13)

The density of the K m distribution is also obtained based on graphs from [23]. For K m < 5%, as shown in [24], the histogram is approximated by the linear OLS, and for K m ≥ 5%—by the exponential dependence. Based on the derived formulas, the distribution functions and functions inverse to the distribution functions necessary for modeling are obtained using known relations. For K m < 5%: f (K m ) = −0.07483 × K m + 0.38281, F(K m ) = −0.03742 × K m2 + 0.38281 × K m , √ 0.1465 − 0.1497 × F(K m ) . K m = 5.115 − 0.0748 For K m ≥ 5%:

(2.14)

2.4 Overrun Risk Management Method Based on Statistical Modeling

f (K m ) = 0.00569 exp(−0.2331 × K m ), F(K m ) = 1 − 0.0244 exp(−0.2331 × K m ),  m) − ln 1−F(K 0.0244 . Km = 0.2331

91

(2.15)

The ratios (2.13)–(2.15) are used in a computer program. Based on the described algorithm, the computer program “Overrun Probability” was developed, which is registered in Rospatent (Federal Service for Intellectual Property), Certificate No. 2008615435 and since 2007 used by Volga-Dnepr airlines. Figure 2.12 shows the operation interface of the program. The initial data for the calculation is obtained from the FI records for each landing, and the runway data is obtained from the aeronautical information collection. Acceptable levels Pmin were set in the form of “average” probability 3 * 10–4 and “high” probability 3 * 10–3 . The Levels were assigned based on the calculation of the calls that ended with real overrun and taking into account the overrun statistics. The procedure for rapid response in cases of exceeding the specified thresholds has been developed. After the airline conducted in 2008, research, which is presented in paragraph 2.3 of this book, the program “Overrun Probability” can be used to assess the risk of rolling out for any airfield.

Fig. 2.12 The working interface of the “Overrun Probability” program

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2 Aircraft Overrun Risk-Reducing Methods

2.4.2 Joint Application of the Program “Overrun Probability” and Multidimensional Statistical Methods of Data Analysis 2.4.2.1

Research Objectives. Primary Data Processing

The use of FI makes a significant contribution to improving safety, but the information content of FI arrays is not yet entirely in demand. Methods of multidimensional statistical analysis of FI, proposed by specialists of MSTU CA I. V. Khamrakulov and B. V. Zubkov in the 80s [25], were not widely used due to a lack of software. Currently, software products, such as STATISTICA-7, with guidelines [26] allow you to perform analysis. Such calculations were performed by Volga-Dnepr airlines in 2006–2008. The calculation methodology and preliminary results were published in 2006 in [27]. At the same time, new programs need to be developed to solve specific tasks. This section provides a methodology for the joint application of the “Overrun Probability” program, conventional statistical methods, and methods of multidimensional statistical analysis. During the analysis, the following tasks were set: 1. 2.

Evaluate the accuracy of maintaining the specified landing parameters by the crews; Identify the presence of hidden factors that determine the safety of landing.

The study uses data on 137 landings of the Volga-Dnepr An-124-100 aircraft in Canada in the winter of 2008–2009. A vector of 15 variables describes each landing: LDA—landing distance available, m; G—landing mass, t; WND—wind component along the runway, m/s, “+”—oncoming, “−”— following; CRFI—Canadian runway adhesion index; H—height of the span of the runway threshold, m; Vtd —landing speed, km/h; dV—deviation of the Vtd landing speed from Vref , km/h; Ltd —removal of the landing point from the runway entrance threshold, m; Ny —usual landing overload, unit g; Vr1 , Vr2 , Vr3 —the speed of switching on, intermediate stop, and turning off the reverse, km/h. Vbr —speed of the beginning of wheel braking on the run, km/h, Ltd —removal of the landing point from the runway entrance threshold, m; P—probability of rolling out by “Overrun Probability”, units. Statistical characteristics of variables are shown in Table 2.4, where M— average, SD—standard deviation, Min—minimum value, Max—maximum value, As—asymmetry, Ex—excess.

329.70

−3.86

2713.00

3109.00

−2.21

2.91

Min

Max

As

Ex

18.42

213.50

16.68

134.27

316.01

3056.97

SD

G

M

LDA

0.46

0.69

2.71

−1.83

0.70

0.40

−4.00

16.00

0.08

0.66

CRFI

3.88

3.71

WND

Table 2.4 Basic statistics of variables

1.00

0.80

18.00

7.00

1.91

11.31

H

2.53

−0.88

288.00

217.00

11.17

260.09

Vtd 9.74

3.55

1.09

−0.25

31.72

−27.29

dV

0.06

0.05

850.00

170.00

124.64

461.05

Ltd

2.02

0.94

1.71

1.04

0.11

1.22

Ny

15.60

−2.00

170.00

0.70

18.10

125.04

Vr1

0.15

0.15

140.00

27.00

19.99

87.45

Vr2

0.19 −0.26

−0.54

2440.00

540.00

394.43

1479.05

Lbr

−0.33

250.00

60.00

41.62

173.36

Vbr

0.88

−0.01

125.00

27.00

16.42

71.85

Vr3

21.04

4.17

0.000230

0.000000

0.000031

0.000013

P

2.4 Overrun Risk Management Method Based on Statistical Modeling 93

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2 Aircraft Overrun Risk-Reducing Methods

The following parameters were considered: H, dV, Ltd , Ny . Their distributions are close to everyday (for example, in Fig. 2.13). Standard values of parameters are defined in the AFM [10], and acceptable deviations are estimated in [28] points. Deviations of parameters beyond the “4” rating are considered unacceptable. Table 2.5 shows the probability of parameters exceeding the “4” rating based on the obtained statistical data. It can be seen that the most likely to exceed the tolerances at the lower limits of the height of the threshold H span and the landing speed Vtd . That is due to pilots’ desire to reduce the risk of rolling out, but simultaneously increases the risk of landing before the runway. As an example of parameter analysis, consider Vr3 . The recommended Vr3 value in the AFM is 90 km/h, but we can use reverse before stopping. At Vr3 < 50 km/h,

N land

L td m

Fig. 2.13 Histogram of the distribution of the removal of the landing point Ltd by the number of landings Nland

Table 2.5 Probabilities of landing parameters exceeding acceptable limits Parameters

Normative value (AFM)

The deviation range is “4”

Deviation limits on “4”

Probability of going beyond “4”

N, m

15

±7

≤8

0.042

≥22

0.000

≤15

0.028

dV, km/h

0

±15

>+15

0.012

Ny , unit

≤1.5

+0.2

≥1.70

0.001

Ltd, m

150–600 from the start of the runway

−50; + 200

≤ 100

0.002

≥800

0.003

2.4 Overrun Risk Management Method Based on Statistical Modeling

95

Nland 60

P

Fig. 2.14 Distribution of overrun probability by the number of landings

the risk of engine damage increases. The probability of such a Vr3 is 0, 092, which we should pay attention to. The probability distribution of rolling out at M = 0.000013, SD = 0.000031, and Max = 0.00023 for small values approaches exponential (Fig. 2.14). The probability threshold set in the “Overrun Probability” program was not exceeded. This can be explained both by the high quality of piloting and the responsible attitude of airport services to the runway condition.

2.4.2.2

Principle Component Analysis Method for the Comprehensive Analysis of Landing Data

From the correlation matrix (Table 2.6), it can be seen that the most correlated with P are the speeds of Vtd , Vbr , runway length, Kadh , removal of landing points Ltd and the beginning of Lbr braking from the runway threshold. Factor analysis was used to identify “enlarged factors” [25] and dependencies, namely the principal components’ method in rotation by the Varimax method. The number of factors k = 5, the minimum eigenvalue of the matrix, is 1,000. From the table of eigenvalues of factors (Table 2.7), it follows that the five main components explain more than 65% of the total variance of the initial features. When interpreting the factor model (Table 2.8), we note that none of the factors carries a determining load for P, and the factor F 1 has the highest load for this parameter. This factor accounts for about 12% of all deviations and has loads close to 1 for the start speed of the V br braking and removing the L br breaking point from the runway threshold. Therefore, this factor can be interpreted as “inhibition”.

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2 Aircraft Overrun Risk-Reducing Methods

Table 2.6 Correlation matrix of the original data

Table 2.7 The cumulative sum of the eigenvalues

The F 2 factor took over all the loads for the speeds related to the reverse. Let’s call it “using reverse”. The F3 factor has the maximum Vtd loads and the deviation of this speed from the AFM. Maintaining speed is an essential element of safe landing and preventing overrun. This factor is referred to as “maintaining the landing speed”. The F4 factor explains the variability of landing point removal and overload. Let’s call it “alignment” and look at it in more detail. Figure 2.15 shows factor loads in F 1 /F 4 coordinates, and Fig. 2.16 shows the overrun and overload probability ratios. It can be seen that Ny and Ltd are located at opposite ends of the F 4 axis, which shows an inverse statistical relationship (r correlation coefficient = −0.47). The sign P in this factor is the opposite of the sign Ny and coincides with the sign Ltd. Figure 2.16 shows that small values of Ny < 1.3 correspond to landings with overrun probabilities. As a result of factor analysis, five enlarged factors were identified with the following degree of significance:

2.4 Overrun Risk Management Method Based on Statistical Modeling

97

Table 2.8 Factor loads (selected values greater than 0.7)

Factor 1 = 2,342—wheel braking on the run; Factor 2 = 2,294—the use of reverse thrust; Factor 3 = 2.104—maintaining landing speed; Factor 4 = 1.760—alignment on landing; Factor 5 = 1,315—characteristics of the runway. The distribution of significance by factors is a reference point for preventive work to prevent rolling out and ensure a safe landing in general. Thus, the use of statistical analysis of FI estimates of the probability of rolling out using a particular program allowed: – identify the parameters with the most significant risk of going beyond the limits of piloting (understating the height of the runway threshold, low landing speed, late shutdown of the reverse), and quantify these risks; – determine the correlation between the probability of rolling out and landing parameters; – to identify the broader factors of safety of landing and distributing them in order of importance; – it clearly shows an increase in the risk of rolling out when the pilot tries to perform the softest possible landing due to long alignment.

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2 Aircraft Overrun Risk-Reducing Methods

Fig. 2.15 The factors of “inhibition” and “alignment”

Nland

P

Ny, unit g.

Nland – number of landings Fig. 2.16 The ratio of the probability of rolling out P and overloading Ny

2.5 Overrun Prognostication

99

2.5 Overrun Prognostication 2.5.1 Modification of “Overrun Probability” Program Mathematical Model for Prognostication Task The program “Overrun Probability” allows you to identify potentially dangerous from rolling out landings from the number of completed ones. That helps us identify hazards and their combinations and take corrective measures to reduce the risk. This method is called “proactive” in managing safety following the ICAO SMM [29]. However, ICAO strongly recommends developing a “forward-looking” approach to safety management. In terms of overrun prevention, the implementation of this approach is the overrun prediction program “Overrun Prognosis”, created based on the “Overrun Probability” program. The scheme of the landing distance of the aircraft in Fig. 2.10 and the probability of event A is evaluated: P(A : (L 1 + L 2 + L 3 ) > L ranway ). The initial data for the calculation is the same as in the “Overrun Probability” program, but in this case, they are all calculated, predicted, or modeled. Below is a list of the source data and its source, and the parameters that need to be predicted are underlined. GL —landing weight of the aircraft calculation of the known takeoff weight and fuel consumption, obtained from the computer flight plan; VT —the speed of the beginning of wheel braking, km/h—is predicted based on past flights of this PIC; D—wind direction, hail;—weather TAF; U—wind speed, m/s.—the weather TAF; Kadh —standard coefficient of adhesion—predicted by a special method; L1 —from the threshold of the runway to the point of touch of the aircraft—is predicted based on past flights of this PIC; L2 —from the point of contact to the point where the wheels start braking—is predicted based on the previous flights of this PIC; L3 —from the starting point of braking to a complete stop—modeled by the Monte Carlo method as well as in the “Overrun Probability” program; LDA—landing distance available equal to the length of the runway. Thus, you need to predict four parameters. Prediction of the Kadh adhesion coefficient. Currently, weather information providers do not officially provide the Kadh forecast. The methodology for predicting Kadh is given in Appendix 5.

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2 Aircraft Overrun Risk-Reducing Methods

Prediction of VT , L1 , L2 . The prediction is based on the analysis of FI for 40–50 previous flights of this PIC. If we consider the average value, the most acceptable result is the median score. We can use a point estimate as a sample median by numbering the sample elements in non-decreasing order and selecting a value: – [(n + 1)/2]—th ordinal statistics, if n is odd; – the average of the [n/2] and [n/2 + 1] ordinal statistics, if n is even. However, any average value distorts the forecast because maintaining particular landing parameters depends on the actual conditions. For example, VT is determined by the AFM by the landing mass [10], which in the predicted flight may differ significantly from the average for 50 flights. As a result, taking the average value of VT , we will knowingly make a significant error in the forecast. The braking speed also depends on wind conditions, runway length, runway condition, and other parameters. Removing the L1 touchpoint from the runway threshold also depends on several parameters. For example, when landing on a short and precipitation-covered runway, the pilot will reduce L1 mostly than when landing on a long and dry runway. The problem of establishing statistical dependence in such cases can be solved by constructing a multidimensional regression model. For the developed model for L1 , L2 , VT , it is proposed to limit the three parameters; the GP’s landing weight, the length of the runway Lrunway, and the coefficient of adhesion of the Kadh . A linear regression model following c [30], for example, for VT will have the form (indexes for G, L, and K are omitted for convenience): VT i = A0 + A1 G i + A2 L i + A3 K i + ei , i = 1, 2, . . . , 50.

(2.16)

where A0 , A1 , A2 , A3 are linear regression coefficients; ei —random errors. Concerning the distribution of ei errors, assumptions from the textbook of Prof. A. I. Orlov [30], namely: – errors have zero mathematical expectations M{ei } = 0; – the results of observations have the same variance D{ei } = σ 2 ; – observation errors are uncorrelated, i.e. cov{ei , ej } = 0. The optimal solution can be obtained using the least-squares method as a solution to a system of linear equations:

2.5 Overrun Prognostication

101

⎧ ⎫ 50 50 50 50     ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ Vi = 50 A0 + A1 G i + A2 L i + A3 Ki ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ i=1 i=1 i=1 i=1 ⎪ ⎪ ⎪ ⎪ 50 50 50 50 50 ⎪ ⎪      ⎪ 2 ⎪ ⎪ G i + A1 G i + A2 L i G i + A3 Ki Gi ⎪ ⎨ VT i G i = A0 ⎬ i=1

i=1

i=1

i=1

i=1

i=1

i=1

i=1

50 50 50 50 50      ⎪ ⎪ ⎪ ⎪ ⎪ VT i L i = A0 L i + A1 G i L i + A2 L i2 + A3 Ki L i . ⎪ ⎪ ⎪ ⎪ i=1 ⎪ ⎪ ⎪ i=1 i=1 i=1 i=1 ⎪ ⎪ ⎪ 50 50 50 50 50 ⎪ ⎪      2⎪ ⎪ ⎪ ⎪ VT i K i = A0 K i + A1 G i K i + A2 L i K i + A3 Ki ⎪ ⎩ ⎭ i=1

(2.17)

i=1

The solution of the system (2.17) in matrix form is given in [30]. Practically, ready-made programs are used for calculations, for example, the STATISTICA package and V. P. Borovikov’s manuals [26]. One of these programs is used in the model for the parameters L1, L2 , and VT . The constructed linear regression will allow us to predict parameters based on Gl, Lranway, and Kadh . Currently, in the test mode, the program includes an average increase in Lland when the reverse is not working following [31]. It is planned to conduct a particular study. According to the data from the textbook [32], the share of the reverse thrust of engines in the total braking force on the run varies from 5–7% on a dry runway to 30–35% on a runway covered with ice. Taking into account the input of these data, the mathematical calculation algorithm, in which the movement of the aircraft on the braking section is modeled by the Monte Carlo method, is similar to the algorithm of the program “Overrun Probability”. Accounting of atmospheric pressure and air temperature of airfield landing The forecast assumes that reduced pressure and increased temperature relative to standard conditions are similar to a conditional reduction in the length of the runway by an amount L to account for atmospheric conditions, The initial data for the calculation L are the results of the calculation of the Lland under the takeoff and landing characteristics program. An example of a calculation L for Gland = 210t is shown in table 2.9. Calculations for a landing mass of up to 340T with an interval of 10t are given in Annex 5. A table of type 2.9 calculates the decrease in Lland for airfield conditions: Gi Gi L Gi T P = L MC A − L T P ,

(2.18)

where L Gi T P is the reduction of the landing distance at the runway pressure p and temperature T specified in the table for the landing mass Gpi ; L Gi MC A —landing distance for international standard atmosphere conditions MCA T = 15 °C and P = 760 mmhg = 1013.2 HPa; L Gi T P —landing distance for the set values T and P. Calculations for (2.18) for G = 330T are shown in Table 2.10 and in Fig. 2.17. Calculations for other Gl are given in Annex 4.

102

2 Aircraft Overrun Risk-Reducing Methods

Table 2.9 Calculation of landing distance (Gl = 330T, calm, runway slope-0) Barometric pressure, mmHg

Temperature, °C 15

20

25

30

35

40

45

760

1670

1710

1750

1790

1820

1850

1910

750

1698

1738

1778

1818

1852

1885

1942

740

1727

1767

1807

1847

1884

1920

1975

730

1758

1798

1838

1878

1918

1957

2009

720

1787

1827

1867

1907

1950

1993

2042

710

1816

1856

1896

1936

1982

2028

2075

700

1844

1884

1924

1963

2012

2061

2106

690

1879

1919

1959

1998

2050

2103

2146

680

1910

1950

1989

2028

2084

2140

2181

670

1942

1981

2020

2059

2118

2176

2217

660

1970

2009

2048

2087

2146

2204

2246

650

1998

2037

2076

2115

2174

2233

2278

640

2027

2066

2105

2144

2203

2261

2308

630

2059

2098

2137

2176

2235

2294

2344

620

2089

2128

2167

2206

2265

2323

2378

610

2121

2160

2199

2238

2297

2357

2415

600

2153

2192

2231

2270

2328

2390

2451

To calculate the pressure at the level runway (QFE) QNH pressure is used from the METAR reports. It can be in HPa (MB) or inches of mercury—Inch. The computation of QFE QNH according to the following procedure. 1. 2. 3.

Convert the pressure QNH to mmHg. QNH [mmHg] = QNH [hPa] * 0.75; QNH [mmHg] = QNH [Inch] * 25.41. For exceeding airfield, calculate P by interpolation according to Table 2.11. Calculate QFE [mmHg] = QNH [mmHg] − P [mmHg].

For example, if the excess of the airfield is 427 m then the P correction is calculated as P = (39.7–35.4)/50 * 427 = 36.7 mm merc. The predicted temperature at the landing airfield is received from the weather center Asia website. The slope of the runway should also be taken into account when forecasting. In the model, an assumption is made to account for the slope: an increase (decrease) in the projected landing distance in the Monte Carlo simulation is equivalent to a decrease (increase) in the available runway length. The average values of the increase (decrease) in the length of the run by 1% of the runway slope depending on the Glend , calculated using the nomogram Fig. 1.68 from the AFM [31], are shown in Fig. 2.18. The slope correction L η is calculated using the formula:

2.5 Overrun Prognostication

103

Table 2.10 Conditional reduction of runway length for Gl = 330 T QFE pressure

Temperature, °C

mm mercury column

HPa

15

20

25

30

35

40

45

760

1013.2

0

40

80

120

150

180

240

750

999.9

28

68

108

148

182

215

272

740

986.5

57

97

137

177

214

250

305

730

973.2

88

128

168

208

248

287

339

720

959.9

117

157

197

237

280

323

372

710

946.5

146

186

226

266

312

358

405

700

933.2

174

214

254

293

342

391

436

690

919.9

209

249

289

328

380

433

476

680

906.5

240

280

319

358

414

470

511

670

893.2

272

311

350

389

448

506

547

660

879.9

300

339

378

417

476

534

576

650

866.6

328

367

406

445

504

563

608

640

853.2

357

396

435

474

533

591

638

630

839.9

389

428

467

506

565

624

674

620

826.6

419

458

497

536

595

653

708

610

813.2

451

490

529

568

627

687

745

600

799.9

483

522

561

600

658

720

781

800

Air temperature, oC

Conditional reduction of runway length, m.

700

+15

+20

+35

600

+25

+40

436 405

400 372 305

300 272 240

200 100 0

180 150 120 80 40 0

215 182 148 108 68 28

250 214 177 137 97 57

287 248 208 168 128 88

674 638

+45

500

339

708

+30

323 280 237 197 157 117

358 312 266 226 186 146

608

476 433

391

380

342

328 289 249 209

293 254 214 174

511 470

547 506

389 350 311 272

417 378 339 300

595 565

533 504

448

624

591

563

476

414 358 319 280 240

576 534

445 406 367 328

653

474 435 396 357

506 467 428 389

536 497 458 419

781 745 720 687 658 627 600 568561 529522 490483 451

760 750 740 730 720 710 700 690 680 670 660 650 640 630 620 610 600 QFE, mm of mercury

Fig. 2.17 Graphs of conditional reduction of runway length, Gp = 330T

104

2 Aircraft Overrun Risk-Reducing Methods

Table 2.11 DR amendment to convert QNH to QFE Excess of airfield above sea level, m

The pressure at the level of the airfield, ISA, mm. merc

The correction Excess of P is the airfield above deviation of sea level, m the airfield pressure from 760 mm

The pressure at the level of the airfield, ISA, mm. merc

The correction P is the deviation of the airfield pressure from 760 mm

0

760.0

0.0

1300

650.0

110.0

50

755.5

4.5

1350

646.0

114.0

100

751.0

9.0

1400

642.1

117.9

150

746.6

13.4

1450

638.1

121.9

200

742.2

17.8

1500

634.2

125.8

250

737.7

22.3

1550

630.4

129.6

300

733.4

26.6

1600

626.5

133.5

350

729.0

31.0

1650

622.7

137.3

400

724.6

35.4

1700

618.8

141.2

450

720.3

39.7

1750

615.0

145.0

500

716.0

44.0

1800

611.3

148.7

550

711.7

48.3

1850

607.5

152.5

600

707.5

52.5

1900

603.7

156.3

650

703.2

56.8

1950

600.0

160.0

700

699.0

61.0

2000

596.3

163.7

750

694.8

65.2

2050

592.6

167.4

800

690.6

69.4

2100

588.9

171.1

850

686.5

73.5

2150

585.3

174.7

900

682.3

77.7

2200

581.7

178.3

950

678.2

81.8

2250

578.0

182.0

1000

674.1

85.9

2300

574.4

185.6

1050

670.0

90.0

2350

570.9

189.1

1100

666.0

94.0

2400

567.3

192.7

1150

662.0

98.0

2450

563.8

196.2

1200

657.9

102.1

2500

560.2

199.8

1250

653.9

106.1

2550

556.7

203.3

L η = −d L · η, where η is the runway slope in%, calculated using the formula: η=

h land − h ex · 100%, L ranway

2.5 Overrun Prognostication

105

4.0%

∆Lland on ,%

3.5% 3.0% 2.5% R² = 0.9892 2.0% 210 220 230 240 250 260 270 280 290 300 310 320 330 Gland, т Fig. 2.18 Increase (decrease) L land 1% slope of the runway for the An-124

h land , h ex exceeding the landing and exit threshold of the runway. Adjusted for the atmospheric conditions and the slope of the runway length L ad j = L ranway − L Gi T P + L η

(2.19)

where L ranway is the length of the airfield runway from the navigation database.

2.5.2 Overrun Prognosticating Algorithm Development for Aborted Takeoff According to IATA world statistics, about 17% of overrun outside the runway from their total number occur on takeoff, mainly during aborted takeoff, in most cases due to engine failure. In the AFM of all aircraft, both Russian and Western production (for example, in [10]), the airworthiness requirements are laid down, according to which the speed of decision-making V1 is calculated before takeoff. If the engine fails at speed less than or equal to V1 , the crew must stop taking off, and at a higher speed—continue it. Speed V1 is calculated using AFM nomograms based on data on the takeoff weight of the aircraft Gtoff , available takeoff distance Lranway , an available distance of aborted takeoff, Kadh on the runway, runway slope, pressure, and temperature at the airfield. When stopping takeoff to V1 , a safe stop of the aircraft is provided within the runway end lane of braking. The “Overrun Prognosis” program makes assumptions (Fig. 2.19): – termination of the takeoff takes place at speed equal to V1 ; – at the moment of stopping the takeoff, wheel braking is activated, traction reverse and aerodynamic braking devices are released (flaps, spoilers); – requirements for safe takeoff termination are tightened:

106

2 Aircraft Overrun Risk-Reducing Methods

Fig. 2.19 Takeoff scheme implemented in the model

(a) (b)

the probability of rolling off the runway is calculated, it is assumed that V1 is equal to the speed of lifting the front leg of VR , (in reality, in some cases, V1 is slightly less than VR ), which compensates for the assumption of instantaneous activation of the braking means.

The probability of an event is predicted P(L V 1 + L 3 > L ranway − 100)

(2.20)

where LV1 is part of the run-up section until the aircraft reaches V1 speed. The value of 100 m entered in the formula (2.20) considers the reduction of the available takeoff distance due to the turn of the aircraft on the runway. The takeoff diagram is shown in Fig. 2.19. As can be seen from the formula (2.20), to calculate the probability of rolling out during an aborted takeoff based on the “Overrun Probability” model, you need to predict the value of only one parameter—LV1 additionally. This value is not directly calculated from AFM nomograms or computer programs for calculating takeoff and landing characteristics, but it can be calculated taking into account the following assumptions: – the speed V1 is always slightly less than the breakaway speed Vba ; – the movement of the aircraft on takeoff can be considered equidistant with sufficient accuracy for practical calculations [31, 32]; – the takeoff length of the aircraft is calculated from the takeoff and landing characteristics nomograms or special computer programs. With these assumptions in mind, we use the formula for the distance L traveled when driving with acceleration a during acceleration from V 1 to V 2 : L=

V22 − V12 . 2a

2.5 Overrun Prognostication

107

Applying the formula twice, after the transformations, we have  LV1 = L

V1 Vba

2 ,

(2.21)

where L P is the length of the run–up on nomograms or programs for calculating the takeoff and landing characteristics. The calculation results using the formula (2.21) for the An-124-100 aircraft for some values of takeoff weight are shown in Table 2.12. The movement of the aircraft on the L 3 section is modeled as for predicting roll-out during landing. Simultaneously, the Kadh is predicted, and corrections for temperature, atmospheric pressure at the airfield, and runway slope are taken into account. The Kadh forecast at the departure airfield is performed in the same way as it is done at the landing airfield. Accounting of atmospheric pressure and air temperature at the departure airport. As we know, the length of the run-up depends on atmospheric conditions—air pressure and temperature. An assumption is made to account for them in the forecast: Table 2.12 Distance from the start of the run-up to the speed point V1 Gtoff, t

V1 , km/h

Vba , km/h

Ll , m

L V1 , m

400

258

277

2351

2040

390

255

275

2236

1923

380

253

275

2120

1794

370

251

275

2005

1670

360

250

270

1920

1646

350

248

270

1830

1544

340

247

270

1745

1460

330

246

270

1675

1390

320

244

270

1600

1307

310

243

270

1530

1239

300

242

270

1475

1185

290

240

260

1415

1206

280

239

260

1355

1145

270

238

260

1310

1098

260

236

260

1250

1030

250

235

260

1200

980

240

233

260

1155

928

230

232

255

1110

919

108

2 Aircraft Overrun Risk-Reducing Methods

reduced atmospheric pressure and increased air temperature from the standard are similar to a conditional reduction in the length of the runway by an amount L. The initial data for the calculation L results from calculating the run-up length using a computer program for calculating the takeoff and landing characteristics, which implements AFM nomograms [10, Fig. 7.23]. Calculations were carried out for Gtoff from 230 to 400t with an interval of 10t. An example of the calculation for Gtoff = 340T is shown in Table 2.13. Values of run-up lengths that correspond to atmospheric conditions for a given mass that do not provide a gradient of 3% in the event of a single-engine failure are shown in bold and indicated by the * icon. However, for forecasting purposes, these values will be taken into account in the program to complete the solution to the problem. The “x” sign in the table indicates combinations of conditions for no calculated values of the AFM’s run-up length [10]. Based on the obtained tables, the run-off length reduction for atmospheric conditions at the airfield is calculated for each Gtoff using formulas similar to (2.13) and (2.15). The results of calculations for Gtoff = 340T are shown in Table 2.14 and in Fig. 2.20. Table 2.13 Calculation of the run-up length using the takeoff and landing characteristics calculation program An-124-100, Gtoff = 340T (calm, runway slope is 0) Barometric pressure, mm of mercury

Temperature, °C 15

20

25

30

35

40

45

760

1750

1775

1885

2035

2205

2380

2565

750

1785

1815

1950

2100

2275

2460

2670

740

1825

1853

2010

2165

2350

2540

2780

730

1855

1890

2055

2215

2405

2610

2865

720

1910

1970

2135

2305

2510

2720

3005

710

1950

2035

2200

2380

2592

2810*

3112*

700

1990

2100

2274

2465

2690

2915*

X

690

2030

2170

2345

2549

2780

3025*

X

680

2070

2235

2420

2640

2875*

X

X

670

2130

2320

2510

2745

2985*

X

X

660

2215

2405

2605

2850

3095*

X

X

650

2300

2500

2715

2960*

X

X

X

640

2400

2610

2835

3080*

X

X

X

630

2495

2710

2945

3220*

X

X

X

620

2595

2820

3065*

X

X

X

X

610

3210*

X

X

X

X

X

X

600

3375*

X

X

X

X

X

X

2.5 Overrun Prognostication

109

Table 2.14 Conditional reduction of runway length for Gtoff = 340T The pressure at the level runway (QFE)

Temperature, °C

mm. mercury column

HPa

15

20

25

30

35

40

45

760

1013.2

0

25

135

285

455

630

815

750

999.9

35

65

200

350

525

710

920

740

986.5

75

103

260

415

600

790

1030

730

973.2

105

140

305

465

655

860

1115

720

959.9

160

220

385

555

760

970

1255

710

946.5

200

285

450

630

842

X

X

700

933.2

240

350

524

715

940

X

X

690

919.9

280

420

595

799

1030

X

X

680

906.5

320

485

670

890

X

X

X

670

893.2

380

570

760

995

X

X

X

660

879.9

465

655

855

1100

X

X

X

650

866.6

550

750

965

X

X

X

X

640

853.2

650

860

1085

X

X

X

X

630

839.9

745

960

1195

X

X

X

X

620

826.6

845

X

X

X

X

X

X

610

813.2

X

X

X

X

X

X

X

600

799.9

X

X

X

X

X

X

X

Air temperature, 0С.

1400

+15

+20

+25

+35

+40

+45

+30

1200

1195 1100

1000

995 890

∆L, m.

800

799 715 630

600

555 415

400

350 285

200

260

305

25 0

65 35

103 75

140 105

220 160

670

524 450

385

200 135

0

465

285 200

240

655

485

280

960 860

750

570

420 350

965 855

760

595

1085

845 745

650 550

465 380

320

760 750 740 730 720 710 700 690 680 670 660 650 640 630 620 QFE, mm. merc.

Fig. 2.20 Graphs of conditional reduction of runway length, Gtoff = 340 T

110

2 Aircraft Overrun Risk-Reducing Methods 11.0% 10.5% 10.0%

∆Lrun, %

9.5% 9.0% 8.5% 8.0% 7.5% 7.0%

230 240 250 260 270 280 290 300 310 320 330 340 350 360 370 380 390 400 Gtoff, t Fig. 2.21 Conditional increase of Lrun by 1% of runway slope for An-124 from Gtoff

Similar calculations are made for takeoff weight from 230 to 400t with an interval of 10t. The results of the calculations are given in Annex 4. The conversion of the QNH pressure to QFE and the predicted air temperature input is performed similarly to the calculation for the landing airfield. The slope of the runway should also be taken into account when forecasting. The same assumption is made in the model: an increase (decrease) in the predicted run-up length in the simulation is equivalent to a decrease (increase) in the runway length. The average increase/decrease in run-up by 1% of the runway slope depending on the Gtoff , calculated according to Fig. 7.23 AFM [10], is shown in Fig. 2.21. The slope correction ΔLη is calculated using the formula: L η = −d L · η, where η is the runway slope in%, calculated using the formula: η=

h land − h ex · 100%, L ranway

h land , h ex exceeding the takeoff and exit threshold of the runway of the takeoff airfield, respectively, from the navigation database. The adjusted runway length used in the program is: L ad j = L ranway − L Gi T,P + L η

(2.22)

where L ranway is the length of the airfield runway from the navigation database. The program “Overrun Prognosis” is used as an alternative module for calculating the probability of rolling out in the ASFPA.

References

111

References 1. van Es GWH (2005) Running out of runway. Analysis of 35 years of landing overrun accidents, National Aerospace Laboratory, NLR-TP-2005-498 2. Airport Services Manual, P. 2. Runway surface condition. Doc. 9137. ICAO (2002), 126 p. 3. Kazakov AP (1975) Methods and tools for evaluating braking conditions on the runway. In: Proceedings of the state. Research Institute “the Aeroproject”, Issue 18. [Electronic resource] Access mode: https://www.gosthelp.ru/text/TrudyVypusk18Ekspluataciy.html 4. On the way to reducing accidents during approach and landing. France, Blagnac Gedex, Airbus (2005), 227 p. 5. Horne WB, Dreber RC (1963) Phenomena of pneumatic tire hydroplaning. NASA TN D-2056 Washington, D.C. 6. Manual for the operation of civil airfields of the Russian Federation (REGA RF-94), “Air transport” (1996), 291 p. 7. GOST R 51901.12-2007 Risk Management. Method of analysis of types and consequences of failures. Publishing House of Standards, Moscow (2012), 41 p. 8. Conducting research and generalizations of incidents that occurred in 2011 related to commercial aircraft rolling out of the runway at the landing stage. Report on research/head of G. D. Sadykov. Moscow region, Sheremetyevo airport, state safety center (2012), 192 p. 9. Runway Overrun Prevention. AC-No: 91-79, FAA USA (2007) 10. Manual for flight operation of the An-124-100 aircraft. Kyiv: ANTK “Antonov” (2003), 1884 p. 11. Cold Weather Operations. ATR Customer Services, March 2011. [Electronic resource]. Access mode: https://www.atraircraft.com/media/downloads/coldweatheroperations_2011_20.pdf 12. Sharov VD (2013) Forecasting and preventing aircraft rolling out of the runway Ed. Lambert, 112 p. 13. Kleine-Bek W. Runway friction characteristics measurement and aircraft breaking. [Electronic resource] access Mode: www.skybrary.aero/bookshelf/books/1412.pdf 14. Benefit-Cost Analysis of Procedures for Accounting for RW Friction on Landing, TP 14082E, Transport Canada (2003) 15. Pavlov MM, Zakharova TI, Sharov VD (2008) Determination of the correlation between the normative coefficient of adhesion on the runway and the Canadian CRFI index. Sci Bull MSTU GA 123:95–103 16. Pavlov MM, Sharov VD, Zakharova TI (2008) Determination of the correlation between the normative coefficient of adhesion on the runway and the Canadian CRFI index: research report. G. Zhukovsky: Federal state unitary enterprise LII named after M. M. Gromov, 50 p. 17. Guide to the organization of collection, processing, and use of flight information in aviation enterprises of the CA of the Russian Federation. Air transport (2001), 80 p. 18. Croll John B (2004) Prediction of aircraft landing distance on winter contaminated RW using the Canadian runway friction index. National Research Council Canada, Ottawa, Ontario 19. Wentzel ES (1972) Operations research Sov. Radio, 550 p. 20. Shevchenko AM, Pavlov BV, Nachinkina GN (2013) Predicting the trajectory of aircraft braking. In: Proceedings of the Southern Federal University. Taganrog Publishing house TTI SFU, no. 3, pp 232–239 21. Biggs DC, Hamilton GB (2002) Runway friction accountability risk assessment – results of a survey of Canadian airline pilots. TP 13941E, Transportation Development Centre, Transport Canada 22. Sharov VD (2008) Method for assessing the risk of rolling out of the runway at Canadian airfields and its application in the Volga-Dnepr airline. In: Proceedings of the Society of Aviation Accident Investigators, no. 20, pp 290–295 23. Causal model for Air Transport Safety. Final report. NLR, Amsterdam (2009) 24. Sharov VD (2007) Methodology for estimating the probability of aircraft rolling out of the runway during landing. Sci Bull MSTU GA 122(12):61–66

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25. Khamrakulov IV, Zubkov BV (1991) Efficiency of using flight information. Transport, Moscow, 175 p. 26. Borovikov VP (2003) STATISTICA. The art of analyzing data on a computer. For professionals, 2nd ed. (+CD). - St. Petersburg, Peter, 688 p. 27. Sharov VD (2006) Using component analysis in-flight information processing. Probl Saf 3:3–6 28. Training programs for flight personnel on the An-124–100 aircraft. RPP of Volga-Dnepr airlines, Part D, Chapter 2, (2008), 314 p. 29. Safety management manual. Doc. 9859. ICAO, 3rd ed. (2013), 300 p. 30. Orlov AI (2012) Organizational and economic modeling: textbook in 3 CH. 3. Statistical methods of data analysis. Moscow, Publishing House of Bauman Moscow State Technical University, 624 p. 31. Bekhtir VP (2005) Practical aerodynamics of the An-124-100 aircraft: textbook. Ulyanovsk, RIO and VOP UVAU CA, p 207 32. Mkhitaryan AM, Laznyuk PS (1978) Flight dynamics: a textbook for aviation universities, 2nd ed., reprint. Moscow, Mashinostroenie, 424 p.

Chapter 3

Practices to Combat External Impact on the Aircraft Navigation Systems in Civil Aviation and Flight Regulatory Management

3.1 Implementation of Methods for Struggle with Unauthorized Actions on Aircraft Navigation Systems 3.1.1 Main Hardware Methods of Protection from Unauthorized Actions The main hardware protection methods, both implemented in existing systems and potentially possible in navigation information receivers, are as follows [1]: – application of correlation processing of a phase-manipulated signal with a broad base—103 –104 ; – introduction of additional frequency channels with increasing signal base; – implementation of spatial selection based on the use of N-element antenna arrays that provide directional reception and directional signal suppression, reducing the side lobes of antenna devices; – implementation of polarizing selection based on the use of two-channel polarizing antennas; – unique signal processing methods; – measurement of spatial-frequency-time parameters of detected signals. An essential step in the fight against such impacts is their recognition. The decision can be made based on determining the signal-to-noise ratio, which shows a deterioration in reliability with increasing interference intensity, or according to the data of an impact detector using algorithms of the theory of statistical solutions [1].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4_3

113

114

3 Practices to Combat External Impact on the Aircraft …

3.1.2 Principal Methods of Aircraft Navigation Systems Stability Enhancement Under Unauthorized Actions Increasing the resistance to external influences of consumer navigation receivers can be implemented by [1, 2]: – improving the characteristics of the consumer’s antenna system; – introduction of additional processing of received signals. Improving the performance of antennas can be implemented in the form of several technical options. Guaranteed regular use of the Global Navigation Satellite System (GNSS), a global navigation satellite system following ICAO documents, is provided at elevation angles greater than 5°. This provision makes it possible to reduce the impact of impacts from ground sources by improving the antenna system’s quality, mainly by reducing the lower side lobes of the antenna pattern (AP). To do this, up to 5° is necessary to reduce the antenna gain within the elevation angles due to the rational placement and shielding of the antennas by the aircraft structures. It would also be best if it used unique antenna designs with a reduced level of the lower lobes of the antenna pattern in the vertical plane. According to experts, such measures can reduce the antenna gain to −35 dB, which gives a real gain in noise immunity to 20–25 dB and a reduction in the suppression range to 10–15 km. The next step in improving the noise immunity of receiving GNSS signals by the consumer can be the use of complex antennas (phased array antennas) that form a directional pattern with a decrease in gain in the direction of the interference stops. Such a solution can give a gain compared to a conventional antenna of 25–35 dB, but even according to the most approximate estimates, it is not acceptable for most civilian consumers due to the sharp increase in equipment costs. According to experts, the cost of such an antenna can be up to 20000 dollars. Besides, the consumer’s board must include equipment for determining the direction of the jamming stop (interference direction finder), which is why the cost of onboard equipment increases even more and, finally, the equipment becomes even more complicated and more expensive if it is necessary to counteract several jamming stops (to form several “zeros” of the directional diagram at the same time). The next possible solution to increase noise protection is to implement polarization selection based on two-channel polarization antennas [2]. This technical solution can give a gain of 5–10 dB, but it is unlikely to be implemented in civilian equipment, since the signal’s polarization in real conditions is a very variable factor, and the issue requires particular study and evaluation. In general, improving the characteristics of the satellite radio navigation system (SRNS) receiver antenna system can give a real improvement in noise immunity by 20 dB. All currently used GNSS systems use powerful signal processing, allowing us to get navigation information at low signal strength in noise conditions. Therefore, the

3.1 Implementation of Methods for Struggle …

115

possibilities for further improvement of such processing are slight. However, several methods can be considered concerning GPS and GLONASS [1]: – introduction of adaptive filtering, application of narrow-band digital rejection, use of transversal filtering to counteract narrow-band interference interrupters; – introduction of adaptive nonlinear signal processing; – switching to direct measurement processing. According to experts, these measures require relatively low costs, but their effectiveness is currently not definitively determined, and therefore the prospects for their widespread implementation soon are not visible. A rough estimate shows that these measures can provide an additional 10 dB gain in protection from external influences on the aircraft’s navigation systems. Thus, for civil consumers to increase noise protection, the most relevant are hardware measures related to the placement of antenna systems, their shielding, and improving signal processing characteristics, giving 25–30 dB of gain in protection from external influences navigation systems of civil aviation aircraft. System-wide solutions for improving the sustainability of GNSS applications include the following measures: – increasing the interference characteristics of each GNSS constellation by increasing the power of radiated signals and introducing new frequency ranges; combined, simultaneous use of several GNSS constellations, for example, the combined use of the Global Positioning System (GPS) and the Global navigation satellite system of Russia (GLONASS); – use of ground-based tools and/or procedural methods for ensuring traffic safety as backup navigation tools. The noise immunity characteristics of individual GPS, GLONASS, and in the future GALILEO constellations can be improved due to two main factors: increasing the potential (increasing the transmitter power and narrowing the onboard AP), the signal emitted by the satellite, and expanding the frequency band by introducing new civilian signals. The results of this analysis are as follows: GPS system. – The power level of the L1 civil signal remains the same for the entire foreseeable future. – Preliminary plans for further development of the system (project GPS-III) provide for a significant increase in the potential for special applications in some regions of the globe by introducing several devices into the GPS system in highly elliptical orbits with a phased array transmitting antenna forming a narrow, controlled AP. The use of this mode by civilian users is problematic. A civilian signal is introduced in the L2 frequency range (L2C) with a radiated power similar to the L1 channel’s power in the 1215–1237 MHz frequency band.

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– A civilian signal is introduced in the frequency range L3 (L5) with radiated power that provides the maximum power value at the receiver input in the frequency band 1164–1188 MHz. Expanding the frequency range by introducing new L2C and L5 signals when they are used simultaneously with L1 eliminates the influence of instability of ionospheric signal propagation and increases the overall noise immunity of the system by 3 dB when L2C is introduced. Another 1.5 dB after L5 is introduced. In general, it can be noted that the introduction of GPS L2 will reduce the suppression range (compared to the −20 dB option, optimal for antenna characteristics) to 12 km, and the introduction of the L5 range to 10 km. System GLONASS The power level of the civil signal L1 does not increase during modernization and is −161 dBW at the consumer receiver’s input. At the GLONASS-M stage, a civil signal is introduced in the L2 range with a power at the consumer’s receiver of −167 dBW (followed by an increase to −161 dBW) frequency range 1237–1254 MHz. At the GLONASS-K stage, a civilian L3 signal is introduced with an undefined power value in the 1188–1205 MHz frequency range. In general, the expansion by introducing new civilian L2 and L3 signals when used simultaneously with L1 eliminates the influence of the ionosphere and increases the system’s resistance to intentional interference, providing similar GPS suppression ranges of up to 12 km after entering L2 and up to 10 km after entering L3. Since the energy gain due to the introduction of new signals cannot effectively solve the problem even in the context of terrorist attacks within the use of a single constellation, the next step is to simultaneously use the signals of several constellations of navigation spacecraft (NS) included in the GNSS. The gain, in this case, is obtained due to the following factors: – further expansion of the general frequency range of satellite navigation; – the use of different principles of signal generation and processing, which requires interference stoppers to use several methods of interference generation, which complicates the task and equipment of the interference transmitter; – increase the number of non-jamming of NS available to the user. This combined use is also valuable because the purpose of combined use is to obtain advantages in the absence of interference, in particular, to improve navigation characteristics such as availability (readiness), integrity, and continuity. The frequency range in each of the civilian sub-ranges is expanded approximately twice: L1-GPSL1 (1563–1587 MHz), GLONASS (1591–1610 MHz). Total frequency band 43 MHz; L2-GPSL2C (1215–1239 MHz), GLONASS (1237–1254 MHz). Total frequency band 41 MHz;

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117

L3-GPSL5 (1164–1188 MHz), GLONASS (1188–1205 MHz). Total frequency band 41 MHz; GPS and GLONASS signals differ in how they are generated and filtered in the user’s receiver. GPS uses code signal separation (CDMA), GLONASS—frequencyselective channel-by-channel system (FDMA). The first requires blocking interference over the entire frequency range for effective suppression, and the second requires frequency-targeted interference for the channels being suppressed. The number of GPS—24 spacecraft (plus 3 back-ups in orbit), GLONASS—24 (bringing the NS group to full-time staff). The specified total number of spacecraft, when used together, may allow: – increase the minimum value of the elevation angle (over 5°) and, accordingly, makes it possible to improve the characteristics of antenna systems at low elevation angles (increase the suppression in the radiation pattern at low elevation angles); – perform a selection of NS by space and exclude the use of NS located in the zone suppressed by interference, while maintaining the readiness and continuity necessary for performing the corresponding operation. Such solutions require the user to have equipment that determines the direction of the jamming device. In general, the combined use of GPS and GLONASS constellations can give an increase in stability of up to 6 dB in the two-frequency version (L1 and L2) and 10 dB in the three-frequency version (L1, L2, L3, or L5), depending on the method of combined use and the type of operation, which reduces the suppression range to 5–6 km. The above estimates of the effectiveness of hardware and system noise protection measures are summarized and presented in Table 3.1. At the same time, the table shows approximate estimates of the cost of implemented measures. This table follows that for traffic safety-critical civil applications using singlefrequency single-system receivers, it is most effective and realistic to introduce AP improvements and combined GNSS with an inertial navigation system (ins) (items 1 and 4 in the table). Improving signal processing in the receiver can be useful and requires further research. The use of two-and three-frequency receivers can provide a real increase in efficiency, especially given that the ability to operate on the second or third frequency in a significant number of operational cases of interference can remove the question terrorists do not suppress these second (third) frequencies. Table 3.2 provides an example of an assessment of the effectiveness of antiterrorist protection measures for the most appropriate interference protection measures for civilian use. These examples show that it is feasible to provide solutions that can effectively deal with deliberate impacts on civil aviation aircraft navigation systems and ensure that the danger zone for performing navigation operations in a local area is reduced to several kilometers.

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Table 3.1 Possible measures to increase the stability of onboard consumer equipment to radio interference Noise protection measures

Possible gain relative Possible cost increase Notes to standard GNSS relative to standard receivers, dB GNSS receivers, %

1 Improving the AP of 10–15 the receiving antenna at low elevation angles

30

Really, in the civil systems of users

2 AP control that 20–25 reduces sensitivity in the direction of the interference source

up to 100

Sufficient only for one jamming device requires knowledge of the direction of jamming

3 Antenna array with polarization of the signal

10–15

up to 50

Does not apply in all conditions of use

4 Improved signal processing in the receiver

up to 20

5–10

Research is required on possible methods and feasibility

5 Combining a GNSS receiver with an ins

10–15

10–300

The cost is determined by the level of INS and tends to decrease

6 The use of dual-frequency receivers L1, L2

5

20–30

7 Using three-frequency receivers

8

40–50

3.1.2.1

Estimation of GALILEO Potential Contribution into Stability Enhancement During Combined Operation with GPS or GNSS

The frequency range of GALILEO. In the L1 band, GALILEO operates in the GPS band (shared use) and assumes the use of two-protection gaps-E1 (1587–1591) and E2 (1559–1563) with a standard 8 MHz band, which were used to protect the GPS and GLONASS, frequency bands. In the L2 band, GALILEO expects to use frequencies from 1261 to 1300 MHz with a total bandwidth of 39 MHz. These frequencies are used for commercial signals and are not used in transport security systems. In the L3 band, it uses the frequency range 1164–1189 MHz (E5A) in conjunction with GPS, and independently frequencies from 1205 to 1216 MHz with a whole band of 11 MHz.

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Table 3.2 Evaluation of the effectiveness of protection measures when using GNSS in a local area during terrorist suppression using a 50-watt (“base”) transmitter Receiver

Increase in noise protection relative to the basic version, dB

Suppression range from the “base” transmitter, km

1

Standard GPS L1 or GLONASS L1 receiver

–

57.0

2

GPS L1 or GLONASS L1 receiver with improved ACCURACY at small angles

15

17.5

3

Receiver integrated with INS

10

10.0

4

Receiver with the entered frequency L2 (two-frequency)

5

6.0

5

Receiver with input frequencies L2 (L3 ) (three-frequency)

8

4.1

6

The receiver that implements GPS/GLONASS integration

5

3.3

Thus, the real contribution of GALILEO to the expansion of the range of navigation frequencies used in transport systems can be 10 MHz compared to the contribution of GPS. If combined systems were used together, using GALILEO instead of GPS would extend the frequency range by 8–9%, resulting in a reduced range compared to the GPS/GLONASS variant of 0.5 km. Advantage of the signal and its processing. GALILEO, working together in the L1 and L3 bands with the GPS, uses the CDMA channel separation code principle, which differs from GPS only in the type of carrier frequency modulation used. Thus, using GALILEO instead of GPS, when combined with GLONASS, does not provide any additional noise protection advantages. Besides, the use of a standard frequency range for different modulation types gives a specific reduction in noise protection (0.5–1.0 dB) due to an increase in the level of intra-system mutual interference. The number of spacecraft used in the GALILEO system does not differ much from the number of GPS devices (30 instead of 27), so when using GALILEO instead of GPS, the total increase in the number of spacecraft in the combined system can be 6–10%. In General, it should be concluded that when combined with GLONASS, the GALILEO system instead of GPS practically does not give any advantages in noise protection.

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3.1.3 Application of Unauthorized Activities Detection Aids for Operation Stability Improvement of the Aircraft Navigation System An essential factor in ensuring the stability of civil aviation aircraft’s navigation systems in conditions of deliberate suppression is the use of means to detect the presence of such interference. Detection of the presence of unauthorized impacts on civilian consumers of GNSS is possible using several technical tools and methods. The main ones are as follows [2]: – System for monitoring the status of spacecraft systems included in GNSS; – Differential stations for correcting navigation coordinates and ensuring information integrity; – Autonomous onboard integrity controls (AAIM and RAIM systems in civil aviation); – Procedural methods of the air traffic monitoring system, especially important for detecting the fact of interference that is poorly detected by instrumental methods (relay, simulation). Detection of deliberate interference can be carried out in a wide area of satellite navigation with general notification of consumers and traffic management services and in a local area where the safety-critical stages of the aircraft flight are carried out. Wide-band interference detection tools It is advisable to include wide-range interference detection tools in the GNSS signal monitoring system, and local ones should be part of the traffic management and control services at airports. Preliminary basic requirements for a wide-band interference monitoring system can be the following [2]: – the system must detect sources of interference of all types based on the ground, on-air carriers (aircraft, unmanned aerial vehicles) operating in the controlled area; – the area of operation of the system should be a region that is associated in size with the GNSS signal monitoring zone; – the system must detect the fact of electronic jamming with accuracy up to the suppressed GNSS range, i.e. L1, L2 , L3 (L5 )—GPS and L1, L2 , L3 —GLONASS; – the system must inform all non-critical consumers about the affected ranges of operation (or complete failure) of GNSS in a 10–20-minute time frame in order for them to take the necessary measures-stop using GNSS or switch to navigation on unaffected channels; – the system must transmit the above information to local monitoring systems with a speed of up to 2 min;

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– requirements for the system’s sensitivity, the power of the interference transmitters detected by it, and the need to determine their location should be determined later. Local interference detection systems Local means of identifying sources of deliberate impacts on civil aviation aircraft navigation systems should have as their primary purpose the rapid identification of transmitters of terrorist suppression for the application of appropriate measures by traffic services and law enforcement agencies. Accordingly, the main requirements for such funds should be the following [1, 2]: • detection of terrorist interference transmitters with power from 1 to 50 W in the area of responsibility of an airport, port, or transport hub; • warning time for interference is up to 10 s; • determination of affected GNSS frequency bands (L1, L2 , L5 —GPS and L1, L2 , L3 —GLONASS) in order to determine the possibility of completing the flight stage of a civil aviation aircraft on non-interfered GNSS frequencies; • determining the location of the source of terrorist interference for prompt action by the relevant authorities or using a spatial selection of GNSS signals to combat interference. The information provided above should be sufficient for the traffic safety authority to make a decision: • if possible, the safe completion of the flight stage of the aircraft and the methods of this completion; • by safely interrupting (terminating) this stage of the flight and leaving for the second round; • on switching to redundant satellite frequencies and/or systems—for consumers equipped with the appropriate equipment; • by switching to standby (ground) navigation tools available in this local area. As already mentioned above, these means of radio interference detection must belong to the traffic service and can be part of local differential stations for correction and monitoring of GNSS signals serving this terminal zone. To do this, it is necessary to combine and record information in the computer complex of the differential station and transmit it to the traffic control center and/or onboard the navigation information consumer. Since the detection of unauthorized impacts on civil aviation aircraft navigation systems should always be available, it is assumed that the detection device should be located in the airport area on the highest structure, most likely on the airport tower. A small study was conducted to determine the necessary sensitivity of cheap and available devices. For the power of the radiation source of 1 W, Fig. 3.1 shows a graph of its detection range and the possible beamwidth of the sensor as a function of the device’s antenna diameter. The figure shows that for reasonable antenna sizes ( 4 leads only to an absolute increase inaccuracy, which is not essential here. Thus, the algorithm for relative navigation definitions with a digital communication channel can be represented by the following scheme:

where kC P AC F T = |xC P AC F T , yC P AC F T , z C P AC F T |T —the global coordinates of CP (materialized), K—the desired coordinates of meteor radiosonde relative to the repeater. It is convenient to use the SRNS property to be a source of high-precision time and apply the following linear algorithm for correcting the measurement results to combine the measurement moments, •

.

N PC P (ti ) = N P(ti + ) − N P [(ti + ) − ti ], CP

(3.1)

where ti + is the moment of measurement of navigation parameters on the CP scale, t i is the moment of measurement of navigation parameters on the meteorological radiosonde scale, and N PC P is the average rate of change of navigation parameters. The requirement of a temporary combination of measurements regulates the mandatory calculation of the measurement moment (ti + ) in the CP and determines the installation of the same navigation equipment in the CP as on the ground. Figure 3.3 illustrates the method of relative navigation definitions with a digital communication channel. The second modification of relative navigation definitions involves making definitions only on the ground [3]. To do this, CP is set to source a secondary navigation field (repeater field SRNS), and additionally receiver. Measurements are performed simultaneously in the SRNS field and the repeater field relative to the ground object’s general time scale. The scheme of this modification of navigation definitions can be represented as

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125

Fig. 3.3 The general structure of relative navigation definitions with a digital communication channel

Since all measurements are made on a ground object, there is no need to combine their moments in time. That greatly simplifies the CP’s hardware, which degenerates into a repeater of SRNS signals without any processing. Let’s consider the requirements for such a communication channel between a CP and a ground object. The channel must provide the necessary size of the working area of the system as a whole. The channel signal must have a limited power density to not create interference on the air for users of the SRNS in standard mode. When copying the SRNS signal (transferring it to a frequency other than the NS), this requirement is provided autoP0 = −53, 0 dB/W, where P0 matically, since the power density will be NC P = F ∼ = is 10 mW—the average radiated power of the repeater, F = 2·103 —the ratio of the SRNS signal bands and the correlator of consumer equipment. The channel must allow selective access, allowing the user to work in a network of u simultaneously operating repeaters of their choice. Multicast systems can be provided by separating their spectra by frequency (analog-GLONASS system) or introducing an additional pseudo-random encoding sequence into the signal format (analog-GPS system). The method of relative navigation definitions with a repeater is shown in Fig. 3.4. There are ways to determine the first object’s coordinates relative to the second object, called below for short relative coordinates, which do not require an accurate geodetic reference of one of the objects. There are at least two such methods that differ in their implementation algorithm. The first method, called the “method of difference correction of the navigation parameter”, is based on measuring the difference in the distance Ri from the same point in space with known coordinates to

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Fig. 3.4 The overall structure of the relative ND with the repeater

the first and second objects, performed at the same time; determining the matrix of guide cosines for the points of the working constellation on the second object; transmitting the measured differences and the matrix of cosines to the first object via the communication channel and calculating the relative coordinates of the second object on the latter using the formula:   θ = G T G −1 G T R

(3.2)

where G is the geometric matrix of the guide cosines of the lines of sight of the j-th KA (KAj ). However, this method requires the presence of high-class precision equipment at the second object, accurate frequency and time synchronization of measurements at the objects, and a communication channel with a large bandwidth for transmitting a significant amount of information to the first object. A significant disadvantage of this method is the deterioration of the determined relative coordinates’ accuracy with the distance of objects from each other. The following discussion describes an improved method for relative navigation data. The feasibility of improving this method of navigation definitions is justified by the advantage mentioned above, namely, the lack of accurate geodetic positioning reference stations, significantly expanding the method’s scope. This method is based on calculating the difference between the coordinates of the first (q1 = || x 1 y1 z1 ||T ) and the second object (q2 = || x 2 y2 z2 ||T ) measured at the same time. Subtracting the simultaneous components of the vectors q1 and q2 allows you to determine the projection of the baseline between objects x = x2 − x1 ; y = y2 − y1 ; z = z 2 − z 1

(3.3)

Arrays of radio navigation parameters can be transmitted to the joint processing point (for example, to the first object) instead of the q2 vector. The disadvantages

3.2 Measurement of Weather Radiosonde Altitude …

127

of this method are the need for accurate frequency and time synchronization of measurements and the deterioration of the determined relative coordinates’ accuracy as objects move away from each other. The reason lies in the errors of knowledge of a priori coordinates of points in space (system errors) and their spatial decorrelation due to different observation angles on different objects. Consider the possibility of improving the accuracy of determining relative coordinates by eliminating the influence of system errors. This goal can be achieved by measuring distances to points in space with known coordinates on the first object and then calculating the second object’s relative coordinates. Besides, this goal is achieved by additional and simultaneous measurement of the increment ((Ri ) of the same distances to the location of the second object, using the distances measured on the first object to determine the coordinates of each point in space relative to the first object (X i , Y i , Z i ) and calculating the inclined distances (Di ) for these coordinates, as well as determining the relative coordinates of the second object (x, y, z) from the formula: Di +  Ri =

 (X i − x)2 + (Yi − y)2 + (Z i − z)2 .

(3.4)

In the proposed method, the distance difference from the same point in space to the first and second objects (Ri ) is additionally measured simultaneously with the distance measurement at the first object. Then, using the distances measured on the first object, determine the coordinates of space points relative to the first object (x i , yi , zi ), for example, by determining the geocentric coordinates of the first object or by directly calculating the relative coordinates for known coordinates of space points relative to each other. Besides, in the proposed method, using certain coordinates (x i , yi , zi ), the inclined ranges (Di ) are calculated for them. The formula determines the relative coordinates of the second object 1/2  + c × t, i = 1 . . . 4, di = (xi − x)2 + (yi − y)2 + (z i − z)2 with replacement di − c · t.—on Di + Ri ; x i , yi , zi —on x i , yi , zi , respectively. x, y, z—on x, y, z, respectively. The essence of the proposed method is explained in Fig. 3.5 and consists of the following. Let there be points in the space T 1 … T M with a priori knew (geocentric) coordinates x i yi zi. There are two dynamic objects 01 and 02 , and the first must determine the coordinates of the second (x, y, z) relative to its location. Taking on the object 01 , (Fig. 3.5) radio navigation signals from M points in space (for example, when working on GPS SRNS for three-dimensional navigation (M = 3), determine the coordinates of the points T i relative to the first object (x i , yi , zi )„ and then the inclined ranges Di . Method of determination—by direct calculation of relative coordinates for known coordinates of points T i, j, K relative to each other (x l,m , yl,m , zl,m ), where l, m ∈ {i, j, k}, are determined, for example,

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3 Practices to Combat External Impact on the Aircraft …

Т1 Т2 Тi = | x i

yi

zi|T

Тm

Di

Di =[ xi2 + yi2 + zi2]1/2 | x y z |T O2 = | x2 y2 z2 |T

O1 = | x1 y1 z1 |T a)

о2

Di i,Т Receiver

Indicator

о1

Di i,Т Receiver Di | x1 y1 z1 i,Т Calculator

Receiving indicator

| x y z |T

c)

b)

Fig. 3.5 Implementation of an improved method for relative navigation definitions

from the formulas: ⎧  1/2 ⎪ Di = xi2 + yi2 + z i2 , ⎪ ⎪ ⎪ 1/2  ⎪ 2 ⎪ ⎨ Dmeasi = (xii − xi ) + (yii − yi )2 + (z ii − z i )2 , 

 2  2  2 1/2 ⎪ ⎪ , Dmeas j = x ji − xi + y ji − yi + z ji − z i ⎪ ⎪ ⎪ ⎪  ⎩ 1/2 Dmeask = (xki − xi )2 + (yki − yi )2 + (z ki − z i )2 .

(3.5)

and, obviously, x ii = yii = zii ≡ 0. At the same time, object 01 determines the difference in distances from the point T i to the first and second objects, for example, by measuring the distance Di  , transmitting it to the object via a communication channel, and calculating Ri using the formula Ri = Di − Di

(3.6)

3.2 Measurement of Weather Radiosonde Altitude …

129

Then determine the desired relative coordinates of the second object (x, y, z) from the formula (3.4). Since the form of the Eq. (3.4) is identical to the form of the equation 1/2  + c × Dt, i = 1, . . . , 4, di = (xi − x)2 + (yi − y)2 + (z i − z)2 a well-known computational algorithm can be used for the calculation process. Determining the three-dimensional vector of relative coordinates is sufficient to create three forms (3.4). The proposed method can be implemented as follows (Fig. 3.5). A receiver indicator is installed on the second object, which, receiving NS signals, measures the distance Di  to all visible NS and determines the coordinate vector of the second object and the system time of the SRNS (T): (T ): (x 2 , y2 , z2 , T ). Then, using a communication channel transmitter (for example, a telemetry one), the following data array is transmitted to the first object: Di  —measured ranges to all visible NS, i—NS numbers, T —the moment of measurement on the SRNS time scale. At the first object (Fig. 3.5), this information is sent to the relative coordinates calculator via the communication channel receiver. The receiver indicator of the first object determines its coordinate vector for the same General moment of time t T (x 1 , y1 , z1 , T ) and passes it to the calculator. The algorithm of the latter is as follows. From the data received via the communication channel, measurements are selected related to the same numbers of the i-th NS and T moments. The range differences are determined using the formula (3.6) using the selected measurements. Using the a priori coordinates of the NS and the properly measured coordinates of the object, the inclined ranges Di and relative coordinates (x i, yi, zi ) are calculated. Based on the results obtained, the desired coordinates (x, y, z) are calculated according to the formula (3.4) following the algorithms described in formulas (3.5) and (3.3). Since the first object determines the second independently’s relative coordinates, in the proposed method, the number of “first” objects is not limited. The location is regulated by the directional diagrams of the transmitter’s antenna device receiver of the communication channel. The advantages of the proposed method are as follows. Denote: δ(di)—error in determining the range Di; δ (Ri )—error in determining the difference in ranges Ri ; δ(X i ), δ(Y i ), δ(Z i )—errors in determining the components of the vector (x i yi zi ); δ(x), δ(y), δ(z)—errors in determining the components of the vector (x, y z). Taking these notations into account, Eq. (3.4) in projections corresponds to the system of equations ⎧ ⎪ ⎨ [(Di + Ri ) + δ(di ) + δ(Ri )]x = X i − x + δ(X i ) + δ(x), [(Di + Ri ) + δ(di ) + δ(Ri )] y = Yi − y + δ(Yi ) + δ(y), . ⎪ ⎩ [(Di + Ri ) + δ(di ) + δ(Ri )]z = Z i − z + δ(Z i ) + δ(z).

(3.7)

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3 Practices to Combat External Impact on the Aircraft …

Ignoring the computational errors, we can write d(di)x = d(xi ), d(di) y = d(yi ), d(di)z = d(z i )

(3.8)

In this case, (3.7) is written as follows: ⎧ ⎪ ⎨ (Di + Ri )x + δ(Ri )x = X i − x + δ(x), (Di + Ri ) y + δ(Ri ) y = Yi − y + δ(y), ⎪ ⎩ (Di + Ri )z + δ(Ri )z = Z i − z + δ(z).

(3.9)

whence it follows that ⎧ ⎪ ⎨ δ(Ri )x = −δ(x), δ(Ri ) y = −δ(y), ⎪ ⎩ δ(Ri )z = −δ(z).

(3.10)

Therefore, in the proposed method, the total error δ  in determining the desired vector of relative coordinates of the second object is determined by the error δ(R) of measuring the radio navigation parameter Ri 1/2   2 1/2 2 2 δ = δ 2 (x) + δ 2 (y) + δ 2 (z) = δ(Ri)x + δ(Ri)y + δ(Ri)z = δ(R) (3.11) and it does not depend on the accuracy of a priori (or calculated—for mobile points in space) coordinates (x i , yi , zi ), as well as on the error in determining their relative coordinates (x i , yi , zi ). It can also be argued that the proposed method eliminates the system errors of the SRNS associated with errors in determining the orbits of the NS and the departure of the onboard time scales. Therefore, the error of the desired relative coordinates is determined by the measurement error and does not depend on the distance of objects from each other. We can also show that if we use the equivalent value Di  instead of the sum (Di + Ri ) in (3.4), then for the same error δ meas in the measurement parameters on objects, the spherical error δ  of the desired coordinates will be determined through the trace of the square matrix:  δ = δmeas S p (H )

(3.12)

the following type   cos21 α2 − cos21 α1 . . . cosi2 α2 − cosi2 α1 , . . . cos2M α2 − cos2M α1 ,   cos21 β2 − cos21 β1 . . . cosi2 β2 − cosi2 β1 , . . . cos2M β2 − cos2M β1 , H =  ..................................................................   cos2 γ − cos2 γ . . . cos2 γ − cos2 γ , . . . cos2 γ − cos2 γ , 1

2

1

1

i

2

i

1

M

2

M

1

       

(3.13),)

3.2 Measurement of Weather Radiosonde Altitude …

131

where cosi α, cosi β, cosi γ are the guiding coines of the i-th point along the x, y, z axes, respectively; M is the number of points in the operational constellation space). Thus, due to the different angles of observation of points in space from objects, replacing the sum (Di + Ri ) with Di  leads to a dependence of the desired relative’s error coordinates on the distance between objects. Therefore, this substitution degrades the accuracy of the desired coordinates. √ Additional advantages of the proposed method are a reduction 2 in the calculation error by a factor of one due to the absence of correlation of errors in determining the vector (x i, yi, zi ) and errors in the desired coordinates (x, y z) and the constancy of the gradient modulus of the field of navigation parameters |G2 | ⎧ ⎫1 ⎧ ⎫1 2 ⎬ / 2 ⎨   d R 2 ⎬ / 2 ⎨   d |G 2 | = = = 1, (3.14) (Di +  Ri ) ⎩ ⎩ ⎭ dj ⎭ dj j=x,y,z j=x,y,z

3.3 Method of Flight Regularity Management in Aviation Enterprise As noted above, the main requirement for the operation of any airline is to ensure the required level of safety. However, the financial risks and losses associated with the forced cancellation of airline flights should also be taken into account by the management and engineering staff [4]. The optimal level of airline flight regularity is determined by a variety of criteria, one of which is the minimum costs incurred when changing the regularity of flights. These include the so-called “commercial” costs required to service irregular flights and the “technical” costs required to increase flights’ regularity. Let’s take a closer look at the methodology for determining the cost of servicing air transportation of delayed, interrupted, and canceled flights. When maintenance operations flight executed regularly (for interrupted, detained, changed) and canceled flights occur mandatory spending, specific Federal aviation rules “General rules of air transportation of passengers, baggage, cargo and requirements to service of passengers, consignors, consignees” (approved by Order of Ministry of Transport of Russia of 28.06.2007 №82). Also, there may also be other expenses that are not mandatory and are not provided for by industry regulations. We will determine the procedure for calculating these so-called “commercial” expenses related to transportation organization [4, 5]. Y = Y1 + Y2 , where

(3.15)

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3 Practices to Combat External Impact on the Aircraft …

Y —expenses for servicing irregular (delayed, interrupted, modified) and canceled flights, Y1 —transportation service expenses defined by FAR №. 82, Y2 —other expenses for servicing irregular and cancelled flights. When a break in the carriage by the carrier in case of a flight delay, flight cancellation due to adverse weather conditions, technical reasons, changes in the route of carriage, the carrier (following FAR-82) is obliged to organize for passengers in points of departure and intermediate points following services, subject to the related operating costs of the airline: Expenses for informing passengers and shippers with whom a passenger air transportation agreement or an air cargo transportation agreement has been concluded about changes in the airline’s flight schedule (Y1.1 ): Y1.1 =



(W1i + X 1i )Ci ,

(3.16)

i

where Y1.1 —expenses for informing passengers and shippers about changes in the aircraft schedule, W1i —the number of passengers who need to be informed about changing the flight schedule at the i-th airport, X 1i —the number of shippers/consignees/agents who need to be informed about changes in the aircraft schedule at the i-th airport—Ci —the average cost of informing one client at the i-th airport along the route of the aircraft. Costs for providing mother and child rooms to a passenger with a child under 7 years of age (Y 1. 2 ): Y1.2. =



W2i ri

(3.17)

i

where Y1.2. —expenses for providing mother and child rooms to a passenger with a child under 7 years of age (Y 1. 2): W2i —number of passengers with irregular flights (changed, aborted, delayed) and canceled flights with a child under 7 years of age who need to arrange access to the mother and child room at the i-th airport when changing the flight schedule., ri —cost of service for one passenger with a child at the age of up to 7 years at the i-th airport. Payment for the operator’s services for two phone calls or two e-mail messages for each passenger when waiting for the flight departure for more than 2 h (Y1. 3 ): Y1.3. =

 i

W3i Z i ,

(3.18)

3.3 Method of Flight Regularity Management in Aviation Enterprise

133

where Y1.3 —the cost of paying for the operator’s services for two phone calls and two e-mail messages for each passenger when waiting more than 2 h for the flight to depart, W3i —number of passengers on flights delayed by more than 2 h at the i-th airport, Zi —the average cost of two phone calls and two messages by the email address of one passenger at the i-th airport. Costs of providing passengers with soft drinks while waiting for the departure of the flight for more than 2 h: Y1.4 =



W4i di ,

(3.19)

i

where Y1.4 —expenses for soft drinks for passengers waiting for departure for more than 2 h, W4i - the number of passengers on flights waiting for departure for more than two hours. di —the average cost of soft drinks per 1 passenger waiting for departure for more than 2 h. Expenses for providing hot meals to passengers when waiting for departure for more than 4 h and then every 6 h during the day and every/ hours at night Y1.5 =



W5i Pi ,

(3.20)

i

Y1.5 —expenses for providing hot meals to passengers on flights waiting more than 4 h for departure, W5i —number of passengers on flights delayed for more than 4 h and then every 6 h during the day and every 8 h at night, Pi —the average cost of hot meals per passenger at the i-th airport. Expenses for hotel accommodation for passengers waiting for departure for more than/ hours during the day and more than 6 h at night: Y1.6 =



W6i n i ,

(3.21)

i

where Y1.6 —hotel accommodation costs for passengers waiting for departure for more than/ hours during the day and more than 6 h at night, W6i —number of flight passengers waiting for departure for more than 8 h during the day and more than 6 h at night at the i-th airport, ni —the average cost of accommodation for one passenger in a hotel at the i-th airport.

134

3 Practices to Combat External Impact on the Aircraft …

Payment for the delivery of passengers by ground transport from the airport to the hotel and back in cases when the hotel is provided by the airline without charging an additional fee:  W7i × ti , (3.22) Y1.7 = (i)

where Y1.7 —expenses for ground transportation of passengers to and from the hotel provided free of charge, W7i —the number of passengers at the i-th airport who must be provided with ground transportation from the airport to the hotel and back in cases when the hotel is provided to the passenger by the airline free of charge, ti —the average cost of delivery per passenger from the i-th airport to the hotel and back. Costs for organizing luggage storage: Y1.8 =



G i × bi ,

(3.23)

(i)

where Y1.8 —expenses for organizing baggage storage for passengers on irregular and canceled flights, Gi —the weight of passengers’ baggage from irregular (delayed, interrupted, modified) and canceled flights at the i-th airport, which requires additional storage costs, bi —the average cost of storing 1 kg of baggage for passengers on irregular and canceled flights. Expenses defined by FAR-82 are calculated as the amount of Y1 = Y1.1 + Y1.2 + Y1.3 + Y1.4 + Y1.5 + Y1.6 + Y1.7 + Y1.8 .

(3.24)

In addition to the listed expenses, airlines may also have other costs for paying for optional services that carriers provide to customers and are not defined by industry regulations [6, 7]. Expenses for loading/unloading baggage, mail, and cargo when the flight is delayed, or the aircraft is replaced: Y2.1 =



Ni × h i ,

(3.25)

(i)

where Y2.1 —expenses for loading and unloading baggage, mail, and cargo in case of irregular flights or cancellations,

3.3 Method of Flight Regularity Management in Aviation Enterprise

135

Ni —the weight of cargo and mail from delayed and canceled flights at the i-th airport, which requires additional work on loading and unloading from the aircraft, hi —the cost of loading and unloading operations for servicing one flight at the i-th airport. Costs for placing and storing mail and cargo from canceled and delayed flights in the warehouse:  Di × bi , (3.26) Y2.2 = (i)

where Y2.2 —expenses for storage and storage of mail and cargo delivered from canceled and delayed flights, Di —the weight of cargo and mail from delayed and canceled flights at the i-th airport that require additional storage costs, bi —the average cost of storing 1 kg of cargo and mail from delayed and canceled flights at the i-th airport. The cost of cargo agent services at airports along with the airline’s route network when servicing delayed and canceled flights. Y2.3 =



Di × Q i ,

(3.27)

(i)

where Y2.3 —the cost of cargo agent services at airports along with the airline’s route network when servicing delayed and canceled flights. The services include payment for re-registration of cargo manifests, consolidation/de-consolidation of cargo shipments, informing customers, registration/re-registration of ground cargo delivery, etc., Qi —the average cost of additional services of cargo agents at the i-th airport per 1 kg of cargo/mail from delayed and canceled flights. Costs for replacement of flight equipment, loading/unloading of flight equipment, and passenger services for delayed and canceled flights: Y2.4 =



W3i × ri ,

(3.28)

(i)

where Y2.4 —expenses for replacement board, loading, and unloading, as well as for loading and unloading of passenger services for delayed and canceled flights, ri —the average cost of loading/unloading of board and passenger services for canceled and delayed flights and the cost of the board that requires replacement according to sanitary rules and regulations. The cost of “interline” in the transfer of passengers, mail, and cargo on flights with other airlines:

136

3 Practices to Combat External Impact on the Aircraft … int int Y2.5 = W pass × R pass + G int crg × kc arg + G mail × kmail ,

(3.29)

where Y2.5 —expenses under agreements on mutual recognition of transport documents (interline agreements) when transferring passengers, cargo, and mail from delayed and canceled flights to flights of other airlines, Wint pass —the number of passengers from delayed and canceled flights transferred to flights of other airlines via interline over the entire route network of the airline during the reporting period, int Gint carg , Gmail —the weight of cargo and mail, respectively, transferred from delayed and canceled flights to flights of other airlines via interline » , kpass , Kgr , kmail —the average amount of Commission paid by interline when transferring passengers, cargo, and mail to flights of other airlines. “Image” losses of the airline that occur when the regularity of flights decreases [7]: Y2.6 = 0, 001 × Dox ,

(3.30)

where Y2.6 —”image” losses of the airline that occur when the level of flight regularity decreases, 0.001—a coefficient that takes into account the share of revenue lost by the airline when the regularity of flights decreases and the corresponding deterioration of the airline’s “image” (up to 0.1% of turnover according to the expert assessment of Rigas Doganis), Dox —the amount of the airline’s revenue for the year. Loss from reduced revenue and cash flow to the airline due to a slower turnover of working capital: cons cons − kact )× B Y2.7 = (ksched

(3.31)

where Y2.7 —loss (lost profit) due to a slowdown in the turnover of the airline’s working capital, resulting from a decrease in the volume of cash flow, revenue, and revenue of the carrier, kcons sched —coefficient of consolidation working capital airlines, is scheduled for the reporting period, kcons act —the actual coefficient of consolidation of working capital obtained as a result of a decrease in revenue/revenue of the airline with a decrease in the frequency of flights, B—the amount of revenue/revenue of the airline for the period. In general, other expenses are determined by the formula: Y2 = Y2.1 + Y2.2 + Y2.3 + Y2.4 + Y2.5 + Y2.6 + Y2.7

(3.32)

3.3 Method of Flight Regularity Management in Aviation Enterprise

137

The “commercial” expenses of an airline determined by us in this study depend on the level of regularity of flights in the inverse proportion, i.e. the lower the regularity of flights, the more significant the airline’s expenses for servicing the transportation of such irregularly performed and canceled flights. Let’s assume that the function of “commercial” expenses Y on the number of irregular and canceled transportations has the form of a directly proportional relationship: Y = ω × a + c,

(3.33)

where Y—“commercial” expenses of the airline, ω—irregularly performed and canceled passenger transportation by the airline, a—average variable airline costs per passenger, C—conditional fixed expenses of the airline when servicing irregular flights. The average variable cost per passenger (a) includes: – – – – – – – – –

to inform the passenger about schedule changes (c), for the provision of mother and child rooms (r), to pay for two phone calls and two e-mail messages (z), for hot meals (p) and soft drinks (d), for hotel accommodation (n), land transfer (t), luggage storage (b), to replace the power supply and passenger service equipment (l), “image” losses of the airline per 1 passenger.

Conditional fixed expenses of the airline (C) that arise when servicing irregular transportation include administrative and economic expenses for maintaining the airline’s management apparatus in crisis and emergencies (employee compensation with social contributions, material costs, depreciation charges for fixed production funds, and other general economic expenses). Below we will focus on the methodology for determining an airline’s technical costs that arise when the level of flight regularity increases. The costs that arise from purposefully increasing the level of flight regularity are necessary for solving a whole range of production, technical, and administrative issues of the airline. These expenses include: 1. 2. 3. 4.

5.

The cost of an additional quantity is high reserved air and ground equipment. Losses when reducing the intensity of flights and flight hours on leasing of aircraft. Organizational costs for business-processes improvement. Expenses for additional flight and lifting equipment, being in reserve, to perform a flight on a reserve or primary aircraft in compliance with the sanitary standards of work and rest of the crew. Personnel costs for ground transportation.

138

6. 7.

3 Practices to Combat External Impact on the Aircraft …

Costs for additional spare parts and logistics property required to perform work to maintain the aircraft’s airworthiness by its technical center. Additional costs for third-party technical services centers under contracts for maintenance and maintenance of aircraft, etc.

In addition to the above cost items, additional costs may also be taken into account for the purchase of new and technically advanced aircraft with improved flight performance and economic characteristics at a higher cost, as well as lost profits in the event of failure to smooth out the waves of arrivals/departures. Below are the formulas for calculating these cost items. The cost of reserve aircraft is calculated by the type of aircraft [4]: es S rAC FT =



ann W j/Tkm ( j)

Whour j × Nseat j × γseat j × V f ligh j

× k rj es × S AC F T j × r (3.34)

where es —the required number of aircraft in reserve for the implementation of the SrACFT planned flight program in value terms for the year, ann —annual volume of work (passenger turnover) for the j-th type of aircraft, W j/Tkm Whourj —average annual flight hours per registered j-type aircraft, Nseatj —number of passenger seats in j-type aircraft, γseatj —coefficient of usage of passenger seats in aircraft of the j-th type, V f ligh j − flight speed of j-type aircraft, kres j —a redundancy factor that takes into account the number of j-type backup aircraft to perform the annual amount of work, S AC F T j —average annual cost of j-type aircraft, r —discount rate. If the reservation coefficient is not set, each airline independently determines the rate of aircraft reservation—for example, one backup aircraft for every 50 scheduled flights. The cost of additional ground equipment is calculated based on the types of ground equipment required [5]: r es Sgr oun = r ×

 i

gr oun

Ni

gr oun

× Si

× kir es ,

(3.35)

where gr oun —the number of required ground vehicles of the i-th type for performing Ni the annual amount of work, gr oun —the average cost of a unit of ground equipment of the i-th type, Si r es ki —reservation coefficient that takes into account the number of reserved ground vehicles of the i-th type. Then es r es S f a= S rAC F T + Sgr oun ,

(3.36)

3.3 Method of Flight Regularity Management in Aviation Enterprise

139

where S f a —the discounted cost of fixed assets, including the cost of aircraft and groundbased aircraft in reserve. When flight hours are reduced for aircraft taken on a financial or operational lease, lease payments are reduced. However, with a decrease in the intensity of flights and flight hours on the aircraft, the cost of air transportation increases (the cost of flight hours, passenger kilometers, ton-kilometers) for the following cost items: – depreciation charges for aircraft and aircraft engines, – deductions for current repairs of aircraft and aircraft engines performed by its aviation technical center or a third-party center based on concluded contracts, – expenses for time-based bonus pay for flight crew and flight attendants, – deductions on social needs, – airport expenses in terms of payment for aircraft basing, – administrative and general expenses. The formula can determine the effect of increasing the cost of air transportation with a decrease in flight hours per scheduled aircraft: FT = E AC f

   j hour es C rj hour − C j hour × Whour j , j

(3.37)

where es C rj hour —cost of flight hours of the j-th type of aircraft when introduced the reserve of the armed forces, es C wthtr j hour —cost of flight hours of the j-th type of aircraft without introduction the reserve of the armed forces, Whour j —flight hours on j-type aircraft for the year. es = C rj hour

es C wthtr = j

E x prj es Whour j

,

E x p wthtr es , Whour j

where E x prj es —annual operating expenses of the armed forces of the j-th type at the reserve of the armed forces, es —annual operating expenses of the armed forces of the j-th type at E x p wthtr j without a reserve of the armed forces. es + E x prj es , E x p j = E x p wthtr j

(3.38)

where Expres j —change in annual operating costs when introducing a backup aircraft.

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3 Practices to Combat External Impact on the Aircraft …

In simplified form es × kr es j , E x prj es = E x p wthtr j

(3.39)

where kr es j —average annual cost of j-type aircraft, If the reservation coefficient is not set kr es j in the airline, then the change in annual operating costs can be determined by cost items using the formula [6]: dep

E x prj es = P jleas + P j

pay

+ P jmaint + P j

soc. pay

+ P j

a/ p

+ P j

+ P jaon (3.40)

where Pleas j —reduction of lease payments for j-type aircraft with the introduction of a reserve aircraft, dep P j —increase in depreciation charges for j-type aircraft with the introduction of a reserve aircraft, —change in the cost of maintenance of aircraft and aircraft engines for Pmaint j the j-th type of aircraft when introducing a backup aircraft, pay P j —increasing costs for time-based premium payments of labor of flight crew and flight attendants,of labor of flight crew and flight attendants, soc.pay —increase in social contributions from time to time-based premium P j payment for staff, a/ p P j .—increase in airport expenses for ACFT type j, Paon j —change in annual expenses for administrative and other needs when introducing a reserve aircraft. With an increase in the inventory of aviation equipment (spare parts and other material and technical equipment), the airline’s turnover of working capital slows down due to an increase in the average annual cost of working capital. The volume of aviation equipment inventory (AEI) is determined based on approved expenditure standards, taking into account the accepted logistics scheme and delivery schedule. In general, the effect of working capital turnover is determined by the formula [7]:   wtgtr es , = Pr od × k rfesi x − k f i x E AEI f

(3.41)

where EAEI f —the effect of slowing down the turnover of working capital when introducing a reserve aircraft, wtht res —coefficients of fixing working capital when introducing a backup kres fix , kfix aircraft without a backup aircraft in the airline. k rfesi x =

r es Cwc , Pr od

(3.42)

3.3 Method of Flight Regularity Management in Aviation Enterprise

141

where res —the average annual cost of working capital when introducing a reserve Cwc aircraft. es = k wthtr f ix

wthtr es Cow , Pr od

(3.43)

where wthtr es Cwc —the average annual cost of working capital when introducing a reserve aircraft. The formula can determine the total amount of so-called “technical” expenses: FT I + E AE . Ctech = S f a + E AC f f

(3.44)

Note that when performing the task of increasing the regularity of flights by introducing reserve aircraft, airlines will incur additional costs due to the need to attract capital to invest in the purchase of reserved aircraft and aviation equipment required to maintain the airworthiness of these aircraft. Besides, airlines will have to pay additional operating costs due to the inevitable increase in air transportation costs in this case [8–15]. When developing a strategy, airlines are guided by the international community’s policy and the country’s transport development strategy when managing flight regularity. Development of air transport approved the Transport Strategy of the Russian Federation, including the formation of a single transport space of Russia based on the balanced development of efficient air transport infrastructure and the availability and quality of air transport services following social standards and for cargo at the level of the innovation development needs of the economy [16–29]. Understanding the current strategy for flight regularity is very important because we cannot make decisions about the future without having a clear idea of the airline’s state and what strategies it is implementing. Various schemes can be used to understand the current strategy. Thompson and Strickland suggest one possible approach. They believe that there are external and internal factors that need to be evaluated in order to understand the strategy being implemented [30–34]. External factors: the volume of air traffic, diversity of airline activities; general character and nature of recent acquisitions of the airline (for example, commercial law) and the sale of part of their own; the structure and focus of the airline over the last period; the possibility, which is focused airline in recent times; the relationship to external threats [35–42]. Internal factors: the airline’s goals, including the regularity of flights; criteria for allocating resources and the current structure of capital investments by type of aircraft; attitude to financial risk, both on the part of management and in accordance with real practice, implemented by financial policy; the level and degree of concentration of efforts in the field of research and innovation; strategies for individual functional areas (regularity of flights, marketing, production, personnel, Finance, research, and development) [43–48].

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Analysis of an airline’s product, one of the characteristics of the regularity of flights, is an essential strategic management tool. It provides a visual representation of how individual parts of the business are interconnected. Using product analysis, we can balance essential business factors such as risk, money flow, and the product life cycle stage. The airline’s strategy for managing flight regularity is selected by management based on the airline’s goals, analysis of key factors that characterize the airline’s condition, taking into account the airline’s product analysis results, and the nature and feasibility of strategies [49–52]. At the same time, they determine the airline’s position compared to competitors using the analysis of competitors’ strengths and weaknesses or SWOT analysis (Strengths + Weaknesses + Opportunities + Threats). The airline’s strengths may be its predominant market share, convenient schedule, high flight regularity level, first-class service, and an effective frequent-flyer program. Weaknesses are the lack of licenses for flights to key destinations, low frequency of flights compared with competitors, and insufficient qualifications of employees of the airline’s commercial department [53]. Opportunities to improve the airline’s position in the transportation market can be as follows: steady growth in the rate of air transportation in the region, increasing the frequency of flights; the start of operation of new modern aircraft with better economic performance and increased comfort; a leading position in the field of ground handling. The airline sees threats in the continuing decline in traffic volumes, in the aggravation of competition, and the widespread use of similar discount systems by competitors for regular airline customers. The analysis allows to identify the main potential opportunities and threats to the airline’s success and helps to decide on which areas to focus the airline’s resources and determine the priority level of the task of significantly increasing the regularity of flights [54]. In addition to the strengths and weaknesses of the competitors, the analysis concerns the financial resources of the airline, the obligations of airlines under the previous strategies, the degree of dependence on the external environment, the chosen strategy to the condition and requirements of the environment, compliance with the chosen strategy and capacity of the firm, admissibility laid down in the strategic risk. Next, we consider the option of “doing nothing”, that is, not changing the airline’s strategy in terms of the quality of the product provided (including the level of regularity of flights), and determine the position that the airline will take in some years. That is followed by identifying strategic alternatives for managing flight regularity and evaluating them based on the following factors: • • • • •

carrying capacity of the aircraft fleet; amount of expenses controlled by the airline; ways of financing the development of the airline; demand in the air transportation market; needs of different market segments;

3.3 Method of Flight Regularity Management in Aviation Enterprise

• • • •

143

types and severity of competition in different areas; amount of expenses not controlled by the airline; regulation of the airline’s activities by international law and national authorities; the capacity of ground infrastructure.

The strategy is defined by combining the strengths of the airline, taking into account environmental factors. Among the airline’s strategic goals may be ensuring safety (to improve safety indicators and bring them to the maximum); increasing the frequency of flights; expanding the regional market (specify a new region); increasing traffic volumes by n% per year; providing affordable prices for tickets (discounts, conducting various programs to encourage customers, particularly Frequent Flyer Program); improving air quality; expanding the range of services (business flights, Charter flights); the flight’s frequency increasing (reducing the connection time when transferring passengers from one flight to another and a half to one hour); the increase of scheduled flights to a week. These strategic goals are achieved primarily through planning the range of services provided, effective resource management, fair pricing or tariff policy, selection of intermediaries and formation of product distribution channels, organization of advertising, and sales promotion. Next, we analyze the sensitivity of the airline’s leading performance indicators to changes in external conditions, such as rising oil prices, currency fluctuations, and so on. Then, the airline’s strategic plan is drawn up, including the management of flight regularity. As it is implemented, the plan is monitored and adjusted if necessary. The strategic plan of the airline management regularity of flights serves a variety of purposes: outlines the direction in which it should develop; controls absence of contradictions between the strategic plans of the different business processes and activities of various departments of the airline (checked the balance of objectives in terms of safety and regularity of flight, airline profitability required by the market product quality the airline, etc.); help to match existing airline resources with existing plans; allows you to relate actual results to desired. The airline’s strategies for managing flight regularity are divided into aggressive, defensive, and retreat strategies. An active, creative, or “aggressive” offensive strategy aims, for example, to increase the level of flight regularity by 5% and thereby increase the volume of traffic by 30%. A defensive, retention, or survival strategy involves the airline maintaining a certain regularity of flights and the lowest possible profitability. The retreat strategy, which is usually forced due to the deterioration of sales of products, involves a gradual curtailment of positions in some regions of activity, by region, or by individual market segments. In the theory and practice of airlines, several methods are used to select strategies by justifying Ansoff matrices, determining the break-even point, selecting priority strategies on the consumer rating scale, and analyzing the matrices of the Boston Advisory group.

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The cost estimate for achieving the strategic level of flight regularity contains a detailed description of the airline’s activities approved by management, indicating the estimated costs for each direction. The following is a procedure for verifying the strategic plan’s implementation and a program for the airline’s actions in an unforeseen situation when there was a failure in operation (contracts with other airlines, hotels, etc.).

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Annex A

Operational Forecasting and Risk Assessment Upcoming Flight Program Aspfaa

1.

Working interface of the program

The principles for the development and operation of ASFPAA are described in Chap. 1 of this book. This annex briefly describes an example of using ASFPAA for operational forecasting on real data of airline “Volga-Dnepr". On the control panel of the working interface of the ASFPAA system, the “Operational forecast” mode is selected (Fig. A.1). 2.

Providing information about the risk of all planned flights

All flights planned for the next day are displayed in the pop-up screen of operational forecasting, with automatic calculation of the total flight risk in value terms in USD. For the convenience of risk assessment, a “traffic light model” is used, in which flights with negligible risks are marked with a green icon, flights with medium risk— yellow, and flights with a risk exceeding the acceptable level—red. In this case, a flight with an unacceptable risk is detected Prestwick–Bourgas 21.10.12, R = 3473USD (Fig. A.2). 3.

Providing detailed information about the risk structure

When the computer mouse clicks on the line with this flight, a page opens with detailed information on the overall risk (Fig. A.3). As can be seen from the information provided, the most significant contribution to the overall risk is made by the risk of the “unsafe runway touch” event (ARC) with the risk value R = 2288.5 USD. 4.

Providing information about significant hazards and general information about the flight.

When we select the event line (in this case, ARC) and click on the “Significant HF events” tab, a page is called that contains information about those hazard factors that make the most significant contribution to the risk under consideration (Fig. A.4). This © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4

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148

Annex A: Operational Forecasting and Risk Assessment Upcoming Flight …

Fig. A.1 Working interface ASFPAA

Fig. A.2 Screen for predicting the risk of upcoming flights

Annex A: Operational Forecasting and Risk Assessment Upcoming Flight …

ADES

Airframe destruction

ARС

Abnormal Runway Contact

BIRD

Birdstrike

DECOM

Decompression of the aircraft

FIRE

Fire

LOC-I-

Loss of Control – In-flight

GCOL

Ground Collision

MAC

Mid-Air Collisions

RE

Runway Excursion

SCF-PP

System Component Failure -Powerplant)

CFIT

Controlled Flight Into or Towards Terrain

SEC

Security Related

Fig. A.3 The detailed forecast for a flight with unacceptable risk

149

150

Annex A: Operational Forecasting and Risk Assessment Upcoming Flight …

1 2 3 4

1. The number of flights in the last 2 months (as untrained) 2. The number of flights at this airport (as untrained) 3. The number of night flights per week 4. The number of flights at difficult airports for a week

Fig. A.4 Analysis of significant HF and information on the PIC of this flight

HF is a reduced speed of passage of the runway threshold and reduced separation speed at takeoff. A page with detailed information about the PIC performing the flight is also displayed. The reasons for the increased probability of these HF are, according to the system, increased fatigue of the PIC due to significant flight time (more than 196 h) and the number of flights (72) over the past three months. It also highlights that this PIC has little experience of flying to this airfield (only 1 flight). 1. 2. 3. 4.

The number of flights in the last 2 months (as untrained) The number of flights at this airport (as untrained) The number of night flights per week The number of flights at difficult airports for a week.

5.

Choosing management solutions to reduce risk

By clicking on a particular tab called page database management decisions (MD DB), which are possible variations of MD with the predicted rating of their impact on the level of risk associated with the hazard (in this case, the reduced efficiency of PIC due to fatigue) and the value of the MD (Fig. A.5).

Annex A: Operational Forecasting and Risk Assessment Upcoming Flight …

151

Fig. A.5 Selection of management decisions from the MD database. Note the “MD cost” Element is currently under approval

In this case, the possible options for MD to reduce the risk are: 1. 2. 3. 4.

perform a test flight to determine the cause of the error; additional monitoring by members of the crew; allow the pilot to rest for 42 h; conduct a training session on the simulator.

The most acceptable may be MD №2 and №3, which will reduce HF’s risk associated with PIC fatigue by 50%. The choice of decision is left to the decision-maker. In this case, it can be the flight control center’s duty shift manager, the duty commander of the flight service, or the general director of the airline.

Annex B

Automated Risk Management System Arms

1. 2.

List of categories by sector (a type of activity) Technology and example of constructing the membership functions of the linguistic variable “Frequency” for evaluating the degree of possibility by fuzzy logic inference.

The membership functions of the terms “Very often”, “Often”, “Sometimes”, “Rarely”, “Extremely rarely” are based on the expert survey method. N experts are involved in the work. Each expert fills out a questionnaire in which they indicate their opinion on whether an element u i (i = 1, n) belongs to a fuzzy set (term) A˜ j ( j = 1, m) in the form of a binary evaluation: 1—the element belongs to the set, 0—does not belong, where n is the total number of elements (intervals), m is the total number of terms. Let’s denote ci,k j the k-th expert’s opinion on whether the i-th element (interval) j-th fuzzy set. The degree to which the ui element belongs to the fuzzy set Aj is calculated using the formula: µ A j (u i ) =

N 1  k c . N k=1 j,i

(B.1)

A survey of eight experts was conducted to construct the membership functions of these terms. The experts were pilot-inspector of the safety inspection, chief engineer of the airline’s CAME, deputy general director for quality and safety, deputy head of the FCR, heads of transportation management services, flight attendants, cargo transportation, and aviation security services.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4

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154

Annex B: Automated Risk Management System Arms

After discussion with experts and considering that the risk assessment was supposed to be carried out quarterly, the question was formulated as follows: how many events during the quarter do you consider that events occur “Extremely rarely”, “Rarely”, etc. The results of the survey are summarized in Table B.1. The range of the number of events from 0 to 150 is accepted, which is divided into intervals of 5 events. The second line from the top shows the midpoints of the intervals for calculating the number of events per 1000 flights, assuming that the average number of flights per quarter is 12000. The results of calculations of the degree of membership according to the formula (B.1) are summarized in Table B.2.

E-4

E-3

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

1

1

Rarely

Sometimes

1

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

1

E-1

E-2

0.2

1

Per 1 thousand flights

Extremely rare

2.5

Events for the quarter—the middle of the interval

1

1

1

0.6

7.5

1

1

1

1

1.0

12.5

Table B.1 Reslts of the expert survey

1

1

1

1

1.5

17.5

1

1

1

1

1.9

22.5

1

1

1

1

2.3

27.5

1

1

1

1

2.7

32.5

1

1

1

1

3.1

37.5

1

1

1

1

3.5

42.5

1

1

1

1

4.0

47.5

1

1

1

1

4.4

52.5

1

1

1

1

4.8

57.5

1

1

1

1

5.2

62.5

1

1

1

1

5.6

67.5

1

1

1

1

6.0

72.5

1

1

1

1

6.5

77.5

1

1

1

1

6.9

82.5

1

1

1

1

7.3

87.5

1

1

1

1

7.7

1

1

1

1

8.1

97.5

(continued)

92.5

Annex B: Automated Risk Management System Arms 155

E-8

E-7

E-6

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

1

1

1

1

-5

Extremely rare

2.5

Events for the quarter—the middle of the interval

Table B.1 (continued)

1

1

1

1

7.5

1

1

1

1

12.5

1

1

1

1

17.5

1

1

1

1

22.5

1

1

1

1

27.5

1

1

1

1

32.5

1

1

1

1

37.5

1

1

1

1

42.5

1

1

1

1

47.5

1

1

1

1

52.5

1

1

1

1

57.5

1

1

1

1

62.5

1

1

1

1

67.5

1

1

1

1

72.5

1

1

1

1

77.5

1

1

1

1

82.5

1

1

1

1

87.5

1

1

1

1

1

1

1

1

97.5

(continued)

92.5

156 Annex B: Automated Risk Management System Arms

E-4

E-3

E-2

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

1

1

1

1

8.5

Extremely rare

Per 1 thousand flights

E-1

102.5

Events for the quarter—the middle of the interval

Table B.1 (continued)

1

1

1

1

9.0

107.5

1

1

1

1

9.4

112.5

1

1

1

1

9.8

117.5

1

1

1

1

10.2

122.5

1

1

1

1

10.6

127.5

1

1

1

1

11.0

132.5

1

1

1

1

11.5

137.5

1

1

1

1

11.9

142.5

1

1

1

1

(continued)

12.3

147.5

Annex B: Automated Risk Management System Arms 157

E-8

E-7

E-6

-5

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Very often

Often

Sometimes

Rarely

Extremely rare

Events for the quarter—the middle of the interval

Table B.1 (continued)

1

1

1

1

102.5

1

1

1

1

107.5

1

1

1

1

112.5

1

1

1

1

117.5

1

1

1

1

122.5

1

1

1

1

127.5

1

1

1

1

132.5

1

1

1

1

137.5

1

1

1

1

142.5

1

1

1

1

147.5

158 Annex B: Automated Risk Management System Arms

0 0 0 0 0 0

Often

Raw

Normalized

Very often

Raw

Normalized

0

0

0

Raw

Normalized

Rarely

0

0

0

0

Extremely rare

0

0

0

0

8.1

0

Normalized

7.7

0

Raw

7.3

0

Per 1 thousand flights

0

1

0 0

0

0

0

0

0

8.5

0

0

0

0

0

0

0

0

0

3

3

5

6

6

6

0 4

0

0

0

0

2

0

0

0

0

0

0

0

0

0

0

0.2

0

0

0

0

9.0

0

0

0

0

0

0

0.5

0

0

0

0.2

0.1

1

0.5

0

0

0

0

9.4

0

0

0

0.2

0.1

1

0.8

0.3

2

0

0

0

0

0

0

0

0

0

0

0

0

0

9.8

0.2

0.3

2

0

0

0

0

0

0

0.3

2

0

0

0

0

0

0

0.3

2

0

0

0

0

0

0

0.3

2

0

0

0

0

0

0

2

2

4

6

6

6

0

0

0

0.3

0

0

0

0

10.2

0

0

0

0.3

0

0

0

0

0

0

1

0

0

0

0

10.6

0.7

0

0

0

0

11.0

0

0

0

1

0.1

0.1

1

0.8

0

0

0

0

11.5

0.1

0

0

1

0.6

5

0

0

0

0

11.9

0.1

0.1

1

0.8

0.6

5

5

0

0

0

0

12.3

0.1

0.1

1

0.8

0.6

(continued)

0.1

0.1

1

0.8

0.6

5

1.00 1.00 0.67 0.33 0.33 0.33 0.33 0.33 0.33 0.33

0.3

2

0

0

0

0

0

0

0.13 0.25 0.25 0.50 0.75 0.75 0.8

1

1

0.13 0.38 0.38 0.63 0.75 0.75 0.75 0.50 0.3

1

0.71 1.00 0.71 0.57 0.29 0.14 0

0

0

0

0

0

6.9

Sometimes

2

0

0

0

6.5

Normalized

4

0

0.63 0.88 0.63 0.50 0.25 0.13 0

5

0

0

0

6.0

0

7

0

0

0

5.6

Raw

5

0.4

0

0

5.2

0

0

0

4.8

1

0

0

4.4

Rarely

0

0

4.0

Normalized

0

0.38 0

3

3.5

1

3.1

8

2.7

Raw

2.3

Extremely rare

1.9

1.0

0.2 0.6

Per 1 thousand flights

1.5

12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5

Events for the quarter—the middle of the interval 2.5 7.5

Table B.2 The results of processing of expert assessments

Annex B: Automated Risk Management System Arms 159

0.6

0.8

2

0.3

0.3

Normalized

Very often

Raw

Normalized

5

Often

Raw

0.1

1

Sometimes

0.17

0

Normalized

Normalized

0

Raw

Raw

7.3

Per 1 thousand flights

Table B.2 (continued)

0.3

0.3

2

0.8

0.6

5

0.17

0.1

1

0

0

7.7

0.4

0.4

3

0.7

0.5

4

0.17

0.1

1

0

0

8.1

0.5

0.5

4

0.67

0.5

4

0.00

0

0

0

0

8.5

0.63

0.63

5

0.5

0.38

3 lePara>

0.00

0

0

0

0

9.0

0.63

0.63

5

0.5

0.38

3

0.00

0

0

0

0

9.4

0.63

0.63

5

0.5

0.38

3

0.00

0

0

0

0

9.8

0.75

0.75

6

0.33

0.25

2

0.00

0

0

0

0

10.2

0.88

0.88

7

0.17

0.13

1

0.00

0

0

0

0

10.6

0.88

7

0.17

0.13

1

0.00

0

0

0

0

0

11.0

0.88

0.88

7

0.17

0.13

1

0.00

0

0

0

0

11.5

0.88

0.88

7

0.17

0.13

1

0.00

0

0

0

0

11.9

1

1

8

0

0

0

0.00

0

0

0

0

12.3

160 Annex B: Automated Risk Management System Arms

Annex C

Application of the Safety Risk Management Method №. 3 in the Airlines

The development and application of method 3 are described in Chap. 2 of the book. This Annex provides examples of using this method in two airlines. 1.

Example of calculating and monitoring DERC for an airline using fuzzy estimation

DERC is calculated according to the methodology given in clause 2.2.1 of the book based on the analysis of events for the reporting period using expert assessments. Table C.1 provides a summary of events taken into account in the weekly monitoring of airline X for year Y. Experts were asked to evaluate the event based on the available information by answering two questions. The experts’ responses are summarized in Tables C.2 and C.3. Processing of expert evaluations was performed with the use of elements of fuzzy set theory (FST). The severity and effectiveness of barriers were considered as linguistic variables (LV). Each LV has 4 terms (Table 1.23). LV terms damage level Catastrophic

Emergency

LV termes efficiency of barriers Average

Minor

High

Average

Minor

Absent

Based on verbal (fuzzy) assessments of experts using the membership functions, as shown below, the exact values of DERC were calculated. The experts were deputy director of the department of prevention of AE, pilotinspector B-747, pilot-inspector Il-76, engineer-inspector of the safety inspection, senior methodologist of the safety inspection. Several calculations are performed based on the survey data.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4

161

Date

03.01

02.02

14.02

12.03

12.05

26.05

30.05

20.06

01.07

15.07

13.08

03.10

21.10

31.10

28.11

№

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

A

B

A

B

B

A

A

B

B

A

A

A

B

A

B

Type of aircraft

Takeoff, decline

push-down

Takeoff

Flight on the echelon

landing

Platform, taxiing after landing

Platform, parking

Climb

Climb

Level flight

Mileage after landing

Climb

Push-down

Push-down

Steering

Flight stage

Table C.1 Events in airline X for 11 months of year Y Main factor

After takeoff, 3–4 rows of landing gear are not removed; before landing, 1 row of landing gear is not allowed

Aileron control rod breakage

Takeoff without proper permission

Temporary loss of radio communication

Rolling out of the runway

Damage to the end of the right plane

In the parking lot, damage to the aircraft as a result of shelling

After takeoff, the oil decreases

The vehicle

The vehicle

Human

Human

Human

Human

Environment

Human

Environment

The vehicle

Surge MDU №1 with subsequent self-shutdown After takeoff, a bird hits the 3rd engine

The vehicle

The vehicle

The vehicle

Human

The vehicle

When the reverse is off on the run, the alarm “Surge of the 3rd engine”

Switching off the MDU-2 by the crew

False triggering of the fire extinguishing system of the 1st engine

Getting into a zone of severe turbulence, injuries to persons on board, and damage to cargo

Engine failure, return from pre-start

Circumstances

162 Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

E5

E4

E3

E2

Insignificant

0

0

0

1

Average

Insignificant

Insignificant

Crash

1

Average

Disaster

0

0

Crash

0

0

1

Average

Disaster

0

1

Insignificant

Crash

0

Average

0

0

Disaster

0

Insignificant

Crash

1

Average

Disaster

0

0

Crash

0

Disaster

E1

1

Events

Expert

0

0

1

0

0

1

0

0

0

1

0

0

0

0

1

0

0

1

0

0

2

1

0

0

0

0

1

0

0

0

1

0

0

0

1

0

0

0

1

0

0

3

0

1

0

0

1

0

0

0

1

0

0

0

0

1

0

0

1

0

0

0

4

0

1

0

0

0

1

0

0

1

0

0

0

0

1

0

0

1

0

0

0

5

1

0

0

0

0

1

0

0

0

1

0

0

0

1

0

0

1

0

0

0

6

0

0

1

0

0

1

0

0

0

0

1

0

1

0

0

0

0

1

0

0

7

0

0

1

0

0

1

0

0

1

0

0

0

1

0

0

0

1

0

0

0

8

0

1

0

0

0

0

1

0

0

0

1

0

0

1

0

0

0

1

0

0

9

0

0

1

0

0

1

0

0

0

1

0

0

0

1

0

0

0

1

0

0

10

Table C.2 Unclear expert assessments of the likely consequences of the situation (damage from the event)

0

0

1

0

0

0

1

0

0

0

1

0

0

0

1

0

0

0

1

0

11

0

1

0

0

0

1

0

0

1

0

0

0

0

0

0

1

1

0

0

0

12

0

0

0

1

0

1

0

0

0

1

0

0

0

0

0

1

0

0

0

1

13

0

0

1

0

0

1

0

0

0

1

0

0

0

0

0

1

0

0

0

1

14

0

0

1

0

0

1

0

0

1

0

0

0

1

0

0

0

1

0

0

0

15

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines 163

E5

E4

E3

E2

0

High

0

0

0

1

Average

High

High

Minor

0

Average

Absent

0

0

Minor

1

1

Average

Absent

0

1

High

Minor

0

Average

0

0

Absent

0

High

Minor

1

Average

Absent

0

0

Minor

0

Absent

E1

1

Events

Expert

0

0

1

0

0

1

0

0

0

1

0

0

0

0

0

1

0

0

0

1

2

1

0

0

0

0

0

1

0

0

0

1

0

0

1

0

0

0

0

0

1

3

1

0

0

0

0

0

1

0

0

0

1

0

1

0

0

0

0

0

1

0

4

1

0

0

0

0

1

0

0

0

0

1

0

1

0

0

0

1

0

0

0

5

1

0

0

0

0

0

1

0

0

0

1

0

1

0

0

0

0

0

1

0

6

Table C.3 Experts unclear assessment of the effectiveness of parry barriers

1

0

1

0

0

1

0

0

0

0

0

1

1

0

0

0

0

0

1

0

7

1

0

0

0

1

0

0

1

0

0

0

1

1

0

0

0

1

0

0

1

8

0

0

1

0

1

0

0

0

0

1

0

0

0

0

0

1

0

1

0

0

9

0

1

0

0

1

0

0

0

0

1

0

0

0

1

0

0

0

1

0

0

10

0

0

1

0

0

0

1

0

0

0

1

0

0

0

1

0

0

0

1

0

11

0

1

0

0

1

0

0

0

0

1

0

0

0

0

1

0

0

1

0

0

12

0

1

0

0

0

1

0

0

0

0

1

0

0

0

1

0

1

0

0

0

13

1

0

0

0

1

0

0

0

0

1

0

0

0

1

0

0

0

0

1

0

14

0

0

1

0

0

1

0

0

0

0

1

0

0

1

0

0

0

1

0

0

15

164 Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

165

The degree of belonging of each event to each category of the severity of consequences is calculated using the formula: n 

Ai−m =

i=1

j

E i−m ,

N

(C.1)

where Ai−m is an estimate of whether (0 or 1) the i-th event belongs to the m-th category of severity, j

E i−m —binary expert assessment (0 or 1) of the i-th event belonging to the m-th severity category, i—event number, in this case i = 1, 2, …, 15, j—number of the expert, in this case j = 1, 2, …, 5, N—is the total number of experts, in this case N = 5, m—number of the severity category (1-accident, 2-accident, 3-medium, 4-minor). Example of calculation for the degree to which event 2 belongs to the “Average” category. i = 2, m = 3. A2−3 =

0+0+1+1+1+0 = 0, 6. 5

The results of calculations of the degree of membership according to the formula (C.1) are summarized in Table C.4. The degree of parry barriers belonging to each event to each type of barrier is calculated using the formula: n 

Bi−k =

i=1

j

E i−k N

,

(C.2)

where Bi−k —the degree of belonging (from 0 to 1) of barriers of the i-th event to the k-th type of barrier effectiveness, j E i−m —assessment of the j-th expert (0 or 1) whether the barriers of the i-th event belong to the k-th type of barrier effectiveness, i—event number, in this case i = 1, 2, …, 15, j—number of the expert, in this case j = 1, 2, …, 5, N—is the total number of experts, in this case N = 5, k—type of barrier effectiveness (1-absent; 2-insignificant, 3-medium, 4-high). Example of calculation for the degree to which event 2 barriers belong to the “Insignificant” efficiency type: i = 2, k = 2. = 0, 4. B2−2 = 0+1+0+0+1 5

Minor

Average

5

1

Grade of membership

0

Grade of membership

The sum of the scores

0

The sum of the scores

0

Grade of membership

0

0

The sum of the scores

Grade of membership

Crash

0

The sum of the scores

Disaster

1

Calculation of № event

Category

0

0

0.6

3

0.4

2

0

0

2

0.2

1

0.8

4

0

0

0

0

3

0.6

3

0.4

2

0

0

0

0

4

0.4

2

0.6

3

0

0

0

0

5

0.4

2

0.6

3

0

0

0

0

6

0.2

1

0.4

2

0.4

2

0

0

7

0.6

3

0.2

1

0.2

1

0

0

8

0

0

0.6

3

0.4

2

0

0

9

0

0

0.8

4

0.2

1

0

0

10

Table C.4 The degree to which the event belongs to the category of severity of consequences (potential damage)

0

0

0

0

1

5

0

0

11

0.4

2

0.4

2

0

0

0.2

1

12

0

0

0.4

2

0

0

0.6

3

13

0

0

0.4

2

0.2

1

0.4

2

14

0.6

3

0.2

1

0.2

1

0

0

15

166 Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

167

The results of calculations of the degree of membership according to the formula (C.2) are summarized in Table C.5. The DERC coefficient of each event is calculated using the formula: D E RCi =

4  4 

Cmk Ai−m Bi−k .

(C.3)

m=1 k=1

The value of the DERC coefficient in the matrix cell corresponds to the severity category m and the barrier efficiency type k. The results of calculating the DERC of each event using the formula (C.3) are summarized in Table C.6. The following data is used for the number of flights of airline X for year Y. Date

ACFT A

ACFT B

General

January

218

79

297

February

261

86

347

March

291

76

367

April

336

83

419

May

272

67

339

June

227

118

345

July

234

66

300

August

234

47

281

September

176

92

268

October

252

80

332

November

256

96

352

December

0

0

0

2757

890

3647

Total

2. Brief conclusions on the results of the DERC assessment of individual events. The result allows us to assess the risk associated with events more objectively and reasonably allocate resources. None of the events that occurred were included in the red zone of the DERC matrix of a particular event, i.e., no emergency measures need to be taken for any of them (stopping the operation of an aircraft type, stopping flights to this airfield, etc.). Nine events 2, 3, 7, 8, 9, 11, 12, 13, 14 on the DERC hit the yellow zone. That means that they need to be thoroughly investigated, existing barriers reviewed, and new ones added. Table C.7 shows these events in descending order of the DERC, the main factors, and the responsibility distribution. The rest of the events were in the green zone. This means that the existing barriers are sufficient to prevent them from developing into AE when these HF occur and the NS repeats.

0.2

Grade of membership

High

Average

Minor

1

The sum of the scores

Absent

0.6

Grade of membership

0.2 3

Grade of membership

The sum of the scores

1

0

Grade of membership

The sum of the scores

0

The sum of the scores

1

Events

Calculation

The effectiveness of barriers

0

0

0.4

2

0.2

1

0.4

2

2

0.2

1

0.2

1

0.4

2

0.2

1

3

0.4

2

0

0

0.6

3

0

0

4

0.6

3

0.2

1

0.2

1

0

0

5

Table C.5 The degree to which the barriers of this event belong to the effectiveness type

0.4

2

0

0

0.6

3

0

0

6

0.2

2

0.2

1

0.4

2

0.2

1

7

0.8

4

0

0

0

0

0.2

3

8

0.2

1

0.4

2

0.2

1

0.2

1

9

0.2

1

0.8

4

0

0

0

0

10

0

0

0

0

1

5

0

0

11

0.2

1

0.6

3

0.2

1

0

0

12

0.2

1

0.4

2

0.4

2

0

0

13

0.4

2

0.4

2

0.2

1

0

0

14

0

0

0.6

3

0.4

2

0

0

15

168 Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

Minor

Average

Accident

Disaster

1

1

Average

High

DERC for events

1

2

High

Minor

4

Average

1

20

Absent

100

10

High

Minor

20

Absent

100

Average

50

High

Minor

100

Average

500

500

Minor

Absent

2500

Absent

Type of DERC efficiency matrix

The DERC for damage category and effectiveness of damage of barriers

Events

2

3

4

5

6

7

8

9

10

11

12

13

14

15

119

0

0

0

0

0

0,96

2,4

24

0

3,2

8

80

0

0

0

0

A

1 quarter

1

0,6

0,2

0

0,2

0

0

0

0

0

0

0

0

0

0

0

0

B

24

0,04

0,2

0,08

0,04

0,32

0,64

6,4

16

0

0

0

0

0

0

0

0

B

6

0,24

0

0,36

0

0,32

0

4,8

0

0

0

0

0

0

0

0

0

A

8

0,16

0

0,24

0

0,48

0

7,2

0

0

0

0

0

0

0

0

0

A

2 quarter

4

0,24

0

0,08

0

0,72

0,48

2,4

0

0

0

0

0

0

0

0

0

A

71

0,08

0

0,08

0,04

0,32

0,32

3,2

8

1,6

1,6

16

40

0

0

0

0

B

75

0,48

0

0

0,36

0,32

0

0

12

1,6

0

0

60

0

0

0

0

B

6

0

0

0

0

0,32

2,56

0

0

0,4

3,2

0

0

0

0

0

0

A

3 quarter

68

0

0

0

0

0,24

0,96

2,4

12

0,8

3,2

8

40

0

0

0

0

A

0

0

0

0

0

100

0

0

0

0

0

0

0

0

0

0

100

B

154

0

0

0

0

0,16

0,64

3,2

0

0

0

0

0

6

24

120

0

A

4 quarter

37

0,08

0

0,08

0

0,16

0,96

1,6

0

0

0

0

0

2

12

20

0

B

73

0

0

0

0

0,32

0,64

1,6

0

0,8

1,6

4

0

8

16

40

0

B

6

0

0

0,24

0

0

0,48

1,6

0

0

2,4

8

0

0

0

0

0

A

03.01. 02.02. 14.02. 12.03. 12.05. 26.05. 30.05. 20.06. 01.07. 15.07. 13.08. 03.10. 21.10. 31.10. 28.11.

1

Table C.6 Calculating the DERC index for each event

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines 169

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

170

Table C.7 Events “correspond” to the DERC levels in descending order of severity №

Event

Type of aircraft

Main factor

DERC

Responsible area

13

Takeoff without proper permission

A

Human

154

LS

2

The strong turbulence on the approach

A

Human (Environment)

119

LS, SOP, CAO

11

Rolling out of the runway after landing

B

Human (Environment)

100

LS

8

Engine oil decrease B

Human

75

LS

14

Aileron thrust break B

The vehicle

73

CAME

7

Collision with a bird

B

Environment

71

CAO

9

Shelling of the ACFT on airfield

A

Environment

68

CAB

12

Temporary loss of radio communication

B

Human

37

LS

15

False alarm of the fire extinguishing system

B

The vehicle

24

CAME

These events are recorded in the database and used during the HRA procedure and for risk monitoring. A moving average is used for weekly monitoring, with a smoothing period of 3 months (91 days). For type A aircraft, the calculation is performed using the formula:  DERCgA

=

i⊂G

DERCiA PGA

× 1000,

(C.4)

where DERCgA is the relative DERC per 1000 flights of ACFT A for the g-th week; DERCiA —DERC value for the i-th event with type an aircraft; G—set of events with type an aircraft 91 days before the monitoring data; PGA —number of flights for 91 days before the monitoring date. Similarly, the DERC is calculated for type B aircraft. It is assumed that the border between the green and yellow zones on the monitoring screen is DERC = 100, and between the yellow and red zones is DERC = 1000. A fragment of the graph is shown in Fig. C.1 (logarithmic scale). The calculation of the quarterly DERC is performed in the same way. Monitoring allows us to track the dynamics of the level of safety in the airline. The graph shows that safety for type B aircraft is significantly lower than for type

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

171

DERC

1000

100

Date of monitoring

Fig. C.1 Monitoring of DERC per 1000 flights for the period 27.03–12.06

A aircraft. Simultaneously, in August, there is an exit of the DERC to the red zone, which indicates the need for urgent measures. The method allows us to determine and track each airfield’s risk, flight stage, and crew. For example, we can get the total risk index of an unstable approach DERCUK A at the k-th airport as DERCUk A =

n 

DERCik ,

i=1

where DERCik is the index of each of the n unstabilized sessions? The graphs are shown in Fig. C.2. The graphs show that monitoring the absolute values of the number of unstable calls and their percentage of the total number of calls gives a real picture of the risk since each call’s dangers are not taken into account. There were two unstable approaches at airfield D, but both were dangerous. The DERC index is used in the SMS enterprises “S7 ENGINEERING” and in the “Meridian” airline SMS to indicate the safety level (Figs. C.3 and C.4). 3. 1.

Example of performing the HRA procedure in an airline. A “list of hazards” is compiled for the main types of activities following the IATA classification (Table C.8).

Risks are formulated by the company’s specialists based on the DERC and other data and expected changes in the airline (increase/decrease in air traffic volumes, changes in flights’ geography, rotation of personnel, structural changes, etc.).

172

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

Fig. C.2 Assessment of airfields for the risk of an unstable approach

Fig. C.3 Distribution of DERC and the number of events on the shop OTO S7 ENGINEERING during the year

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

173

Fig. C.4 Monitor DERC in JSC “Airline” “Meridian”

2.

The risk assessment of each hazard is performed using the “hazard risk Assessment” tool, an MSExcel table of the ARMS group, adapted in the Russian airline (see Fig. C.5).

Let’s look at the calculation using the example of the event “Emergency when meeting with dangerous weather events”. 1. 2.

Fill in the field 1 hazard Name: “Emergency when encountering dangerous weather events”. Fill in field 2 hazard Description: “When encountering dangerous weather events, especially at mountain airfields at the arrival/departure stages, the aircraft may get into an emergency”. We must also fill in the five gray fields below.

3.

4.

Write down the scenario of the event in field 3: “there is a dangerous weather event in the area of the mountain airfield. The crew does not take measures to bypass it, and when it hits it, it does not fully perform the SOP. As a result, the movement parameters of the aircraft go beyond the limits of the AFM, injuries to persons on board with a threat to their lives, as well as serious damage to the cargo”. Fill in the three gray cells: – – – –

initiating event—“Presence of a dangerous weather event”; intermediate event—“Aircraft hit a dangerous weather event” and the possible final event—The “Accident". Enter in the two gray cells of field 4 “description of barriers".

Cargo formation, loading, unloading, (CGO)

Flight planning, weather and air navigation support (DSP)

Ground handling (GRH)

General organization and management of AL SI (It is proposed to include ASS issues in the list 6.2 (ORG) of hazards in accordance with the IATA approach.)

Aviation security (SEC)

4

5

6

7

SEC

SI (We assume that the problems for the safety related to ground handling and problems related to the overall organization and management of the Al are formulated by the safety inspection (SI))

CATC

TMS

7.1

SI 6.1No permission for An-124-100 to land in the 17/40 configuration

6.1

The danger of an aircraft being fired at a parking lot in an Afghan airport

Lack of qualified flight personnel

The danger of PVA when towing and taxiing to a Parking lot in the CIS

Deficiencies in ornithological support in a/p cost center

4.2 5.1

Getting into unpredictable dangerous weather events

4.1

Errors when loading and securing cargo in CIS vehicles

False alarm of the fire extinguishing system

3.1

2.2

Unstable approach D-18 T engine surge

2.1

1.2

3

CAME

Emergency situation when encountering dangerous weather events

Hazard description

1.1

Technical operation (MNT)

№

2

All airline stewardess

Flight operations (FLT)

1

FS

Type of activity

№

Table C.8 The list of hazards in the airline X in III-IV quarters

174 Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

175

Barriers to prevention: 1. Forecasting dangerous weather events. 2. Detection of dangerous weather events by the crew in flight.

Parry barrier: 1. Strict implementation of the SOP when meeting a dangerous weather event.

6.

Perform a risk assessment in field 5 “Risk assessment”. We must select three frequency values and the final event: – – – –

for the triggering event, select “In one flight out of 100”; for prevention barriers-choose “In one IE out of 10”; for parry barriers - “Barriers” “in one IE out of 1 thousand”; as the final event-select “Accident” from the drop-down menu.

All these assessments are based on an expert survey. 7.

In field 6 of the “Assessment result” will automatically receive a risk assessment. In this case, this is the orange level, which requires the development of “Urgent warning measures". As such, measures (management decisions-MD) can be offered: 1. Development of measures to increase situational awareness of crews about dangerous weather events. 2. Additional training on actions when getting into dangerous weather events with a test on the simulator.

8.

We assess the level of residual risk if the MD is implemented.

Let MD №1 increase its awareness of the crew so that only 1 in 100 cases of a dangerous weather event will be unexpected. Let’s assume that MD №2 will increase the parry level so that only 1 hit in a complex phenomenon out of 1000 will lead to an accident. We enter this data into the program and automatically get a blue risk level that prescribes “monitoring the situation” (see Fig. C.6). The “hazard risk assessment” program has been used since 2013 in the Meridian airlines SMS.

176

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

Fig. C.5 Initial hazard risk assessment

Annex C: Application of the Safety Risk Management Method №. 3 in the Airlines

Fig. C.6 The residual risk of danger after the implementation of measures

177

Annex D

Preparing Data for Use in the “Overrun Prognosis” Program

This appendix provides a method for predicting the coupling coefficient and the results of additional calculations necessary for the “Overrun Prognosis” operation. 1.

Methods for prediction of the coefficient of friction.

Currently, the K adh forecast is not officially provided by weather information providers. Therefore, when developing the “Overrun Prognosis” program, we developed our forecasting methodology based mainly on expert assessments. The method is based on the use of information about the actual value of the K adh in the actual weather report METAR (Meteorological Aerodrome Report), predicted weather events (precipitation) from the aviation forecast TAF (Terminal Aerodrome Forecast). The calculation method is as follows: Input data: – – – –

summary of the actual weather of the departure and arrival airfield METAR; computer flight plan—CFP; TAF airport of departure and arrival; expert evaluation of airfields on the parameter “Efficiency of airfield services for runway preparation”; Procedure of settlements.

1.

In the METAR weather report of the departure and arrival aerodrome, an 8-digit f group of runway status is allocated, and the actual one is determined K adh by it at the time of calculation.

Simultaneously, all options for presenting data on the runway state are considered f (K adh it can be indicated by a numerical or descriptive characteristic of braking performance, the runway state group is not always present in METAR, and so on.)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4

179

180

2. 3. 4.

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

CFP is determined at the time of departure and arrival. The TAF weather forecast at the time of departure/arrival determines the predicted weather events and precipitation type. f The actual K adh one is adjusted downwards by a coefficient W < 1 for predicted weather events and precipitation according to Table D.1: f

W = K adh W K adh

(D.1)

W obtained by formula (D.1) is corrected for the coefficient Ai The value of K adh ≥ 1, which takes into account the efficiency of the airfield service in preparing the runway: W Ai . K adh = K adh

The Ai coefficient for each i-th airfield is determined based on expert assessments. Table D.2 shows the results of calculations of the conditional reduction of the runway length depending on the air temperature and atmospheric pressure at the landing airfield for different values of the landing mass of the An-124-100 aircraft. Figure D.1 shows graphs of the conditional reduction of the runway length for landing An-124-100 aircraft. Figure D.2 and Table D.3 shows the results of calculations of the conditional reduction of the runway length depending on the air temperature and atmospheric pressure at the landing airfield for different values of the landing mass of the An124-100 aircraft.

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

181

Table D.1 Correction factor for predicting K adh p/p

Weather phenomena (type of precipitation)

The code name for a weather phenomenon

Weather forecast factor W

1

Strong supercooled precipitation, forming ice

+FZRA

0.5

2

Strong supercooled precipitation, forming ice

FZRA

0.5

3

Strong supercooled precipitation, forming ice

FZRA

0.5

4

Heavy supercooled drizzle

FZRA

0.5

5

Heavy supercooled drizzle

FZRA

0.5

6

Freezing rain

PL (+PL, −PL)

0.5

7

General snowstorm

+BLSN

0.6

8

Heavy snow (wet, heavy rain)

+SN, +SHSN

0.6

9

Heavy snow and rain (wet, heavy rain)

+SNRA, +SHSNRA

0.6

10

Weak supercooled drizzle that forms ice

−FZDZ

0.6

11

Heavy snow and rain (wet, heavy rain)

SNRA, SHSNRA

0.6

12

Heavy snow (wet, heavy rain)

SN, SHSN

0.7

13

Heavy rain with snow (heavy rain, +RASN, +SHRASN heavy rain)

0.75

14

Heavy rain with snow (heavy rain, RASN, SHRASN heavy rain)

0.8

15

Heavy snow (wet, heavy rain)

+RA, +SHRA

0.8

16

Hail

GR

0.8

17

Snow grains, snow groats

SG, GS

0.8

18

Rain, light rain (heavy rain, heavy RA, −RA, −SHRA, SHRA rain)

0.8

19

Heavy rain with snow (heavy rain, −RASN, −SHRASN heavy rain)

0.9

182

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program Condional reducon of the available runway length from the temperature and pressure on the landing airfield Gland=210t

350

Condional reducon of runway length, m.

327 +15

300

+25

+20

+30

+35

313

+45

+40

299 285 270 257

250

243

200

199 168 153

150 139 124 109

100

50

0

96

100 85 71 55 39 23

86 73 72 60 59 48 43 32 27 16 11 0 760 750 740

116 99 83 67 51 35

730

147

132

127

113 95 79 63 47

107 91 75 59

720

710

181

180

161

157

140

134 117 100 84

119 102 86 70

700

690

230 212

214 196

211

161 144 126 109

147 130 113 96

670

173 156 139 121

660

185 168 151 134

650

198 181 164 147

640

253 236

251 238

224

199 186

172

680

224

279 265

264

249

237

303

290

277

212 195 178 161

630

225 208 191 174

620

239 222 205 188

610

202

600

Barometric pressure at runway level, mm mercury.

Condional reducon of the available runway length from the temperature and pressure on the landing airfield Gland=250t 450

Condional reducon of runway length, m.

400

+15

+30

+25

+20

+35

+45

+40

394 376 364

358

350

342 324 308

300

292

250

241 220 204

200 188 171 153

150 136 100

107 91 74 54 34 14

90 75 60 40 20 0

50

0 760

750

144 124 104 84 64 44

125 107 88 68 48 28

120

740

730

720

157

140 119 99 79 59

133 113 93 73

710

192

172 147 127 107 87

700

690

196 176 156 136

680

670

660

271 251 231 211

270

240

180 160 140 120

165 145 125 105

256 236 216 196

240 220 200 180

225 205 185 165

650

288 268 248 228

286

285

210 190 170 150

640

304 284 264 244

301

255

209

334 318

316

300

270

226

219

197

180

162

277 256

259 238

348

331

630

620

610

Barometric pressure at runway level, mm mercury.

Fig. D.1 Graphs of conditional reduction of runway length for landing, an-124-100 aircraft

600

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

183

Condional reducon of the available runway length from the temperature and pressure on the landing airfield Gland=290t 600 568

Condional reducon of runway length, m.

+15

+20

+30

+25

+35

+40

541

+45

523

514

500

498

489 439 415

400

393 366 339 309

300

286 261 236 210

200

122 102 82

169 145 120 93 66 39

100

54 28 0

145 123 101 74 47 20

224

197

185 163

181

161 134 107 80

250

153 126 99

326

287

296

279

274

257

249

265

228

215

198

191

185

169 142

163

344

321

333

236

201

350

303

274

172 145 118

374

355

338

244

221

362 308

316 295

227

403

380

382

339

267

238

215

191

168 141 114 87 60

275

250

409 385

407

405

318 293

433

429

361

335

307

454

452

426

383

362

477

473

462

206

0

760

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

Barometric pressure at runway level, mm mercury. Condional reducon of the available runway length from the temperature and pressure on the landing vehicle Gland=330T 800 781 745 720

708

700

687

Condional reducon of runway length, m.

674

+15

+30

+25

+20

638

+45

+40

+35

608

600 576 547 511

500 476 405

400 372 339 305

300 272

100

180 150 120 80

248

237

208

226

197

186

108 68

57

168

137

157

128

97

117

88

391

380

561

529

506

522

497

474

490

467

483

445

458

451

417

435

428

406

419

378

396

389

367

357

476 448 389 350

319

339

311

289

280

300

254

272

249

240

214

209

293

533 504

328

342

565

563

534

414

600

595 568

358

266

214 177

433

312

280

250

40 0

323

215 182 148

240 200

287

358

436

627

624

591

536

506

470

658

653

328

174

146

28

0

760

750

740

730

720

710

700

690

680

670

660

650

640

Barometric pressure at runway level (QFE), mm mercury

Fig. D.1 (continued)

630

620

610

600

184

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program Condional reducon of the available runway length from the temperature and pressure at the take-off airfield Gtoff=240t

Condional reducon of runway length, m.

1800 1600

1565

+15

1400

+30

+25

+20

+35

+40

1485

+45

1405

1375

1325 1170 1100 955 890 800

815

760 640

600

575 515

460

200 0

400

355

195 105 35 20

750

740

730

215 120 85

720

400

355

315

268 180 85 65

230 140 55 40

520

475

430

382

335

295

255 160 70 15 0

760

445

640

595

545

495

287 190 125

255 155 105

575 450 335 235 155

870 810

750 675

625

380 275 175

430 320 215

540 480

665 600

600 540

475

420 360

310

260

725

538 480

425

370

865 800

735 670

600

560 500

1005 935

865

845

735

690 630

1100 1015

945

910

775

1175

1135

980

845

710

700

1210

1055

1030

1000

400

1295

1245

1200

710 700 690 680 670 660 650 640 Barometric pressure at runway level, mm mercury.

630

620

610

600

Condional reducon of the available runway length from the temperature and pressure at the take-off airfield Gtoff=340T 1400

+15

+20

+35

+30

+25

+40

+45

1255

Condional reducon of runway length, m.

1200

1195 1115

1100

1030

1000

1030 970

890

860 815

800

760

760

465 415 305

135 0

760

65 35

750

103 75

740

140 105

730

220 160

655 570

845

485

745 650

550

524 465

420 380

350 285

260 200

595

450 385

350 285

25 0

630 555

525

200

670

655 600

455

960 860

750

715

710

400

965 855

842 799

790

630

600

995

940

920

1085

320 280

240 200

720 710 700 690 680 670 660 Barometric pressure at runway level, mm mercury.

650

640

630

620

Fig. D.2 Graphs of conditional reduction of runway length for landing, an-124-100 aircraft

1137

1149

1161

1173

1185

1196

1210

1222

1235

1247

1260

1273

1287

1300

1314

1328

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1345

1331

1317

1304

1290

1277

1265

1252

1239

1226

1212

1201

1189

1177

1165

1153

1142

20

1362

1348

1334

1321

1307

1294

1282

1270

1256

1243

1228

1217

1205

1193

1181

1169

1158

25

1379

1365

1351

1338

1324

1311

1299

1287

1273

1260

1245

1233

1221

1209

1197

1185

1174

30

1405

1391

1377

1364

1350

1337

1325

1312

1298

1283

1266

1253

1239

1225

1211

1199

1186

35

1429

1416

1403

1390

1375

1363

1350

1338

1322

1306

1287

1273

1258

1242

1226

1212

1198

40

1453

1439

1425

1411

1396

1383

1369

1356

1340

1325

1307

1294

1279

1265

1250

1235

1222

45

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1126

15

760

The pressure at the level runway (QFE)

Temperature, °C

The pressure at the level runway (QFE)

0

202

188

174

161

147

134

121

109

96

84

70

59

47

35

23

11

15

219

205

191

178

164

151

139

126

113

100

86

75

63

51

39

27

16

20

236

222

208

195

181

168

156

144

130

117

102

91

79

67

55

43

32

25

Temperature, °C

Conditional reduction of the runway, m—calculation

Landing distance for Gland = 210t (calm, runway slope 0)—by AFM

253

239

225

212

198

185

173

161

147

134

119

107

95

83

71

59

48

30

279

265

251

238

224

211

199

186

172

157

140

127

113

99

85

73

60

35

327

313

299

285

270

257

243

230

214

199

181

168

153

139

124

109

96

45

(continued)

303

290

277

264

249

237

224

212

196

180

161

147

132

116

100

86

72

40

Table D.2 Tables of the calculated conditional reduction of the runway length depending on the air temperature and atmospheric pressure at the landing airfield for different values of the landing mass Gland, An-124-100

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 185

30

35

40

45

1156

1167

1179

1191

1203

1215

1226

1240

1252

1266

1279

1293

1307

1323

1338

1353

1369

760

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1388

1372

1357

1342

1326

1312

1298

1285

1270

1256

1242

1231

1219

1207

1195

1183

1172

1407

1391

1376

1361

1345

1331

1318

1304

1289

1274

1258

1247

1235

1223

1211

1199

1188

1426

1410

1395

1380

1364

1350

1337

1323

1308

1293

1276

1263

1251

1239

1227

1215

1204

1454

1439

1423

1409

1393

1379

1365

1352

1335

1319

1300

1285

1270

1255

1241

1229

1216

1480

1467

1452

1437

1422

1408

1394

1380

1363

1344

1324

1308

1291

1273

1256

1242

1228

1504

1490

1475

1461

1445

1430

1414

1400

1382

1365

1346

1331

1315

1299

1282

1266

1252

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

213

197

182

167

151

137

123

110

96

84

70

59

47

35

23

11

0

15

232

216

201

186

170

156

142

129

114

100

86

75

63

51

39

27

16

20

251

235

220

205

189

175

162

148

133

118

102

91

79

67

55

43

32

25

270

254

239

224

208

194

181

167

152

137

120

107

95

83

71

59

48

30

mm of mercury St

25

15

20

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, deg. C

Conditional reduction of runway length Gland = 220t., m

An-124-100 Landing distance Gland = 220t (calm, runway slope 0)

Table D.2 (continued)

298

283

267

253

237

223

209

196

179

163

144

129

114

99

85

73

60

35

348

334

319

305

289

274

258

244

226

209

190

175

159

143

126

110

96

45

(continued)

324

311

296

281

266

252

238

224

207

188

168

152

135

117

100

86

72

40

186 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

1206

1219

1233

1246

1259

1271

1287

1301

1316

1329

1343

1357

1373

1388

1403

1419

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1438

1422

1407

1392

1376

1362

1348

1335

1320

1305

1289

1277

1264

1251

1237

1224

1211

20

1457

1441

1426

1411

1395

1381

1368

1354

1339

1324

1307

1295

1282

1269

1255

1242

1229

25

1476

1460

1445

1430

1414

1400

1387

1373

1358

1343

1326

1313

1300

1287

1273

1260

1247

30

35

1504

1489

1473

1459

1443

1429

1415

1402

1385

1369

1350

1335

1320

1305

1289

1275

1261

40

1531

1517

1502

1487

1472

1458

1444

1430

1413

1394

1374

1358

1341

1323

1306

1290

1274

45

1557

1541

1526

1512

1495

1480

1464

1450

1432

1415

1396

1381

1365

1349

1332

1316

1301

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1193

15

760

Temperature, °C

Barometer.pressure, mm. mercury column

226

210

195

180

164

150

136

123

108

94

78

66

53

40

26

13

0

15

245

229

214

199

183

169

155

142

127

112

96

84

71

58

44

31

18

20

264

248

233

218

202

188

175

161

146

131

114

102

89

76

62

49

36

25

Temperature, °C

Conditional reduction of runway lengthGland = 230t., m The pressure at the level runway (QFE)

An-124-100 Landing distance Gland = 220t (calm, runway slope 0)

Table D.2 (continued)

283

267

252

237

221

207

194

180

165

150

133

120

107

94

80

67

54

30

311

296

280

266

250

236

222

209

192

176

157

142

127

112

96

82

68

35

364

348

333

319

302

287

271

257

239

222

203

188

172

156

139

123

108

45

(continued)

338

324

309

294

279

265

251

237

220

201

181

165

148

130

113

97

81

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 187

20

1244

1258

1274

1289

1303

1317

1335

1350

1366

1379

1393

1407

1423

1438

1453

1469

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1488

1472

1457

1442

1426

1412

1398

1385

1370

1355

1337

1323

1309

1294

1278

1264

1250

25

1507

1491

1476

1461

1445

1431

1418

1404

1389

1374

1357

1343

1329

1314

1298

1284

1270

30

1526

1510

1495

1480

1464

1450

1437

1423

1408

1393

1376

1363

1349

1334

1318

1304

1290

35

1554

1539

1523

1509

1493

1479

1465

1452

1435

1419

1400

1385

1370

1354

1337

1321

1305

40

1582

1567

1552

1537

1522

1508

1494

1480

1463

1444

1424

1408

1391

1373

1355

1337

1320

45

1609

1593

1577

1562

1545

1530

1514

1500

1482

1465

1446

1431

1415

1399

1382

1366

1350

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1230

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

239

223

208

193

177

163

149

136

120

105

87

73

59

44

28

14

0

15

258

242

227

212

196

182

168

155

140

125

107

93

79

64

48

34

20

20

277

261

246

231

215

201

188

174

159

144

127

113

99

84

68

54

40

25

Temperature, °C

Conditional reduction of runway length Gland = 240t., m

An-124-100 Landing distance Gland = 240t (the wind, the runway slope 0)

Table D.2 (continued)

296

280

265

250

234

220

207

193

178

163

146

133

119

104

88

74

60

30

324

309

293

279

263

249

235

222

205

189

170

155

140

124

107

91

75

35

379

363

347

332

315

300

284

270

252

235

216

201

185

169

152

136

120

45

(continued)

352

337

322

307

292

278

264

250

233

214

194

178

161

143

125

107

90

40

188 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

20

1284

1298

1314

1329

1343

1357

1375

1390

1406

1420

1435

1450

1466

1481

1498

1514

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1534

1518

1501

1486

1470

1455

1440

1426

1410

1395

1377

1363

1349

1334

1318

1304

1290

25

1554

1538

1521

1506

1490

1475

1460

1446

1430

1415

1397

1383

1369

1354

1338

1324

1310

30

1574

1558

1541

1526

1510

1495

1480

1466

1450

1435

1417

1403

1389

1374

1358

1344

1330

35

1604

1588

1571

1556

1540

1525

1510

1496

1479

1462

1442

1427

1410

1394

1377

1361

1345

40

1634

1618

1601

1586

1570

1555

1540

1526

1508

1489

1467

1450

1432

1414

1395

1377

1360

45

1664

1646

1628

1612

1594

1578

1562

1547

1529

1511

1490

1474

1458

1441

1423

1406

1390

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1270

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

244

228

211

196

180

165

150

136

120

105

87

73

59

44

28

14

0

15

264

248

231

216

200

185

170

156

140

125

107

93

79

64

48

34

20

20

284

268

251

236

220

205

190

176

160

145

127

113

99

84

68

54

40

25

Temperature, °C

Conditional reduction of runway length Gland = 250t., m

An-124-100 Landing distance Gland = 250t (the wind, the runway slope 0)

Table D.2 (continued)

304

288

271

256

240

225

210

196

180

165

147

133

119

104

88

74

60

30

334

318

301

286

270

255

240

226

209

192

172

157

140

124

107

91

75

35

394

376

358

342

324

308

292

277

259

241

220

204

188

171

153

136

120

45

(continued)

364

348

331

316

300

285

270

256

238

219

197

180

162

144

125

107

90

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 189

20

1324

1338

1354

1369

1383

1397

1415

1430

1446

1461

1477

1492

1509

1525

1543

1559

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1580

1564

1546

1530

1513

1498

1482

1467

1451

1435

1417

1403

1389

1374

1358

1344

1330

25

1601

1585

1567

1551

1534

1519

1503

1488

1472

1455

1437

1423

1409

1394

1378

1364

1350

30

1622

1606

1588

1572

1555

1540

1524

1509

1493

1476

1458

1443

1429

1414

1398

1384

1370

35

1654

1637

1620

1604

1586

1571

1556

1541

1523

1505

1484

1468

1451

1434

1417

1401

1385

40

1686

1669

1651

1635

1618

1602

1587

1572

1553

1533

1510

1492

1474

1455

1435

1417

1400

45

1718

1699

1680

1662

1643

1626

1610

1594

1575

1556

1535

1518

1500

1483

1464

1447

1430

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1310

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

249

233

215

199

182

167

151

136

120

105

87

73

59

44

28

14

0

15

270

254

236

220

203

188

172

157

141

125

107

93

79

64

48

34

20

20

291

275

257

241

224

209

193

178

162

145

127

113

99

84

68

54

40

25

Temperature, °C

Conditional reduction of runway length Gland = 260t., m

An-124-100 Landing distance Gland = 260t (the wind, the runway slope 0)

Table D.2 (continued)

312

296

278

262

245

230

214

199

183

166

148

133

119

104

88

74

60

30

344

327

310

294

276

261

246

231

213

195

174

158

141

124

107

91

75

35

408

389

370

352

333

316

300

284

265

246

225

208

190

173

154

137

120

45

(continued)

376

359

341

325

308

292

277

262

243

223

200

182

164

145

125

107

90

40

190 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

20

1363

1378

1395

1411

1428

1443

1462

1479

1497

1514

1532

1549

1569

1587

1607

1626

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1650

1630

1611

1593

1573

1555

1538

1521

1502

1484

1465

1450

1433

1417

1400

1385

1347

25

1673

1654

1635

1616

1597

1579

1562

1545

1526

1507

1487

1472

1455

1439

1422

1407

1391

30

1697

1678

1658

1640

1620

1603

1586

1569

1550

1531

1510

1494

1477

1461

1444

1429

1413

35

1733

1714

1694

1676

1656

1639

1621

1605

1584

1563

1540

1522

1502

1483

1464

1447

1429

40

1770

1750

1730

1712

1692

1675

1657

1640

1618

1595

1570

1550

1528

1507

1484

1465

1446

45

1806

1785

1762

1742

1721

1702

1683

1665

1643

1622

1598

1579

1559

1538

1517

1498

1479

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1303

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

323

304

284

266

246

229

211

194

176

159

140

125

108

92

75

60

0

15

347

327

308

290

270

252

235

218

199

181

162

147

130

114

97

82

44

20

370

351

332

313

294

276

259

242

223

204

184

169

152

136

119

104

88

25

Temperature, °C

Conditional reduction of runway length Gland = 270t., m

An-124–100 Landing distance Gland = 270t (the wind, the runway slope 0)

Table D.2 (continued)

394

375

355

337

317

300

283

266

247

228

207

191

174

158

141

126

110

30

430

411

391

373

353

336

318

302

281

260

237

219

199

180

161

144

126

35

503

482

459

439

418

399

380

362

340

319

295

276

256

235

214

195

176

45

(continued)

467

447

427

409

389

372

354

337

315

292

267

247

225

204

181

162

143

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 191

20

1401

1418

1437

1454

1472

1488

1510

1528

1548

1567

1587

1606

1628

1649

1671

1692

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1719

1697

1675

1655

1633

1613

1594

1575

1554

1534

1512

1496

1478

1461

1442

1425

1408

25

1745

1724

1702

1682

1659

1640

1621

1602

1580

1559

1536

1520

1502

1485

1466

1449

1432

30

1772

1751

1729

1708

1686

1667

1647

1628

1607

1586

1563

1544

1526

1509

1490

1473

1456

35

1812

1791

1769

1748

1726

1707

1687

1668

1645

1622

1596

1575

1554

1533

1512

1493

1474

40

1853

1831

1809

1788

1766

1747

1727

1708

1684

1658

1629

1607

1583

1559

1534

1513

1492

45

1894

1870

1845

1822

1798

1777

1756

1736

1711

1687

1661

1639

1617

1594

1570

1549

1528

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

308

287

265

244

222

203

183

164

144

126

104

88

70

53

34

17

0

15

335

313

291

271

249

229

210

191

170

150

128

112

94

77

58

41

24

20

361

340

318

298

275

256

237

218

196

175

152

136

118

101

82

65

48

25

38

367

345

324

302

283

263

244

223

202

179

160

142

125

106

89

72

30

mm of mercury St

1384

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland = 280t., m

An-124–100 Landing distance Gland = 280t (the wind, the runway slope 0)

Table D.2 (continued)

428

407

385

364

342

323

303

284

261

238

212

191

170

149

128

109

90

35

510

486

461

438

414

393

372

352

327

303

277

255

233

210

186

165

144

45

(continued)

469

447

425

404

382

363

343

324

300

274

245

223

199

175

150

129

108

40

192 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

20

1454

1473

1494

1514

1533

1552

1576

1597

1619

1640

1662

1683

1708

1730

1755

1778

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1808

1784

1760

1737

1713

1691

1670

1649

1625

1603

1579

1560

1541

1521

1500

1481

1462

25

1837

1814

1789

1767

1742

1721

1699

1678

1655

1632

1606

1587

1568

1548

1527

1508

1488

30

1867

1843

1819

1796

1772

1750

1729

1708

1684

1661

1635

1615

1595

1575

1554

1535

1516

35

1911

1888

1863

1841

1816

1795

1773

1752

1727

1701

1672

1649

1625

1602

1579

1557

1536

40

1957

1932

1907

1886

1860

1839

1817

1796

1769

1741

1709

1684

1658

1631

1603

1579

1556

45

2002

1975

1948

1923

1896

1873

1849

1827

1800

1773

1743

1720

1695

1670

1644

1619

1597

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1434

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

344

321

296

274

249

228

206

185

163

142

118

99

80

60

39

20

0

15

374

350

326

303

279

257

236

215

191

169

145

126

107

87

66

47

28

20

403

380

355

333

308

287

265

244

221

198

172

153

134

114

93

74

54

25

Temperature, °C

Conditional reduction of runway length Gland = 290t., m

An-124-100 Landing distance Gland = 290t (the wind, the runway slope 0)

Table D.2 (continued)

433

409

385

362

338

316

295

274

250

227

201

181

161

141

120

101

82

30

477

454

429

407

382

361

339

318

293

267

238

215

191

168

145

123

102

35

568

541

514

489

462

439

415

393

366

339

309

286

261

236

210

185

163

45

(continued)

523

498

473

452

426

405

383

362

335

307

275

250

224

197

169

145

122

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 193

20

1506

1528

1551

1573

1595

1615

1642

1665

1690

1713

1737

1760

1787

1812

1839

1865

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1897

1817

1844

1820

1793

1769

1745

1722

1697

1672

1645

1625

1603

1581

1558

1536

1515

25

1929

1903

1877

1852

1825

1801

1778

1755

1729

1704

1675

1655

1633

1611

1588

1566

1545

30

1962

1936

1909

1884

1857

1834

1810

1787

1761

1736

1708

1685

1663

1641

1618

1596

1575

35

2010

1986

1958

1933

1906

1882

1859

1836

1808

1780

1748

1723

1697

1671

1645

1621

1598

40

2060

2033

2006

1981

1955

1931

1907

1884

1855

1823

1788

1761

1732

1703

1672

1646

1620

45

2110

2081

2050

2023

1994

1968

1942

1918

1888

1859

1826

1800

1773

1746

1717

1690

1665

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

380

354

327

302

275

252

228

205

180

157

130

110

88

66

43

21

0

15

412

332

359

335

308

284

260

237

212

187

160

140

118

96

73

51

30

20

444

418

392

367

340

316

293

270

244

219

190

170

148

126

103

81

60

25

477

451

424

399

372

349

325

302

276

251

223

200

178

156

133

111

90

30

mm of mercury St

1485

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland = 300t., m

An-124-100 Landing distance Gland = 300 T (the wind, the runway slope 0)

Table D.2 (continued)

525

501

473

448

421

397

374

351

323

295

263

238

212

186

160

136

113

35

625

596

565

538

509

483

457

433

403

374

341

315

288

261

232

205

180

45

(continued)

575

548

521

496

470

446

422

399

370

338

303

276

247

218

187

161

135

40

194 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

20

1569

1592

1618

1642

1666

1689

1719

1744

1771

1796

1822

1847

1877

1903

1933

1961

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1996

1968

1939

1912

1883

1857

1831

1806

1778

1751

1722

1699

1675

1651

1625

1602

1578

25

2031

2003

1974

1947

1918

1892

1866

1841

1813

1786

1755

1732

1708

1684

1658

1635

1612

30

2067

2038

2009

1982

1953

1927

1902

1877

1848

1821

1790

1766

1741

1717

1691

1668

1644

35

2119

2091

2062

2035

2006

1980

1955

1929

1899

1869

1834

1807

1778

1750

1722

1695

1669

40

2173

2144

2115

2088

2059

2033

2007

1982

1950

1916

1878

1848

1817

1785

1752

1723

1694

45

2226

2195

2163

2133

2101

2074

2045

2019

1987

1955

1919

1891

1861

1832

1800

1771

1744

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1546

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

415

387

357

331

301

276

250

225

198

173

143

120

96

72

46

23

0

15

450

422

393

366

337

311

285

260

232

205

176

153

129

105

79

56

32

20

485

457

428

401

372

346

320

295

267

240

209

186

162

138

112

89

66

25

Temperature, °C

Conditional reduction of runway length Gland = 310t., m

An-124-100 Landing distance Gland = 310 T (the wind, the runway slope 0)

Table D.2 (continued)

521

492

463

436

407

381

356

331

302

275

244

220

195

171

145

122

98

30

573

545

516

489

460

434

409

383

353

323

288

261

232

204

176

149

123

35

680

649

617

587

555

528

499

473

44

409

373

345

315

286

254

225

198

45

(continued)

627

598

569

542

513

487

461

436

401

370

332

302

271

239

206

177

148

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 195

20

1632

1657

1685

1711

1738

1763

1794

1822

1852

1879

1907

1934

1966

1995

2027

2057

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

2095

2065

2033

2004

1973

1945

1917

1890

1859

1830

1799

1774

1747

1721

1693

1668

1642

25

2133

2103

2071

2042

2011

1983

1955

1928

1898

1868

1835

1810

1783

1757

1729

1704

1678

30

2171

2141

2109

2080

2049

2021

1993

1966

1936

1906

1872

1846

1819

1793

1765

1740

1714

35

2229

2198

2166

2138

2106

2078

2050

2023

1990

1957

1920

1891

1860

1829

1798

1769

1741

40

2286

2255

2224

2195

2163

2135

2107

2080

2045

2009

1968

1935

1901

1867

1831

1799

1768

45

2342

2309

2275

2243

2209

2179

2149

2120

2085

2051

2012

1982

1950

1917

1883

1852

1822

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1606

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

451

421

389

360

328

301

273

246

216

188

157

132

105

79

51

26

0

15

489

459

427

398

367

339

311

284

253

224

193

168

141

115

87

62

36

20

527

497

465

436

405

377

349

322

292

262

229

204

177

151

123

98

72

20

Temperature, °C

Conditional reduction of runway length Gland = 320t., m

An-124-100 Landing distance Gland = 320 T (the wind, the runway slope 0)

Table D.2 (continued)

565

535

503

474

443

415

387

360

330

300

266

240

213

187

159

134

108

30

623

592

560

532

500

472

444

417

384

351

314

285

254

223

192

163

135

35

736

703

669

637

603

573

543

514

479

445

406

376

344

311

277

246

216

45

(continued)

680

649

618

589

557

529

501

474

439

403

362

329

295

261

225

193

162

40

196 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

20

1698

1727

1758

1787

1816

1844

1879

1910

1942

1970

1998

2027

2059

2089

2121

2153

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

2192

2160

2128

2098

2066

2037

2009

1981

1950

1919

1884

1856

1827

1798

1767

1738

1710

25

2231

2199

2167

2137

2105

2076

2048

2020

1989

1959

1924

1896

1867

1838

1807

1778

1750

30

2270

2238

2206

2176

2144

2115

2087

2059

2028

1998

1963

1936

1907

1878

1847

1818

1790

35

2328

2297

2265

2235

2203

2174

2146

2118

2084

2050

2012

1982

1950

1918

1884

1852

1820

40

2390

2357

2323

2294

2261

2233

2204

2176

2140

2103

2061

2028

1993

1957

1920

1885

1850

45

2451

2415

2378

2344

2308

2278

2246

2217

2181

2146

2106

2075

2042

2009

1975

1942

1910

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

mm of mercury St

1670

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

483

451

419

389

357

328

300

272

240

209

174

146

117

88

57

28

0

15

522

490

458

428

396

367

339

311

280

249

214

186

157

128

97

68

40

20

561

529

497

467

435

406

378

350

319

289

254

226

197

168

137

108

80

25

Temperature, °C

Conditional reduction of runway length Gland = 330t., m

An-124–100 Landing distance Gland = 330t (the wind, the runway slope 0)

Table D.2 (continued)

600

568

536

506

474

445

417

389

358

328

293

266

237

208

177

148

120

30

658

627

595

565

533

504

476

448

414

380

342

312

280

248

214

182

150

35

720

687

653

624

591

563

534

506

470

433

391

358

323

287

250

215

180

40

781

745

708

674

638

608

576

547

511

476

436

405

372

339

305

272

240

45

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 197

1126

1150

1169

1190

1210

1230

1253

1276

1310

1355

1405

1455

1510

1560

1620

1680

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1800

1740

1680

1625

1565

1510

1460

1415

1366

1330

1290

1255

1225

1189

1165

1145

1125

20

1930

1870

1800

1745

1680

1625

1570

1515

1466

1430

1385

1350

1315

1280

1240

1210

1180

25

2065

1995

1930

1870

1805

1750

1695

1640

1585

1540

1490

1450

1410

1365

1330

1295

1260

30

2230

2155

2075

2005

1935

1880

1820

1760

1710

1660

1610

1560

1515

1475

1430

1390

1350

35

2420

2340

2260

2190

2110

2040

1970

1910

1845

1780

1720

1675

1625

1580

1531

1490

1450

40

2600

2520

2445

2370

2290

2220

2150

2090

2015

1950

1880

1830

1780

1715

1655

1600

1545

45

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

570

510

450

400

345

295

245

200

166

143

120

100

80

59

40

16

0

15

690

630

570

515

455

400

350

305

256

220

180

145

115

79

55

35

15

20

820

760

690

635

570

515

460

405

356

320

275

240

205

170

130

100

70

25

955

885

820

760

695

640

585

530

475

430

380

340

300

255

220

185

150

30

mm. mercury column

1110

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gtoff = 230t.,

An-124–100 Landing distance Gtoff = 230t (calm, runway slope 0)

1120

1045

965

895

825

770

710

650

600

550

500

450

405

365

320

280

240

35

1490

1410

1335

1260

1180

1110

1040

980

905

840

770

720

670

605

545

490

435

45

(continued)

1310

1230

1150

1080

1000

930

860

800

735

670

610

565

515

470

421

380

340

40

Table D.3 Tables of the calculated conditional reduction of the runway length depending on the air temperature and atmospheric pressure at the landing airfield for different values of the landing mass Gland, An-124-100

198 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

30

35

40

45

1156

1167

1179

1191

1203

1215

1226

1240

1252

1266

1279

1293

1307

1323

1338

1353

1369

760

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1388

1372

1357

1342

1326

1312

1298

1285

1270

1256

1242

1231

1219

1207

1195

1183

1172

1407

1391

1376

1361

1345

1331

1318

1304

1289

1274

1258

1247

1235

1223

1211

1199

1188

1426

1410

1395

1380

1364

1350

1337

1323

1308

1293

1276

1263

1251

1239

1227

1215

1204

1454

1439

1423

1409

1393

1379

1365

1352

1335

1319

1300

1285

1270

1255

1241

1229

1216

1480

1467

1452

1437

1422

1408

1394

1380

1363

1344

1324

1308

1291

1273

1256

1242

1228

1504

1490

1475

1461

1445

1430

1414

1400

1382

1365

1346

1331

1315

1299

1282

1266

1252

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

213

197

182

167

151

137

123

110

96

84

70

59

47

35

23

11

0

15

232

216

201

186

170

156

142

129

114

100

86

75

63

51

39

27

16

20

251

235

220

205

189

175

162

148

133

118

102

91

79

67

55

43

32

25

270

254

239

224

208

194

181

167

152

137

120

107

95

83

71

59

48

30

mm of mercury St

25

15

20

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =220t., m

An-124-100 Landing distance Gland =220t (calm, runway slope 0)

Table D.3 (continued)

298

283

267

253

237

223

209

196

179

163

144

129

114

99

85

73

60

35

348

334

319

305

289

274

258

244

226

209

190

175

159

143

126

110

96

45

(continued)

324

311

296

281

266

252

238

224

207

188

168

152

135

117

100

86

72

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 199

30

35

40

45

1193

1206

1219

1233

1246

1259

1271

1287

1301

1316

1329

1343

1357

1373

1388

1403

1419

760

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1438

1422

1407

1392

1376

1362

1348

1335

1320

1305

1289

1277

1264

1251

1237

1224

1211

1457

1441

1426

1411

1395

1381

1368

1354

1339

1324

1307

1295

1282

1269

1255

1242

1229

1476

1460

1445

1430

1414

1400

1387

1373

1358

1343

1326

1313

1300

1287

1273

1260

1247

1504

1489

1473

1459

1443

1429

1415

1402

1385

1369

1350

1335

1320

1305

1289

1275

1261

1531

1517

1502

1487

1472

1458

1444

1430

1413

1394

1374

1358

1341

1323

1306

1290

1274

1557

1541

1526

1512

1495

1480

1464

1450

1432

1415

1396

1381

1365

1349

1332

1316

1301

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

226

210

195

180

164

150

136

123

108

94

78

66

53

40

26

13

0

15

245

229

214

199

183

169

155

142

127

112

96

84

71

58

44

31

18

20

264

248

233

218

202

188

175

161

146

131

114

102

89

76

62

49

36

25

283

267

252

237

221

207

194

180

165

150

133

120

107

94

80

67

54

30

mm of mercury St

25

15

20

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =230t., m

An-124-100 Landing distance Gland =220t (calm, runway slope 0)

Table D.3 (continued)

311

296

280

266

250

236

222

209

192

176

157

142

127

112

96

82

68

35

364

348

333

319

302

287

271

257

239

222

203

188

172

156

139

123

108

45

(continued)

338

324

309

294

279

265

251

237

220

201

181

165

148

130

113

97

81

40

200 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

1244

1258

1274

1289

1303

1317

1335

1350

1366

1379

1393

1407

1423

1438

1453

1469

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1488

1472

1457

1442

1426

1412

1398

1385

1370

1355

1337

1323

1309

1294

1278

1264

1250

20

25

1507

1491

1476

1461

1445

1431

1418

1404

1389

1374

1357

1343

1329

1314

1298

1284

1270

30

1526

1510

1495

1480

1464

1450

1437

1423

1408

1393

1376

1363

1349

1334

1318

1304

1290

35

1554

1539

1523

1509

1493

1479

1465

1452

1435

1419

1400

1385

1370

1354

1337

1321

1305

40

1582

1567

1552

1537

1522

1508

1494

1480

1463

1444

1424

1408

1391

1373

1355

1337

1320

45

1609

1593

1577

1562

1545

1530

1514

1500

1482

1465

1446

1431

1415

1399

1382

1366

1350

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

239

223

208

193

177

163

149

136

120

105

87

73

59

44

28

14

0

15

258

242

227

212

196

182

168

155

140

125

107

93

79

64

48

34

20

20

277

261

246

231

215

201

188

174

159

144

127

113

99

84

68

54

40

25

296

280

265

250

234

220

207

193

178

163

146

133

119

104

88

74

60

30

mm of mercury St

1230

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =240t., m

An-124-100 Landing distance Gland =240t (the wind, the runway slope 0)

Table D.3 (continued)

324

309

293

279

263

249

235

222

205

189

170

155

140

124

107

91

75

35

379

363

347

332

315

300

284

270

252

235

216

201

185

169

152

136

120

45

(continued)

352

337

322

307

292

278

264

250

233

214

194

178

161

143

125

107

90

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 201

1284

1298

1314

1329

1343

1357

1375

1390

1406

1420

1435

1450

1466

1481

1498

1514

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1534

1518

1501

1486

1470

1455

1440

1426

1410

1395

1377

1363

1349

1334

1318

1304

1290

20

25

1554

1538

1521

1506

1490

1475

1460

1446

1430

1415

1397

1383

1369

1354

1338

1324

1310

30

1574

1558

1541

1526

1510

1495

1480

1466

1450

1435

1417

1403

1389

1374

1358

1344

1330

35

1604

1588

1571

1556

1540

1525

1510

1496

1479

1462

1442

1427

1410

1394

1377

1361

1345

40

1634

1618

1601

1586

1570

1555

1540

1526

1508

1489

1467

1450

1432

1414

1395

1377

1360

45

1664

1646

1628

1612

1594

1578

1562

1547

1529

1511

1490

1474

1458

1441

1423

1406

1390

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

244

228

211

196

180

165

150

136

120

105

87

73

59

44

28

14

0

15

264

248

231

216

200

185

170

156

140

125

107

93

79

64

48

34

20

20

284

268

251

236

220

205

190

176

160

145

127

113

99

84

68

54

40

25

304

288

271

256

240

225

210

196

180

165

147

133

119

104

88

74

60

30

mm of mercury St

1270

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =250t., m

An-124-100 Landing distance Gland =250t (the wind, the runway slope 0)

Table D.3 (continued)

334

318

301

286

270

255

240

226

209

192

172

157

140

124

107

91

75

35

394

376

358

342

324

308

292

277

259

241

220

204

188

171

153

136

120

45

(continued)

364

348

331

316

300

285

270

256

238

219

197

180

162

144

125

107

90

40

202 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

1324

1338

1354

1369

1383

1397

1415

1430

1446

1461

1477

1492

1509

1525

1543

1559

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1580

1564

1546

1530

1513

1498

1482

1467

1451

1435

1417

1403

1389

1374

1358

1344

1330

20

25

1601

1585

1567

1551

1534

1519

1503

1488

1472

1455

1437

1423

1409

1394

1378

1364

1350

30

1622

1606

1588

1572

1555

1540

1524

1509

1493

1476

1458

1443

1429

1414

1398

1384

1370

35

1654

1637

1620

1604

1586

1571

1556

1541

1523

1505

1484

1468

1451

1434

1417

1401

1385

40

1686

1669

1651

1635

1618

1602

1587

1572

1553

1533

1510

1492

1474

1455

1435

1417

1400

45

1718

1699

1680

1662

1643

1626

1610

1594

1575

1556

1535

1518

1500

1483

1464

1447

1430

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

249

233

215

199

182

167

151

136

120

105

87

73

59

44

28

14

0

15

270

254

236

220

203

188

172

157

141

125

107

93

79

64

48

34

20

20

291

275

257

241

224

209

193

178

162

145

127

113

99

84

68

54

40

25

312

296

278

262

245

230

214

199

183

166

148

133

119

104

88

74

60

30

mm of mercury St

1310

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =260t., m

An-124-100 Landing distance Gland =260t (the wind, the runway slope 0)

Table D.3 (continued)

344

327

310

294

276

261

246

231

213

195

174

158

141

124

107

91

75

35

408

389

370

352

333

316

300

284

265

246

225

208

190

173

154

137

120

45

(continued)

376

359

341

325

308

292

277

262

243

223

200

182

164

145

125

107

90

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 203

1363

1378

1395

1411

1428

1443

1462

1479

1497

1514

1532

1549

1569

1587

1607

1626

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1650

1630

1611

1593

1573

1555

1538

1521

1502

1484

1465

1450

1433

1417

1400

1385

1347

20

25

1673

1654

1635

1616

1597

1579

1562

1545

1526

1507

1487

1472

1455

1439

1422

1407

1391

30

1697

1678

1658

1640

1620

1603

1586

1569

1550

1531

1510

1494

1477

1461

1444

1429

1413

35

1733

1714

1694

1676

1656

1639

1621

1605

1584

1563

1540

1522

1502

1483

1464

1447

1429

40

1770

1750

1730

1712

1692

1675

1657

1640

1618

1595

1570

1550

1528

1507

1484

1465

1446

45

1806

1785

1762

1742

1721

1702

1683

1665

1643

1622

1598

1579

1559

1538

1517

1498

1479

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

323

304

284

266

246

229

211

194

176

159

140

125

108

92

75

60

0

15

347

327

308

290

270

252

235

218

199

181

162

147

130

114

97

82

44

20

370

351

332

313

294

276

259

242

223

204

184

169

152

136

119

104

88

25

394

375

355

337

317

300

283

266

247

228

207

191

174

158

141

126

110

30

mm of mercury St

1303

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =270t., m

An-124-100 Landing distance Gland =270t (the wind, the runway slope 0)

Table D.3 (continued)

430

411

391

373

353

336

318

302

281

260

237

219

199

180

161

144

126

35

503

482

459

439

418

399

380

362

340

319

295

276

256

235

214

195

176

45

(continued)

467

447

427

409

389

372

354

337

315

292

267

247

225

204

181

162

143

40

204 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

1401

1418

1437

1454

1472

1488

1510

1528

1548

1567

1587

1606

1628

1649

1671

1692

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1719

1697

1675

1655

1633

1613

1594

1575

1554

1534

1512

1496

1478

1461

1442

1425

1408

20

25

1745

1724

1702

1682

1659

1640

1621

1602

1580

1559

1536

1520

1502

1485

1466

1449

1432

30

1772

1751

1729

1708

1686

1667

1647

1628

1607

1586

1563

1544

1526

1509

1490

1473

1456

35

1812

1791

1769

1748

1726

1707

1687

1668

1645

1622

1596

1575

1554

1533

1512

1493

1474

40

1853

1831

1809

1788

1766

1747

1727

1708

1684

1658

1629

1607

1583

1559

1534

1513

1492

45

1894

1870

1845

1822

1798

1777

1756

1736

1711

1687

1661

1639

1617

1594

1570

1549

1528

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

308

287

265

244

222

203

183

164

144

126

104

88

70

53

34

17

0

15

335

313

291

271

249

229

210

191

170

150

128

112

94

77

58

41

24

20

361

340

318

298

275

256

237

218

196

175

152

136

118

101

82

65

48

25

388

367

345

324

302

283

263

244

223

202

179

160

142

125

106

89

72

30

mm of mercury St

1384

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =280t., m

An-124-100 Landing distance Gland =280t (the wind, the runway slope 0)

Table D.3 (continued)

428

407

385

364

342

323

303

284

261

238

212

191

170

149

128

109

90

35

510

486

461

438

414

393

372

352

327

303

277

255

233

210

186

165

144

45

(continued)

469

447

425

404

382

363

343

324

300

274

245

223

199

175

150

129

108

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 205

1454

1473

1494

1514

1533

1552

1576

1597

1619

1640

1662

1683

1708

1730

1755

1778

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1808

1784

1760

1737

1713

1691

1670

1649

1625

1603

1579

1560

1541

1521

1500

1481

1462

20

25

1837

1814

1789

1767

1742

1721

1699

1678

1655

1632

1606

1587

1568

1548

1527

1508

1488

30

1867

1843

1819

1796

1772

1750

1729

1708

1684

1661

1635

1615

1595

1575

1554

1535

1516

35

1911

1888

1863

1841

1816

1795

1773

1752

1727

1701

1672

1649

1625

1602

1579

1557

1536

40

1957

1932

1907

1886

1860

1839

1817

1796

1769

1741

1709

1684

1658

1631

1603

1579

1556

45

2002

1975

1948

1923

1896

1873

1849

1827

1800

1773

1743

1720

1695

1670

1644

1619

1597

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

344

321

296

274

249

228

206

185

163

142

118

99

80

60

39

20

0

15

374

350

326

303

279

257

236

215

191

169

145

126

107

87

66

47

28

20

403

380

355

333

308

287

265

244

221

198

172

153

134

114

93

74

54

25

433

409

385

362

338

316

295

274

250

227

201

181

161

141

120

101

82

30

mm of mercury St

1434

15

760

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =290t., m

An-124-100 Landing distance Gland =290t (the wind, the runway slope 0)

Table D.3 (continued)

477

454

429

407

382

361

339

318

293

267

238

215

191

168

145

123

102

35

568

541

514

489

462

439

415

393

366

339

309

286

261

236

210

185

163

45

(continued)

523

498

473

452

426

405

383

362

335

307

275

250

224

197

169

145

122

40

206 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

1485

1506

1528

1551

1573

1595

1615

1642

1665

1690

1713

1737

1760

1787

1812

1839

1865

760

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1897

1817

1844

1820

1793

1769

1745

1722

1697

1672

1645

1625

1603

1581

1558

1536

1515

1929

1903

1877

1852

1825

1801

1778

1755

1729

1704

1675

1655

1633

1611

1588

1566

1545

1962

1936

1909

1884

1857

1834

1810

1787

1761

1736

1708

1685

1663

1641

1618

1596

1575

30

35

40

45

2010

1986

1958

1933

1906

1882

1859

1836

1808

1780

1748

1723

1697

1671

1645

1621

1598

2060

2033

2006

1981

1955

1931

1907

1884

1855

1823

1788

1761

1732

1703

1672

1646

1620

2110

2081

2050

2023

1994

1968

1942

1918

1888

1859

1826

1800

1773

1746

1717

1690

1665

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

380

354

327

302

275

252

228

205

180

157

130

110

88

66

43

21

0

15

412

332

359

335

308

284

260

237

212

187

160

140

118

96

73

51

30

20

444

418

392

367

340

316

293

270

244

219

190

170

148

126

103

81

60

25

477

451

424

399

372

349

325

302

276

251

223

200

178

156

133

111

90

30

mm of mercury St

25

15

20

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =300t., m

An-124-100 Landing distance Gland =300T (the wind, the runway slope 0)

Table D.3 (continued)

525

501

473

448

421

397

374

351

323

295

263

238

212

186

160

136

113

35

625

596

565

538

509

483

457

433

403

374

341

315

288

261

232

205

180

45

(continued)

575

548

521

496

470

446

422

399

370

338

303

276

247

218

187

161

135

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 207

1546

1569

1592

1618

1642

1666

1689

1719

1744

1771

1796

1822

1847

1877

1903

1933

1961

760

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

1996

1968

1939

1912

1883

1857

1831

1806

1778

1751

1722

1699

1675

1651

1625

1602

1578

2031

2003

1974

1947

1918

1892

1866

1841

1813

1786

1755

1732

1708

1684

1658

1635

1612

2067

2038

2009

1982

1953

1927

1902

1877

1848

1821

1790

1766

1741

1717

1691

1668

1644

30

35

40

45

2119

2091

2062

2035

2006

1980

1955

1929

1899

1869

1834

1807

1778

1750

1722

1695

1669

2173

2144

2115

2088

2059

2033

2007

1982

1950

1916

1878

1848

1817

1785

1752

1723

1694

2226

2195

2163

2133

2101

2074

2045

2019

1987

1955

1919

1891

1861

1832

1800

1771

1744

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

415

387

357

331

301

276

250

225

198

173

143

120

96

72

46

23

0

15

450

422

393

366

337

311

285

260

232

205

176

153

129

105

79

56

32

20

485

457

428

401

372

346

320

295

267

240

209

186

162

138

112

89

66

25

521

492

463

436

407

381

356

331

302

275

244

220

195

171

145

122

98

30

mm of mercury St

25

15

20

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =310t., m

An-124-100 Landing distance Gland =310T (the wind, the runway slope 0)

Table D.3 (continued)

573

545

516

489

460

434

409

383

353

323

288

261

232

204

176

149

123

35

680

649

617

587

555

528

499

473

441

409

373

345

315

286

254

225

198

45

(continued)

627

598

569

542

513

487

461

436

404

370

332

302

271

239

206

177

148

40

208 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

1606

1632

1657

1685

1711

1738

1763

1794

1822

1852

1879

1907

1934

1966

1995

2027

2057

760

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

2095

2065

2033

2004

1973

1945

1917

1890

1859

1830

1799

1774

1747

1721

1693

1668

1642

2133

2103

2071

2042

2011

1983

1955

1928

1898

1868

1835

1810

1783

1757

1729

1704

1678

2171

2141

2109

2080

2049

2021

1993

1966

1936

1906

1872

1846

1819

1793

1765

1740

1714

30

35

40

45

2229

2198

2166

2138

2106

2078

2050

2023

1990

1957

1920

1891

1860

1829

1798

1769

1741

2286

2255

2224

2195

2163

2135

2107

2080

2045

2009

1968

1935

1901

1867

1831

1799

1768

2342

2309

2275

2243

2209

2179

2149

2120

2085

2051

2012

1982

1950

1917

1883

1852

1822

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

451

421

389

360

328

301

273

246

216

188

157

132

105

79

51

26

0

15

489

459

427

398

367

339

311

284

253

224

193

168

141

115

87

62

36

20

527

497

465

436

405

377

349

322

292

262

229

204

177

151

123

98

72

25

565

535

503

474

443

415

387

360

330

300

266

240

213

187

159

134

108

30

mm of mercury St

25

15

20

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =320t., m

An-124-100 Landing distance Gland =320T (the wind, the runway slope 0)

Table D.3 (continued)

623

592

560

532

500

472

444

417

384

351

314

285

254

223

192

163

135

35

736

703

669

637

603

573

543

514

479

445

406

376

344

311

277

246

216

45

(continued)

680

649

618

589

557

529

501

474

439

403

362

329

295

261

225

193

162

40

Annex D: Preparing Data for Use in the “Overrun Prognosis” Program 209

35

40

45

1698

1727

1758

1787

1816

1844

1879

1910

1942

1970

1998

2027

2059

2089

2121

2153

750

740

730

720

710

700

690

680

670

660

650

640

630

620

610

600

2192

2160

2128

2098

2066

2037

2009

1981

1950

1919

1884

1856

1827

1798

1767

1738

1710

2231

2199

2167

2137

2105

2076

2048

2020

1989

1959

1924

1896

1867

1838

1807

1778

1750

2270

2238

2206

2176

2144

2115

2087

2059

2028

1998

1963

1936

1907

1878

1847

1818

1790

2328

2297

2265

2235

2203

2174

2146

2118

2084

2050

2012

1982

1950

1918

1884

1852

1820

2390

2357

2323

2294

2261

2233

2204

2176

2140

2103

2061

2028

1993

1957

1920

1885

1850

2451

2415

2378

2344

2308

2278

2246

2217

2181

2146

2106

2075

2042

2009

1975

1942

1910

600

610

620

630

640

650

660

670

680

690

700

710

720

730

740

750

760

799,9

813,2

826,6

839,9

853,2

866,6

879.9

893.2

906.5

919.9

933.2

946.5

959.9

973.2

986.5

999.9

1013.2

HPa

483

451

419

389

357

328

300

272

240

209

174

146

117

88

57

28

0

15

522

490

458

428

396

367

339

311

280

249

214

186

157

128

97

68

40

20

561

529

497

467

435

406

378

350

319

289

254

226

197

168

137

108

80

25

600

568

536

506

474

445

417

389

358

328

293

266

237

208

177

148

120

30

658

627

595

565

533

504

476

448

414

380

342

312

280

248

214

182

150

35

720

687

653

624

591

563

534

506

470

433

391

358

323

287

250

215

180

40

781

745

708

674

638

608

576

547

511

476

436

405

372

339

305

272

240

45

Note The sign ( *) indicates the calculation results under conditions when the minimum gradient of set 3 is not provided%; The sign (X) indicates combinations of conditions that go beyond the design operating conditions.

1670

760

30

mm of mercury St

25

15

20

The pressure at the level runway (QFE)

Temperature, °C

Barometer.pressure, mm. mercury column

Temperature, °C

Conditional reduction of runway length Gland =330t., m

An-124-100 Landing distance Gland =330t (the wind, the runway slope 0)

Table D.3 (continued)

210 Annex D: Preparing Data for Use in the “Overrun Prognosis” Program

Annex E

Using the “Overrun Prognosis” Program as a Module Asfpaa

As part of the ASFPAA project, a DRAS RE was developed to predict overrun of the runway during takeoff and landing. However, due to the importance of this event for the airline, it was decided to include “Overrun Prognosis” in the ASFPAA as an additional module for alternative rollout forecasting. This allows you to automate the entry of all data into “Overrun Prognosis through” the ASFPAA information support system, which combines all the airline’s databases. A schematic diagram of the forecasting process is shown in Fig. E.1. The program is being used in test mode. We can get an idea of the interface and the general procedure for using the program in Figs. E.2, E.3 and E.4. The program provides a mode for manually changing input data—“probability rolling calculator” (Fig. E.5). To change any parameter of the input data, click on it with the mouse and increase or decrease the parameter using the slider. This option can be useful during pre-flight or pre-flight training. The pilot can see what predicted parameter values the system has calculated based on a summary of information from his 50 flights and what is the risk of rolling out at this airfield if he acts “as usual”. It can enter a value, for example, for the distance of the touch point from the runway threshold, different from what the program assumes for its previous flights, and get a new probability value. We can assess how the risk of rolling out the Kadh is affected. We can change any parameters of the upcoming landing.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. D. Sharov et al., Risk Management Methods in the Aviation Enterprise, Springer Aerospace Technology, https://doi.org/10.1007/978-981-33-6017-4

211

212

Annex E: Using the “Overrun Prognosis” Program as a Module Asfpaa

Fig. E.1 Diagram of the forecasting process of rolling out

Fig. E.2 The choice of flight to calculate the probability of rolling out

Annex E: Using the “Overrun Prognosis” Program as a Module Asfpaa

Fig. E.3 Information about the source data used for the calculation

Fig. E.4 The results of the calculation for the landing aerodrome

213

214

Annex E: Using the “Overrun Prognosis” Program as a Module Asfpaa

The slider changes the parameter

Fig. E.5 Probability calculator rolling out