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Mathematical Research Systems Analysis and Simulation 1988 II edited by A.Sydow • S.G.Tzafestas R. Vichnevetsky
Band 47 A K A D E M I E - V E R L A G BERLIN
In this series original contributions of mathematical research in all fields are contained, such as — research monographs — collections of papers to a single topic — reports on congresses of exceptional interest for mathematical research. This series is aimed at promoting quick information and communication between mathematicians of the various special branches.
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Systems Analysis and Simulation 1988 II
Mathematical Research Wissenschaftliche Beiträge herausgegeben von der Akademie der Wissenschaften der DDR Karl-Weierstraß-Institut für Mathematik
Band 47 Systems Analysis a n d Simulation 1988 II
Mathematische Forschung
Systems Analysis and Simulation 1988 II: Applications Proceedings of the International Symposium held in Berlin (GDR), September 12-16,1988
edited by Achim Sydow Spyros G. Tzafestas Robert Vichnevetsky
Akademie-Verlag Berlin 1988
Herausgeber : Prof. Dr. Achim Sydow, Zentralinstitut prozesse
der Akademie
der Wissenschaften
Prof. Dr. Spyros G. Tzafestas,
Computer
University
Athens
Technical
of Athens,
Prof. Dr. Robert Vichnevetsky, University,
New
Schriftenreihe
3-05-500655-0
ISSN
0138-3019
Erschienen
im Akademie-Verlag
(c) Akademie-Verlag Lizenznummer:
Berlin
Division,
Science,
National
Rutgers
vom Originalmanuskript
der
Berlin, DDR-1086
Berlin,Leipziger
Str.3-4
1988
202-. 100/501/88 VEB Kongreß-
Republic und Werbedruck,
1095
Bestellnummer: 05600
werden
in the German Democratic
Gesamtherstellung: LSV
Engineering
Dept. of Computer
Informations-
Berlin
reproduziert.
ISBN
Printed
der DDR,
Brunswick
Die Titel dieser Autoren
für Kybernetik und
763 960 2
(2182/47)
9273
Oberlungwitz
PREFACE The
present volume contains the papers which were accepted for
tation
at
the
3rd International Symposium for
Systems
presen-
Analysis
and
Simulation held in Berlin (GDR), September 12-16, 1988. It
is
already a tradition to meet a broad international in systems analysis,
experts sium.
community
modelling and simulation at this
of
sympo-
This fact shows the requirements for a forum of presentation
and
discussion of new developments and applications of modelling and simulation in systems analysis. To
the great interest in this field one has to take into
con-
sideration the developed role of computer simulation as a powerful
realize
tool
of problem solving. been
More and more areas in sciences and production have
investigated by mathematical models and computer simulation.
Bio-
logical sciences and social sciences are even by now influenced by
this
trend. The
model
use on the computer has been very much improved in
decision
support systems.
Parallel simulation will provide drastic shortening of
computing
Parallel simulation and model based
time.
decision
support
systems are brought in the focus of international activities. Numerical mathematics," algorithms or
systems theory and control sciences provide with
supporting the modelling process itself based on
analytic
modelling
methods.
simulation
Such simulation systems equipped with tools
and graphics for representing results are real model
for
support
systems. A
new important impact comes from artificial intelligence by
processing.
knowledge
Expert systems may help decision making in case of
mathematical models.
missing
Expert systems may also support teaching and using
simulation systems. New teria
application areas are investigated. control
problems
of
qualitative
Applications sciences
Complex systems with multicri-
problems are in the scope of the symposium analysis of small
in engineering sciences,
scale
as
nonlinear
economy and management,
well
as
systems. natural
and social sciences are examined but also mixed problems
from
different areas. The
state
efficient
of
computer technique and programming
conditions for simulations.
environment
set
Personal computer are even
up used
for simulation more and more. The
symposium
reflects
the state of the art
and
trends
in
systems
analysis, modelling and simulation.
5
The 3rd International Symposium is organized by 'the Central Institute of Cybernetics and Information Processes of the Academy of Sciences of the GDR (ZKI) with cosponsorship of the -International Association for Mathematics and Computers in Simulation (IMACS), -International Federation of Automatic Control (IFAC), -International Institute for Applied Systems Analysis Laxenburg (IIASA), -Scientific Society of Measurement and Automation (WGMA) in the Chamber of Technology (KdT) of the GDR, -Mathematical Society (MG) of the GDR. The papers included in these proceedings were not formally refereed. The authors themselves are fully responsible. The international Program Committee consisted of: W. Ameling (FRG), P. Borne (France), L. Dekker (The Netherlands), S. Deng (PRC), A. A. Dorodnicyn (USSR), K. H. Fasol (FRG), W. Findeisen (Poland), 0. I. Franksen (Denmark), V. Hamata (Czechoslovakia), C. Hu (PRC), A. Javor (Hungary), K. Kabes (Czechoslovakia), V. V. Kalashnikov (USSR), V. Kempe (GDR), E. J. H. Kerckhoffs (The Netherlands), R. KlGtzler (GDR), R. Kulikovski (Poland), A. Kurzhansky (Austria, USSR), N. Levan (USA), A. H. Levis (USA), T. I. Oren (Canada), M. Peschel (GDR), P. D. Dieu (Vietnam), F. Pichler (Austria), K. Reinisch (GDR), W. Schirmer (GDR), B. Schmidt (FRG), V. V. Solodovnikov (USSR), F. Stanciulescu (Romania), J. M. Svirezhev (USSR), M. Thoma (FRG), I. Troch (Austria), S. G. Tzafestas (Greece), .G. C. Vansteenkiste (Belgium), R. Vichnevetsky (USA) , A. Sydow (GDR). Many thanks should be given to the members of this committee for the very helpful cooperation. Special thanks are said to Prof. Dr. V. Kempe, Director of the ZKI, for his great support in preparing and performing the symposium. Furthermore, great gratitude is to express to Prof. Dr. R. Vichnevetsky (USA), IMACS-President, Prof. Dr. B. Tamm (USSR), IFAC-President, Prof. Dr. R. H. Pry (USA), IIASA-Director, Prof. Dr. W. Richter (GDR), Chairman of WGMA, Prof. Dr. R. KlStzler (GDR), Chairman of MG for help and encouragement. A lot of the hard preparation work was done by the Department for Systems Analysis and Simulation of the Central Institute of Cybernetics and Information Processes. The editor expresses his thanks to all col-
6
leagues and friends who were very much engaged in the research work and in the preparation. First of all I would like to name Dr. P. Rudolph and Dr. A. Wittmüß who helped to prepare the proceedings. Furthermore I thank these colleagues and Dr. K.Bellmann, Dr. W.Jansen, Dr. E.Matthäus, Dr. E. Straubel and all the other colleagues for engaged cooperation for years in developing this research area. Mrs. Ch. Fröhlich and Mrs. J. Obretenov should be named for speedy service in preparing the manuscript. Mrs. S. Böttcher made an excellent job as organizer. Last not least I would give my thanks to the publishers, especially Mrs. R. Helle and Mrs. 6. Reiher, for their assistance and cooperation. Finally I would like to express my expectation also on behalf of the coeditors Prof. Dr. S. G. Tzafestas and Prof. Dr. R. Vichnevetsky that also the third symposium will be a contribution to the further development in systems analysis, modelling and simulation as well as a place for cooperation and communication like the first both.
April 1988
Achim Sydow On behalf of the editors
7
TABLE OF
CONTENTS
1. Complex Systems Analysis for Environmental 1.1. B a s i c P r o b l e m s Page,B. /INVITED PAPER/: Environmental Protection
Problems
R e v i e w of A p p l i e d I n f o r m a t i c s
Dorrer.G.A.; Ovezgeldyev,0.G.; Dmitriev,M.G.; Application of O p t i m a l Control M e t h o d s to N a t u r e P r o c e s s e s in A r i d Z o n e Puta.H.; Gerecke,D.: A Repetitive Algorithm Using Actual Climate Data
M a n s i m o v , M.R. : Simulation of
Working
Irrigation
17
21 24
K o r b i c z , J . ; P a r m a s t e , I . L . ; G a w l o w i c z , P . : S i m u l a t i o n of U r b a n Air Pollutant Processes and Monitoring Stations Location Problem
29
Carl,P.; S t e n c h i k o v , G . L . : S t r u c t u r a l A n a l y s i s of t h e tic Response to a Nuclear War
33
1.2. A c i d
1.3.
in
Clima-
Rain
Sydow,A.; Bellmann,K.; Straubel.R.; Imming,I.; Kaschenz.H.; Damrath,U.; Hofmann.G.; Anders,S.: PEMU - An Impact Model Based Environment Protection Decision Support System
37
Lasch,P.; Model,N.; Bellmann,K.: The PEMU/Air Pollutant transport M o d e l B a s e d on Emittent-Receptor-Point-Transmission Calculation
42
Forestry Bellmann,K.; Lasch,P.; Hofmann.G.; Anders,S.; Schulz,H.: PEMU F o r e s t - I m p a c t - M o d e l FORSTK. A Pine Stand Decline Wood Supply Model «
1.4. W a t e r
The and
45
Systems
Richter,J. /INVITED Agro-Ecosystems
PAPER/:
.
Water and Matter Dynamics
G r a e b e r , P . - W . : A u t o m a t i c a l C o n t r o l S t r a t e g y of S y s t e m s in t h e G r o u n d w a t e r Zone
in
Non-Technical
Popescu,Th.D.; Demetriu,S.; Marinescu,V.: Modelling F o r e c a s t i n g of R i v e r F l o w s U s i n g S t o c h a s t i c M o d e l s
and
51 61 65
Bereziriski, M. ; Petryczek,G.: Application of Trajectory D e c o m p o s i t i o n M e t h o d of t h e W a t e r Q u a l i t y C o n t r o l P r o b l e m
69
Braun,P.; Rudolph,P.; A l b r e c h t , K.-F. : C o m p u t e r A i d e d s i o n for W a t e r Q u a l i t y M a n a g e m e n t
75
Deci-
Gnauck,A.; Rathke,P.; Straubel.R.; Wittmuess,A.: ParetoOptimal Cost Division for t h e D e s i g n of Sewage Water T r e a t m e n t P l a n t s b y m e a n s of t h e D S S R E H 2. C o m p l e x S y s t e m s A n a l y s i s for I n d u s t r i a l 2.1. B a s i c P r o b l e m s
79
Automation
Gutenbaum,J. /INVITED PAPER/: Two-Level Heuristic for P a r a l l e l M a c h i n e s S c h e d u l i n g Zhou Shichang; Cao Xinming; Yao Guiyou: The State D i a g r a m of a H y d r a u l i c C o n t r o l S y s t e m
Algorithm 84 Variable
90
Valdes Pardo,V.G.: Decision-Making Simulations with the Micro-CED Programming System
94
G e s c h k e , S. ; Kempe.N.; Schlawatzky,H.: Application of H i e r a r c h i c a l C o m p u t e r S y s t e m for P i c o s e c o n d T e c h n o l o g y Wrycza,St.: Some C o m m e n t s on C o n s t i t u e n t s of Information Systems Development Methodologies Singer,D.: Systems
Model
Based
Default
a
Contemporary
Data Generation
for
Net
98 102 107
W i n n i c k i . A . ; P l o s z a j s k i , G . : O p t i m i s a t i o n P r o b l e m for B y - P a s s Routes Creation in t h e C a s e of a Teletransmission Line Break-Down 121 Hydro,K.: S u b s t i t u t i o n a l R o u t i n g for T r u n k L i n e in t h e T e l e t r a n s m i s s i o n N e t w o r k
Break-Down
Holena.M. : Object System Structure and Behaviour Analysis Database Design
in
125 131
Schiele,K.; Koch.M.; Nachbar,M.: Computer-Aided Elaboration of R e s e a r c h S t r a t e g i e s - The E x p e r t S y s t e m P H A R M E X 136 2.2. M a n u f a c t u r i n g
S y s t e m s and
CIM-Structures
Bobeanu.C.; Neagu.G.; Filip.F.: A Simulation O p t i m i z a t i o n A l g o r i t h m for O p e r a t i o n s C o n t r o l in F M S Lorenz,P.; Tolujev.J.: S i m u l a t i o n of M a n u f a c t u r i n g Goals, Methods, Tools and Problems
Based Systems.
Krug,W.; Blume,F.; Erfurth.F.: Modeling and Simulation Automated Welding Manufacturing System Voigt,G.: On the Application Simulation Processes for the Manufacturing Systems
2.3.
of
of Advisory Systems Planning and Control
and of
Yashkov,S.F.: T h e N o n - S t a t i o n a r y D i s t r i b u t i o n of N u m b e r C a l l s in t h e M / G / l P r o c e s s o r - S h a r i n g Q u e u e
of
140 144 150
154 158
Robotics Tenreiro Machado.J.A.; Martins de Carvalho,J.L.: pulator Systems : Analysis and Control Tenreiro Machado,J.A.; Costa,A.M.C.; Matos,J.S.: Robot Manipulators
Martins Dedicated
de Carvalho,J.L. Computer System for
Miernik,J.W.; Zaremba,M.B.: Performance Robotic Systems Using Rendezvous Networks 2.4. A u t o m a t i o n of C o n t i n u o u s
Industrial
Robot Mani-
Approximation
of
163
167 171
Systems
Matsevity,Yu.M.; Moultanovsky,A.V.: Solution of P a r a m e t e r Optimization and C o n t r o l P r o b l e m s in T h e r m a l Systems by m e a n s of a Local A d a p t i v e F i l t e r 175 Vajta.M.; Rijnsdorp,J.E.; van der Meulen,S.F.; Verberne,J.F.C: O p t i m i z a t i o n of H e a t D e l i v e r y t o B u i l d i n g s U s i n g an I m p r o v e d B u i l d i n g M o d e l
Apartment
179
Zhou Shichang; Cao Xinming; Gao Chengrui; Yang Yanyong: Performance Measurement and Mathematical Model Identification of t h e H y d r a u l i c S e r v o C o n t r o l S y s t e m of a Vacuum Electric-Arc Furnace 183 10
Zhou Shichang; Cao Xinming; Gao Chenrui; Yang Yanyong: Identification of the Mathematical Model of a Servo Valve by means of a Square Wave Signal 190 Piiaciriski, J.: Analog Simulation of Transient Behavior of Converters with the Application of Dynamic Thyristor Models 194 Hvala,N.; Strmcnik,S.; £erneti,i*)on elementary area element which is constrained by j)',^'*and Jid . Here («^j 5 ) are variables of curvilinear coordinated system, U) - initial direction of magistral ( 0 £ idi.H5T) and J> - relative distance from a centre - water source ). The pressure u*, for which normal phytomass volume S equals zero, is called the crytical pressure. If u u * , we have 3 > 0 - it is possible the stable management and by u » u * we have the desertification. For our model we have also S-*0, in origin neighborhood, where u .
Siberian Technological Institute, Krasnoyarsk, 660049, USSR ^FTI AN TS3R, Ashkhabad, 744000, USSR ^Turkmengydromet,Ashkhabad,744000,USSR
21
This fact corresponds the physics sence and observations i n the areas of watering-place the plant cover is destroyed, as a rule. 2.
Parameters identification of spreading processes
in.
There are often appeared the inverse problems under the models parameters estimation on the base of remote sensing side by side w i t h the problems of direct m o d e l l i n g of natural
processes.
ihs wave fronts transfer speeds are represented the greatest i n terest from the dynamical parameters. In our study the two approaches for this problem solution are proposed.
2.1
Parameters estimation of wave front speeds
indicatrix.
Let consider some dynamical set contours o n the earth surface are fixed in sequential time moments, i.e. we have the spreading processes
3oundary
w h i c h we approximate the points sequence,
is the two-dimontional vectors set 'dJCp&l . At first we identify the contours sequence i n topographic m a p s . Then changed sets dJCcfa 1 are used for normal speeds set construction. Further w i t h the help of s t a tistical analysis methods we find the formules parameters for describing of the normal speeds set.
2.2
Optimal guaranted estimates of spreading
processes.
/ere we use the results [ 2 ] by control systems
attainability
sets estimates on the base of the ellipsoids. Let us consider the spreading process parameters estimate p r o b lem by observed fronts positions
(A front is characterized by the c i r -
cumscribed ellipse). Let have the ellipse
E(t) = £[ci({)>
1
^{xeR :
sequence
(x-a)7HU~
a)< i j
, a - the
ellipse centre, II is the matrix defining the ellipse amounts and orien tation. Our problem - find the C, f, K , where x = C x + K u + f-, x e
22
P r o m £ 2 J it is known, that X € i * 5 f M d r ^ i =
+A
= L
J=
fit*
l ( o c - x ! ) T (T(-t^)(jc-X J i
).
arbitrary function,
Further we propose the next algorithm: I.
J-calculated ellipsoid estimate JC
estimate
E(iz)
approximates to o b s e r v e d
,i.e.
' « W - H W c X l e . t x(it)= QUz).
T h e n constructed
functions
t and get the forecasting ellipsoidal
exprapolate
by
estimates.
Numerical experiments, w h i c h were fulfilled for the dynamics
pre-
dictions of the eolian land forms n e a r the Sarykamysh lake on the basis of two set forth approaches have s h o w n the satisfactory c o n f o r m i t y
bet-
w e e n calculations and observations.
Literature i l l Dorrer, G.A.: Dynamical systems parameters estimate by their a t t a i n ability sets. Avtomatika and Telemechanica. (1986) I, 39-46 (In R u s s i a n ) [2] Konstantinov, G.N., G.V. Sidorenko : O u t e r estimates of the control systems attainability sets. Izv. AN 333R. T e c h n . k y b e r n e t i c s . (I98S) 3, 28-34 ( In Russian)
23
A REPETITIVE WORKING IRRIGATION ALGORITHM USING ACTUAL CLIMATE DATA Puta, H.
1)
and Gerecke, D.
2)
1. INTRODUCTION For
a long -time biologists and scientists in agriculture are engaged
develope growth models (see e.g. 1980).
Descriptive
detail.
van KEULEN,
1986;
AUGUSTIN,
models have the benefit to be as true as possible in
Control models represent as correct as possible the input-output
behavior and contain the control parameters explicitely. is
to
SCHMIDT,
The latter
one
suitable for design of control strategies but it is more difficult to
build
it
original been
directly.
For this end a deskriptive model can be
substitute.
In this paper a control model for sugar beets
available (HOFFMANN,
1977),
1980) and also a soil water model
with respect to the water amount which comes from
irrigation.
Both
models have been developed by the
as has
(KOITZSCH,
which enables to ascertain the water supply of the plant
waterstress) and/or
useful
(called rainfall
Academy
of
Agriculture of the GDR. Using these two models it is possible to give the farmer a computer aided help
for
irrigation
decisions taking into account the
present
growth
state and climate data from the past.
2. MODEL DISCUSSION The
growth
model of HOFFMANN (1980) can be at least
described
by
thev
following nonlinear differential equations:
where body
B(t)=f x *f 2 *f 3
(1)
Z(t)=f 1 *f 2
(2)
W(t)=Z(t)-B(t)
(3)
Z is the total dry matter or nettophotosynthesis rate of both W and the leaves B.
The anabolite distribution between leaves
the and
the total dry matter describes f^. The f^ are as following: f =0.01*f(R,t)*B(t),
(4)
f ^ K ^ / E ^ K ^ R *B 2 / 3 +K *WS+K *(ST-2)+K *(ST-2)2 +K,*WS*(ST-2)+K„*ln(TFZ)+Ka*WS*ln(TFZ)) /
2)
24
0
7
Institute of Technology, Ilmenau, GDR Institute for Cybernetics and Information Processes, Berlin, GDR
(5)
f 3=a0-a1#B-a2*-t+a.5#ST1' 2-a4*WS-a5*T+afi*ln< TFZ)-a? *t*ln(TFZ)
(6)
The coefficients K^ are linear interpolated within the four growth intervals: 45...63, 64...87, 88...129, 130...180th day. Waterstress WS and nitrogen content ST can be treated as the control variables, because they are directly conneted with the effects of irrigation and fertilization on the growing behaviour of the plant. Positive values of WS means water demand. Uncontrollable inputs are the radiation R and the outside temperature T. The soil quality is given by the parameter TFZ. The K^ and the constants a^ has been ascertained by regression. The function f(R,T) gives a proper weight every day on the nettoassimilation rate in dependence of the actual courses for global radiation and temperature. Analytical models are available for R(t) and T(t) but it is also possible to use measured data. The waterstress WS represents the state of the water supply of the plant. The soil water model of KOITZSCH (1977) can be used to elaborate the actual WS in dependence of the water supply in the past and the actual growth state of the plant. To get reference courses for WS(t) we applied a dynamic optimization procedure (ARNOLD, PUTA, 1986) with the goal to maximize the yield of dry matter of the body at harvest time. In agreement with the definition of HOFFMANN (1980) the course for waterstress results in about WS(t) - 0 without a short period between 9 0 t h and 110 t h day. During this period the waterstress could be a bit positiv (water demand) for better qualification of the roots. Up to the availability of a proper nitrogen soil model, nitrogen courses ST(t) from the practical experience have to be used. For given values or courses, resp., of the discussed control variables it is possible to use the growth model, eqs. (1) to (5), to predict the yield of dry matter of the body and dry matter of the leaves for any day during the growing season of about 135 days (from 165 t h to 300 t h day of the year).
3. THE IRRIGATION ALGORITHM For application it is desirable to make control decisions (here for irrigation) with respect to the annual climate situation and adapted to the actual growth state of the plant. For this task are available the growth model and the soil water model. The latter one allows the computation of waterstress in dependence of climate date as inputs and using the growth model. The inversion of the soil water model would be desirable to compute the necessary irrigation for required values of waterstress. But this is because of the algorithmic structure of this model impossible. Therefore another strategie will be proposed.
25
At
a
given
actual time t g during the growing period we know
the
real
climate
from t„ (initial time) up to t . With this information we 0 % simulate this time interval and compute the actual growth state of
plant using both of the models. With predicted climate data
for
can the
the in-
terval from t a to t. I(t„I is the estimated harvest time) it is now possible to give a irrigation proposal (fig. 1). The prediction results may be applied as long as there are not too deviations fall.
If
between estimated this
not hold,
and real climate including
large
expected rain-
a new prediction is needed at a new time
Therefore the algorithm should have repetitive character.
A fragment
the repetitive irrigation algorithm is shown in figure 2.
Fig. 1
Choosing t
between t•o n and t. 'f
-- input WS r e f, tig., t a , tf - - compute WS at g r o w t h model
ta
P {OAIS - W S r e f > > 0
using
soil
water
and
?>
n g i v e one i r r i g a t i o n u n i t i e . g . 1mm)
n
P -(sum of p r o p o s e d i r r i g a t i o n > - 3 0 m m ?)•
store WS
- - input : t a
prediction results
--
—
of
i r r i g a t i o n : amount & d a t e
climate
P -C t e s t o f WS d e s i r e d
n
Fig. 2 26
The repetitive irrigation algorithm
?>
and
expected
yield
t . a
of
4. SIMULATION RESULTS The dialogoriented algorithm (GERECKE, 1988) allows the prediction of five variants of expected climate on the screen of the 8 Bit PC 1715. The program needs about 50 kByte of memory and is written in TURBO-PASCAL. The protocol is shown in figure 3.
Ergebnisprotokoll 1.Variante 2.Variante 3.Variante
4.Variante 5.Variante
Blattmasse 90 dt/'ha 79 dt/ha 83 dt/ha 72 dt/ha 41 dt/ha Ruebenmasse 227 dt/ha 201 dt/ha 203 dt/ha 177 dt/ha 94 dt/ha Zuckerertrag 134 dt/ha 114 dt/ha 117 dt/ha 97 dt/ha 45 dt/ha Gesamterloes 8936.-M/ha 8947.-M/ha 7846.-M/ha 10023.-M/ha 4234.-M/ha Transportkosten 1555.-M/ha 1538.-M/ha 1392.-M/ha 1705.-M/ha 958.-M/ha Beregnungskosten 225.-M/ha 338.-M/ha 338.-M/ha 225.-M/ha 0.-M/ha >> Gewinn