Building Thermal Performance and Sustainability 9811991383, 9789811991387

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Lecture Notes in Civil Engineering

Vijayalaxmi J.

Building Thermal Performance and Sustainability

Lecture Notes in Civil Engineering Volume 316

Series Editors Marco di Prisco, Politecnico di Milano, Milano, Italy Sheng-Hong Chen, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China Ioannis Vayas, Institute of Steel Structures, National Technical University of Athens, Athens, Greece Sanjay Kumar Shukla, School of Engineering, Edith Cowan University, Joondalup, WA, Australia Anuj Sharma, Iowa State University, Ames, IA, USA Nagesh Kumar, Department of Civil Engineering, Indian Institute of Science Bangalore, Bengaluru, Karnataka, India Chien Ming Wang, School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

Lecture Notes in Civil Engineering (LNCE) publishes the latest developments in Civil Engineering—quickly, informally and in top quality. Though original research reported in proceedings and post-proceedings represents the core of LNCE, edited volumes of exceptionally high quality and interest may also be considered for publication. Volumes published in LNCE embrace all aspects and subfields of, as well as new challenges in, Civil Engineering. Topics in the series include: . . . . . . . . . . . . . . .

Construction and Structural Mechanics Building Materials Concrete, Steel and Timber Structures Geotechnical Engineering Earthquake Engineering Coastal Engineering Ocean and Offshore Engineering; Ships and Floating Structures Hydraulics, Hydrology and Water Resources Engineering Environmental Engineering and Sustainability Structural Health and Monitoring Surveying and Geographical Information Systems Indoor Environments Transportation and Traffic Risk Analysis Safety and Security

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Vijayalaxmi J.

Building Thermal Performance and Sustainability

Vijayalaxmi J. Department of Architecture School of Planning and Architecture, Vijayawada Vijayawada, Andhra Pradesh, India

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-19-9138-7 ISBN 978-981-19-9139-4 (eBook) https://doi.org/10.1007/978-981-19-9139-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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

Dedicated to all my Students

Foreword

The rapid progress in the domain of sustainable built environment has been comprehensively synthesized in this book Building Thermal Performance and Sustainability by Dr. Vijayalaxmi J. This book provides chapters with multiple dimensions of data collection and analysis for the researchers and postgraduate students of architecture. The contribution by Dr. Vijayalaxmi J. includes real-time data and experiments conducted in full-scale buildings. The uniqueness of her work is that, it can add value to the early-stage design decisions of architects in the specific context. The work can also be carried by researchers in other parts of the world by following the experimental methodology of this book. This book forms a valuable addition to the existing body of knowledge in this domain and will open up new opportunities and perspectives for students, researchers, and practitioners. Dr. Vijayalaxmi J. is one of the efficient and resilient people I have met, and she is also very courageous. I have observed her keenly through easy times and hard times, and it is no surprise that she has persevered to write this book. I congratulate her and the publishers on this achievement.

Dr. Srikonda Ramesh Director, School of Planning and Architecture, Vijayawada Vijayawada, Andhra Pradesh, India

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Preface

The book ‘Building Thermal Performance and Sustainability’ as part of ‘Lecture Notes in Civil Engineering’ highlights three aspects of architectural research. The book presents chapters of architectural research based on real-time data obtained through field studies. The research papers presented in this book are a result of systematic research conducted after identification of a research gap through the latest review of literature and present new knowledge which can help in making performance-based design decisions. Broad topics covered in this book include heat stress of occupants, methods to assess thermal performance of buildings, and smart materials and nanotechnology in architecture. The book also presents papers which are ‘methodology’ based. The methodology can be applied in other contexts to achieve desired results. This book is a valuable reference for postgraduate and doctoral students of architecture and professionals interested in built environment and allied fields to understand the strategies, tact, and methods of a scientific approach to assess building performance. As a professor of architecture, I have come across students whose main hurdle in presenting research is the lack of data validation. This book demonstrates how data generated or collected from one source can be validated. The book is a result of spending long hours on understanding how architects can contribute to the society through their research in a non-invasive way through the use of simple passive strategies to achieve sustainable architecture. Some chapters of the book deal with the implication of what is being done in the contemporary world which will help practicing architects make early-stage design decisions. I hope that the research included in this book pertaining to heat stress, methods of assessing thermal performance, and use of smart materials and nanotechnology for non-invasive retrofit will help academicians, postgraduate and doctoral students, and practicing architects in their research and decisions. Finally, I express my sincere thanks to Dr. Loyola D’Silva, Publishing Editor at Springer for LNCE Series, Ms. Saranya Devi Balasubramanian and Ms. Coral

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Zhou, Project Coordinator of the book at Springer, for their great support during the preparation of the final book. Dr. Vijayalaxmi J. Professor and Head, Department of Architecture, School of Planning and Architecture, Vijayawada An Institute of National Importance Under the MoE, GoI Vijayawada, India

Contents

Building Thermal Performance and Sustainability Issues . . . . . . . . . . . . . . 1 Aspects to Pertaining to Building Thermal Performance . . . . . . . . . . . . . 1.1 Heat Stress in Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Performance of Buildings—Assessment Methods . . . . . . . . . . . . . 1.3 Smart Materials and Nanotechnology for Sustainability . . . . . . . . 2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 3 9 10

Appropriate Heat Stress Index to Assess Heat Stress in Built Environment in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Heat Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Heat Stress in Industries and Rural Houses . . . . . . . . . . . . . . . . . . . 1.3 Heat Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Heat Stress Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 11 11 12 12 14 20 21 21

Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone Hot-Humid Climate of Andhra Pradesh, India . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Heat Stress and Heat Stress Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Objectives of This Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Materials and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Study Design and Study Population . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Site Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Indoor Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Results of Assessment of Indoor WBGT . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Comparison of WBGT in All the Five Houses . . . . . . . . . . . . . . . . 5.2 Comparison of Outdoor and Indoor Temperatures in the Five Houses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 23 24 25 25 25 26 27 33 33 34

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5.3

The Difference in Indoor WBGT Between the Conventional RCC Roof and Other Houses with Reed Roof . . . . . . . . . . . . . . . . 5.4 Comparison of WBGT of RCC Roof and Reed Roof . . . . . . . . . . 5.5 WBGT Limits for Moderate Work in All Five Houses . . . . . . . . . 6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Strengths and Limitations of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34 35 35 37 38 38 39

Methods of Assessing Thermal Performance of Buildings . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methods of Assessing the Thermal Performance . . . . . . . . . . . . . . . . . . . . 2.1 Understanding Steady-State Models . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Understanding Empirical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Understanding Dynamic Models—Software and Tools . . . . . . . . . 3 Improvements in Simulation Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 41 42 42 46 48 50 51 51

Steady-State Assessment of Vertical Greenery Systems on the Thermal Resistance of the Wall and Its Correlation with Thermal Insulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Thermal Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 54 55 55 60 65 67 69

Thermal Performance of Bamboo Flat Roof Slab and RCC Flat Roof Filler Slab in Warm-Humid Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methods and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Field Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Study Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Study on Thermal Performance Parameters Before Field Study and Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 The Time Lag and Decrement Factor . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Thermal Performance Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Distribution of Air and Surface Temperature During Field Study . . . . . . 5 Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Temperature Range, Minimum, and Maximum . . . . . . . . . . . . . . . 5.2 Comparative Study with Respect to Parameters on Thermal Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71 72 73 73 74 74 77 77 77 78 81 81 83

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6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

84 84 85

Empirical and Dynamic Simulation-Based Assessment of Indoor Thermal Performance in Naturally Ventilated Buildings . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Components of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Climate Characteristics of the Region . . . . . . . . . . . . . . . . . . . . . . . 2.3 Context of Relational Real-Scale Building . . . . . . . . . . . . . . . . . . . 3 Design of the Relational Experimental Set-Up . . . . . . . . . . . . . . . . . . . . . . 4 Data Collection Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Generation of Predictive Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Building Modeling and Simulation Settings for DesignBuilder and Rhino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Variables and Constrains for the Models Using DesignBuilder and Rhino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Outdoor Data Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Correlation Between Indoor Temperature from Real-Scale and Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Analysis of Real-Scale and Simulated Results . . . . . . . . . . . . . . . . 7 Validation of Predictive Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Use of Ceiling Fans in a Hot-Humid Climate . . . . . . . . . . . . . . . . . 7.2 Use of Flyscreens in a Hot-Humid Climate . . . . . . . . . . . . . . . . . . . 8 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Study of Indoor Thermal Performance Due to Varying Ceiling Heights in a Hot-Humid Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Ceiling Heights in Building Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Components of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Results of the Simulation for Varying Ceiling Heights Using DesignBuilder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Impact of Ceiling Height Variation in Rooms with Changing Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Impact of Ceiling Height Variation in Rooms with Changing Sizes of Openings . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 87 88 89 89 90 90 92 93 93 94 94 95 95 107 107 109 109 109 111 112 113 115 115 116 118 118 119 119 119 122 122

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4.4

Rate of Increase in Indoor Temperature with Increase in Ceiling Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 The Indoor Temperature for All Ceiling Heights Coincided Twice a Day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Validation of Simulation Results for Varying Ceiling Height . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

122 123 124 125 126

Optimization of the Integrated Daylighting and Natural Ventilation in a Commercial Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methodology and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Measurement Tools Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Measurements and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Base Case Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Design Constants and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Base Case Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Base Case Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Inputs Used for Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Findings and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Scope and Limitations of the Study . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

129 129 130 130 131 132 132 135 135 136 137 138 140 140 147 149 149

A Methodology to Optimize Thermal Conditions of Built Forms for Humans and Birds in a Birds Sanctuary . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Site Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Study and Assessment of the Parameters for Ideal Design Values . . . . . . 4.1 Orientation and Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Aspect Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Roof Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Window Wall Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Perforated Screens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Shading Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

151 152 153 154 155 155 156 156 157 160 161 161 164

Applications of Smart Building Materials in Sustainable Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 2 Classification of Smart Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

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2.1 Type 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Type 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Application of Smart Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Chromogenic Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Suspended Particle Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Polymer Dispersed Liquid Crystals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Electrochromic Glass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Phase Change Materials (PCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Shape Memory Alloys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Nanotechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Nanotechnology-Based Thermal Insulation Materials . . . . . . . . . . . . . . . . 12 Energy Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Photovoltaics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Piezoelectric Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

166 167 168 168 168 168 169 170 171 172 172 173 173 174 175 175

An Analytical Assessment and Retrofit Using Nanomaterials of Rural Houses in Heat Wave-Prone Region in India . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Materials and Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Details of Houses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Indoor Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Simulation and Validation of the Model . . . . . . . . . . . . . . . . . . . . . 3 Results and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Correlation Between Measured and Simulated Temperature . . . . 3.2 Monthly Comfort Hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Embodied Energy Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Thermal Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Correctives for Enhanced Thermal Performance . . . . . . . . . . . . . . . . . . . . 4.1 Embodied Energy with Aerogel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

177 177 178 178 181 182 184 184 184 184 184 187 187 188 190 190

About the Author

Dr. Vijayalaxmi J. completed her Ph.D. and postdoctoral research with specialization in sustainable built environment. With over 27 years of teaching and research experience, she has worked primarily in the domain of climate responsive built environment and thermal performance of buildings. She is currently the professor and the Head of the Department of Architecture and the former Dean (Research) at the School of Planning and Architecture, Vijayawada, an Institute of National Importance under the Ministry of Education, Government of India.

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Building Thermal Performance and Sustainability Issues

Abstract The book ‘Building Thermal Performance and Sustainability’ as part of ‘Lecture Notes in Civil Engineering’ highlights the various aspects of architectural research in the domain of Building Thermal Performance leading to a sustainable built environment. The book has chapters based on the sections pertaining to (i) heat stress in buildings, (ii) methods of assessing thermal performance in buildings, and (iii) smart materials and nanotechnology in architecture. The chapters under each of the sections present various aspects of architectural research based on realtime data obtained through field studies. The first section deals with the issues that the rural houses face due to heat wave and the impact on heat stress due to the envelope building materials. The second section focuses on the various ways to assess the performance of the buildings which can enable conscious design decisions at early stages. The third section addresses the way in which correctives can be carried out using nanotechnology with the case study. The research papers are a result of systematic research conducted after the identification of a research gap through the latest review of the literature and present new knowledge which can help in making performance-based design decisions, especially building in a hot-humid climate. The book also presents chapters that are ‘methodology’ based. The methodology can be applied in other contexts to achieve desired results. This book is a valuable reference for postgraduate and doctoral students of architecture and professionals interested in the built environment and allied fields to understand the strategies, tact, and methods of a scientific approach to assess building performance.

1 Aspects to Pertaining to Building Thermal Performance Buildings are primarily built to protect the inhabitants from the onslaught of weather. Building envelope is the most important element that protects the humans from the vagaries of weather. The selection of these building materials depends upon various factors and is a crucial factor to enhance building thermal performance. The three thrust areas that this book addresses are

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_1

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Building Thermal Performance and Sustainability Issues

i. Heat stress in buildings ii. Methods of assessing thermal performance in buildings iii. Smart materials and nanotechnology in architecture.

1.1 Heat Stress in Buildings Heat stress is a reality in most regions of hot-humid climates and can lead to severe health issues such as fatigue, nausea, dizziness, fainting, and even death. The number of human deaths due to heat wave in India has increased progressively since 1971, 1987, 1997, 2001, 2002, 2013, and 2015. In recent years, India has registered the highest number of deaths due to heat wave events compared to the previous three decades. The only way to protect oneself from heat wave is to avoid the outdoor environment during those periods. The disaster mitigation advisory suggests that people must remain indoors during times of heat wave. In rural areas of India, the use of air conditioners is not prevalent due to economic reasons. Most of the houses also back up as workplaces of small-scale home-based industries such as snack making, beedi rolling, tailoring and embroidery, book binding. The already non-conducive environment can further escalate the inner body temperature of the occupants due to work stress. Hence, it becomes important to assess the indoor thermal performance of rural houses in heat stress conditions. Chapter “Appropriate Heat Stress Index to Assess Heat Stress in Built Environment in India” deals with the various indices that have evolved to assess heat stress in buildings. The chapter also discusses the most appropriate heat stress index for the hot-humid climate which is the WBGT. The use of WBGT is demonstrated in Chapter “Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone Hot-Humid Climate of Andhra Pradesh, India” through the assessment of the heat stress index of rural houses in heat wave-prone hot-humid climate of Andhra Pradesh, India. Andhra Pradesh is an important province/state in India. It is one of the states which is listed in the National Disaster Management Manual as being worst hit due to heat wave causing mortality (Fig. 1). Of the three worst-hit states among Andhra Pradesh, Telangana, and Orissa, Andhra Pradesh recorded mortality of 1422 persons and 723 persons in the years 2015 and 2016, respectively, (Fig. 1). Andhra Pradesh accounted for 70% and 66.75% of the total deaths due to heat stress in 2015 and 2016, respectively. This indicates the need for intervention to ensure comfortable indoors to reduce/stop the loss of human lives. A ground-level information on the existing conditions of heat stress in rural houses is presented in Chapter “Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone Hot-Humid Climate of Andhra Pradesh, India”. The heat stress of houses using four different walling material and one roofing material is assessed. The roofing is made of local reed known as ‘rail gaddi’ (meaning grass grown along the railway tracks). The walls of the four houses are made of 55 mm thick reed walls with 7.5 mm thick mud plaster on both sides, 270 mm thick random rubble with mud mortar and mud plaster on both sides, 230 mm thick brick wall with mud mortar and cement

1 Aspects to Pertaining to Building Thermal Performance

3

Fig. 1 Mortality due to heat wave in the three most affected states in India

plaster on both side painted white, aerated cement blocks, and 150 mm thick cement block with cement mortar and no plaster on both sides. These are the representative and generic building materials used in rural Andhra Pradesh. The heat stress in these houses is assessed and compared with the heat stress in a house with conventional building material, namely 230 mm burnt brick wall with cement mortar on both sides and a reinforced cement concrete roof. Among the four walling materials analyzed in this study, the brick wall in combination with the reed roof performs better than the other three walling materials, namely reed, stone, and cement concrete blocks. It shows least discomfort compared to the other houses and enables 75% work and 25% rest time. Its indoor temperature is lower than the outside temperature by 3 °C to 4 °C. In all the other four houses, work for only 50% of the time is possible before reaching heat stress levels. The conventional house with brick wall and RCC roof has higher indoor temperature than the other houses. The study results also show that it is more stressful and difficult to work in a concrete-roofed house as compared to the traditional reed-roofed house, when walling material is brick. The building envelope materials of all the houses create thermally uncomfortable indoors even to carry out work within limits of heat stress. This clearly shows the need to reassess the building materials used in rural houses of heat wave-prone zone of Andhra Pradesh to reduce the heat stress of inhabitants. Further, this study also reiterates the need to retrofit existing rural houses to safeguard the inhabitants from heat stress.

1.2 Performance of Buildings—Assessment Methods The performance of a building is a measure to assess how the building functions for the criteria designated. The designated criteria can be temperature, humidity, ventilation (wind) daylighting, acoustics, etc. The envelope of a building acts as its skin and is responsible to protect the inhabitants from the onslaught of weather. With the ‘internationalization’ of building materials and construction techniques,

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the building envelope has become standardized with no regard to the requirements of the climate. This can result in an extremely uncomfortable indoor environment. To nullify the discomfort, there is a resort to active means of enhancing indoor thermal performance. The repercussions result in the consumption of electricity to support the active aids. Nearly 30% of a country’s annual electricity consumption is by the building sector. Hence, buildings also contribute to the GHG. To reduce this consumption, there is a need to understand the ‘performance gap’ in buildings. Performance Gap: Every building is designed with anticipation that it would provide a thermally conducive environment. The difference between the actual performance and the anticipated performance is called a ‘performance gap’. To understand the performance gap, we have to know. i. The intended thermal performance of the building ii. The actual performance of the building. Buildings can consume twice the energy that for which they were initially designed. Hence, it is necessary to know the methods for assessing the thermal performance of buildings. There are three methods of assessing the thermal performance of buildings. Methods of Assessing Thermal Performance in Buildings The various methods of assessing the thermal performance of buildings along with their advantages, limitations, and process involved are described in Chapter “Methods of Assessing Thermal Performance of Buildings”. The following are the methods of assessing the thermal performance of buildings: a. b. c. d.

Steady-state method Empirical method Dynamic method Mixed methods.

The forthcoming Chapters “Steady State Assessment of Vertical Greenery Systems on the Thermal Resistance of the Wall and its Correlation with Thermal Insulation, Thermal Performance of Bamboo Flat Roof Slab and RCC Flat Roof Filler Slab in Warm Humid Climate, Empirical and Dynamic Simulation-Based Assessment of Indoor Thermal Performance in Naturally Ventilated Buildings, Study of Indoor Thermal Performance due to Varying Ceiling Heights in a Hot-Humid Climate, Optimization of the Integrated Daylighting and Natural Ventilation in a Commercial Building, A Methodology to Optimize Thermal Conditions of Built Forms for Humans and Birds in a BirdsSanctuary”demonstrate either one or more of the methods in combination. Chapters “Empirical and Dynamic Simulation-Based Assessment of Indoor Thermal Performance in Naturally Ventilated Buildings and A Methodology to Optimize Thermal Conditions of Built Forms for Humans and Birds in a Birds Sanctuary” are methodology-based researches, wherein the methodology to achieve a particular target is demonstrated. The application of research like this is very vast. The methodology can be followed in various other contexts to assess and take appropriate decisions.

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Table 1 Methodology of assessment used in chapters Sl. No.

Chapter No.

Chapter title

1

Chapter 5

Steady State Assessment of Vertical Steady state Greenery Systems on the Thermal Resistance of the Wall and its Correlation with Thermal Insulation

Method adopted

2

Chapter 6

Thermal Performance of Bamboo Empirical + steady state Flat Roof Slab and RCC Flat Roof Filler Slab in Warm Humid Climate

3

Chapter 7

Empirical and dynamic simulation-based assessment of indoor thermal performance in naturally ventilated buildings

Methodology-based empirical + dynamic

4

Chapter 8

Study of indoor thermal performance due to varying ceiling heights in a hot-humid climate

Dynamic

5

Chapter 9

Optimization of the Integrated Empirical + dynamic Daylighting and Natural Ventilation in a Commercial Building

6

Chapter 10

A Methodology to optimize thermal Methodology-based dynamic conditions of built forms for humans and birds in a birds sanctuary

The thermal performance assessment using the above methods is demonstrated in the forthcoming chapters as given in Table 1. Chapter “Steady State Assessment of Vertical Greenery Systems on the Thermal Resistance of the Wall and its Correlation with Thermal Insulation” deals with the vertical greenery systems on the thermal resistance of the wall and its correlation with thermal insulation using the steady-state method. The study pertains to the resistive capacity of the façade due to vertical greening system due to the foliage. In this study, the relation of this influence on the insulative capacity of the façade is studied by adopting three construction types in brick masonry with and without vegetation, namely. . Full brick thick construction . Living wall system . Insulated brick cavity wall. This shows that the insulation has more influence in increasing the resistance of construction type over the resistance offered by foliage. Also, it is observed that in the case of an insulated cavity wall, there is an opportunity to obtain a better thermal resistance with lesser material if the living wall system is integrated into the façade serving the functional purpose of the wall. This may lead to cost reduction, material efficiency, and decrease in the carbon footprint of the building. This study uses the steady-state heat transfer of the wall and foliage in green wall systems.

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The combination of the empirical method and steady-state method to assess the thermal performance of bamboo flat roof slab and RCC flat roof filler slab in a warm humid climates is shown in Chapter “Thermal Performance of Bamboo Flat Roof Slab and RCC Flat Roof Filler Slab in Warm Humid Climate”. Two nearby residences in the Thiruvananthapuram district of Kerala, India, have been identified for the study of bamboo-mud roof slab and RCC filler roof slab for field study. Whirling thermo-hygrometer is used to monitor DBT & WBT (°C), Fluke TiS40 9 Hz Thermal Imager is used to monitor indoor and outdoor surface temperature (°C) with an accuracy of ±2 °C, and KUSAM-MECO 909-Anemometer is used to monitor wind speed (m/sec) with an accuracy of ±3% full scale, for every one hour time interval for adjacent days in identified two residences in the summer month of March 2022 to evaluate the thermal performance of both the slabs in critical weather conditions. With the help of the instruments, real-time field study empirical data of indoor climate parameters is obtained. The time lag, decrement factor, and Thermal Performance Index are calculated using the steady-state method. But the input parameter (indoor air temperature (Ti), outdoor air temperature (To), outer surface temperature (Tso), and inner surface temperature (Tsi)) are got from field studies. Thus, Chapter “Thermal Performance of Bamboo Flat Roof Slab and RCC Flat Roof Filler Slab in Warm Humid Climate” is a demonstration of how a combination of the empirical method and steady-state method can be used for assessing the thermal performance of buildings. The results show that for the RCC filler slab thermal performance: The indoor air temperature is about 0.5–1.5 °C lower during peak hot hours compared to the outdoor temperature. There is a decrement factor of 0.43 and a time lag of 2 h with a TPI of 158.75% The bamboo-mud slab thermal performance shows that the indoor air temperature is about 2–3 °C lower during peak hot hours compared to the outdoor temperature. There is a decrement factor of 0.36 and a time lag of 6 h with a TPI of 76.25%. Hence, the bamboo-mud flat roof slab performs better than the RCC filler slab in terms of thermal performance for the same walling material. Chapter “Empirical and dynamic simulation-based assessment of indoor thermal performance in naturally ventilated buildings” studies the empirical and dynamic simulation-based assessment of indoor thermal performance in naturally ventilated buildings. In this study, to carry out the empirical studies, two experimental full-scale houses are built for the capture of indoor air temperature data. The thermal performance of a building is a consequence of the design, orientation, and building materials. Several parameters influence the thermal performance of ventilated buildings. To understand the impact of multiple parameters, a predictive model needs to be developed with which the required parameter can be changed and the thermal performance assessed. This single model can be used to assess several design parameters, which otherwise may be impossible to study, thus enabling design for optimized thermal performance. To validate the dynamic predictive model, the indoor air temperature from the two experimental houses is monitored for conditions of 14 opening sizes, in rooms of 8 orientation for a duration of 4 months during the summer season in the hot-humid climate of Chennai, India.

1 Aspects to Pertaining to Building Thermal Performance

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The predictive model, similar to the experimental building, is simulated using Design Builder and Rhino and assesses the indoor thermal performance for 14 opening sizes in eight room orientation. The results of the indoor thermal performance are compared and validated with the field results. To ensure that the predictive model functions with accuracy for other varying factors, the impact of two other commonly used factors, namely ceiling fan and fly screen, is also generated and compared with field study results. Since the results are found to correlate well, the generated dynamic model can be considered a predictive model to assess the impact of many other factors on indoor thermal performance. The use of this predictive model is demonstrated in Chapter “Study of indoor thermal performance due to varying ceiling heights in a hot-humid climate” through the study of indoor thermal performance due to varying ceiling heights in a hot-humid climate. This study explores the impact of changing ceiling height for 10 variations, 11 opening sizes, and 8 room orientations on indoor thermal performance which is studied. The number of parameters involved is more, and the combination of parameters also increases. Besides, it is not possible to conduct this research completely by experimental method. It would be almost close to impossible to identify 880 building samples with the combination of orientation, opening size, and ceiling height for the same room size, envelope material, and climate type with no variation in microclimate. Hence, the dynamic method which is based on the experimental method is the best way to conduct studies such as these. The results of the dynamic study are validated through field study available in combinations of room orientation, opening size, and ceiling heights. The result of the study shows that an increase in ceiling height correlated with an increase in indoor air temperature during the day and a decrease in air temperature in the evening. But the increase is not linear. The increase in indoor temperature reduces exponentially as the ceiling height increases for every subsequent 30 cm. Also, the results show that irrespective of the ceiling height, size of openings, or room orientation, the indoor temperature during twice a day is the same in all rooms. The reason for this phenomenon is not yet been explored. One of the important aspects of building performance and sustainability is daylighting. Chapter “Optimization of the Integrated Daylighting and Natural Ventilation in a Commercial Building” studies the optimization of integrated daylighting and natural ventilation in a commercial building. This study has a relatively small part based on empirical field study data collection, and a large part of the study is based on dynamic simulation. The empirical study is carried out to substantiate the validity of the simulation model. Energy efficiency can be practiced in a commercial building by integrating a building with a more efficient natural ventilation and daylighting system, as this would reduce the dependency on artificial lighting and HVAC systems that account for more than 50% of the total building energy. As commercial buildings are one of the energy-intensive building typologies, maximizing the natural ventilation and daylighting potential can make the building more resilient. For this study, the atrium space, which forms a central connectivity point in a commercial space, is selected

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and optimized for maximum natural ventilation and daylighting while maintaining occupant comfort. A field study of an existing commercial building, similar to the proposed case, is conducted, and data is collected for validation. A quantitative analysis is done to study the impact of various natural ventilation and daylighting strategies on indoor thermal and visual comfort through simulations. After carrying out 127 iterations each for three design cases of the atrium, it is found that among the 11 design variables selected, the window-to-wall ratio and the type of glazing have the most impact on the daylighting and thermal comfort of the space. The opening schedule, vent area, and the size of the opening have the maximum impact on natural ventilation. Results for such a large number of conditions, which include window-to-wall ratio, façade type (height of fenestration from slab level), façade glazing (11 options), roof glazing type (11 options), roof window shading coefficient, percentage of external window area opening, percentage of roof window area opening, infiltration (each), vent area (m), roof vent area (m), natural vent set-point temperature cannot be found through field studies with same ‘constant’ factors of the building. In such cases, only dynamic study methods with the help of simulation software can be used for studies. A methodology to optimize thermal conditions of built forms for humans and birds in a bird sanctuary forms Chapter “A Methodology to optimize thermal conditions of built forms for humans and birds in abirdssanctuary”. This chapter is based completely on the dynamic simulation method and looks at arriving at a methodology to design buildings in a bird’s sanctuary to enable co-existing of migratory birds and humans. The study demonstrates the architectural intervention conditions to ensure indoor thermal comfort for humans and outdoor thermal conditions for birds (which are sensitive to even small microclimate variations). Ecological sustainability should be a holistic approach, where we consider all the biotic and abiotic factors of an ecosystem. Due to climate change, changes in vegetation patterns, the rapid increase of urbanization, depletion of resources, etc., in many ecological cases, we have crossed the line of conservation and now we face the phase of rejuvenation or revival of an eco-sensitive space. For the benefit of flora and fauna and environment, much research is done, but the consideration of other species is very less. Sanctuaries being home to 80% of the migratory and native birds have to be rejuvenated by making them a vital space for avian habitat. The site chosen for this study is the Chitrangudi Birds Sanctuary in Tamil Nadu, India. To achieve the optimal thermal conditions for the birds in and around a bird sanctuary, many stages of analysis and strategies benefiting both humans and birds have to be considered. The strategies derived after analysis concerning the site considering human adaptive thermal comfort and bird breeding temperature of each species are—roof, WWR%, shading device, the shape of the building, and jaali. Quantification of different passive strategies that balance both indoor comfort for humans and outdoor comfort for birds is carried out. The various conditions for which iterations are carried out are: . Orientation and form—10 conditions . Aspect ratio—11 ratio each for two perimeter conditions (22 conditions of aspect ratio)

1 Aspects to Pertaining to Building Thermal Performance

. . . . . . .

9

Roof structure and material—15 conditions of various pitch angles and overhang WWR and height—20 opening condition of WWR Building height—7 height conditions Sill heights and window heights—5 conditions each Level of openings—8 conditions Perforated screens—6 configurations Shading devices—8 conditions.

for optimizing indoor air temperature as well as lighting levels. Future studies can include noise. This study establishes a methodology for optimizing indoor comfort for humans with minimum disturbance to the requirement of avian microclimate in a bird sanctuary. This methodology can be followed in other sanctuaries to ensure safe human interventions to assist flora and fauna.

1.3 Smart Materials and Nanotechnology for Sustainability Applications of smart building materials in sustainable architecture are dealt with in Applications of Smart Building Materials in Sustainable Architecture. For advances in material research, there is a growing interest in the knowledge of smart materials and their application in improving energy efficiency and the indoor environmental quality of a building. Smart materials can sense and react to their environment, and thus, they behave like living systems. Smart materials and technology produce a useful effect in response to an external condition. They can be combined to provide changing and dynamic solutions for problems encountered while designing for energy efficiency. This paper is an introduction to the characteristics of smart materials and their application in the construction industry. Due to their small scale, smart materials enable us to design dynamic thermal environments. Smart materials are applied for façade systems, lighting systems, and energy systems. By focusing on the phenomena rather than the material artifact, the use of smart materials has the potential to dramatically increase the sustainability of buildings. We can save energy by operating discretely and locally only when necessary. This chapter forms a part of An Analytical Assessment and Retrofit Using Nanomaterials of Rural Houses in Heat Wave-Prone Region in India, which demonstrates the application of smart material in the form of nanotechnology in the retrofit of heat wave-prone rural houses to enhance indoor thermal performance. Rural houses contribute to balancing the sustainability of energy-intensive urban buildings. This is because they are low on embodied energy and operational energy. The rural houses of heat wave-prone regions need to be studied to understand the methods to safeguard the inhabitants from heat stress through retrofit measures. In An Analytical Assessment and Retrofit Using Nanomaterials of Rural Houses in Heat Wave-Prone Region in India, the embodied energy of heat wave-prone rural houses is assessed, and retrofit measures to enhance indoor thermal performance are demonstrated. The embodied energy of the houses before and after the use of retrofit is also analyzed and compared. The Indian state of Andhra Pradesh experiences intense

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Building Thermal Performance and Sustainability Issues

heat wave in the summer months. It is important to assess the indoor comfort hours of rural houses which are built with locally available materials because of economic constraints. This study aims to gage the embodied energy and heat conductance of the houses in the heat wave-prone hot and humid climate of Vijayawada, Andhra Pradesh and suggest retrofit to better the indoor thermal environment. HTC-AMV06 Thermometer is used for field measurements of indoor dry bulb temperature and humidity, and globe thermometer is used for outdoor temperature data on a summer day in April. Thermal energy models are simulated in energy plus and correlated with recorded data to validate the models. Validated models are used for computing indoor comfort hours. Embodied energy analysis shows that a house made with a reed wall and mud plaster with a reed roof has the lowest embodied energy (473.5 MJ/m2). It is only 9.47% of the conventional house which has very high embodied energy (5002.2 MJ/m2). Comfort hours for all the houses lie in a narrow range of 51.4– 47.18% irrespective of the variation in embodied energy. Aerogel, when used as an insulation material, reduces indoor temperature by 11.09 °C in cement block houses and 6.17 °C in random rubble houses. Hence, use of nanomaterials is a viable retrofit measure to safeguard the inhabitants from heat stress. Government intervention and policy are of utmost importance to ensure the same.

2 Conclusion As a professor of architecture, I have come across students whose main hurdle in presenting research is the lack of data validation. The results of the work in this book can fulfill this gap. The book is a result of the intense work of researchers working in the field of sustainable architecture. The output of the book bridges the architectural research methods and their application in the contemporary world through a thorough understanding of sustainable building materials, construction techniques, and its quantified consequences on thermal performance. Energy efficiency is an urgent necessity in the construction sector to achieve sustainable development. Energy use is associated with rising GHG levels in the atmosphere, leading to climate change. When people start to see the benefits of saving energy, they naturally find it easier to adopt this concept. Material selection is key in the early stages of design. Design process and methodology also play an important part in a sustainable built environment. These 12 chapters make up for a holistic understanding of the areas which need immediate study intervention such as assessing heat stress based on geographic locations, revisiting the heat stress index of the people based in different geographies (due to clothing levels, acclimatization, etc.), impact of the use of alternative building materials, construction technology, approach to the eco-sensitive environment, ways to optimize daylighting in commercial buildings, optimization of factors affecting the built environment. This book can stimulate further research ideas leading to conclusions that can have a major impact on the design of built forms for better thermal performance and sustainability.

Appropriate Heat Stress Index to Assess Heat Stress in Built Environment in India

Abstract Global warming is on the rise, and the occurrence of heat stroke due to exposure to high temperature and humidity has become more prevalent. The country over the years has experienced severe heat wave with implications of illness and death. Most of the studies on heat stress have been assessed in urban areas. In contrast, there is very less research on the impact and significance of heat stress on rural habitations. The United Nations and Red Cross have warned that due to heat wave, human life may become unsustainable in the coming years. This study aims to look at the impact of heat stress on human life and the appropriate index to measure heat stress. A detailed study has been made to understand the current Indian scenario. Various methods of assessing heat stress and indices such as rational, empirical, and direct indices are studied to understand the best assessing method for assessing its impact in rural homes. An extensive assessment should be made for various geographical locations to understand the climate parameters and physiological needs to combat heat stress as heat stress indicators cannot be generic. It is found that Wet Bulb Globe Temperature (WBGT) is the best index for measuring that stress.

1 Introduction 1.1 Heat Stress When the human body is not able to release the heat produced because of the metabolic activities performed naturally, heat stress occurs. This causes a rise in body’s core temperature and an increased heart rate. As a result of this, the body stores heat leading to loss of concentration, sickness, irritability, and the yearning for hydration. This often causes fainting and sometimes death if immediate action of cooling down of the body is not taken. Factors contributing to heat stress are radiant heat source, high air temperature, high humidity, strenuous physical activities, and direct physical contact with hot objects [1].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_2

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Appropriate Heat Stress Index to Assess Heat Stress in Built …

1.2 Heat Stress in Industries and Rural Houses Heat stress in humans is observed by lack of sweating, high body temperature, xeroderma, hot spasms, fatigue, and heat stroke which may lead to death along with some neurological side effects such as paralysis and unconsciousness. Exposure to high temperature combined with high humidity and physical activity can lead to heat exhaustion, which can lead to heat stroke. Thus, the vulnerability to heat stress and its related illness is high in hot and humid climates. The scenario becomes even worse in rural households and small-scale industries dependent on strenuous physical labor, activity as the people cannot afford active comfort systems and thermally comfortable dwellings. In such a case where climate, a nature bound variable and lifestyle of people in rural households and small-scale industries, cannot be altered with, informed decisions about appropriate use of building materials for the house envelope can be of benefit to the occupants in countering the harsh environment. The choice of buildings and building materials plays a crucial role in reducing indoor heat stress. Heat stress in rural housing and small-scale industries can be attributed to lack of quality housing, overcrowding, and lack of heat reducing technology, and most of the people continue working in such conditions to support livelihood [2].

1.3 Heat Wave Heat wave occur when high atmospheric pressure air enters an area and stays there for more than two days. Air from the upper environmental zones is pulled down, where it gets compacted temperature increase. A heat wave can stay for a few weeks or days due to the region’s high centralization of weight, which makes it difficult for other climate frameworks to enter the area. The more extended the framework remains in a zone, the sultrier the region becomes prone to heat wave. The high pressure makes winds faint to non-existent and prevents clouds from entering the region. The combination of all of these factors together creates the exceptional condition called a heat wave [3] The Indian Meteorological Department (IMD) defines a heat wave in India as when air temperature is more than 40 °C (104 °F) in the plains or more than 30 °C (86 °F) in the hills. When temperature is more than 46 °C (114.8 °F), IMD defines it as severe heat wave. Heat wave occur mostly in May and June or they may extend until July. Lately, the country has experienced an increase in the frequency and length of heat wave. The Indian Institute of Tropical Meteorology has identified several factors responsible for this, including delayed monsoon and ‘El Niño Modoki’, a type of El Niño [3].

1 Introduction Table 1 Mortality due to heat wave in the year 2017

13 Deaths due to heat wave during the year 2017 State West Bengal

No. of deaths 2

Jharkhand

4

Telangana

100

Odisha Maharashtra

17 16

Andhra Pradesh

236

Total

375

Source Ministry of Earth Sciences, Govt of India

1.3.1

Indian Scenario

Heat-related mortality have been on the rise in India over the past few decades. The National Disaster Management Authority had released a report according to which, the number of heat-related fatalities was roughly documented as 22,562 between 1992 and 2015 [4]. In India, 15% of fatalities between 2001 and 2012 were natural disaster deaths caused by heat stroke being the second-leading cause [5]. Health Vulnerability Index (HVI) is conceptualized as a multidimensional complex index that considers biophysical attributes as well as population-level attributes while measuring the effect caused by heat wave. Majorly, central part of the country has ‘high’ and ‘very high’ HVI. With a higher common population, these states have been at the lower end of health, education, economic, and growth indicators [3]. It has been observed that from April to June, stations from the North and Central India observe higher temperatures and are called Core Heat Wave Zone (CHZ). Core Heat Wave Zone (CHZ) is most prone to heat wave. Rajasthan, Uttar Pradesh, Delhi, Madhya Pradesh, Gujarat, Chhattisgarh, West Bengal, Bihar, Orissa, Jharkhand, Maharashtra, and Andhra Pradesh are all included in the CHZ [3]. Table 1 shows the number of deaths due to heat wave in Indian states. Pre-monsoon or summer (according to IMD categorization, March, April, and May) seasonal average air temperatures for 2022 are 1.24 °C warmer than baseline trends during 1971–2000 climate. This is lower than the anomaly by 1.45 °C recorded in the pre-monsoon season of the year 2010 but warmer than the 1.20 °C anomaly registered during the same period of time in 2016 [1]. The yearly land surface temperature and pre-monsoon air anomaly over the Indian subcontinent are depicted in Fig. 1 (2015–22).

1.3.2

Heat Stress in Andhra Pradesh

Andhra Pradesh is a southern state in India. The state is classified under warmhumid climate. In India, heat wave mortality has claimed 4620 lives in the last four

14

Appropriate Heat Stress Index to Assess Heat Stress in Built …

Fig. 1 Anomaly in annual and pre-monsoon air and land surface temperature over Indian subcontinent (2015–22); Source Somvanshi [1]

years, with Andhra Pradesh accounting for 90% of these fatalities. Andhra Pradesh is one of the affected provinces in the list of the ten heat-related deaths with the highest mortality rates worldwide from 2001 to 2010 [6]. The Government of Andhra Pradesh annually issues a heat wave action plan for the state. It tries to implement heat response activities with an integrated approach. The key components of this action plan include monitoring the climatic conditions, warning system, build public awareness, and outreach in terms of heat-related illness, health capacity building, and mapping high-risk areas. Throughout such action plans and the awareness on buildings and its materials, the impact of built spaces and nature on indoor heat stress is ignored. There is need to relook into these factors while planning mitigation strategies [7].

1.4 Heat Stress Indices In any thermal environment for people, the effects of the six basic climatic elements are combined into a single number known as the Heat Stress Index. Its value varies depending on the thermal stress a person in a hot climate experiences. These six parameters are detailed in Table 2. The index value (measured or calculated) can be used to establish safe limits in design or at work places. The definitive Heat Stress Index has been the subject of extensive research, and there is debate over which is the best. Assessment of the thermal stress and the translation of the stress in terms of physiological and

1 Introduction

15

Table 2 Six factors in determining the thermal comfort (Source Epstein and Moran [8]) Parameter

Symbol

Also

1

Dry bulb temperature To = ‘0.5(Ta + MRT) (To approx. = 2/3Ta + 1//3Tg)

(Ta)

To

2

Black globe temperature MRT = (1 + 0.22V0.5)(Tg–Ta) + Ta

(Tg)

MRT

Environmental

3

Wind velocity

(V)

4

Wet bulb temperature

(Tw)

rh; VP

5

Metabolic rate

(M)

met

6

Clothing

Behavioral

Insulation

(Clo)

Moisture permeability

(im)

Where rh relative humidity, VP vapor pressure, met a metabolic rate unit (1 met = 50 kcal/h/m2), T = operative temperature, an index of the combined effects of dry bulb and radiant temperature; first degree estimate for a sunny clear day is: To = Ta + 5 (°C)

psychological strain are complex. Heat Stress Index is related to the thermal sensation as shown in Table 3. Goldman (1988), for instance, lists 32 Heat Stress Indices, although likely there are at least twice as many used globally, while Epstein and Moran [8] list further more as shown in Table 4. All indices must take all six fundamental parameters into account when applying; however, many do not. Indices will be used differently in different contexts and circumstances, which is why there are so many of them. Sweating is considered the primary source of stress on the body, either directly or indirectly by most of the Heat Stress Indices. For instance, the strain on the body increases as more perspiration is needed to maintain internal body temperature and heat balance. A mechanism to calculate a person’s sweating capacity is necessary for a Heat Stress Index to accurately anticipate heat strain and describe the human thermal environment [9]. Tools for evaluating hot environments and forecasting likely thermal strain on the body are provided by Heat Stress Indices. Limit values based on Heat Stress Indices will show when that strain is most likely to become intolerable. Work practices for hot workplaces are widely established, and the processes of heat stress are generally understood as shown in Fig. 2. These include being aware of the symptoms of heat exhaustion, participating in acclimatization programs, and replacing lost water. However, there are still a lot of casualties, and it seems that these lessons need to be learnt again. Leithead and Lind (1964) reported a thorough assessment and came to the conclusion that of the following three factors, one or more factors could contribute to heat disorders:

16

Appropriate Heat Stress Index to Assess Heat Stress in Built …

Table 3 Comfort vote and thermal sensation in association to the physiological zone of thermal effect Vote (b)

Thermal sensation (c)

Comfort sensation (d)

Zone of thermal effect (e)

Heat Stress Index (f)

9

Very hot

Very uncomfortable

Incompensable heat

80

+3

8

Hot

Uncomfortable

+2

7

Warm

Slightly uncomfortable

+1

6

Slightly warm

0

5

Neutral

– 1

4

Slightly cool

– 2

3

Cool

Vote (a)

– 3

2

Cold

1

Very cold

40–60 Sweat evaporation

20

Compensable Comfortable

Vasomotor compensable

Slightly uncomfortable

Shivering compensable

Uncomfortable

Incompensable cold

0

Based on: Goldrnan (1988) (a) Thermal scale according to ASRAE 55 (b) Thermal scale according to Rohles (f) The ‘Heat Strain Index’ (HSI) is the ratio of demand for sweat evaporation to capacity of evaporation (Ereq/Emax)7. This denotes also the percent of skin wettedness, which is a good predictor of warm discomfort.

. The presence of conditions like dehydration or a lack of acclimatization . Lacunae of sufficient understanding of the risks of heat, among the individuals at risk or the supervising authority . Unavoidable or accidental situations that expose people to extremely high heat stress. Indicators of heat stress might be rational, empirical, or direct. Direct indices are based on the temperature of equipment created to imitate how the human body could behave, whereas empirical indices are based on formulating equations from human physiological responses (such as sweat loss). Calculations using the equation for heat balance serve as the foundation for rational indices [9]. The effects of the surrounding air on the human body vary from person to person and are a very complicated process. It depends on many factors like occupation, clothing, and metabolic rate. Hence, it is difficult to define heat stress without approximations.

1 Introduction Table 4 Proposed systems for rating heat stress (Source Epstein and Moran [8])

17 Year

Index

Prescribed by

1905

Wet bulb temperature (Tw)

Haldane

1916

Kata thermometer

Hill

1923

Effective temperature (ET)

Houghton and Yaglou

1929

Equivalent temperature (Teq)

Dufton

1932

Corrected effective temperature (CET)

Vernon and Warner

1937

Operative temperature (OpT)

Winslow

1945

Thermal acceptance ratio lonides (TAR)

1945

Index of physiological effect (Ep)

Robinson

1946

Corrected effective temperature (CET)

Bedford

1947

Predicted 4-h sweat rate (P4SR)

McArdel

1948

Resultant temperature (RT)

Missenard

1950

Craig Index (I)

Craig

1955

Heat Stress Index (HIS)

Belding and Hatch

1957

Wet bulb globe temperature (WBGT)

Yaglou and Minard

1957

Oxford Index (WD)

Lind and Hellon

1957

Discomfort Index (DI)

Thom

1958

Thermal Strain Index (TSI)

Lee and Henschel

1959

Discomfort Index (DI)

Tennenbaum

1960

Cumulative Discomfort Index (CumDI)

Tennenbaum

1960

Index of physiological strain (Is)

Hall and Polte

1962

Index of thermal stress (ITS)

Givoni

1966

Heat Strain Index (corrected) (HSI)

McKams and Brief

1966

Prediction of heart rate (HR)

Fuller and Brouha

1967

Effective radiant field (ERF)

Gagge (continued)

18 Table 4 (continued)

Appropriate Heat Stress Index to Assess Heat Stress in Built … Year

Index

Prescribed by

1970

Predicted mean vote (PMV)

Fanger

1970

Prescriptive zone

Lind

1971

New effective temperature (ET)

Gagge

1971

Wet globe temperature (WGT)

Botsford

1971

Humid operative temperature

Nishi and Gagge

1972

Predicted body core temperature

Givoni and Goldman

1972

Skin wetness

Kerslake

1973

Standard effective temperature (SET)

Gagge

1973

Predicted heart rate

Givoni and Goldman

1978

Skin wettedness

Gonzales

1979

Fighter index of thermal stress (FITS)

Nunneley and Stribley

1981

Effective Heat Strain Index (EHSI)

Kamon and Ryan

1982

Predicted sweat loss (ms/w)

Shapiro

1985

Required sweating (SWreq)

ISO 7933

1986

Predicted mean vote (modified) (PMV)

Gagge

1996

Cumulative Heat Strain Index (CHSI)

Frank

1998

Physiological Strain Index (PSI)

Moran

1999

Modified Discomfort Index (MDI)

Moran

2001

Environmental Stress Index (ESI)

Moran

2005

Wet bulb dry temperature Wallace (WBDT)

2005

Relative humidity dry temperature (RHDT)

Wallace

1 Introduction

19

Fig. 2 Health effects of heat stress on humans

Two most globally used indices are: . Heat Stress Index (HSI) . Wet Bulb Globe Temperature Index (WGBT). 1.4.1

Heat Stress Index

The ratio of the amount of evaporation needed to maintain heat balance (Ereq) to the amount of evaporation that could be attained in the environment at its maximum (Emax), expressed as a percentage, is the Heat Stress Index [10]. The HSI scale produces a percentage. It is the proportion of evaporative cooling demand to capacity needed to keep the body’s internal temperature under control. There are three levels to it. When there is a high requirement for evaporation to sustain thermoregulation, the first level is referred to as ‘very strong heat stress’, which is designated at above 80% on the HSI scale. High temperatures (36.5 °C), a high relative humidity of 42%, and very little wind speed (0.01 m/s) characterize the weather. The second level on the HSI scale is 40–80% and denotes ‘strong heat stress’. Third level on the scale is 20–40% implying ‘moderate heat stress’ and requires small amount of sweating to be balanced [11]. The HSI can also account for how much heat is being lost through evaporation in the hot-humid climatic zone. As a result, the HSI is a crucial index for these climatic circumstances.

20

Appropriate Heat Stress Index to Assess Heat Stress in Built …

Heat Stress Index (HSI) =

Required evaporative heat (Ereq) × 100 Maximum evaporative heat (Emax)

(1)

where Ereq = evaporative heat loss required to maintain the body in thermal equilibrium Emax = maximum evaporative capacity of the climate [12] HSI accounts for the amount of heat stress in hot and humid climate zones when heat is lost through evaporation [11]. Hence, it is an important index in these climates particularly from an occupant perspective as it gives the direct estimate of the evaporation needed by the body for thermoregulation.

1.4.2

Wet Bulb Globe Temperature

The Wet Bulb Globe Temperature (WBGT) is the most widely used index around the world. This was evolved by the United States Navy [8]. WGBT = 07Tw + 0.1Ta + 0.2Tg

(2)

The index is modified for indoor conditions as follows: WGBT = 07Tw + 0.3Tg

(3)

where Tw is wet bulb temperature Ta is dry bulb temperature. Tg is black globe temperature. It has been adopted as an ISO standard (ISO 7243). Although WGBT is the most popular measure, it appears to overstate the severity of heat stress in hot and humid climate. WBGT defines a temperature of 28 °C and over as ‘very high’ heat stress. In a study by [11], most of the readings according to WGBT fell into a ‘very high heat stress’ zone, while the responses taken from thermal comfort vote showed just one-fourth of the conditions as too warm. Unlike other indices, WGBT accounts for globe temperature, which can give a better picture about the radiation from wall and roof surfaces in the indoors making it a widely used index to study and understand indoor heat stress conditions.

2 Discussion HSI relates with direct physiological activity of the body but lacks to include the effect of radiation from the surrounding surfaces. It is comparable to the body’s thermoregulation principles in hot, humid settings since it is a ratio of the evaporation needed to sustain thermoregulation to that of the highest achievable evaporation rate

References

21

in the local conditions. In the hot-humid climate, sweating and evaporative processes are essential for preventing heat stress. People would experience less heat stress if they perspire enough and could let enough of their perspiration to disperse. From an occupant perception point of view, HSI is the most relevant as it reflects the occupants’ physiological conditions [11]. WGBT overestimates the heat stress conditions in hot-humid climate but accounts for the globe temperature which has a significant impact in detecting the impact of surface radiation from walls and roof in an indoor environment indoors making it a widely used index to study and understand indoor heat stress conditions and to compare between different building envelope materials. In four significant Indian cities, a comparison of heat indices using observed (IMD) and model-simulated data was conducted. Annual cycles of heat indices, namely WGBT, UTCI, Humidex, Heat Index (HI), were plotted for Chennai, Kolkata, Delhi, and Mumbai. The study found WGBT to be the best index [13].

3 Conclusion Though several studies are done in India to understand heat stress in work places, residences, and industries, the appropriate heat index has not yet been determined by a thorough investigation. India is such a diverse country, and various parts of the country experience different climates. An extensive assessment should be made for various geographical locations to understand the climate parameters and physiological needs to combat heat stress. Thermal perception analysis in terms of TSV and TCV is very crucial to get an actual picture of heat stress. Heat wave shall only increase in the future, and a country-level mapping of heat risk zones and assessment using appropriate indices is essential to plan for early warning systems and mitigation measures. Heat action plans shall have to consider the impact of built environment especially in rural areas.

References 1. Somvanshi A (2022) Understanding the heat conditions of North India in the context of national and regional heat stress. New Delhi: centre for science and environment 2. Mukhopadhyay B, Weitz AC, Das K (2021) Indoor heat conditions measured in urban slum and rural village housing in West Bengal, India. Build Environ 3. Rohilla A (2020) Estimation of heat stress of over major Indian cities. Roorkee 4. Government of India (2018) Demography | Jalna [Internet]. Jalna. Available online at https:// jalna.gov.in/about-district/demography/. Accessed May 01 2018 5. Paul S, Bhatia V (2016) Heat stroke—emerging as one of the biggest natural calamity in India. Int J Med Res Prof 2:15–20 6. Guleria S, Gupta AK (2018) Heat Wave in India docmentation of state of Telangana and Odisha. New Delhi: National Disater Management 7. Govt of Andhra Pradesh (2020) Heat wave action plan 2020

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Appropriate Heat Stress Index to Assess Heat Stress in Built …

8. Epstein Y, Moran DS (2006) Thermal comfort and heat stress indices. Ind Health, 288–298 9. Parsons KC (2011) Assessment of heat stress and heat stress indices. Retrieved from iloencyclopedia of occupational health and safety: https://www.iloencyclopaedia.org/part-vi-16255/ heat-and-cold/item/682-assessment-of-heat-stress-and-heat-stress-indicies 10. Belding H, Hatch T (1955) Index for evaluating heat stress in terms of resulting physiological strain. Heating Pip Air Conditioning, 129–36 11. Chindapol S, Blair J, Osmond P, Prasad D (2017) A suitable thermal stress index for the elderly in summer tropical climates. Procedia Eng, 932–943 12. Beshir MY, Ramsey JD (1988) Heat stress indices: a review paper. Int J lnd Ergon, 89–102 13. Dash SK, Dey S, Salunke P (2017) Comparative study of heat indices in India based on observed and model simulated data. Curr World Environ, 504–520 14. Pradyumna A, Bendapudi R, Zade D (2018) Managing the Increasing Heat Stress in rural areas. Springer, Cham

Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone Hot-Humid Climate of Andhra Pradesh, India

Abstract Heat stress results in human distress and sometimes mortality. This is significant in the rural context where the economically vulnerable have no access to active means to enhance indoor comfort. This study deals with assessing the Heat Stress Index of five house types. Four houses have reed roofs and different walling materials in rural Andhra Pradesh, while one house is made of conventional building materials. Field measurements of indoor climatic parameters of the five houses are captured using data loggers. Heat Stress Index and Wet Bulb Globe Temperature (WBGT) are assessed using the Bernard and Pourmoghani method on August 27, 2019, from 5.00 am to 9.00 pm. The indoor heat stress is compared with a conventional house from the same location. It is found that occupants of houses built with concrete roofs are more vulnerable to heat stress than the reed roof for the same walling material. The WBGT of the brick wall with a reed roof is lower than other walling materials for most parts of the day. The WBGT of the houses shows a difference of up to 2 °C. Occupants’ thermal sensations through closed format rating scale questionnaire show that the house of reed wall felt most uncomfortable for most parts of the day as expressed by 100% of the occupants. The time of discomfort sensation and the WBGT limits correlate. Since the intensity of discomfort reported is high, in-depth assessments of WBGT are required for heat wave-specific geographic locations to avert the adverse effects of heat stress for millions of rural population.

1 Introduction Globally, there has been an increase in the intensity, duration, and frequency of heat wave, with a trend toward worsening due to global warming [1]. Heat wave have become a serious challenge in India, particularly in recent years. Long-duration heat wave, all over the world, during the summer and prevailing warmth are becoming increasingly likely because of a warming planet [2]. In the last 4 years, mortality due to heat wave has claimed 4620 lives in India, with 90% of these deaths in Andhra Pradesh [3]. The Ministry of Earth Sciences, in 2016, reported that of the 1600 people who died due to unpredictable weather conditions, 557 were due to severe heat wave [3]. The 10 highest mortality heat events © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_3

23

24

Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

around the world from 2001 to 2010 feature Andhra Pradesh as one of the affected provinces. Heat wave have been classified as a disaster phenomenon related to hydrometeorological environmental conditions [3]. It has also seen poor prominence in risk mitigation of heat syndrome and its severe health consequences. Several factors pertaining to environmental factors, socio-demographic factors, state of health, and behavior factors are responsible for heat stress [4]. Of all these factors, informed decisions about the use of appropriate building materials can avoid serious health consequences of heat wave [5, 6], which include heat rash, syncope, cramps, exhaustion, stroke, and ultimately death. During conditions of heat wave, the advisory from the National Institute of Disaster Management, Government of India, is to stay indoors. The NIDM also advises building houses to ensure that the indoors do not get warm during those times [3]. It is also true that the rural houses of India are small single- or double-roomed houses, with people living in cramped conditions lacking sufficient ventilation. The lifestyle of the women entails continuous indoor work, domestic work, and small-scale product making. This puts the inhabitants at risk of heat stress with added factors such as low- and middle-income, resulting in no access to air conditioning [7]. With this background in perspective, a study of the indoor climatic parameters to assess the heat stress in the rural houses in Andhra Pradesh is carried out. Many studies have been done to understand occupational heat stress in industries and workplaces in India [5, 8, 9]. This study gains significance because, as of now, WBGT measurements and studies are usually not carried out in the context of rural India [11]. Because of this, there remains a significant knowledge gap concerning the heat stress inside rural houses occupied by low socio-economic groups in tropical climates and built structures.

2 Heat Stress and Heat Stress Index Heat stress is a concern for people residing in densely populated areas closer to the equator, as temperatures are predicted to increase in relation to changing climate [1, 7]. In the year 2015 alone, the heat wave in India caused more than 2300 deaths. (Guleria and Gupta 2016). India witnessed one of the most severe heat wave conditions during April 2016, which contributed to many heat-related deaths. There is a strong correlation between high outdoor temperature and death rates [12–14]. When temperatures are high for longer periods, indoor thermal stress is a common feature. Standard operating procedures given by the National Institute of Disaster Management, India, state that houses must be designed appropriately to reduce heat stress. However, this seldom happens in rural houses in India, which are primarily smaller, compact, and lack ventilation. It is important to understand aspects of design features and the amount of thermal stress in rural houses. The thermal stress is calculated using the Heat Stress Index. The Heat Stress Index or apparent temperature is a generic measure of how hot it feels. The basic factors of indoor heat stress depend only on temperature and humidity

4 Materials and Methodology

25

[15], considering that the indoor air movement in rural houses is negligible. To use a Heat Stress Index that is simple, reliable, unambiguous, and easy to interpret, the WBGT is the most preferred [16–20]. Lemke and Kjellstrom [21] studied various ways of arriving at WBGT to identify and develop a globally valid way of assessing heat stress using climatic data such as air temperature, wind speed, solar radiation, and dew point temperature. He did so by comparing the various ways of estimating indoor WBGT as published with specific benchmarks for recommending a method. After analyzing the works of [22– 27] on methods and equations to arrive at WBGT, the methods evolved by Bernard and Pourmoghani [22] and Liljegren et al. [25] are found to be the most appropriate to calculate the indoor and outdoor WBGT, respectively. The National Institute of Disaster Management has suggested a classification of heat index based on indoor air temperature but does not give any basis or studies leading to this suggestion. Therefore, this research follows [22] method to assess indoor WBGT.

3 Objectives of This Study i.

To assess the impact of varying walling building material on indoor WBGT when the roofing material is a reed, which is the most commonly used roofing material in rural areas ii. To assess the WBGT of a conventional house made with brick as a walling material and concrete as a roofing material iii. To evaluate and compare the WBGT for moderate activity in houses with varying roofing materials but the same walling material iv. To compare the indoor WBGT of various rural house types with the WBGT of a house built with a conventional house. These studies are carried out to understand the contribution of envelope materials and roof materials to indoor WBGT and hence provide suggestions for protecting from heat stress. In low-resource situations, such as rural India, locally available materials are used extensively due to ease of procurement, ease of maintenance, and cost [5]. This study will also throw light on the impact of using modern building materials such as burnt bricks and reinforced cement concrete roofs on indoor WBGT.

4 Materials and Methodology 4.1 Study Design and Study Population A study is conducted in five rural houses in the heat wave-prone zone of Vallabhapuram village in Andhra Pradesh. Prior approval for the study is obtained from

26

Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

the Vallabhapuram Panchayat Office and the occupants of the house. Field measurements of the five houses are collected during the hottest part of the year in the month of April 2019. In hot-humid regions such as Andhra Pradesh, the mere act of being outdoors can induce heat stress due to high temperatures and humidity. Being indoors also leads to heat stress in habitations of low socio-economic houses in rural India. The rural houses are mostly single room with a veranda attached. Hence, there is moderate activity at all times inside the house, especially by the womenfolk. This study focuses on assessing indoor WBGT for moderate activity in the rural houses of Andhra Pradesh.

4.2 Site Location The five houses are located in Vallabhapuram, a typical rural village in Andhra Pradesh, located 26.9 km away from the state capital of Vijayawada at a latitude of 16.34 °N and longitude of 80.7195 °E. The location of the village is shown in Fig. 1. The months from May to September are very hot with outdoor average maximum ranging between 44 and 37 °C in the summer of 2019.

Fig. 1 Location map of Vallabhapuram village in Andhra Pradesh State of India

4 Materials and Methodology

27

Fig. 2 Location of the five sample houses studied for heat stress

The sample houses are selected to include the following: i.

Similarity of form, size, and microclimate. The outdoor spaces are similar in size and activity use ii. Different walling material of four houses with similar roofing material. Reed is abundantly available in rural areas and is predominantly used in the study area iii. One sample house from the study area was built with conventional materials— 230 mm-thick brick wall with cement mortar and roofing of reinforced cement concrete (RCC). The envelope building materials of the five houses are representative examples of other houses in the village. All five houses are along one street, within a distance of 200 m, with similar microclimatic influences as shown in Fig. 2. The plans, sections, walling material data, and roofing material data are given in Table 1, while the walling detail, roofing detail, and view of the house are given in Table 2.

4.3 Indoor Data Collection Field measurements of indoor air temperature, wind speed, and relative humidity are recorded simultaneously in all five houses on August 27, 2019, from 5.00 am to 9.00 pm as the heat stress is calculated for the working period of the day. The digital thermo-anemometer HTC-AVM-06 with an accuracy of ±0.1 °C is used to continuously monitor indoor climate parameters. The instruments are kept in the center of the room at a height of 1.1 m from floor level as per ASHRAE 55 Protocol (ANSI/ASHRAE 55, 2010), where occupancy is maximum since the sides of the rooms are used for placing household items or storage (Fig. 3). Also, all the houses are single-celled room with kitchens kept outside the house.

Reed, Casuarina poles bamboo splits, Palmyra timber

Country wood

16.5 m2

220 mm thick reed with bamboo support

Country wood

32.85 m2

Roof

Doors

Area

270 mm thick random rubble with mud mortar and mud plaster on both sides

House 2 Random Rubble + Mud Plaster

PCC 18 mm red oxide flooring

55 mm thick reed walls with 7.5 mm thick mud plaster on both sides

House 1 Reed + Mud Plaster

Flooring 24 mm thick 600mm× 600mm Tandur stone

Wall

Plan

Items

36.8 m2

Country wood

Reed roof with bamboo support

Kadappa stone fixed with cement mortar

32.2 m2

Country wood

Reed roof with bamboo support

Clay tile fixed with PCC

32.60 m2

Country wood (continued)

RCC roof slab 150 mm thick

Tandur stone with PCC mortar

Exposed 230 mm thick brick wall

House 4 House 5 Cement Concrete Block + Brick + Cement Mortar Cement Mortar

230 mm thick brick wall with 150 mm thick cement mud mortar and cement plaster block with cement mortar on both sides painted white and no plaster on both sides

House 3 Brick + Mud Mortar + Cement Plaster

Table 1 Plans, section, building material data of the five sample houses

28 Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

Section

Items

House 1 Reed + Mud Plaster

Table 1 (continued)

House 2 Random Rubble + Mud Plaster

House 3 Brick + Mud Mortar + Cement Plaster

House 4 House 5 Cement Concrete Block + Brick + Cement Mortar Cement Mortar

4 Materials and Methodology 29

Interior view of the house

Roof detail

Wall detail

Items

House 1 Reed + Mud Plaster

House 2 Random Rubble + Mud Plaster

House 3 Brick + Mud Mortar + Cement Plaster

House 4 Cement Concrete Block + Cement Mortar

Table 2 Walling detail, roofing detail, the interior living conditions, and exterior view of the five sample houses

(continued)

House 5 Brick + Cement Mortar

30 Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

House view

Items

House 1 Reed + Mud Plaster

Table 2 (continued)

House 2 Random Rubble + Mud Plaster

House 3 Brick + Mud Mortar + Cement Plaster

House 4 Cement Concrete Block + Cement Mortar

House 5 Brick + Cement Mortar

4 Materials and Methodology 31

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Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

Fig. 3 Monitoring of indoor climate parameters

Qualitative assessments of the perception of heat stress are carried out using a closed format rating scale of 7 based on ASHRAE Standard 55–2004:5, which requires perceptions to be rated on seven-point thermal sensation scale ranging from hot, warm, slightly warm, neutral, slightly cool, cool, and cold. The questions are translated into the local language (Telugu) by the interviewer while collecting the indoor climatic data. Every house had three or four occupants, and the questionnaires are collected from each of them. Computation of WBGT From the indoor climatic data of air temperature, air velocity, and relative humidity, the WBGT is calculated using a macro developed from the formula by [22]. Approximations made for indoor WBGT include that the globe temperature is set equal to the ambient temperature and the minimum wind speed is set to 0.1 m/s and ultimately calculating indoor WBGT with the UTCI-WBGT excel heat stress calculator (http:// www.climatechip.org/excel-wbgt-calculator). The National Institute of Disaster Management gives the heat index based on air temperature, the basis for which is not known. Hence, the WBGT based on [22] is followed using the climate chip calculator. The WBGT (indoor) analysis of data is done to compare: i. WBGT of all five houses under conditions of moderate activity ii. WBGT of Houses 1, 2, 3, and 4 comprising of varying walling material but with a roof made of the same material, that is, reed iii. WBGT of Houses 4 and 5, where walling material is brick, but roofing is reed and reinforced cement concrete, respectively. The index for WBGT is based on ACGIH (2018) for moderate workload activity, assuming there would be at least moderate activity in a single-celled house, mostly by

5 Results of Assessment of Indoor WBGT

33

women, as is prevalent in this part of the world. This is because almost all the household work, such as cooking, washing, cleaning, drawing water, is done manually without the aid of machines.

5 Results of Assessment of Indoor WBGT 5.1 Comparison of WBGT in All the Five Houses A comparison of the WBGT of all five houses is shown in Fig. 4 with the WBGT limits for various work percentages. The work carried out indoors includes domestic work such as preparation for cooking, cleaning the house, folding clothes, sewing. It can be seen from the graph that the house that has reed as its walling material has WBGT limits to perform work for 25% time with 75% rest time until noon. Thereafter, up to 50% activity can be performed. The occupants and the subjects who recorded the readings, however, felt that the house is extremely uncomfortable for most parts of the day after 8.00 am. From 10.00 am to 1.00 noon, the house with brick walls and a reed roof could accommodate 75% of work and 25% of rest time. In all the other houses, it is possible to work for only 50% of the time. These work limits are concerning the body reaching heat stress levels.

Fig. 4 Comparison of indoor WBGT of all the five houses

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Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

Fig. 5 Comparison of outdoor temperature and indoor temperature in the five houses

5.2 Comparison of Outdoor and Indoor Temperatures in the Five Houses The house with ACC wall and reed roof shows better thermal performance than the other houses for most parts of the day. The house with a stone wall and brick wall with a reed roof has a lower temperature than the outside by about 3 to 4 °C. The conventional house with brick walls and RCC roof is warmer than the other houses (Fig. 5). The occupant survey shows that the house with reed as its walling and roofing material is warmer until 3.00 pm. for all four occupants. All the other four houses are comfortable from 5.00 am to 9.00 am. Beyond that, the indoors feel uncomfortable to all the occupants. There is a difference between feeling uncomfortable and reaching the limits of heat stress, yet the results indicate that even though rural people have been working within limits of heat stress, the conditions are thermally uncomfortable.

5.3 The Difference in Indoor WBGT Between the Conventional RCC Roof and Other Houses with Reed Roof The WBGT of the house with brick wall and reed roof is less than the houses that with reed, stone, and cement concrete blocks as walling materials for most parts of the day, which is from 8.00 am to 5.00 pm. The maximum WBGT difference is 2 °C (Fig. 6). After 5.00 pm, the reed house has a lower WBGT than the brick house by 1.5 °C. The WBGT in the peak afternoon in all the houses is comparatively lower than in the late evenings because of the thermal mass of the wall material. Hence, the

5 Results of Assessment of Indoor WBGT

35

Fig. 6 Difference in WBGT between brick house and other walling materials

indoor temperature and therefore heat stress tend to peak during the late afternoon as the heat continues to be released from the wall due to the time lag of the walls.

5.4 Comparison of WBGT of RCC Roof and Reed Roof The analysis of WBGT for houses that have brick as the walling material, but different roofing materials, namely the reed roof and the conventional reinforced cement concrete roof, is analyzed as shown in Fig. 7. The WBGT of the brick house with reinforced cement concrete roofing is lower than the WBGT of the brick house with the reed roof in the morning hours up to 8.00 am. After 8.00 am, the WBGT in the reinforced cement concrete-roofed house is higher than the reed-roofed house. This indicates that it is more difficult to work in a concrete-roofed house as compared to the traditional reed-roofed house when walling material is the same.

5.5 WBGT Limits for Moderate Work in All Five Houses The limits of work and intermediate rest for all the houses are analyzed in Fig. 8 for the moderate intensity of work. i.

For the reed wall and reed roof house, the WBGT is 25% work from 7.00 am to 10.30 am. In normal Indian rural households, the work activity by women happens primarily during this time period. Thus, the occupants are exposed to more heat stress during the morning hours than the other times because of high activity levels ii. The stone wall with reed roof house shows the WBGT as 25% work from 8.30 am to 9.30 am. This one hour is also a high activity period for a regular household

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Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

Fig. 7 WBGT of brick houses with varying roofing material

Fig. 8 Percentage of work to ensure within limits of prescribed WBGT

6 Discussion

37

iii. The house comprising of brick wall with a reed roof has a WBGT of 25% work from 5.30 am to 8.30 am, causing maximum heat stress to the occupants who work indoors for more than 25% of the time iv. The house made of CCB walls and a reed roof has a WBGT of 50% work for the entire period. When the roof is made of reed, a WBGT of 25% is possible for 3.5 h in the house with reed wall, 1 h in stone wall house, 3 h in brick wall house, and zero hours in the CCB wall house.

6 Discussion The current climate and heat levels cause distress in indoors in all five rural houses. This affects the women more than the other occupants, as women have to work indoors. Thus, the increased activity levels cause increased heat stress. Hence this study shows that there can be a significant impact on the health of the occupants if this issue is not addressed to reduce indoor heat stress. Studies also show that any increase in outdoor heat due to climate change [28] will affect the health of workers [29]. Occupants who are indoors to protect themselves from the outdoor heat wave do not seem to be at an advantage because the extent of physical and psychological strain due to heat stress depends upon activity levels and heat exposure [9]. This calls for an urgent need to provide solutions for the envelope design and appropriate building materials to reduce the indoor heat stress in the heat wave-prone rural area of Andhra Pradesh. The results of this study on houses in rural Andhra Pradesh correlate well with the previous studies by [8, 10] which indicates that overheated buildings cause indoor distress because of high heat stress. Simple passive technologies and advances passive technologies can provide an effective solution to provide relief from indoor heat stress [30]. Previous studies by Latha et al. [5] suggest that building material choice can be an important factor to avert heat gain. Since rural Andhra Pradesh is economically challenged, the villagers use the reed, which is available free of cost as it grows along the banks of water bodies and along rail tracks. Use of an appropriate building material finish which averts heat can be an easy and effective solution to combat heat and provide thermal comfort. Such a strategy of using appropriate heat-reflecting finish or a finish that increases the time lag can offer a sustainable solution to enable a comfortable indoors. In this study, a strong correlation between indoor heat stress and indoor thermal comfort is observed. In all five houses, the heat stress is high during morning hours. Hence, the occupants, especially women, are subjected to higher heat stress and discomfort during the morning hours than the other times because of high activity levels. When compared with the ACGIH 2018 Standards for WBGT limits, the occupants and subjects who are present in the house experience the houses to be extremely uncomfortable for the larger part of the day even under sedentary conditions.

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Assessment of Heat Stress Index of Rural Houses in Heat wave-Prone …

The National Institute of Disaster Management advises people to stay indoors during heat wave. However, it is important to make indoors comfortable for people to stay in during times of heat wave. Hence, further investigations using evidencebased research should be carried out to provide guidelines on envelope materials and design considerations to enable indoors to be thermally comfortable during times of heat wave.

7 Strengths and Limitations of the Study i.

This is the first study to explore the heat stress in rural houses of heat wave-prone Andhra Pradesh, where the socio-economic situation of people does not enable the use of active means of cooling such as coolers or air conditioners ii. The findings of the study gain significance to prevent indoor heat stress because the only way to safeguard from the heat wave is to remain indoors. Any research or policy based on the findings will help in the well-being of a large number of rural populations in the hot-humid climate of a developing country such as India iii. The field study of this research is based on five sample houses representative of the houses built in rural Andhra Pradesh. The main weakness of this study is the limited number of sample houses and the field study data about one typical summer day’s temperature. Also, the house sizes are not the same. Four houses are of similar size, while the random rubble-walled house is much smaller iv. The study is limited to only one village in rural Andhra Pradesh and therefore cannot be generalized to the entire rural community in India.

8 Conclusion i.

Among the four walling materials analyzed in this study, the brick wall in combination with the reed roof performs better than the other three walling materials, namely reed, stone, and cement concrete blocks. It shows the least discomfort compared to the other houses and enables 75% of work and 25% of rest time. Its indoor temperature is lower than the outside temperature by 3 to 4 °C ii. In all the other four houses, work for only 50% of the time is possible before reaching heat stress levels iii. The conventional house with brick walls and RCC roofs has higher indoor temperature than the other houses. The study results also show that it is more stressful and difficult to work in a concrete-roofed house as compared to the traditional reed-roofed house when walling material is brick iv. The building envelope materials of all the houses create thermally uncomfortable indoors even to carry out work within limits of heat stress.

References

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The use of appropriate building materials can enable the socio-economically weaker section of the rural population from safeguarding themselves against heat stress.

References 1. Perkins-Kirkpatrick SE, Lewis SC (2020) Increasing trends in regional heatwaves. Nat Commun 11:3357 2. Rupp DE, Li S, Massey N, Sparrow SN, Mote PW, Allen M (2015) Anthropogenic influence on the changing likelihood of an exceptionally warm summer in Texas, 2011. Geophys Res Lett 42:2392–2400 3. Guleria S, Gupta AK (2018) Heatwave in India documentation of State of Telangana and Odisha (2016). National Institute of Disaster Management, New Delhi. 4. Alessandrini J, Ribéron J, Da D (2019) Energy and Buildings Will naturally ventilated dwellings remain safe during heatwaves? Energy Build 183:408–417 5. Latha PK, Darshana Y, Venugopal V (2015) Role of building material in thermal comfort in tropical climates—a review. J Build Eng 3:104–113. https://doi.org/10.1016/j.jobe.2015. 06.003 6. Vijayalaxmi J, Sekar SP (2013) Thermal performance of naturally ventilated residential buildings with various room orientations in the hot-humid climate of Chennai, India. J Archit Plann Res 1–22 7. Lucas RA, Epstein Y, Kjellstrom T (2014) Excessive occupational heat exposure: a significant ergonomic challenge and health risk for current and future workers. Extrem Physiol Med 3:14 8. Venugopal V, Chinnadurai JS, Lucas RAI, Kjellstrom T (2015) Occupational heat stress profiles in selected workplaces in India. Int J Environ Res Public Health 13(1):1–13 9. Venugopal Vidhya PK, Latha RS, Manikandan Krishnamoorthy R, Omprashanth RL, Johnson P (2021) Epidemiological evidence from south Indian working population—the heat exposures and health linkage. J Eposure Sci Environ Epidemiol 31(1):1–10. https://doi.org/10.1038/s41 370-020-00261-w 10. Venugopal V, Latha PK, Rekha S, Manikandan K, Kjellstrom T (2019) O7E.2â…Risk factors for heat strain â“ comparing indoor and outdoor workers in the changing climate scenario. Occup Environ Med 76(1):A68.3–A69. https://doi.org/10.1136/OEM-2019-epi.184 11. Dash SK, Dey S, Salunke P, Dalal M, Saraswat V, Chowdhury S, Choudhary RK (2017) Comparative study of heat indices in india based on observed and model simulated data. Curr World Environ 12(3) 12. Armstrong BG, Chalabi Z, Fenn B, Hajat S, Kovats S, Milojevic A, Wilkinson P (2011) Association of mortality with high temperatures in a temperate climate: England and Wales. J Epidemiol Commun Health 65(4):340–345 13. Hajat S, Vardoulakis S, Heaviside C, Eggen B (2014) Climate change effects on human health: projections of temperature-related mortality for the UK during the 2020s, 2050s, and 2080s. J Epidemiol Commun Health 14. Vardoulakis S, Solazzo E, Lumbreras J (2011) Intra-urban and street scale variability of BTEX, NO2 and O3 in Birmingham, UK: Implications for exposure assessment. Atmos Environ 45(29):5069–5078 15. Sherwood SC (2018) How important is humidity in heat stress? J Geophys Res Atmos 123:11808–11810 16. Labour Institutes (1999) Directorate general of factory advice service & Labour Institutes, India 4(4):100 17. Parsons KC (2003) Human thermal environments: the effects of hot, moderate and cold temperatures on human health, comfort and performance, 2nd edn. Taylor & Francis, London, UK

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18. Epstein Y, Moran DS (2006) Thermal comfort and the heat stress indices. Ind Health 44:388– 398 19. Liang C, Zheng G, Zhu N, Tian Z, Lu S, Chen Y (2011) A new environmental heat stress index for indoor hot and humid environments based on Cox regression. Build Environ 46(12):2472– 2479 20. Alimohamadi I, Falahati M, Farshad A, Zokaie M, Sardar A (2015) Evaluation and validation of heat stress indices in Iranian oil terminals. Int J Occup Hyg 4:21–25 21. Lemke B, Kjellstrom T (2012) Calculating workplace WBGT from meteorological data : a tool for climate change assessment. Ind Health 267–278 22. Bernard TE, Pourmoghani M (1999) Prediction of workplace wet bulb global temperature. Appl Occup Environ Hyg 14:126–134 23. Hunter C, Minyard O (1999) Estimating wet bulb globe temperature using standard meteorological measurements 24. Tonouchi M, Murayama K, Ono M (2006) WBGT forecast for prevention of heat stroke in Japan. In: Sixth symposium on the urban environment. ams forum: managing our physical and natural resources: successes and challenges 25. Liljegren JC, Carhart R, Lawday P, Tschopp S, Sharp R (2008) Modeling wet bulb globe temperature using standard meteorological measurements. J Occup Environ Hyg 5:645–655 26. McPherson MJ (2008) Subsurface ventilation and environmental engineering, 2nd edn. Chapter 17. Physiological reactions to climatic conditions. Mine ventilation services Inc., 27. Gaspar AR, Quintela D (2009) Physical modeling of the globe and natural wet bulb temperatures to predict WBGT heat stress index in outdoor environments. Int J Biometeorol 28. IPCC (2018) Global Warming of 1.5 °C: An IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Intergovernmental panel on climate change; Geneva, Switzerland 29. Kjellstrom T, Holmer I, Lemke B (2009) Workplace heat stress, health and productivity–an increasing challenge for low and middle-income countries during climate change. Glob Health Action 2 https://doi.org/10.3402/gha.v2i0.2047 30. Jeyasingh V, Sekar SP (2003) OTTV controls—its formulation and relevance—an analysis. Sustain World 6:601–610

Methods of Assessing Thermal Performance of Buildings

Abstract Buildings are responsible for approximately 30% of CO2 emissions and about 40% of the world’s energy usage. This energy is mainly used in buildings for thermal comfort. Built structures are one of the major energy consumers that are eventually to blame for environmental degradation in these times of escalating environmental concerns. Dealing with the building’s energy demand will help us mitigate this issue. Heat transfer from the outside to the inside occurs within the building envelope, and the quantities are determined using some fundamental ideas. To better understand the building’s energy requirements, the thermal performance of the structure can be evaluated a number of methods. The goal of this research is to investigate the suitability of various methodologies for evaluating the thermal performance of buildings. To comprehend their applications and adaptability, the three methods of evaluating thermal performance—numerical, simulation, and physical data—have been investigated.

1 Introduction Buildings are one of the main energy end-use sectors in many developed nations, consuming more energy than both industry and transportation combined. Numerous studies have been carried out globally to increase building energy efficiency as a result of this problem [1]. Thermal comfort is influenced by a wide range of physical factors. These factors include the ambient air temperature, the radiant field around the subject, the air velocity over the subject, the ambient air humidity, the clothing worn the subject is wearing, and the subject’s activities. Without the thermal protection of the building exterior and heating or cooling, controlling body heat release at significant external temperature fluctuations usually requires adjusting heat transmission around the human alone (by changing clothing or sweating). To regulate changes in interior temperature, the dynamic storage of heat in building elements is crucial. Buildings with high thermal performance use the least amount of energy possible to keep its occupants healthy and productive while also maintaining a comfortable indoor atmosphere [2]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_4

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Methods of Assessing Thermal Performance of Buildings

Evidently, the amount of energy needed to maintain a zone in a comfortable and healthy state depends on a number of variables [3], including the temperature inside, the humidity within, the chemical composition inside, the rate of ventilation, the temperature of the surfaces, and the PPD level (predicted percentage of dissatisfied). The increase in indoor comfort enables research on a variety of controllers for HVAC (heat, ventilation, and air conditioning) systems, energy, lighting, and new technologies like smart meters, occupancy detectors, sensor technologies, and building-to-grid characterization [4]. Typically, a building uses between 50 and 70% of its total energy to maintain thermal comfort. As a result, several academics have looked into creating an appropriate control system based on a thermal model of buildings that will be able to accommodate preferences for indoor quality in the best possible way [1]. The need for assessing building thermal performance has come far along to understanding and optimizing indoor comfort for users. It calculates the temperature variation inside the building over time. Duration of uncomfortable periods can be detected to improve comfort conditions. The effectiveness of building design is checked, and thus, it helps in evolving energy-efficient buildings [5]. This paper focuses on the effectiveness and suitability of employing the simulation method of assessment for the thermal performance of buildings after a description of several building energy performance methodologies and some of the available tools.

2 Methods of Assessing the Thermal Performance The various methods of assessing a building’s indoor thermal performance (as shown in Fig. 1) can be (i) numerical method, (ii) field study-based real-time data collection method, and (iii) the dynamic method is based on building simulation. The assessment method can be any one of these or a combination of two or more of these types.

2.1 Understanding Steady-State Models 2.1.1

Characteristics of Steady-State Method

This method assumes idealized weather conditions (like a sinusoidal pattern for 24 h). It uses thermal response factors (admittance, decrement factor, and surface factor) that are based on a 24 h frequency. Numerical models are increasingly employed to help project, exercise, and management decision-making. By making the assumption that the building’s interior temperature remains constant throughout the whole year, steady-state models produce good results for calculating the maximum necessary loads. The Flemish energy performance regulation semi-steady-state model (EPW) has been found to compute the overall energy demand more accurately than software that uses dynamic

2 Methods of Assessing the Thermal Performance

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Assessment Steady-state method

i. Numerical methods (Energy Balance Equation) ii. Transfer Function Method (TFM) Degree Days Method iii. Autoregressive Moving Average model

Physical Data collection

i. Thermometers ii. Anemometers iii. Lux meters iv. Thermal camera v. Sonometers vi. Hygrometers

Dynamic Method

i. Simulation method ii. Thermal Network iii. Transfer functions iv. Computer emulations v. ARMA Parametric Models

Fig. 1 Assessment methods of building thermal performance

models. Simplified methods, which are not based on dynamic simulation but on steady-state calculations, lack the capability of high-resolution analysis and do not stimulate cutting-edge improvements in building performance [2]. Because it is possible for two walls with the same U-value to absorb and release heat at different rates, Mohammad and Shea suggested that the value of envelope thermal properties (U-value) derived from steady-state calculations is not a suitable indicator of the thermal performance of building elements by itself. In steady-state analysis, just the material’s thermal conductivity is considered; the effect of the material’s ability to store heat is ignored. In order to choose the materials for the envelope that have the best possible mix of thermal comfort and energy efficiency, it is crucial to assess the dynamic behavior of the entire structure (Mohammad and Shea). For example, the indoor heat balance as a momentary phenomenon (Fig. 2) is a result of Fig. 2 Indoor heat balance parameters

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Methods of Assessing Thermal Performance of Buildings

(1) Conduction through the building element (2) Convection to the air (3) Shortwave radiation absorption and reflectance. (The incident shortwave radiation is from the solar radiation entering the zone through windows and emittance from internal sources such as lights) (4) Longwave radiant interchange. (The longwave radiation interchange includes the absorption and emittance of low-temperature radiation sources, such as all other zone surfaces, equipment, and people). The combination of all the above for a moment or as an average of a few hours/one day can be computed as q '' LWX + q '' SW + q '' LWS + q '' ki + q '' sol + q '' conv = 0 where: q '' LWX = Net longwave radiant exchange flux between zone surfaces q '' SW = Net shortwave radiation flux to surface from lights. q '' LWS = Long wave radiation flux from equipment in the zone. q '' ki = Conduction flux through the wall. q '' sol = Transmitted solar radiation flux absorbed at the surface. q '' conv = Convective heat flux to zone air. Similarly, the outside heat balance can be given as shown in Fig. 3 q '' asol + q '' LWR + q '' conv − q '' ko = 0 where, q '' asol = Absorbed direct and diffuse solar (short wavelength) radiation heat flux. Fig. 3 Outdoor thermal balance

2 Methods of Assessing the Thermal Performance

45

q '' LWR = Net long wavelength (thermal) radiation flux exchange with the air and surroundings. q '' conv = Convective flux exchange with outside air. q '' ko = Conduction heat flux (q/A) into the wall.

2.1.2

Advantages of Steady-State Method

i. It significantly simplifies the experiment or calculations ii. This method can interpret if condition ‘A’ is better or worse than condition ‘B’ at a moment or under a set of prefixed conditions iii. For the given condition, the outcome can be very accurate as it involves mathematical equations iv. While performing studies on comparative assessments between two conditions or comparing the same building for two different conditions, the steady-state method can give a fair idea about which building performs better in comparison v. Therefore, a steady-state method is a good starting point to understand the thermal performance of buildings. 2.1.3 i.

Limitations of Steady-State Method

The momentary value is not representative of the performance throughout the month or year, because the building is not subject to a constant heat load and other environmental conditions ii. The steady-state methods only provide information on the peak environmental conditions expected in the building iii. It uses an extreme value of one weather variable (outside air temperature) iv. The building designer only has one hourly value available and no trends with which to investigate the value v. It can provide associated maximum heating and cooling loads vi. Steady-state methods only provide information for a momentary or one-hour design condition (depending upon the input parameter). It provides one-hour summer and one-hour winter design conditions in buildings vii. Because of these inadequacies, this method does not assist the building designer to achieve low environmental impact design solutions viii. Use of the steady-state methods does not provide the building designer with the information required for making informed decisions on the best design options.

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Methods of Assessing Thermal Performance of Buildings

2.2 Understanding Empirical Models 2.2.1

Characteristics of Empirical Research

• Research Questions—A set of research questions that are arrived at after assessing the research gap is an important aspect of empirical research. The research gap is arrived at through the existing review of the literature. Hence, the methodology also forms part of the research gap. Sometimes, this research gap can also be perceived as a hypothesis that is tested using the data from empirical research. • Definition of the Research Variables—The constants and variables of the study must be clearly defined. In empirical research, all the aspects of all the targets cannot be studied. Hence, it is necessary to define the variables and constants. For example, in the study of the impact of ventilation due to window size, the variables can be window size, room orientation, and window location. The constants will be geographical location, envelope material, furniture position, and outdoor vegetation. 2.2.2

Advantages of Empirical Method

i.

Empirical research is research based on actual field studies and measurements taken from real-time ground-level data ii. Empirical research is based on actual knowledge and does not depend on existing numerical and theoretical information iii. This method uses verifiable methods to collect data for research outcomes. The method of data collection is systematic and scientific, backed by sufficient justification iv. Data required for empirical research can be quantitative or qualitative. Empirical research depends upon the data collected through systematic scientific methods and not on pre-conceived perceived notions. 2.2.3 i.

ii. iii. iv. v. vi.

Disadvantages of Empirical Method

The instrument/data loggers are required to collect information pertaining to the building performance (temperature, humidity, anemometer, lux meter, sonometer, etc.) The instruments should have a high degree of accuracy Uncalibrated instruments can give erroneous results The data collection process can be laborious and time-consuming Parameters need to be recorded at regular intervals of 5–6 min (10–12 readings per hour) It is important to determine the noise-to-signal ratio in monitored sensors and to eliminate the noise.

2 Methods of Assessing the Thermal Performance

2.2.4

47

Empirical Research Data Collection Methods

• Quantitative data collection—Numerical data obtained through appropriate measuring instruments that can be quantified is collected from field studies such as – – – – – – –

Indoor air temperature—measured with a thermometer Indoor wind speeds—measured with an anemometer Indoor humidity—measured with a hygrometer Solar radiation on the envelope—measured with a thermal imagery camera Wet Bulb Globe Temperature—measured with a WBGT meter Lighting level—measured with a lux meter Acoustic levels—measured with a sonometer.

• Qualitative data collection—This data cannot be collected using contraception. The data will be non-numeric and will depend upon the questionnaire survey. For example, a survey pertaining to the perception of thermal comfort, feeling of safety, perception of joy, etc. • Experimental data collection—Data is collected under controlled conditions of specific constants and specific variables. The constants can become variables in order to understand the impact of change of one factor. The impact of one variable when all things remain constant can be done by the experimental method. So, it is a reliable method to understand cause and effect between two parameters. For example: – – – –

Size of the window and indoor air temperature Position of windows and indoor air speed Orientation of room and indoor air speed Color of external walls and indoor air temperature.

Sometimes, the experimental method can involve challenging or questioning conventional assumptions or theories to arrive at conclusions that could be different from pre-conceived notions. • Observation—This is a qualitative method that involves gathering a large number of data pertaining to user behavior in the natural environment of the variables. The results can serve as primary empirical data. The advantage of empirical data is that it can be verified and validated any number of times. Since the data is collected from field studies such as experiments, documentation, survey, and observation, the data cannot be incorrect. However, the conditions under which the raw data is collected have to be explicitly defined to avoid ambiguity. Since the methodology of data collection is scientific, the experimental process can be revisited for confirmation. The credibility and integrity of the data are very high, provided the scientific basis is clearly explained.

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Methods of Assessing Thermal Performance of Buildings

2.3 Understanding Dynamic Models—Software and Tools 2.3.1

Characteristic of Dynamic Models

The thermal performance of a building can be predicted with appropriate thermal simulation tools. Building simulation offers the potential to effectively solve issues with both the construction process and building performance. It provides dynamic analysis with the help of building modeling and simulation. Thermal loads on the building’s embodied energy and energy consumption can be assessed using dynamic simulation. The entire architectural design can be revisited to work toward reducing energy consumption based on the simulation output. The thermal performance of very complex structures can be assessed with dynamic simulation. In order to prevent disasters in terms of energy performance, most of the simulation software is designed for early design stage decisions. The boundary conditions under which the building is simulated are completely in the hands of the user. Figure 4 shows the different heads for the boundary conditions. Each of the heads has a multitude of subheads. This also provides an opportunity for the user to have comparisons of which subhead (parameter) would give the best solution. It must be understood that each of the heads, at a particular moment, is a steady-state numerical method based on the fundamental heat transfer principle (Fig. 5). Thus, the basics of the combination of all the subheads for a period of time makes simulation a dynamic and realistic method to understand the behavior of buildings. Development, evaluation, use in practice, and standardization of models and programs are becoming more and more crucial. ‘Effective building performance simulation can reduce the environmental impact of the built environment, improve indoor quality and productivity, and facilitate future innovation and technological

Fig. 4 Input data for a simulation program

2 Methods of Assessing the Thermal Performance

49

Fig. 5 Input data for energy calculations (Source [6])

progress in construction’, claims Prof. Jan Hensen, a former president of IBPSA (International Building Performance Simulation Association). According to Prof. J. A. Clarke, former president of IBPSA, ‘simulation allows users to understand project interrelationships and performance parameters, identify potential problem areas, and thus implement and test appropriate design solutions. The resulting project is more energy conscious and provides higher levels of comfort and air quality’ [7]. Numerous applications are included with building simulation. The Building Energy Simulation Tools (BESTs) directory offers a fairly clear classification of the calculation tools, covering analyses of systems, materials, and the building as a whole as shown in Fig. 6 [6].

2.3.2 i. ii. iii. iv. v.

vi.

Advantages of Dynamic Models

The information obtained using simulation has determined when and why the peak loads occur in the different zones in the building This is more realistic, as the weather file in the simulation software is an average of 30 years Even though it is not the real data, it is closer to the realistic data With simulation, the long-term changes in weather data are taken in to consideration The information provided by dynamic simulation allows the building designer to understand what is happening in the building over a period of time and check out the reasons for the peak loads Using dynamic simulation will provide the building designer with the required information to optimize energy use in the building.

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Methods of Assessing Thermal Performance of Buildings

Fig. 6 Energy simulation software (Source [6])

2.3.3

Disadvantages of Dynamic Models

i. It needs a skill to understand and learn the simulation software. Any error in an entry or assumption of boundary conditions can lead to a completely erroneous outcome ii. It is extremely time-consuming and is dependent upon the system configuration of the computer.

3 Improvements in Simulation Tools Simulation tools need to be improved in terms of user interfaces and the underlying software architecture. Some simulation tools, such as Energy Plus, have user interfaces that are separate from the underlying model, and these interfaces do not allow visualization of the building during the design process. Some simulation tools, like IES Virtual Environment and EDSL TAS, have built-in user interfaces, but their underlying architecture is essentially a black box, closed to the user. Decoupling the simulation engine from the user interface, as found in DesignBuilder and in JEPlus/JEPlus + EA which use the EnergyPlus engine, can result in enhanced functionality of the combined product. Decoupling the development of the simulation tool and simulation engine has resulted in DesignBuilder and JEPlus/JEPlus + EA achieving advanced multi-objective optimization capability much quicker than some other simulation tools. The days of single simulations are numbered, and that multiobjective optimization will become the predominant mode of simulation analysis in the future. A method of zero carbon design was demonstrated that took into

References

51

account well in excess of 900 million possibilities for a single design. It is intuitively understandable that this approach will lead to more considered designs and to more exploration of alternatives in comparison with designs based on a handful of simulation runs. Therefore, most dynamic simulation tools will have multi-objective optimization capability in the future [5].

4 Conclusion The paper briefs about the different methods of assessing thermal performance— numerical, experimental, and simulation in order to understand their appropriateness, use, and versatility. The steady-state method gives an idea of the momentary situation based on basic principles of heat transfer. In order to understand the behavior of buildings for a longer span of time, the number of calculations is very vast and can sometimes be difficult to interpret. The experimental method is a systematic investigation to capture field study data. It permits the researcher to define and control research constants and variables to achieve the most relevant research outcomes. The sampling size, data collection method, and analysis all comprise good research. It gives first-hand information about the cause and effect. Since it involves field experiments, field data collection, and primary observation, it serves as a validation for steady-state and dynamic methods. However, the data collection process can be time-consuming and expensive and must be planned meticulously. Advanced simulation methods are a prerequisite for energy-efficient and thermally comfortable design. It is possible to be able to run more accurate simulations, leading to more confident and competent designs. The more advanced models are created, and the more validation of simulation methods are done through postoccupancy evaluation of buildings. A significant advancement that would improve the effectiveness and accuracy of modeling is to take lessons from complexity science and transition from the simulation models of today to models that recreate building behavior from the ground up.

References 1. Pérez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy build 40(3):394–398 2. Bagheri A, Feldheim V, Ioakimidis CS (2018) On the evolution and application of the thermal network method for energy assessments in buildings 3. Kontes GD, Giannakis GI. Horn P (2017) Using thermostats for indoor climate control in office buildings: the effect on thermal comfort 4. Katipamula S, Underhill R, Goddard J, Taasev (2012) Small and medium-sized commercial building monitoring and controls needs: a scoping study. Pacific Northwest National Laboratory, Richland, WA, USA

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Methods of Assessing Thermal Performance of Buildings

5. Jankovic L (2017) Designing zero carbon buildings using dynamic simulation methods 6. Corrado V, Fabrizio E (2019) Steady-state and dynamic codes, critical review, advantages and disadvantages, accuracy, and reliability. In: Handbook for energy effieciency in buildings 7. Hensen J, Lamberts R (2011) Building performance simulation for design and operations 8. Mohammad S, Shea A (2013) Performance evaluation of modern building thermal envelope designs in the semi-arid continental climate of Tehran 9. Bell PA (1981) Physiological, comfort, performance, and social effects of heat stress

Steady-State Assessment of Vertical Greenery Systems on the Thermal Resistance of the Wall and Its Correlation with Thermal Insulation Vijayalaxmi J. and Kiranjee Gandham

Abstract Vegetation coupled with buildings proved to be efficient in mitigating excessive cooling, heating loads of buildings through achieving thermal comfort, microclimatic cooling, and control of insolation through the building envelope. This is possible through the shading effect, insulation, cooling by evapotranspiration, and wind barrier effects of the foliage layer. This study focuses on assessing the thermal resistance of the façade generated through the addition of vertical greenery systems in a steady state adopting a theoretical approach. A total of nine construction types are considered of varying insulation and vegetation strategies, and the influence of thermal insulation of the structure upon the resistive capacity of the façade improved by vertical greenery systems is evaluated. It is found that the effect of foliage in increasing the resistive capacity in cases with less insulated envelope is greater with a green façade showing 12.76% and living wall system showing 93.6%, and with the increase in the insulation of the construction type, the greening measures showed less impact in increasing the resistive capacity. The percentage increases system, respectively. The theoretical approach is adopted due to the complex metabolic processes in plants. This theoretical approach does not consider any other effect caused by the plant layer except the resistance generated by foliage when used in vertical greenery systems. Further, this study tries to explore conventional, vertical greenery system facades and their influence on material efficiency. The results will be useful to architects in designing energy-efficient and sustainable buildings.

Nomenclature VGS

Vertical Greenery System

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_5

53

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Vijayalaxmi J. and K. Gandham

1 Introduction ASHRAE Standard 55 suggests that thermal comfort is the state of mind that expresses satisfaction with the thermal environment present within a space [1]. It is important to attain the desired levels of comfort within the built environment, and this consumes a large quantum of energy produced globally. Several recent studies show that on a larger scale, this has led to excess consumption of fossil fuel and emission of greenhouse gases which eventually resulted in global warming. To mitigate these effects, the United Nations General Assembly, in 2015, adopted a universal agenda for sustainable development forward 2030 that proposed “Make cities and human settlements inclusive, safe, resilient and sustainable” as one of the 17 main objectives [2]. It is recommended that low impact development systems (LID) or green infrastructure (GI) can be incorporated at multiple scales ranging from building level to city level [3]. Building envelope coupled with vegetation is one such LID system that adopts the use of green roofs and vertical greenery systems (VGS) to attain thermal comfort indoors. However, there is extensive research carried out on green roofs, and the potential of vertical greenery facades/systems is yet to be fully explored [4]. The majority of studies carried out on VGS stated that they reduce overheating and improve thermal comfort. Widiastuti [5] have conducted experiments on scaled building models with different leaf coverages (Leaf Area Index), and the data obtained showed that the reduction in temperature is more prominent when the building façade is covered by greater vegetation. Also, the readings of radiant temperature around vegetative surfaces are low when compared to shading devices made from masonry and metals. The results obtained are validated through CFD simulation which confirmed the potential of vegetated facades in the reduction of surface temperature and reported a phase change of 2 h. The study by Lin [6] investigated the effect of vegetative facades on the thermal behavior of transition spaces, and the results showed that the conditions of the thermal environment improved with the increase of green façade volume. This effect is greater in living walls followed by wall surfaces with direct and indirect plants, and the greater temperatures are recorded on a bare wall. The test is carried out in a hot-humid climate on four types of VGS [7]. The Leaf Area Index is the major contributor to the impact of the thermal behavior of the walls that have integrated VGS, and the reduction in temperature is more or less independent of the orientation of the wall. However, the energy-saving potential of the vegetated walls depends on the orientation and form of the envelope [8]. Also, the thermal regulation behavior of the vegetated wall systems greatly depends on climatic conditions, vegetation type, plant intensity, and orientation with a temperature reduction of almost 6.1 °C on sunny days and 4 °C on cloudy days when compared to a bare wall, and greatest temperature reductions are observed to be dominant at the locations closer to the ground [9]. While most of the studies focus on the thermal regulation ability of vertical greenery systems, some studies are also carried out on the energy-saving potential of building envelopes integrated with vegetative surfaces. Vegetative surfaces shading walls and windows reduce the energy needed for air conditioning by 50–70% with

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

55

an 8% reduction in annual energy consumption. However, the relative humidity in the case of the envelope with VGS is found to be 42.93% greater than the envelope without VGS, which may cause conditions of discomfort [10]. Energy savings of 34% are observed in the analytical study due to reduced indoor temperatures and delayed transfer of solar heat [3]. A reduction of up to 3.1 W/m2 is observed in a fullscale experiment performed in China by Xing et al. in 2018. This study also reported lower values of daily average indoor air temperature in a room with VGS than in a room with bare walls. In 2018, a study by Vox et al. found that the thermal effects of the facade are guided by solar radiation, wind velocity, and relative humidity of the air, and the highest cooling effect is observed at a wind speed of 3–4 M/s, relative humidity range of 30–60%, and solar radiation higher than 800 W/m2 . Also, the performance of an envelope with VGS depends on WWR. A wall with 50% WWR achieved an energy reduction of 9–11%, whereas a wall with 70% WWR attains 3–6% of energy savings [11]. From the literature review, it is evident that most of the studies and experiments are limited to the calculation of indoor air temperature and wall surface temperatures to investigate the effect of VGS on the thermal environment. Also, the studies mostly restricted themselves to the evaluation of thermal performance comparison among different types of VGS, while only a few studies are carried out on the effect of insulation of the facade and its influence on the performance of VGS. The majority of the works have adopted the approach of simulating the model and validating the result by the use of field measurements using a scaled model or vice versa. Moreover, most recent studies have stated that a literature gap exists in terms of the quantitative effect on a building’s thermal performance, and the potential of VGS in the field of sustainable and energy-efficient design is yet to be explored. The simulation of leaf properties is a complex phenomenon and needs a good knowledge of computer programming. Also, the dynamic state requires instantaneous values and readings from field experiments. Therefore, this study focuses on assessing the thermal resistance of the facade generated through the addition of vertical greenery systems (VGS) in a steady state adopting a theoretical approach. Even though the steady-state method does not provide actual and accurate results, it generates reliable outcomes.

2 Methodology 2.1 Thermal Resistance There is an electrical analogy which is used in heat transfer of building components. If Q is the current (heat transfer in buildings) for a voltage difference (temperature difference in buildings) T 1 –T 2 , then resistance R can be obtained from Eq. 1

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Vijayalaxmi J. and K. Gandham

T1 − T2 Q˙ = R Thermal resistance is the ability of a building material to resist heat flow. When the thermal resistance of a material is high, it will transfer less heat through it. Also, when the material is thicker, it will have less conductivity to allow heat transfer. Therefore, thermal resistance is measured in R values and is calculated using the equation R = L/k. L relates to the thickness of the product, and k (which is a constant) relates to its thermal conductivity. The higher the R rating, the greater is its insulation capacity. The overall thermal resistance of a composite material having thickness L 1 , L 2 , and L 3 with conductivity K 1 , K 2 , and K 3 , respectively, over an area of A1 , A2 , and A3 , respectively, is R = R1 + R2 + R3 =

L1 L2 L3 + + . k1 A1 k2 A2 k3 A3

For the estimation of the thermal resistance of vertical greenery systems, this study has adopted a numerical model followed by Ottele [12] for assessing the thermal resistance of VGS in steady-state conditions. Ottele [12] has stated that the insulation capacity of the vertical green surfaces is mainly due to the trapping of stagnant air layer inside the foliage, filtering of sun’s radiation, and preventing of moving wind along the building façade which is put forth by Peck et al. (1999). Also, the study considers the envelope as a system of layers with individual resistances connected in series as shown in Fig. 1 (where 1,2 … n is the number of layers, T is the temperature in K between each layer, and R is the respective resistances of each respective layer). The same principle is applied in simplifying the façade with vertical greenery systems where a direct green façade is treated as a homogenous layer of foliage and the living wall system is conceptualized as a combination of a homogenous foliage layer, wet soil, and a cavity as in Figs. 2 and 3. For steady-state conditions, the rate of heat transfer through each surface area must be the same and the layers through which the heat transfer takes place in the green façade q1 and façade covered with living wall systems q2 as shown in Figs. 2 and 3.

Fig. 1 Thermal resistances of construction can be conceptualized as a group of resistances in series

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

57

Fig. 2 Schematization of vertical greenery system with direct greening on the façade [12]

) ( q1 = heat transfer through green façade = (Te − Ti )/ Re: plant + Rwall + Ri (1) ) ( q2 = heat transfer through LWS = (Te − Ti )/ Re: plant + Rcavity + Rwall + Ri (2) However, Ottele [12] did not determine the resistance of the foliage/plant layer according to the literature and adopted the theoretical approach of “Adapted surface

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Vijayalaxmi J. and K. Gandham

Fig. 3 Schematization of living wall system (LWS) based on planter box [12]

resistance due to stagnant air” in the estimation of the thermal resistance of the foliage layer considering that leaves of plants create an almost stagnant layer of air just adjacent to the foliage layer and with the condition that exterior surface resistance is equal to interior surface resistance in case of vegetated facades (Ri = Re-plant ). The resistances offered by the green facade and LWS in steady state can be deduced using Eqs. 1 and 2. Te Re Re: plant Rplant RLWS Rcavity Rwall Ri Ti

Exterior air temperature Resistance offered by an external surface layer Changed/adapted exterior surface resistance due to foliage/plant Thermal resistance of climbing plant Resistance of living wall system Resistance of cavity Resistance of wall/structure Resistance offered by internal surface Interior air temperature.

In order to quantify the resistance offered by incorporating the vegetative layer, the following models as shown in Fig. 4 are considered and evaluated for theoretical

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

59

resistance using above-mentioned adaptive external surface resistance due to the stagnant air layer caused by foliage. 1. Full brick thick wall + climbing plant (green façade) + living wall system based on the planter box 2. Cavity wall + climbing plant (green façade) + living wall system based on the planter box 3. Insulated cavity wall + climbing plant (green façade) + living wall system based on the planter box. From Figs. 1, 2, and 3, the above models can be schematized as a series of resistances, and the total air-to-air resistance can be found using the adapted external surface resistance due to the plant layer assuming stagnant layer of air just outside the wall surface due to the presence of foliage. The results obtained are presented in the following section.

Fig. 4 Sections of the construction types with dimensions (Source [12]—illustrations by author)

60

Vijayalaxmi J. and K. Gandham

3 Findings In the case of a full brick thick wall of 20 cm (1-a), a climbing plant is added to the bare façade to configure Case (1-b), and Case (1-c) is obtained by adding a living wall system based on a planter box. Table 1 shows the air-to-air resistance of each type of wall of category 1. It can be observed that when the resistance offered by Cases 1-b and 1-c is compared over the resistance offered by the Case 1-a, there is an increase of 12.76% in total resistance in the case of (1-b) and 93.60% increase in the overall resistance in the case of (1-c) compared to base case, i.e., bare full brick thick wall. The percentage increase in resistance of vegetated brick wall with no insulation, i.e., R1-b can be obtained by the following Eq. 3. R1-b = [(R1-b − R1-a )/R1-a ] ∗ 100

(3)

R1-c = [(R1-c − R1-a )/R1-a ] ∗ 100

(4)

The above Eq. 4 gives the percentage increase in resistance of the living wall system based on a planter box supported on a brick wall with no insulation. These equations can be adopted to calculate the percentage increase in the total resistance of each of the categories with varying vegetative measures. In the case of uninsulated cavity wall (2-a), the same configuration of vegetation is applied as in the case of models (1-b) and (1-c) on the outer brick layer. The total air-to-air resistance of the models (2-a), (2-b), and (2-c) is calculated using the schematization shown in Figs. 2 and 3. It can be observed from Table 2 that improvement in the resistance offered by the foliage cover showed an increase of 8.67% in the case of vegetated façade over uninsulated cavity wall (2-b) and 63.55% in the case of living wall system based on planter box (2-c) over the base case of a bare-faced cavity wall with no vegetation. Further, in the case of an insulated cavity wall with no vegetation (3-a), there is an addition of EPS insulation of 10 cm thickness. A similar vegetation strategy for Cases 3-b and 3-c is followed as in previous cases. From the data presented in Table 3, there is an increase of resistance of 1.77% in the case of (3-b) and nearly 13% in the case of (3-c) over the base case scenario of (3-a). From the above observations, it can be deduced that with an increase in the insulation and thickness of the wall in the case of 1, 2, and 3 categories—the percentage increase in the resistance of the structure has gradually increased from category 1 to category 2, but the increase in the percentage of resistance has spiked in the case of category 3 in comparison to category 1 irrespective of vegetation. The percentage increase in the resistive capacity of each case when compared to the case of a bare full brick thick wall (1-a) can be seen in Fig. 5. And, within the same category, the percentage increase in resistance of the model is greatest for the living wall system based on planter boxes, and in the case of the only greened facade, it is less than that of the percentage increase seen in the LWS case. Also, with an

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

61

Table 1 Total resistance of 1-a, 1-b, and 1-c all models with an uninsulated brick wall S. No.

Material layer Thickness d (m)

Thermal conductivity λ (W/m K)

Interior surface resistance

Thermal resistance (d/λ) in m2 K/W for models with a full brick thick wall (1) 1-a Bare wall

1-b Direct green

1-c LWS system

0.12

0.12

0.12

1

13 mm 0.013 cement plaster

0.50

0.03

0.03

0.03

2

Brick masonry

0.200

0.84

0.24

0.24

0.24

3

13 mm 0.013 cement plaster

0.50

0.03

0.03

0.03

4

Cavitya (only for 1-c)

0.050

NA

NA

0.18

5

LWS planter boxesa (only for 1-c)

0.200

NA

NA

0.20

6

Plant layerb Exterior surface resistancec

0.06

NA

NA

(Adapted) green façade exterior surface resistanced

NA

0.12

0.12

0.47

0.53

0.91

12.76

93.60

Total air-to-air resistance in m2 K/W

1.00

% Increase in the resistance capacity of the total wall 0.00 with the addition of VGS a

Applicable only for models with living wall systems based on planter boxes. The thermal conductivity of the planter box is taken equal to that of wet soil b Thermal resistance of the plant layer is not calculated as per the literature c Exterior and interior surface resistances for non-vegetated systems and resistance of cavity are taken from existing standards d Adapted surface resistances due to stagnant air layer

increase in the insulation of the non-vegetated base case in each category, there is a phenomenal decrease in the capacity of the foliage to increase the total resistive capacity of the structure. The resistive capacity increase due to foliage in category 1 with less insulation is more than doubled in the case of the LWS system when compared to the base case scenario (1-a). However, the same vegetation strategies have very less impact on the percentage improvement of the resistive capacity in category 3—insulated cavity wall model with a high level of insulation when compared to the other categories. The increase

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Vijayalaxmi J. and K. Gandham

Table 2 Total resistance of 2-a, 2-b, and 2-c all models with an uninsulated cavity wall S. No.

Material layer Thickness d (m)

Thermal conductivity λ (W/m K)

Interior surface resistance

Thermal resistance (d/λ) in m2 K/W for models with uninsulated cavity wall (2) 2-a Bare wall

2-b Direct green

2-c LWS system

0.12

0.12

0.12

1

13 mm 0.013 cement plaster

0.50

0.03

0.03

0.03

2

Brick masonry (inner leaf)

0.100

0.62

0.16

0.16

0.16

3

Cavity between brickwork

0.050

0.18

0.18

0.18

4

Brick masonry (outer leaf)

0.100

0.84

0.12

0.12

0.12

5

13 mm 0.013 cement plaster

0.50

0.03

0.03

0.03

6

Cavitya (only for 2-c)

0.050

NA

NA

0.18

7

LWS planter boxesa (only for 1-c)

0.200

NA

NA

0.20

8

Plant layerb Exterior surface resistancec

0.06

NA

NA

(adapted) green façade exterior surface resistanced

NA

0.12

0.12

Total air-to-air resistance in m2 K/W

0.69

0.75

1.13

% Increase in the resistance capacity of the total wall with the addition of VGS

0.00

8.67

63.55

a

1.00

Applicable only for models with living wall systems based on planter boxes. The thermal conductivity of the planter box is taken equal to that of wet soil b Thermal resistance of the plant layer is not calculated as per the literature c Exterior and interior surface resistances for non-vegetated systems and resistance of cavity are taken from existing standards d Adapted surface resistances due to stagnant air layer

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

63

Table 3 Total resistance of 3-a, 3-b, and 3-c all models with an insulated cavity wall S. No.

Material layer Thickness d (m)

Thermal conductivity λ (W/m K)

Interior surface resistance

Thermal resistance (d/λ) in m2 K/W for models of a cavity wall with insulation (3) 3-a Bare wall

3-b Direct green

3-c LWS system

0.12

0.12

0.12

1

13 mm 0.013 cement plaster

0.500

0.03

0.03

0.03

2

Brick masonry (inner leaf)

0.100

0.620

0.16

0.16

0.16

3

EPS insulation

0.100

0.037

2.70

2.70

2.70

4

Cavity between brickwork

0.050

0.18

0.18

0.18

5

Brick masonry (outer leaf)

0.100

0.840

0.12

0.12

0.12

6

13 mm 0.013 cement plaster

0.500

0.03

0.03

0.03

7

Cavitya (only for 2-c)

0.050

NA

NA

0.18

8

LWS planter boxesb (only for 1-c)

0.200

NA

NA

0.20

9

Plant layerb Exterior surface resistancec

0.06

NA

NA

(adapted) green façade exterior surface resistanced

NA

0.12

0.12

3.40

3.46

3.84

Total air-to-air resistance in m2 K/W

1.000

(continued)

in the percentage of resistance of wall LWS system (3-c) over the base Case (3-a) is only 12.96% and 1.77% in the case of greened façade (3-b) as shown in Fig. 6. The influence of the greened façade does not show a high impact on the percentage increase of resistance over the non-vegetated case, but a prominent increase can be seen in category 1 and gradually the influence of foliage diminishes with the increase

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Vijayalaxmi J. and K. Gandham

Table 3 (continued) S. No.

Material layer Thickness d (m)

Thermal conductivity λ (W/m K)

% Increase in the resistance capacity of the total wall with the addition of VGS

Thermal resistance (d/λ) in m2 K/W for models of a cavity wall with insulation (3) 3-a Bare wall

3-b Direct green

3-c LWS system

0.00

1.77

12.96

a

Applicable only for models with living wall systems based on planter boxes. The thermal conductivity of the planter box is taken equal to that of wet soil b Thermal resistance of the plant layer is not calculated as per the literature c Exterior and interior surface resistances for non-vegetated systems and resistance of cavity are taken from existing standards d Adapted surface resistances due to stagnant air layer

Fig. 5 Percentage of increase in the total thermal resistance over full brick thick wall (1-a)

in the number of layers of the wall, i.e., the insulative capacity of the wall in categories 2 and 3. The foliage influence on the resistance of the structure relates inversely to the thermal insulation of the structure. And, with the same insulation, the greening system with the soil layer has a higher influence in increasing the resistance to heat flux through the envelope than a vegetative strategy where the foliage layer merely twines along the surface of the wall. The greater ability of thermal resistance in the LWS case is due to the presence of soil substrate/growing medium which inhibits the heat flow to inner layers, thus regulating the thermal transmission.

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

65

Fig. 6 Percentage of increase in the total thermal resistance over the non-vegetated base case due to foliage

4 Discussion From the above scenarios used for estimating the theoretical heat resistance, the respective values of thermal resistance and transmittance values for each of the construction types are presented in Table 4. All of these values directly correspond to the energy savings for either cooling or heating at the building level where these are used for opaque external walls. It is to be noted that these improvements are dependent upon the insulation adopted for the envelope. The lesser the insulation in the structure, the greater the ability of greening strategies in decreasing the energy loads of the building. The construction type of uninsulated brick wall showed that the transmittance is halved by the use of the same strategy which has very less impact in reducing the thermal transmittance in insulated cavity walls with larger insulating capacity. From this, it can be stated that among the thermal insulation of the envelope and the resistance offered by foliage, the insulation is more dominant. Further, it can be seen that in category 3, there is no major change in the U-value among (3-a), (3-b), (3-c), and the type (3-c) has better thermal resistance than the (3-a) even if the external brick layer is completely removed and if the LWS based on the planter box (3-c*) is integrated to the envelope. This can be observed in Fig. 7 and Table 5. From Table 5, it can be seen that the modified construction type (3-c*) has a benefit of a 3.39% increase in the resistive capacity to insulated cavity wall (3-a) with a lesser number of bricks which will have a positive impact on many factors

Uninsulated cavity wall (2)

Cavity wall with insulation (3)

0.53

0.47

2.13

Total resistance (m2 K/W)

Total transmittance (W/m2 K) 1.89

0.40

1.10

0.91

0.55

1.44

0.69

0.25

1.33

0.75

0.45

0.88

1.13

0.60

0.29

3.40

0.35

0.29

3.46

0.55

0.26

3.84

0.70

1-a 1-b 1-c 2-a 2-b 2-c 3-a 3-b 3-c Bare wall Direct green LWS system Bare wall Direct green LWS system Bare wall Direct green LWS system

Full brick thick wall (1)

The thickness of the wall including 0.20 foliage in m

Type of the wall

Table 4 Summarized resistance and transmittance values of the a, b, and c types of 3 categories

66 Vijayalaxmi J. and K. Gandham

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

67

Fig. 7 Alternative to achieve the same thermal resistance with lesser construction material

such as cost of construction, materials and in sustainable point of view reduces the embodied energy. In the same manner, up to 16% of the increase in resistive capacity over (2-a) is seen in category 2, if a similar modification of removing the external brick layer is made in (2-c). Also, in the case of category 3, all the types of construction, namely (3-a), (3-b), and (3-c), have their U-values in the range of ECBC + criteria which mandates the transmittance of an exterior opaque wall to be less than 0.34 for climate zones other than temperate and cold in India as per BEE.

5 Conclusion Greening the envelope with green roofs and vertical greenery systems is one of the green infrastructure measures gaining importance in recent times for its benefits regarding sustainable habitat development. Particularly, vertical greenery systems correspond to a larger reduction in energy loads and keep indoor conditions at comfort levels. Foliage present in the vertical greenery systems (VGS) improves the resistive capacity of the façade to heat transmission through the building envelope. In this study, the relation of this influence on the insulative capacity of the façade is studied by adopting three construction types in brick masonry with and without vegetation. And, it is found that the influence of increasing the resistive capacity of the wall is inversely related to the insulation of the structure. In category 1, with full brick thick construction type with less insulation, the influence of VGS is nearly doubled for LWS system based on planter boxes, and it has diminished to nearly 13% increase in the case of insulated brick cavity wall, i.e., category 3. This shows that the insulation has more influence in increasing the resistance of construction type over the resistance offered by foliage. Also, it is observed that in the case of an insulated cavity wall, there is an opportunity to obtain better thermal resistance with lesser material if the LWS system is integrated into the facade serving the functional purpose of the wall.

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Vijayalaxmi J. and K. Gandham

Table 5 Thermal resistance of 3-a and 3-c*, the case with insulated cavity wall S. No. Material layer

Thickness Thermal conductivity Thermal resistance (d/λ) in m2 K/W for models of d (m) λ (W/m K) a cavity wall with insulation (3) 3-a 3-c* Bare wall LWS system

Interior surface resistance

0.12

0.12

1

13 mm cement plaster

0.013

0.500

0.03

0.03

2

Brick masonry (inner leaf)

0.100

0.620

0.16

0.16

3

EPS insulation

0.100

0.037

2.70

2.70

4

Cavity between brickwork

0.050

5

Brick masonry (outer leaf)

0.100

0.840

0.12

6

13 mm cement plaster

0.013

0.500

0.03

7

Cavity1

0.050

8

LWS planter boxesa (only for 1-c)

9

Plant layerb

(only for 2-c)

0.18

NA

0.18

NA

0.20

Exterior surface resistancec

0.06

NA

(adapted) green façade exterior surface resistanced

NA

0.12

Total air-to-air resistance in m2 K/W

3.40

3.51

% Increase in the resistance capacity of the total wall with the addition of VGS

0.00

3.39

0.200

1.000

For modified option 3-c*, the outer layer of brickwork from base Case 3-a is removed

a Applicable only for models with living wall systems based on planter boxes. The thermal conductivity of the planter box is taken equal to that of wet soil b Thermal resistance of the plant layer is not calculated as per the literature c Exterior and interior surface resistances for non-vegetated systems and resistance of cavity are taken from existing standards d Adapted surface resistances due to stagnant air layer

Steady-State Assessment of Vertical Greenery Systems on the Thermal …

69

This may lead to cost reduction, material efficiency, and a decrease in the carbon foot print of the building. The environmental and economic benefits and the limitations in the applicability of VGS-integrated facades would be taken up for further study.

References 1. ANSI/ASHRAE (2017) Standard 55: 2017, Thermal environmental conditions for human occupancy. ASHRAE, Atlanta 2. Assimakopoulos M-N, De Masi RF, de Rossi F, Papadaki D, Ruggiero S (2020) Green wall design approach towards energy performance and indoor comfort improvement: a case study in Athens. Sustainability 12(9):3772. https://doi.org/10.3390/su12093772 3. Palermo SA, Turco M (2020) IOP conference series: earth and environmental science, vol 410, p 012013 4. Radi´c M, Brkovi´c Dodig M, Auer T (2019) Green facades and living walls—a review establishing the classification of construction types and mapping the benefits. Sustainability 11(17):4579. https://doi.org/10.3390/su11174579 5. Widiastuti R, Zaini J, Caesarendra W (2020) Field measurement on the model of green facade systems and its effect on building indoor thermal comfort. Measurement. https://doi.org/10. 1016/j.measurement.2020.108212 6. Lin H, Xiao Y, Musso F, Lu Y (2019) Green façade effects on thermal environment in transitional space: field measurement studies and computational fluid dynamics simulations. Sustainability 11(20):5691. https://doi.org/10.3390/su11205691 7. Kokogiannakis G, Darkwa J, Badeka S, Li Y (2019) Experimental comparison of green facades with outdoor test cells during a hot humid season. Ener Build 185:196–209 8. Pérez G, Coma J, Sol S, Cabeza LF (2017) Green facade for energy savings in buildings: the influence of leaf area index and facade orientation on the shadow effect. Appl Ener 187:424– 437. https://doi.org/10.1016/j.apenergy.2016.11.055 9. Cuce E (2017) Thermal regulation impact of green walls: An experimental and numerical investigation. Appl Ener 194:247–254. https://doi.org/10.1016/j.apenergy.2016.09.079 10. Widiastuti R, Prianto E, Budi WS (2018) Investigation of the thermal performance of green facade in tropical climate based on the modeling experiment. Int J Archit Eng Constr 7(1):26–33 11. Bano P, Dervishi S (2021) The impact of vertical vegetation on the thermal performance of high-rise office building facades in a Mediterranean climate. Ener Build 236:110761 12. Ottele M (2011) The green building envelope—vertical greening. Ph.D. thesis, Delft, Netherlands

Thermal Performance of Bamboo Flat Roof Slab and RCC Flat Roof Filler Slab in Warm-Humid Climate Vijayalaxmi J. and Sanjay Antony

Abstract The thermal performance of roof envelopes is one of the major contributors in a building to the indoor temperature and its associated comfort level. Roof envelopes by alternative construction techniques with the involvement of renewable materials like bamboo and stabilized mud are studied and incorporated to achieve the required thermal comfort. In Kerala with its Warm and Humid climate, even though reinforced cement concrete roofs are majorly constructed for residential buildings in recent times, as per research, it does not achieve the required thermal performance with respect to adverse impacts of an increase in temperature, which further pave the way to attain higher heat gain during the summer season. This study is done with respect to the comparative analysis of the thermal performance in real time of flat RCC filler slab roofs and bamboo-mud made flat slabs in two residential buildings in Kerala. Using the required parameters, thermal performance is evaluated through field study, and comparative analysis is carried out. Due to the lower decrement factor of 0.36, a time lag of 6 h and TPI of 76.25%, lower surface temperature, higher outdoor to indoor air temperature variation with 2–3 °C (during peak hot hours), and presence of more air cavity inside bamboo poles than in the filler material of filler slabs, flat bamboo-stabilized mud roof envelope exhibits better thermal performance than flat roof filler slab. This research can add more value to the usage of alternative construction methods over conventional, ones to achieve required thermal performance as well as to minimize overconsumption of non-renewable resources.

Abbreviations RCC f ϕ CO2

Reinforced Cement Concrete Decrement Factor Time Lag (h) Carbon Dioxide

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_6

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1 Introduction Building construction execution and operations accounts for 36% use of global final energy and 40% of energy-related carbon dioxide (CO2 ) emissions in 2017 [1]. By 2020, CO2 emissions in the building sector will be reduced by an estimated 10% to 11.7 Gt, a level which has not been seen since 2007 [2]. The two major reasons for the negative environmental impact on the construction sector are steel and concrete products [3, 4], both are intensely mined that consume high energy to get produced and recycled. The usage of sustainable construction materials has received higher demand recently due to most of the conventional building materials ending up in landfills after the demolition process. Such materials should be highly renewable and should have a low impact ecologically so as to be categorized as sustainable [5]. Carbon sequestration is a method of storing captured CO2 from the atmosphere or from a source like a power plant based on fossil fuel for a very long time under the earth’s surface. It is a very natural, inexpensive, and effective process to sequester CO2 from the atmosphere through plants [6]. Bamboo is one of the major among them with the fastest growing ability. Since it is a renewable, sustainable, and eco-friendly material, it is highly recommended as an alternative building material [7, 8]. Bamboo actually consumes 1 ton of CO2 per culm from the surrounding atmosphere during its growth period [9]. In India, more than 30% of the energy in total is consumed by the building sector. With an annual growth rate of 6.7% over the past 10 years, the residential sector is the second largest electricity consumer [10]. Rapidly increased requirement for mechanical means of indoor space cooling is led majorly by the steady increase in electricity demand [11]. Better thermal performance can be effectively provided when there is an energy handover with the surrounding atmosphere as part of the response of a building thermally. Improving the building envelope’s thermal performance further minimizes the dependency on indoor cooling systems based on mechanical energy which impacts the required thermal comfort of occupants [12]. Different methods like software/numerical [13– 17], experimental, survey, and field studies [18–22] are carried out for calculating the thermal performance of different wall/roof/floor envelope systems. Studies on the thermal performance of roof envelop made of the bamboo-mud slab and RCC filler slab using field study data are very limited. Roof envelopes of buildings in warm-humid climatic zone are majorly exposed regularly to higher solar radiation and more heat to the indoor spaces are transferred through roofs compared to other building envelopes in low-storied buildings [23]. The most used roofing envelope component for residential buildings in Kerala State of India is concrete with 49% majority among the total number of census houses [12, 24]. RCC roofing envelopes exchange high heat fluxes to indoor spaces with high discomfort conditions in warm climates [11]. However, there is a lack of information regarding the thermal response of flat roof slabs made using alternative building materials like bamboo along with stabilized mud and necessary protection layers in the warm-humid climate. This study focuses to compare the real-time thermal performance of exposed flat filler slabs and flat bamboo-mud roof slabs under

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Fig. 1 Map showing the two selected residences in Thiruvananthapuram, Kerala

ambient conditions. This study would be suitable to explore more on the adaptation of renewable materials like bamboo in the modern construction industry.

2 Methods and Methodology 2.1 Study Area 2.1.1

Topography

The capital district of Kerala state in India is Thiruvananthapuram. It lies on the South-West Coast of Kerala (Fig. 1). The geographical location of Thiruvananthapuram is 8.5° N 76.9° E. The mean city elevation above sea level is 4.9 m. The Geological Survey of India has categorized Thiruvananthapuram in the Seismic Zone III. Thiruvananthapuram is rich with the backwaters like Vellayani, Thiruvallam, and Aakulam, and Karamana and Killi rivers.

2.1.2

Climate

Thiruvananthapuram has a tropical monsoon climate with warm-humid categorization. The maximum and minimum mean temperature is 34 and 21 °C, respectively. During the monsoon season, relative humidity increases to about 90%. First showers in early June along the path of the S-W monsoons are being received in Thiruvananthapuram first. It receives intense rainfall of 1827 mm per year. The city receives rainfall from the N-E monsoons which also start during the month of October. Hence, the city is classified as a warm-humid climate zone [25].

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2.2 Field Study 2.2.1

Roof Sample Selection for Field Study

Selection criteria for identification of bamboo-mud roof slab and RCC Filler roof slab for field study are identified from warm-humid climate, and they are as follows: (i) Exposed terrace floor of a Residential building which is free from outdoor shading, (ii) flat slab over bedrooms occupied by the users, (iii) no attic space or false ceiling, (iv) roof surface area of 11 m2 approximately, (v) comparable roof slab thickness of 100–150 mm, and (vii) outer wall envelope of the selected rooms are made using similar walling material. Two nearby residences in the Thiruvananthapuram district of Kerala, India, have been identified for the study (Figs. 2 and 3). Construction details of both roof samples are shown in Figs. 5 and 7.

2.3 Study Methodology The study methodology is subdivided into three phases as shown in Fig. 4. Phase one commenced with a literature study on thermal performance parameters impacting the field study. Field study with the selection of required roof samples for study, its real-time thermal performance monitoring, and recording of temperature distribution is included in phase two. Phase three is included with comparative analysis of the real-time temperature ranges and thermal performance parameters, respectively. To calculate required thermal performance parameters using Eqs. (1–3), real-time monitoring of Indoor Air Temperature (Ti), Outdoor Air Temperature (To), Outer Surface Temperature (Tso), and Inner Surface Temperature (Tsi) are needed. For Fig. 2 Residence with RCC filler slab

Fig. 3 Residence with bamboo slab

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Fig. 4 Methodology

Fig. 5 Construction detail of bamboo-mud roof

recording the abovementioned data, as shown in Fig. 6, Whirling Thermo-hygrometer is used to monitor DBT and WBT (°C), Fluke TiS40 9 Hz Thermal Imager is used to monitor indoor and outdoor Surface Temperature (°C) with an accuracy of ± 2 °C, and KUSAM-MECO 909-Anemometer is used to monitor wind speed (m/sec) with an accuracy of ± 3% full scale, for every one-hour time interval for adjacent days in identified two residences in the summer month of March 2022 to evaluate the thermal performance of both the slabs in critical weather conditions.

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Fig. 6 The Instruments used for measuring Indoor temperature, thermo-hygrometer and Fluke TiS40 9 Hz Thermal

Fig. 7 Construction detail of RCC filler slab—on site

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77

3 Study on Thermal Performance Parameters Before Field Study and Data Collection 3.1 The Time Lag and Decrement Factor Two major performance parameters on thermal aspects are associated with thermal resistance which defines the storage of heat and capacity of distribution of the building envelope; these are time lag and decrement factor [26]. The decrement factor is the ratio of heat wave amplitude between the outside and inside surfaces of a building envelope. Time lag (ϕ) indicates the difference in time between the inside surface peak temperature and the outside surface peak temperature developed within 24 h [27]. The formulae for calculating the decrement factor and time lag are as follows: f = Tsi max −Tsi min /Tso max −Tso min

(1)

ϕ = t(Tsi max)−t(Tso max)

(2)

where Tso max, Tsi max, Tso min, and Tsi min are the maximum and minimum outer and inner surface temperatures, respectively. t(Tsi max) and t(Tso max) denote the time in hours when the outer and inner surface temperatures are at their peak values, respectively [26]. Higher the time lag and reduced decrement factor minimizes the fluctuations in the inner surface temperature and thus generates high thermal resistance on components of the envelope [28].

3.2 Thermal Performance Index The Thermal Performance Index is a parameter under typical climatic conditions which focuses on the thermal property of an opaque building component. TPI is mainly used to compare and analyze different wall/roof/floor envelopes based on the inside peak surface temperature, and for easy comparative study, it is denoted in percentage. The formula for calculating TPI is given by Eq. (3). TPI = (Tsi max −30) × 100/8

(3)

where Tsi max is the maximum inner surface temperature [29]. A TPI rating of 100 denotes an 8 °C increase in inner surface temperature over the reference temperature of 30 °C in unconditioned buildings. A maximum TPI denotes maximum inner surface temperature; hence, more radiant heat will be transferred from the building’s outer envelope to indoor spaces which further leads to high discomfort for the users [30].

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4 Distribution of Air and Surface Temperature During Field Study For the study of the performance in thermal aspects of the two selected roof slab typologies, hourly temperature data of adjacent days in the month of March with similar outdoor temperatures is recorded and represented as line graphs in Figs. 8 and 9. In both roof typologies shown in Figs. 8 and 9, it is clear that Tsi and Tso fluctuate more compared to To and Ti. Distribution of Tsi and Tso during the hot period depicts that the top of the roof is getting heated up faster and reaches its peak value higher in the RCC filler slab compared to the bamboo-mud slab, but the surface of the ceiling heats up at a very less pace and its higher value is obtained later. The top of the roof

Fig. 8 Hourly temperature variations in RCC filler slab

Fig. 9 Hourly temperature variations in bamboo-mud slab

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Fig. 10 Tsi and Tso in filler slab at 1 am

surface cools down at a faster pace than the inner surface and thus reaches a similar temperature pattern after a hot period. Figures 10, 11, 12 and 13 clearly shows the higher fluctuation in Tso over the RCC filler slab from lower surface temperature during the early morning period to higher surface temperature during the noon and afternoon period. Meanwhile, the minimal fluctuation in Tsi of the filler slab throughout the day signifies the presence of two inverted terracotta Mangalore tiles and the cavity present inside it. Figures 14, 15, 16 and 17 shows only a moderate fluctuation in Tso and Tsi on the bamboo-mud slab. Fig. 11 Tsi and Tso in filler slab at 6 am

Fig. 12 Tsi and Tso in filler slab at 12 pm

Fig. 13 Tsi and Tso in filler slab at 6 pm

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Fig. 14 Tsi and Tso in bamboo-mud slab at 1 am

Fig. 15 Tsi and Tso in bamboo-mud slab at 6 am

Fig. 16 Tsi and Tso in bamboo-mud slab at 12 pm

Fig. 17 Tsi and Tso in bamboo-mud slab at 6 pm

Figure 18 shows the air temperature difference variation from To to Ti. Both roof typologies perform well during the daytime with decreased Ti than To and night-time with increased Ti than To.

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Fig. 18 Outdoor to Indoor air temperature difference in filler slab and bamboo-mud slab

5 Comparative Analysis 5.1 Temperature Range, Minimum, and Maximum From Fig. 18, bamboo slab performs better in terms of outdoor to indoor air temperature variations both during the day and night hours. Majorly during the peak hot hour at 1 and 2 pm, air temperature variation is 2–3 °C in the room with bamboo-mud slab while it is 0.5–1.5 °C in the room with RCC Filler slab. From Figs. 19 and 21, even at a higher outdoor maximum temperature range of 33–35 °C, the outside maximum surface temperature is 45.1 °C in the bamboo-mud slab which is lower compared to 55.2 °C in the RCC filler slab. The effect of the same is reflected in the maximum indoor surface temperature also. From Figs. 20 and 22, in the bamboo-mud slab, even in a higher To fluctuation of 11.5 °C, fluctuation in Tso and Tsi is lower (20.4 and 7.3 °C) compared to filler slab with lower To fluctuation of 8 °C, fluctuation in Tso and Tsi is higher (24.9 and 10.7 °C). Fig. 19 Max and min temperature—filler slab

82 Fig. 20 Temperature difference of filler slab

Fig. 21 Max and Min Temp.—bamboo-mud slab

Fig. 22 Temp. difference in bamboo-mud slab

Vijayalaxmi J. and S. Antony

Thermal Performance of Bamboo Flat Roof Slab and RCC Flat Roof … Table 1 Decrement factor and time lag

Roof typology

Decrement factor

83 Time lag (hours)

RCC filler slab

0.43

2

Bamboo-mud slab

0.36

6

5.2 Comparative Study with Respect to Parameters on Thermal Performance 5.2.1

Time Lag and Decrement Factor

Below mentioned Table 1 shows the calculated value of the decrement factor and time lag using Eqs. (1) and (2), respectively. RCC Filler Slab has a decrement factor of 0.43 and a time lag of 2 h. Bamboo-mud slab has a decrement factor of 0.36 and a time lag of 6 h which shows higher efficiency in minimizing the high Tsi fluctuations than that of the RCC filler slab.

5.2.2

Comparative Analysis of Thermal Performance Index

Figure 23 depicts the TPI of two slab typologies evaluated with Eq. (3). BIS standardizes a TPI of 125% as the maximum for roof variants in the warm-humid climate zone. Calculated TPI depicts that the flat RCC filler slab has a higher TPI rating of 158.75, and the bamboo-mud slab has a very lower TPI rating of 76.25. Higher surface temperature and increased heat transfer to indoor spaces are indicated by higher TPI values. Through the comparative analysis in temperature distribution and in thermal performance parameters over two selected roofing typologies, it is identified that the bamboo-mud slab performs better than the RCC filler slab in terms of its decrement factor of 0.36, a time lag of 6 h, and TPI of 76.25% and higher outdoor to indoor air temperature variation with 2–3 °C (during peak hot hours). The presence of maximum cavity cross-sectional area in bamboo than the flipped Mangalore tile Fig. 23 TPI (%) of bamboo-mud slab and RCC filler slab

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combination in RCC filler slab is one of the major contributors in minimizing the heat transfer through the slab, which leads to lower ceiling temperature and further reduced indoor temperature during higher outdoor temperature fluctuations.

6 Discussion A study on real-time thermal performance in naturally ventilated residences during the summer season of exposed RCC bare slab in a warm-humid climate of the same location (Kerala, India) shows the least thermal performance in terms of decrement factor (0.89), time lag (1 h 10 min), and Thermal Performance Index (229%) when it was compared to RCC slabs covered with different roof coverings like Mangalore tiles, concrete tiles, and ceramic tiles [12]. This further strengthens the result arrived in the present study.

7 Conclusion Based on the study, the real-time thermal performance of two roof typologies, both with the wall of country burnt 23 cm brick constructed using rat-trap bond, is as follows: . RCC filler slab thermal performance: The indoor air temperature is about 0.5– 1.5 °C lower during peak hot hours compared to the outdoor temperature. There is a decrement factor of 0.43 and a time lag of 2 h with a TPI of 158.75% . Bamboo-mud slab thermal performance: The indoor air temperature is about 2– 3 °C lower during peak hot hours compared to the outdoor temperature. There is a decrement factor of 0.36 and a time lag of 6 h with a TPI of 76.25% Hence, the bamboo-mud flat roof slab performs better than the RCC filler slab in terms of thermal performance for the same walling material. Studies on the comparison of two different alternate building materials and construction techniques are limited. Most of the studies focus on comparing the alternate building materials and construction technique to the conventional technique. This study gains significance, as it elaborates on the thermal performance, time lag, and decrement factor of building built with alternate and appropriate building materials and technology. This establishes a new benchmark for comparison and achieving thermal performance. Acknowledgements The authors extend sincere gratitude to the Centre of Science and Technology for Rural Development (COSTFORD), Laurie Baker Centre for Habitat Studies (LBC), Thiruvananthapuram, Kerala, and Ar. Ashams Ravi for their kind cooperation in the conduct of the study. The authors also extend sincere gratitude to Dr. Shailaja Nair, Prof. Aysha S., and Ar. Veena, R.S., Department of Architecture, College of Engineering Trivandrum, Kerala, for providing support with required instruments during the field study for data collection.

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References 1. International Energy Agency (2018) 2018 Global status report: towards a zero-emission, efficient and resilient buildings and construction sector. United Nations Environment Programme, Global Alliance for Buildings and Construction 2. United Nations Environment Programme (2021) 2021 global status report for buildings and construction: towards a zero-emission. Efficient and Resilient Buildings and Construction Sector, Nairobi 3. Lehne J, Preston F (2018) Making concrete change: innovation in low-carbon cement and concrete, Chatham House Report, Energy Environment Resources, London, UK, pp 1–66 4. Habert G, Miller SA, John VM, Provis JL, Favier A, Horvath A, Scrivener KL (2020) Environmental impacts and decarbonization strategies in the cement and concrete industries. Nat Rev Earth Environ 1:559–573 5. Det Udomsap A, Hallinger P (2020) A bibliometric review of research on sustainable construction, 1994–2018. J Clean Prod 254:120073 6. Mathew I et al (2017) What crop type for atmospheric carbon sequestration: results from a global data analysis. Agr Ecosyst Environ 243:34–46 7. Vijayalaxmi J, Singha HR (2021) Use of Bamboo as a construction material in the North-East and Southern vernacular settlements of India. ISVS e-J 8(4):86–100 8. Goh Y, Yap SP, Tong TY (2020) Bamboo: the emerging renewable material for sustainable construction. Encycl Renew Sustain Mater 2:365–376. https://doi.org/10.1016/B978-0-12-803 581-8.10748-9 9. Mali PR, Datta D (2019) Experimental evaluation of bamboo reinforced concrete beams. J Build Eng 28:101071 10. Central Statistics Office (2019) Energy statistics 2019, twenty sixth issue, pp 1–123 11. Harkouss F, Fardoun F, Biwole PH (2018) Passive design optimization of low energy buildings in different climates. Energy 165:591–613 12. Joshima VM, Naseer MA, Lakshmi E, Prabha J (2021) Assessing the real-time thermal performance of reinforced cement concrete roof during summer—a study in the warm humid climate of Kerala. J Build Eng 41(8):102735 13. Jannat N, Hussien A, Abdullah B, Cotgrave A (2020) A comparative simulation study of the thermal performances of the building envelope wall materials in the tropics. Sustain Times 12(12) 14. Mnasri F, Bahria S, Slimani MEA, Lahoucine O, El Ganaoui M (2020) Building incorporated bio-based materials: experimental and numerical study. J Build Eng 28(December 2018):101088 15. Far C, Far H (2019) Improving energy efficiency of existing residential buildings using effective thermal retrofit of building envelope. Indoor Built Environ 28(6):744–760 16. Lee J, Kim S, Kim J, Song D, Jeong H (2018) Thermal performance evaluation of low income buildings based on indoor temperature performance. Appl Ener 221(2018):425–436 17. Juanico LE (2020) Thermal insulation of roofs by using multiple air gaps separated by insulating layers of low infrared emissivity. Construct Build Mater 230:1–10 18. Vijayalaxmi J, Sekar SP (2013) Thermal performance of naturally ventilated residential buildings with various room orientations in the hot-humid climate of Chennai, India. J Architect Plan Res 1–22 19. Dilli AS, Naseer MA, Varghese Teney J (2010).Climate responsive design for comfortable living in warm-humid climate: the need for a comprehensive investigation of Kerala vernacular architecture and its present status. Des Principles Pract 4(2):29–38 20. Al Waheed Hawila A, Abdelatif Merabtine J (2021) A statistical-based optimization method to integrate thermal comfort in the design of low energy consumption building. J Build Eng 33:101661 21. Talukdar MSJ, Talukdar TH, Singh MK, Baten MA, Md. Shahadat Hossen J (2020) Status of thermal comfort in naturally ventilated university classrooms of Bangladesh in hot and humid summer season. J Build Eng 32(7):101700

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22. Atmaca AB, Zorer G, Gedik J (2020) Determination of thermal comfort of religious buildings by measurement and survey methods: examples of mosques in a temperate-humid climate. Ener Build 195(1–4) 23. Kabre C (2010) A new thermal performance index for dwelling roofs in the warm humid tropics. Build Environ 45(3):727–738 24. Office of the Registrar General & Census Commissioner, India. Population Census (2011) Houselisting and Housing Census Data, Table H-03 B: Census houses by predominant material of the roof (excluding locked/vacant houses), Kerala—2011. Ministry of Home Affairs, Government of India. https://censusindia.gov.in/nada/index.php/catalog/10029 25. NBC (2016) National building code 2016, clause 6.2, Part 8 building services, Section 1, Lighting and Natural Ventilation, page 9 of volume 2 26. Asan H (2006) Numerical computation of time lags and decrement factors for different building materials. Build Environ 41:615–620 27. Manu S, Shukla Y, Rawal R, Thomas LE, de Dear R (2016) Field studies of thermal comfort across multiple climate zones for the subcontinent: India model for adaptive comfort (IMAC). Build Environ 98:55–70 28. Muscio A, Akbari H (2017) An index for the overall performance of opaque building elements subjected to solar radiation. Ener Build 157:184–194 29. Kisan M, Sangathan S (1987) Handbook on functional requirements of buildings (other than industrial buildings). In: CED 12: functional requirements in buildings, SP 41 30. Kiran Kumar DEVS, Puranik S (2017) Thermal performance evaluation of a mineral based cement tile as roofing material. Indoor Built Environ 26(3):409–421

Empirical and Dynamic Simulation-Based Assessment of Indoor Thermal Performance in Naturally Ventilated Buildings

Abstract This study attempts to investigate a parametric model to assess the thermal performance of naturally ventilated residential buildings for various parameters. A predictive model using DesignBuilder and Rhino is generated for 14 opening sizes in rooms along eight orientations in the hot-humid climate of Chennai city. The results of indoor temperature simulated from the model and collected from field measurements are compared and found to correlate well. The model is validated with two commonly used influencing factors, namely ceiling fan and flyscreen, and is found to correlate well with field measurements. The South-West room showed better thermal performance. For the same opening size in different room orientations, there is a temperature variation of 4 °C. The indoor average temperature is higher in rooms oriented along the cardinal directions than in the semi-cardinal directions. There is not much variation in indoor temperature for openings below 35%. Precautions must be taken to ensure that the outdoor temperature at site correlates with the EPW data. The model can be used to test the implications of the affecting factors to arrive at an optimized design at an early design stage resulting in enhanced thermal comfort. The results are useful in assessing the implications of changing one or more parameters on the indoor thermal performance in rooms with varying orientation and opening size. Since the model is based on the most prevailing and preferred building configurations, the study will assist architects and designers in optimizing for the most effective design of naturally ventilated buildings.

1 Introduction The energy sector, globally, contributes to two-thirds of greenhouse gases and CO2 emissions [1]. In India, the building sector consumes over 30% of the Country’s annual consumption [2]. The residential buildings account for 75% of this consumption and have increased more than four times since 1997 [3]. In Chennai city, which has a hot-humid climate, the total electricity consumption by the residential sector accounts for 53.36% during 2017. This calls for a need to reduce energy use in buildings. The thermal performance of a building is a consequence of the design, orientation, and building materials. A number of parameters influence the thermal © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_7

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performance of ventilated buildings. In order to understand the impact of multiple parameters, a predictive model needs to be developed with which the required parameter can be changed and the thermal performance assessed. This single model can be used to assess a number of design parameters, which otherwise may be impossible to study, thus enabling design for optimized thermal performance.

1.1 Background of the Study In a hot-humid climate, physiological comfort in unconditioned buildings can be achieved by providing comfort ventilation [4]. Several studies have pointed to the use of innovative designs in natural ventilation systems in order to reduce air-conditioning [5]. A study by Kubot et al. [6] presented that occupants prefer to apply daytime ventilation to enhance the indoor thermal environment. The character of buildings in hot-humid climate is dominated by air flows in buildings to enhance thermal performance [7]. Therefore, adequate ventilation is a primary concern in building’s design [8]. In a building with good cross-ventilation, the indoor temperature follows the ambient temperature [9]. The thermal performance of a facade contributes to determining the heat gains in buildings that determine the indoor environment. Hence, design data and material data are the two important components in naturally ventilated building in hot-humid climates that should lay emphasis on design data and material data. Every factor of the contributory component has an influence on the indoor thermal environment. There is a need to create a predictive model that would permit changes in parameters based on study requirements as it offers valuable information for decision-making at an early design stage [10]. Therefore, this paper seeks to: (i)

Assess the indoor thermal performance of rooms for 14 opening sizes with a window-to-floor ratio (WFR) of 100, 85, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 10, and 5% in eight room orientations of North-East (NE), South-East (SE), South-West (SW), North-West (NW), Northeast-Northwest (NE-NW), Northeast-Southeast (NE-SE), Southeast-Southwest (SE-SW), and NorthwestSouthwest (NW-SW) with a real-scale experimental building from which field measurements will be taken (ii) Simulate a model similar to the experimental building using DesignBuilder and Rhino and assess the indoor thermal performance for 14 opening sizes in eight room orientation (iii) Compare and analyze the results of the indoor thermal performance from the real-scale experimental building and the simulated model to establish a predictive model (iv) Validate the predictive model by comparing the impact of two other commonly used factors, namely ceiling fan and flyscreen.

2 Methodology

89

2 Methodology The factors involved in natural ventilation are complex. If all the factors are studied simultaneously, the results inferred cannot be deduced to a single criterion. Due to the dynamism involved, the factors cannot be evaluated easily with good accuracy [11]. In order to remove the uncertainty, a new relational has been adapted.

2.1 Components of Study A number of components are involved in determining the indoor thermal environment (Fig. 1). Out of these, material and design factors can be manipulated by an architect to achieve indoor thermal comfort. Comfort ventilation provides physiological relief in hot-humid climate. Provision of higher indoor air velocity could also risk the increased solar radiation. For large fenestration size, the ingress of heat gain may be detrimental to indoor thermal comfort [12]. In this paper, the thermal performance is assessed for rooms with changing orientation and opening sizes. Both these are ‘variables’ while other factors pertaining to building material and design are ‘constants’ (Fig. 2). The constant components such as climate, building materials, color, and shading of opening are arrived through a primary survey of 240 houses in the study area and define the scope and limitation of this paper. A relational model, which is a full-scale house, is used for field measurement. This would also cope with the incommensurability by studying one factor at a time when all other factors remain the same.

Fig. 1 Basis of the predictive model and various factors that can be studied with the predictive model

90

Empirical and Dynamic Simulation-Based Assessment of Indoor …

Fig. 2 Research design showing methodology, scope, and limitations

2.2 Climate Characteristics of the Region The relational real-scale experimental house is located in Chennai city, India. It is classified as a hot and humid climate according to the Bureau of Indian Standards, SP:41 [13]. The climatic data of the study area, Kelambakkam in Chennai, where the experimental house is built, is shown in Fig. 3. Solutions must be established to optimize thermal performance during the hottest part of the year (March to July, in this case). Hence, this study is conducted from March to July.

2.3 Context of Relational Real-Scale Building A real-scale house is designed on the basis of the locational context after a study of the various factors involved.

2.3.1

Building Typology

Since residences account for 84.0% (Fig. 4) of the total buildings in the state of Tamil Nadu [14], a large number of buildings will benefit from understanding ways to enhance the indoor thermal performance of residences.

2 Methodology

91

Fig. 3 Annual temperature and relative humidity data for the study area. Source All India meteorological data book

Fig. 4 Percentage of buildings under each typology in the state

The experimental house is built based on the regulatory norms prescribed by the building regulatory body, the Chennai Metropolitan Development Authority.

2.3.2

Building Materials

The most prevalent walling and roofing material for residences in Tamil Nadu is burnt brick with cement plaster on both sides and reinforced cement concrete, respectively [14]. 63.5% of residences use burnt brick with cement plaster on both sides, while 52.9% of residential buildings use reinforced cement concrete as the roofing material. Hence, the relational experimental building is constructed with a burnt brick wall with cement mortar and reinforced cement concrete roof.

92

Empirical and Dynamic Simulation-Based Assessment of Indoor …

Fig. 5 Dimensions of prevalent and preferred rooms in housing settlement. Source Primary survey conducted by the author

2.3.3

Room Sizes

The results of a primary survey of 240 residences in Velachery area in Chennai city suggest that 3.66 m × 3.66 m (12' × 12' ) is the most prevalent and most preferred size for habitable rooms (Fig. 5). All the houses had a ceiling height of 3.04 m (10' –0'' ) or 3.20 m (10' –6'' ). The room sizes in the relational model are 3.66 m × 3.66 m, based on the above study. 2.3.4

Opening Size and Position

Studies have shown that air speeds inside rooms are consistently high if the inlet and outlet sizes are the same [15–17]. Hence, this research provides the inlet and outlet opening sizes to be the same. A study by Gao and Lee [18] found that windows placed in opposite or perpendicular directions facilitate better natural ventilation performance. In a study done with computational simulation using EnergyPlus, Moret et al. [19] established that windows kept open for 24 h continuously provided the best results for all indicators. In this study, the inlet and outlet openings of the same size were placed perpendicular to each other and monitored for 24 h continuously. Chennai city has hot and humid climate according to the BIS [13]. March to July are the hottest months of the year. Hence, this study is conducted from March to July.

3 Design of the Relational Experimental Set-Up The relational model is a house unit representative of residential typology and construction practices in Chennai city. The floor plan is given in Fig. 6.

5 Generation of Predictive Model

93

Fig. 6 Plan of the real-scale experimental building and data capture method

4 Data Collection Method Indoor air temperature data for 24 h is captured with AZ8904 thermo-anemometer during the hottest part of the year. The instrument has an accuracy of 0.1 °C. The data is recorded with RS232 software with real-time clock memory. The measurement is taken at a height of 1.1 m above the floor level in line with ASHRAE standard protocol 55 [20] in all eight rooms, as shown in Fig. 6. The study buildings have a provision of sequentially closing the walls to achieve the desired floor-to-wall ratio. Subsequently, walls along the vertical opening edges are built using 230 mm burnt brick to arrive at the desired WFR.

5 Generation of Predictive Model Several predictive models, which have been successfully put to future research, have been developed in the past. Marincioni et al. [21] developed a prediction model to assess moisture risk for internal insulation using Monte Carlo analysis and suggested that the models predicted quite accurately. In an experiment by Liu et al. [22], the ventilation in subterranean structures is predicted and compared with the simulation results. Since the outcomes are found to be correlating satisfactorily, the simulation model is developed to be a predictive model. Thermal comfort studies by Hawila et al. [23], on the optimization of use of glass on the building elevation, are carried with numerical simulation, the DoE, and the optimization method. The results are verified with field results for SHGC and U-Value of glass. But more parameters need to be included in the model. In a thermal comfort study of cockpit by Schminder and Gardhagen [24], the results of the simulation model and real scenario revealed an agreement of ± 0.7 and ± 0.9 °C for globe temperature and WBGT, respectively. A simple and precise model based on field studies is the basis for a successful simulation [25]. For the purpose of this study, a simulation model is generated using DesignBuilder and Rhino.

94

Empirical and Dynamic Simulation-Based Assessment of Indoor …

Fig. 7 View of building model in DesignBuilder and Rhino with 100% WFR

5.1 Building Modeling and Simulation Settings for DesignBuilder and Rhino DesignBuilder uses the simulation engine ‘EnergyPlus’ to assess thermal simulation of naturally ventilated buildings. The simulation analysis performed in this research for a residential prototype building in Chennai uses weather data obtained from the EnergyPlus Weather file (EPW). Then, the building is modeled according to the existing dimensions using the available tools (Fig. 7). The building has 4 zones and is simulated for two orientations: 0° and 45° and for the 14 opening conditions. Sunshades are of 0.1 m thickness at 2.1 m height. In the model data input, the option for HVAC is off, and natural ventilation is selected for calculation. In the activity tab, the template is changed to the ASHRAE 62.1 Residential. No change has been made to occupancy. In the construction tab, the data pertaining to envelop materials is specified along with their thermal values from the Energy Conservation Building Code Residential manual [26]. For the second model, Rhinoceros is used for simulation with Grasshopper as a parametric design add program. Honeybee, which is used to calculate indoor air temperature, is an energy simulation plug-in for Grasshopper with EnergyPlus as its engine. The model shown in Fig. 7 is for 40% WFR.

5.2 Variables and Constrains for the Models Using DesignBuilder and Rhino The variables considered are openings with WFR of 100, 85, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 10, and 5% with cross-ventilation. All the design and material specifications that are assigned are based on a real-scale building. The analysis correlates with the actual date of data capture for each WFR opening percentage. The properties of the building materials are given in Table 1.

6 Results

95

Table 1 Boundary conditions of building material in the simulating model Element Material Wall

Roof

Thickness Conductivity Density Specific heat R-value U-value

External air film

0.04

Plaster

0.01

0.721

1762

840

0.014

Brick

0.23

0.98

1920

800

0.235

Plaster

0.01

0.721

1762

840

0.014

Internal Air film

0.13

External air film

0.04

Plaster

0.01

0.721

1762

840

0.014

RCC

0.15

1.58

2288

880

0.095

Plaster

0.01

0.721

1762

840

0.014

Internal air film

2.3

3.0

0.17

6 Results 6.1 Outdoor Data Input Since the indoor temperature in naturally ventilated rooms is influenced by the outdoor temperature, the error percentage, and standard deviation of the error percentage and coefficient of determination is carried out using linear regression analysis of site temperature and the EPW file for all the days of data collected as shown in Table 2. On 3rd April, 19th April, and 6th May, when indoor temperature data is collected for 70, 60, and 40% openings, respectively, the error percentage and standard deviation of the error percentage are the least. The errors are 0.13, − 0.18, and − 0.09%, respectively, while the standard deviations are 2.1, 2.08, and 2.49, respectively. The coefficient of determination is 0.92, 0.89, and 0.92. On 3rd May of and 8th July, when indoor temperature data is collected for 45 and 15% openings, the error is − 7.46 and 18.71%, respectively; the standard deviations are 5.5 and 3.5, respectively, while the coefficients of determination are 0.88 and 0.69, respectively. This shows the least correlation as compared to other opening conditions. For all other opening conditions, the outdoor site temperature correlates well with the EPW file data. Hence, other than for 45 and 15% openings, further validation can be carried out for any other openings. The actual site data must correlate well with the EPW file.

T i = 0.64 * T s + 9.68 T i = 0.77 * T s + 5.25 T i = 0.60 * T s + 10.76 T i = 0.64 * T s + 9.69 T i = 0.58 * T s + 11.30 T i = 0.63 * T s + 9.49 T i = 0.64 * T s + 8.80 T i = 0.70 * T s + 7.57

T i = 0.63 * T r + 10.48

T i = 0.71 * T s + 7.54

T i = 0.59 * T s + 11.69

T i = 0.61 * T s + 11.21

T i = 0.54 * T s + 13.05

T i = 0.59 * T s + 11.20

T i = 0.62 * T s + 9.92

T i = 0.68 * T s + 8.86 0.7969

NE

NW

SE

SW

NW-SW

SE-SW

NE-NW

NE-SE

100%—23rd March

Predictive equation Predictive with DB equation with Rhino

Room orientation

Opening % and date of data collection

0.79

0.67

0.59

0.72

0.73

0.69

0.81

0.71

R2 with DB

0.76

0.64

0.59

0.73

0.72

0.66

0.84

0.67

R2 with Rhino

1.00

4.65

1.94

0.32

− 0.56

− 0.66

3.15

0.38

Error % with DB

3.01

6.63

3.87

2.37

1.46

1.39

5.03

2.25

Error %with Rhino 4.84

Error % of To

(continued)

2.54

SD of T o

Table 2 The predictive equation, coefficient of correlation of indoor air temperature between the actual and simulated data, and correlation of outdoor climatic data

96 Empirical and Dynamic Simulation-Based Assessment of Indoor …

T i = 0.59 * T s + 9.66 T i = 0.99 * T s − 0.94 T i = 0.44 * T s + 14.93 T i = 0.41 * T s + 15.77 T i = 0.44 * T s + 14.91 T i = 0.46 * T s + 14.53 T i = 0.94 * T s + 1.23 T i = 0.87 * T s + 3.15 T i = 0.86 * T s + 3.53 T i = 0.78 * T s + 5.90

T i = 0.40 * T s + 16.13

T i = 0.91 * T s + 1.97

T i = 0.37 * T s + 17.63

T i = 0.39 * T s + 17.07

T i = 0.39 * T s + 16.99

T i = 0.48 * T s + 14.37

T i = 0.84 * T s + 4.61

T i = 0.65 * T s + 9.97

T i = 0.77 * T s + 6.90

T i = 0.70 * T s + 9.03

NW

SE

NW-SW

SE-SW

NE-NW

NE-SE

NE

SE

SW

NW-SW

70%—3rd April

T i = 0.59 * T s + 10.78

T i = 0.38 * T s + 17.37

NE

85%—27th March

Predictive equation Predictive with DB equation with Rhino

Room orientation

Opening % and date of data collection

Table 2 (continued)

0.95

0.89

0.8 931

0.95

0.78

0.67

0.76

0.62

0.66

0.77

0.70

R2 with DB

0.96

0.94

0.86

0.92

0.69

0.70

0.68

0.69

0.63

0.76

0.65

R2 with Rhino

− 0.66

−0.76

0.23

0.05

3.53

4.47

1.94

4.12

1.91

2.21

2.21

Error % with DB

1.52

1.12

1.76

1.61

5.30

5.98

3.87

5.94

3.93

3.52

3.05

Error %with Rhino

0.13

4.26

Error % of To

(continued)

2.10

7.04

SD of T o

6 Results 97

Predictive equation Predictive with DB equation with Rhino T i = 0.93 * T s + 1.50 T i = 0.87 * T s + 2.91 T i = 0.94 * T s + 1.01 T i = 0.92 * T s + 2.16 T i = 1.02 * T s − 0.63 T i = 0.86 * T s + 4.06 T i = 0.94 * T s + 1.96 T i = 0.78 * T s + 6.32 T i = 1.03 * T s − 0.72 T i = 0.80 * T s + 5.63 T i = 0.93 * T s + 2.05

T i = 0.79 * T s + 6.19

T i = 0.75 * T s + 6.84

T i = 0.87 * T s + 3.55

T i = 1.04 * T s − 0.43

T i = 0.67 * T s + 10.43

T i = 0.70 * T s + 9.41

T i = 1.21 * T s − 5.51

T i = 0.90 * T s + 3.52

T i = 1.34 * T s − 9.21

T i = 0.88 * T s + 3.93

T i = 0.99 * T s + 0.55

SE-SW

NE-NW

NE-SE

60%—19th April NE

NW

SE

SW

NW-SW

SE-SW

NE-NW

NE-SE

Opening % and date of data collection

Room orientation

Table 2 (continued)

0.71

0.82

0.94

0.79

0.92

0.78

0.94

0.80

0.84

0.96

0.96

R2 with DB

0.93

0.86

0.95

0.93

0.95

0.88

0.97

0.92

0.91

0.95

0.96

R2 with Rhino

− 1.85

− 1.72

− 3.06

− 2.41

− 2.38

− 0.19

− 2.67

− 2.74

0.30

1.19

− 0.23

Error % with DB

− 0.12

0.92

− 0.90

− 0.66

− 0.78

− 0.18

− 0.03

− 0.16

2.41

3.14

1.75

Error %with Rhino

− 0.18

Error % of To

(continued)

2.08

SD of T o

98 Empirical and Dynamic Simulation-Based Assessment of Indoor …

T i = 1.08 * T s − 2.03 T i = 0.89 * T s + 3.96 T i = 1.26 * T s − 6.97 T i = 0.74 * T s + 8.18 T i = 1.17 * T s − 7.57 T i = 0.61 * T s + 11.6 T i = 1.05 * T s − 1.15 T i = 2.95 * T s − 59.48 T i = 1.97 * T s − 31.02 T i = 2.58 * T s − 47.53 T i = 2.45 * T s − 44.19

T i = 0.51 * T s + 15.1

T i = 0.79 * T s + 7.07

T i = 1.18 * T s − 4.50

T i = 0.65 * T s + 11.29

T i = 1.13 * T s − 6.18

T i = 0.59 * T s + 12.61

T i = 0.98 * T s + 1.21

T i = 2.81 * T s − 54.77

T i = 1.65 * T s − 19.96

T i = 2.39 * T s − 41.52

T i = 2.36 * T s − 41.15

NW

SE

SW

NW-SW

SE-SW

NE-NW

NE-SE

NE

NW

SE

SW

45%—3rd May

T i = 1.07 * T s − 1.37

T i = 1.19 * T s − 8.05

NE

50%—21st April

Predictive equation Predictive with DB equation with Rhino

Room orientation

Opening % and date of data collection

Table 2 (continued)

0.94

0.86

0.86

0.87

0.93

0.64

0.94

0.89

0.94

0.70

0.92

0.94

R2 with DB

0.96

0.85

0.87

0.88

0.94

0.62

0.97

0.91

0.94

0.73

0.95

0.93

R2 with Rhino

− 5.21

− 6.50

− 3.92

− 6.04

− 2.06

− 0.93

5.73

− 3.04

− 3.27

− 3.44

− 3.07

− 3.56

Error % with DB

− 4.63

− 5.55

− 1.73

− 5.25

− 1.43

0.39

6.41

− 1.59

− 2.70

− 2.42

− 1.31

− 2.87

Error %with Rhino

− 7.45

3.77

Error % of To

(continued)

5.52

2.96

SD of T o

6 Results 99

40%—6th May

Opening % and date of data collection

Predictive equation Predictive with DB equation with Rhino T i = 1.85 * T s − 25.85 T i = 2.92 * T s − 58.57 T i = 2.51 * T s − 47.31 T i = 2.65 * T s − 50.92 T i = 1.48 * T s − 15.17 T i = 1.27 * T s − 9.15 T i = 1.22 * T s − 7.30 T i = 1.32 * T s − 10.45 T i = 1.19 * T s − 6.59 T i = 1.46 * T s − 15.0 T i = 1.46 * T s − 15.19 T i = 1.46 * T s − 14.87

T i = 1.70 * T s − 20.65

T i = 2.84 * T s − 55.90

T i = 2.27 * T s − 38.94

T i = 0.98 * T s + 1.21

T i = 1.29 * T s − 8.93

T i = 1.06 * T s − 2.05

T i = 1.18 * T s − 6.02

T i = 1.28 * T s − 9.09

T i = 1.10 * T s − 3.57

T i = 1.34 * T s − 10.82

T i = 1.21 * T s − 6.56

T i = 1.29 * T s 8.93

Room orientation

NW-SW

SE-SW

NE-NW

NE-SE

NE

NW

SE

SW

NW-SW

SE-SW

NE-NW

NE-SE

Table 2 (continued)

0.90

0.96

0.96

0.96

0.92

0.86

0.89

0.90

0.93

0.82

0.90

0.96

R2 with DB

0.95

0.96

0.96

0.96

0.91

0.83

0.88

0.89

0.92

0.86

0.91

0.95

R2 with Rhino

0.50

− 0.76

− 0.12

0.80

0.17

0.58

− 0.14

− 1.16

− 4.90

− 4.99

− 5.75

− 5.08

Error % with DB

0.96

1.57

1.03

1.55

0.54

1.02

1.64

0.30

− 4.17

− 3.32

− 4.99

− 3.62

Error %with Rhino

− 0.09

Error % of To

(continued)

2.48

SD of T o

100 Empirical and Dynamic Simulation-Based Assessment of Indoor …

0.98

T i = 0.91 * T s + 3.06 T i = 1.01 * T s + 0.03

T i = 0.91 * T s + 2.61 T i = 1.00 * T s + 0.20 T i = 0.84 * T s + 4.45 T i = 0.82 * T s + 5.38 T i = 1.00 * T s − 0.64 T i = 0.93 * T s + 1.95

y = 0.85 * T s + 5.64

y = 1.05 * T s - 1.96 T i = 1.04 * T s 1.30 T i = 0.84 * T s + 5.03

T i = 0.86 * T s + 4.89

y = 0.93 * T s + 2.22

y = 0.86 * T s + 4.16

y = 0.85 * T s + 5.64

T i = 0.81 * T s + 5.66

T i = 0.74 * T s + 8.26

T i = 0.89 * T s + 3.41

T i = 0.93 * T s + 2.27

NW

SW

NE-SE

NW-SW

NE-NW

SE-SW

NE

SE

NW

SW

30%—5th June

0.98

T i = 1.06 * T s − 2.07

T i = 0.93 * T s + 2.48

SE

0.91

0.87

0.92

0.90

0.98

0.92

0.84

0.79

0.83

0.82

T i = 1.06 * T s − 2.07

T i = 0.84 * T s + 5.57

NE

R2 with DB

35%—3rd June

Predictive equation Predictive with DB equation with Rhino

Room orientation

Opening % and date of data collection

Table 2 (continued)

0.86

0.86

0.86

0.86

0.78

0.74

0.78

0.80

0.83

0.79

0.82

0.82

R2 with Rhino

− 0.68

− 0.74

− 1.04

0.08

− 1.29

− 2.28

− 2.11

− 2.22

− 3.99

− 2.05

− 1.99

− 2.53

Error % with DB

0.09

1.20

0.27

0.97

− 0.75

− 0.17

− 1.39

− 0.48

− 1.92

− 1.05

− 1.35

− 0.09

Error %with Rhino

− 1.31

− 3.19

Error % of To

(continued)

7.08

5.64

SD of T o

6 Results 101

25%—19th June

Opening % and date of data collection

Predictive equation Predictive with DB equation with Rhino T i = 0.87 * T s + 3.67 T i = 0.78 * T s + 6.47 T i = 0.97 * T s + 0.71 T i = 0.98 * T s + 0.36 T i = 0.66 * T s + 10.65 T i = 0.55 * T s + 13.98 T i = 0.67 * T s + 10.24 T i = 0.55 * T s + 14.18 T i = 0.62 * T s + 11.9 T i = 0.47 * T s + 16.74 T i = 0.62 * T s + 12.08 T i = 0.62 * T s + 11.98

T i = 0.82 * T s + 5.32

T i = 0.72 * T s + 9.09

T i = 0.85 * T s + 4.59

T i = 0.99 * T s + 0.28

T i = 0.81 * T s + 4.77

T i = 1.01 * T s 1.77

T i = 0.91 * T s + 1.86

T i = 0.93 * T s + 0.58

T i = 0.81 * T s + 4.48

T i = 1.42 * T s − 15.48

T i = 0.97 * T s − 0.86

T i = 0.98 * T s − 0.95

Room orientation

NE-SE

NW-SW

NE-NW

SE-SW

NE

SE

NW

SW

NE-SE

NW-SW

NE-NW

SE-SW

Table 2 (continued)

0.67

0.61

0.74

0.43

0.58

0.63

0.58

0.51

0.88

0.90

0.87

0.89

R2 with DB

0.85

0.80

0.72

0.73

0.72

0.80

0.67

0.68

0.84

0.86

0.86

0.86

R2 with Rhino

− 4.30

− 5.24

− 5.35

− 4.93

− 5.14

− 3.53

− 4.10

− 4.35

− 0.28

− 0.84

− 1.91

− 0.26

Error % with DB

− 3.06

− 3.19

− 3.67

− 2.87

− 3.76

− 2.11

− 2.72

− 2.15

0.59

0.64

0.16

0.53

Error %with Rhino

− 3.99

Error % of To

(continued)

10.77

SD of T o

102 Empirical and Dynamic Simulation-Based Assessment of Indoor …

T i = 1.71 * T s − 20.45 T i = 2.20 * T s − 36.80 T i = 2.02 * T s − 30.69 T i = 2.10 * T s − 33.31 T i = 1.88 * T s − 26.87 T i = 2.06 * T s − 32.22 T i = 2.21 * T s − 36.49 T i = 0.54 * T s + 8.6 T i = 0.50 * T s + 10.17 T i = 0.62 * T s + 6.11 T i = 0.47 * T s + 11.05

T i = 1.41 * T s − 10.99

T i = 1.91 * T s − 27.20

T i = 1.75 * T s − 21.72

T i = 1.65 * T s − 18.79

T i = 1.54 * T s − 15.63

T i = 1.68 * T s − 19.55

T i = 1.85 * T s − 24.73

T i = 1.00 * T s − 0.93

T i = 1.13 * T s − 5.87

T i = 1.15 * T s − 6.47

T i = 1.07 * T s − 3.41

SE

NW

SW

NE-SE

NW-SW

NE-NW

SE-SW

NE

SE

NW

SW

15%—8th July

T i = 2.19 * T s − 36.08

T i = 1.79 * T s − 22.97

NE

20%—24th June

Predictive equation Predictive with DB equation with Rhino

Room orientation

Opening % and date of data collection

Table 2 (continued)

0.92

0.84

0.90

0.90

0.95

0.94

0.94

0.94

0.93

0.96

0.88

0.97

R2 with DB

0.41

0.60

0.47

0.45

0.92

0.93

0.95

0.95

0.94

0.94

0.88

0.95

R2 with Rhino

18.80

19.29

18.95

19.43

− 5.72

− 5.78

− 4.45

− 5.24

− 5.09

− 4.36

− 5.78

− 5.45

Error % with DB

18.96

19.49

19.32

19.64

− 3.27

− 2.99

− 2.65

− 3.31

− 3.42

− 2.39

− 4.26

− 3.17

Error %with Rhino

18.71

0.52

Error % of To

(continued)

3.50

3.96

SD of T o

6 Results 103

10%—10th July

Opening % and date of data collection

Predictive equation Predictive with DB equation with Rhino T i = 0.62 * T s + 6.00 T i = 0.57 * T s + 7.64 T i = 0.57 * T s + 7.77 T i = 0.57 * T s + 7.65 T i = 1.08 * T s − 4.48 T i = 0.91 * T s + 1.10 T i = 0.96 * T s − 0.40 T i = 0.80 * T s + 4.69 T i = 1.06 * T s − 3.75 T i = 0.89 * T s + 1.76 T i = 0.84 * T s + 3.51 T i = 1.12 * T s T s − 5.90

T i = 1.08 * T s − 3.96

T i = 1.00 * T s − 1.27

T i = 0.83 * T s + 4.94

T i = 1.11 * T s − 4.93

T i = 0.67 * T s + 8.79

T i = 0.68 * T s + 8.95

T i = 0.829 * T s + 4.08

T i = 0.58 * T s + 11.8

T i = 0.74 * T s + 6.98

T i = 0.706 * T s + 8.26

T i = 0.59 * T s + 11.76

T i = 0.94 * T s + 0.33

Room orientation

NE-SE

NW-SW

NE-NW

SE-SW

NE

SE

NW

SW

NE-SE

NW-SW

NE-NW

SE-SW

Table 2 (continued)

0.74

0.50

0.61

0.52

0.35

0.59

0.56

0.49

0.76

0.84

0.81

0.92

R2 with DB

0.67

0.54

0.58

0.61

0.37

0.56

0.55

0.62

0.50

0.64

0.65

0.60

R2 with Rhino

4.19

2.80

2.83

3.42

2.96

4.25

2.83

4.30

18.90

18.76

18.81

19.08

Error % with DB

5.80

4.13

4.77

5.25

4.14

5.30

4.96

6.01

19.24

19.01

19.10

19.46

Error %with Rhino

0.61

Error % of To

(continued)

7.58

SD of T o

104 Empirical and Dynamic Simulation-Based Assessment of Indoor …

T i = 1.09 * T s − 3.74 T i = 1.24 * T s − 8.71 T i = 1.13 * T s − 4.79 T i = 1.19 * T s − 6.78 T i = 0.95 * T s + 0.92 T i = 0.88 * T s + 3.20 T i = 1.13 * T s − 4.91 T i = 1.05 * T s − 1.82 T i = 1.05 * T s − 1.68 T i = 1.05 * T s − 1.88 T i = 1.05 * T s − 1.73

T i = 0.71 * T s + 9.72

T i = 0.85 * T s + 5.17

T i = 1.19 * T s − 6.66

T i = 0.80 * T s + 6.37

T i = 0.57 * T s + 14.56

T i = 1.31 * T s − 10.44

T i = 1.16 * T s − 5.72

T i = 0.80 * T s + 6.36

T i = 0.79 * T s + 6.93

T i = 0.82 * T s + 6.04

T i = 0.80 * T s + 6.36

SE

NW

SW

NE-SE

NW-SW

NE-NW

SE-SW

NE

SE

NW

SW

50% with flyscreen—26th April

T i = 1.32 * T s − 11.24

T i = 0.94 * T s + 2.06

NE

5%—18th July

Predictive equation Predictive with DB equation with Rhino

Room orientation

Opening % and date of data collection

Table 2 (continued)

0.93

0.98

0.96

0.94

0.80

0.86

0.74

0.78

0.91

0.79

0.84

0.90

R2 with DB

0.99

0.99

0.99

0.99

0.86

0.57

0.89

0.86

0.85

0.90

0.87

0.90

R2 with Rhino

− 1.06

− 2.04

− 1.7

− 1.03

− 0.04

− 1.43

− 1.17

− 0.46

− 1.19

0.46

0.81

0.56

Error % with DB

0.08

0.15

0.08

0.13

2.20

1.72

1.69

2.07

− 1.94

− 2.5

− 1.9

− 2.36

Error %with Rhino

4.37

0.21

Error % of To

(continued)

3.18

3.75

SD of T o

6 Results 105

T i = 1.24 * T s − 6.79 T i = 1.13 * T s − 3.70 T i = 0.82 * T s + 5.38 T i = 1.20 * T s − 5.61

T i = 1.11 * T s − 2.27

T i = 0.98 * T s + 1.50

T i = 0.68 * T s + 10.68

T i = 1.06 * T s − 0.85

NE

SE

NW

SW

50% with ceiling fan—24th April

Predictive equation Predictive with DB equation with Rhino

Room orientation

Opening % and date of data collection

Table 2 (continued)

0.80

0.87

0.74

0.80

R2 with DB

0.78

0.92

0.73

0.74

R2 with Rhino

− 3.52

− 2.79

− 3.31

− 3.69

Error % with DB

− 2.12

− 0.20

− 1.73

− 1.96

Error %with Rhino − 2.83

Error % of To 3.90

SD of T o

106 Empirical and Dynamic Simulation-Based Assessment of Indoor …

6 Results

107

6.2 Correlation Between Indoor Temperature from Real-Scale and Simulation Model The indoor air temperature data for all 14 opening sizes in the rooms along eight orientations is analyzed by comparing the readings obtained from i. Actual indoor air temperature data captured using instruments ii. Indoor air temperature obtained by simulation using DesignBuilder and Rhino. The objective of this analysis is to a. Assess the error percentage between the actual and simulated indoor air temperatures b. Arrive at the predictive equation and the coefficient of correlation between the actual and simulated indoor air temperatures c. Suggest a predictive model. 6.2.1

Thermal Performance Concerning Room Orientation

The NE room is most comfortable when the opening sizes are large (100–60%). The SW room is most comfortable when the opening size is small (50–5%). The NW room is most uncomfortable when the opening size is small. For the same opening size, during certain periods, it is possible to have indoor temperature reductions of up to 4 °C between rooms of varying orientations; indoor average temperature is higher in rooms oriented along the cardinal directions than semi-cardinal directions.

6.2.2

Thermal Performance Concerning the Opening Sizes

The indoor room temperatures in rooms with large openings (100–50%) follow the trend of the outside temperature, except in the NW room. The NW room is warmer than the other seven rooms. This is likely because of the lack of adequate breeze to remove the hot air inside as the prevailing wind direction is East and South-East. When the opening percentages are between 50 and 35%, the SE, SW, and SE-SW rooms are the least warm. Besides, the changes in the opening percentage have less impact on the indoor thermal conditions when the openings are less than 35%. In the same room, it is possible to reduce the indoor temperature during certain periods by up to 5 °C by changing the opening size.

6.3 Analysis of Real-Scale and Simulated Results The error percentage of indoor air temperature for most opening sizes and orientations is lower for results obtained from DesignBuilder as compared to Rhino. The

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error percentage of DesignBuilder values is closer to the error percentage of the outside temperature. The last error of the outside temperature is − 0.090% for 40% opening date. This results in a close correlation of an error of 0.32% on average, between the actual and simulated indoor temperatures, as shown in Fig. 8. Linear regression analysis of the field study and simulated indoor air temperature is shown in Table 2. It can be seen that the real-time temperature matches very well with the simulation results for most hours of the day. It is concluded that when the outdoor temperature of the EPW file of the simulation software matches closely with that of the meteorological data, the simulation results match the real-time data very well.

Fig. 8 Comparison of the indoor temperature of real-scale building and simulation studies

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7 Validation of Predictive Model The simulation model is validated by verifying the accuracy of the thermal performance under the following two conditions: Ceiling Fan and Flyscreen.

7.1 Use of Ceiling Fans in a Hot-Humid Climate Residential buildings in the tropics rely on ceiling fans to provide comfort. In a study by Kubot et al. [6], almost 98% of houses in Malaysia use ceiling fans. Due to the economy and ease, they are used on a large scale in the domestic sector. Ceiling fans are the most commonly used passive means to bring comfort in the housing sector of Chennai city [27]. Its impact on indoor temperature is important to be studied.

7.1.1

Indoor Thermal Performance with Ceiling Fan

Field measurement of indoor air temperature in the North-East room for 50% WFR is collected on 6th June with AZ8904 digital thermo-anemometers. The method of data collection is similar to the data capture of natural opening conditions.

7.1.2

Impact of Ceiling Fans for 50% Opening

The indoor temperature data from the real-scale building and the simulated model with a ceiling fan in operation is compared for an opening size of 50%, as shown in Fig. 9. It is found that the coefficient of determination between the actual data from the real-scale building and the simulated model is approximately 0.8, indicating a good correlation. The error percentage is approximately 1.5 and 3.3% with DesignBuilder and Rhino, respectively, while the error between the actual outside temperature and EPW file is 2.83% with a standard deviation of 3.9. When the error percentage between the actual outside temperature and EPW file is less, the predictive model gives closer to the real results.

7.2 Use of Flyscreens in a Hot-Humid Climate Tropical cities like Chennai have to deal with mosquitoes and flies. It is important to prevent their entry into the dwellings, as these insects spread deadly infections like malaria, filariasis, dengue, and yellow fever [28]. Since the use of mosquito repellent and lotions can cause respiratory side effects, the best protection is with a flyscreen. These can prevent direct solar radiation and wind inside the building [29].

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Empirical and Dynamic Simulation-Based Assessment of Indoor …

Fig. 9 Indoor air temperature of rooms along the cardinal in the presence of ceiling fan

7.2.1

Indoor Thermal Performance with Flyscreen

Indoor air temperature in the cardinal rooms is captured in the presence of a flyscreen for a sample opening size of 50% on the summer day of 26th April with AZ8904 digital thermo-anemometers. Using the predictive model, the indoor air temperature of the four rooms is simulated.

7.2.2

Experimental and Simulated Data with Flyscreen for Opening Size of 50%

The indoor temperature data in the presence of a flyscreen from the real-scale building and the simulated model is compared for an opening size of 50%, as shown in Fig. 10. It is observed that the coefficient of determination between the actual data from a real-scale building with a flyscreen and the simulated model is approximately 0.95, indicating a high level of association. The error percentage between real-time data and the simulated air temperature using DesignBuilder and Rhino is − 1.4 and 0.11%, respectively, while the error between actual site temp and EPW file is 4.3% with a standard deviation of 3.18. The predictive model can generate indoor air temperature with a high level of correlation with the actual data in the presence of a flyscreen. There is an increase of about 2 °C in the presence of flyscreen for an opening of 50%.

8 Results and Discussion

111

Fig. 10 The indoor temperature from the real-scale model and the simulation results

8 Results and Discussion The results of the studies in the eight rooms in various orientations and for 14 opening conditions are discussed below. In practice, the opening sizes normally range below 70%. The model incorporates openings for permanent ventilation with an opening size of 100–5% of the floor area for research and understanding. i. Behavior of rooms with openings for natural ventilation The indoor room temperature in rooms with large openings (100–50%) follows the trend of the outside temperature, except for the NW room. This is because the prevailing wind direction is East and South-East, resulting in the accumulation of heat in the NW room and hence a rise in temperature. When the opening percentages range between 50 and 35%, the rooms that are least hot are SE, SW, and SE-SW rooms. When the rooms have an opening size smaller than 35%, the room that has the least number of hot hours is the SW room. The changes in the opening percentage have less impact on the indoor thermal conditions when the opening sizes are less than 35%. ii. Behavior of rooms with openings for natural ventilation and ceiling fan When the opening sizes are 100–50%, there is no significant difference in the indoor temperature. The heat gained due to solar radiation and the heat from the ceiling is dissipated by the air movement of the ceiling fan and ventilation through the large openings. As the opening percentage reduces from 45 to 25%, the presence of a ceiling fan reduces the indoor temperature and enhances the indoor performance

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proportionate to the size of the openings. The maximum reduction in indoor temperature with a ceiling fan occurs when the opening percentage is 45% of the floor area. When the opening size is between 25 and 15%, the presence of the ceiling fan neither increases nor decreases the indoor temperature. For opening sizes of less than 15%, the presence of the ceiling fan increased the indoor temperature. The opening sizes are not large enough to throw the warm air out. The combined heat from the ceiling and the lack of adequate outdoor ventilation increases the indoor temperature. iii. Behavior of rooms with openings for natural ventilation and flyscreen When the opening sizes are large (100–50%), the indoor temperature is elevated by 3 °C. This is because of the ingress of solar radiation leading to increasing indoor temperature. When the opening percentage is between 50 and 35%, the indoor temperature rises by 2 °C. It is found that when the openings are 35–20%, there is not much impact of the fly screens on the indoor air temperature. This is because the impact of solar radiation is responsible for increasing indoor temperature and the wind from outside, which may increase or decrease indoor temperature neutralize each other. As a result, there is no significant difference in indoor temperature in the presence of fly screens when openings are less than 35% of the floor area. iv. Significance of Outdoor climatic data The accuracy of the predictive model depends on the input parameters, especially the correlation between the outdoor climatic data and EPW file data. Since the model correlates well with the real-scale experimental data, this predictive model can be used to assess the impact of other contributory components, and the optimized architectural design solutions can be outlined with predictions of thermal performance.

9 Conclusion This study presents the optimization method with various parameters in a naturally ventilated building. The impact of combined impact of solar radiation and air movement on indoor air temperature can be assessed using simulation models. It is inferred from the results of the simulation and field studies that the South-West direction provides better thermal performance for Chennai city during the summer months contrary to the belief that the West side is the hottest. This is because of the combination of solar radiation and wind, which helps dissipate the heat and the use of sunshades. For the same opening size, the indoor average temperature of the rooms along the semi-cardinal performs better than the cardinal rooms. Hence, it is advisable to orient the building along semi-cardinal directions for naturally ventilated rooms in Chennai city to maximize the thermal performance. For maximizing the thermal performance, the opening sizes must be bigger than 35%, as there is no significant difference for openings less than 35%.

References

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This study proves that when the actual outdoor temperature correlates well with the EPW file of the simulation software, the predictive model can be used for further studying the impact of other affecting factors in naturally ventilated buildings such as ceiling height, room sizes, building materials, and impact of occupancy, which would otherwise be challenging to study with a real-scale model or with field measurements alone. Acknowledgements This work is supported by the All India Council for Technical Education, Government of India, under the TAPTEC scheme. The author would like to express sincere thanks to MASA 2018-20, SPAV, for their generous support.

References 1. Birol F (2015) Energy and climate change. World Energy Outlook Special Report. International Energy Agency (IEA), Paris, France, p 200 2. Ministry of Statistics and Programme Implementation (MoSIP) (2018) Energy Statistics 2018. Government of India, New Delhi 3. Central Electricity Authority (CEA) (2017) Growth of electricity sector in India from 1947– 2017. CEA, Government of India, New Delhi 4. Vijayalaxmi J, Sekar SP (2013) Thermal performance of naturally ventilated residential buildings with various room orientations in the hot-humid climate of Chennai, India. J Archit Plann Res 1–22 5. Nayar PP (2012) Improving thermal comfort in residential spaces in the wet tropical climate zones of India using passive cooling techniques: a study using computational design methods. University of Southern California, California, United States, p 180 6. Kubot T, Toe D, Chyee H, Ahmad S (2009) The effects of night ventilation technique on indoor thermal environment for residential buildings in hot-humid climate of Malaysia. Ener Build 41:829–839 7. Wang L, Wong Nyuk H, Li S (2007) Facade design optimization for naturally ventilated residential buildings in Singapore. Ener Build 39(8):954–961 8. Ben-David T, Racks A, Lo LJ, Wen J, Waring MS (2019) Optimizing ventilation: theoretical study on increasing rates in offices to maximize occupant productivity with constrained additional energy use. Build Environ 106314 9. Brager GS, de Dear R (2001) ‘Climate, comfort and natural ventilation: a new adaptive comfort standard for ASHRAE Standard 55’, moving thermal comfort standards into the 21st century. Loughborough University, Windsor, pp 60–77 10. Bouchlaghem N (2000) Optimising the design of building envelopes for thermal performance. Autom Constr 10(1):101–112 11. Germano M (2007) Assessing the natural ventilation potential of the Basel region. Ener Build 39(11):1159–1166 12. Aluclu I, Dalgic A (2005) A case study on natural ventilation characteristics of the Diyarbakir Surici (old city) Municipality Building in Turkey. Build Environ 40(11):1441–1449 13. SP:41 (1987) Handbook on functional requirement of buildings (other than industrial buildings). Bureau of Indian Standards, New Delhi 14. Census Data (2011) http://censusindia.gov.in/2011census/hlo/hlo_highlights.html 15. Givoni B (1961) Window location in residential buildings. Research report, Building research station Technion, Israel Institute of Technology, Haifa 16. Wang L, Nyuk Hien W (2006) The impacts of ventilation strategies and facade on indoor thermal environment for naturally ventilated residential buildings in Singapore. Build Environ 42(12):4006–4015

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17. Bureau of Indian Standards (2016) Approach to sustainability. National Building Code of India, SP 7: 2016, vol 2 18. Gao CF, Lee WL (2011) Evaluating the influence of openings configuration on natural ventilation performance of residential units in Hong Kong. Build Environ 46:961–969 19. Moret Rodrigues A, Santos M, Gomes M, Glória DR (2019) Impact of natural ventilation on the thermal and energy performance of buildings in a Mediterranean climate. Buildings 9(5):123 20. ANSI/ASHRAE Standard 55-2010 (2010) Thermal environment conditions for human occupancy 21. Marincioni V, Marr GA, Altamirano-Medina H (2018) Development of predictive models for the probabilistic moisture risk assessment of internal wall insulation. Build Environ 137:257– 267 22. Liu Y, Xiao Y, Chen J, Augenbroe G, Zhou T (2019) A network model for natural ventilation simulation in deep buried underground structures. Build Environ. https://doi.org/10.1016/j.bui ldenv.2019.01.045 23. Hawila AA, Merabtine A, Troussier N, Bennacer R (2019) Combined use of dynamic building simulation and metamodeling to optimize glass facades for thermal comfort. Build Environ https://doi.org/10.1016/j.buildenv.2019.04.027 24. Schminder J, Gårdhagen R (2018) A generic simulation model for prediction of thermal conditions and human performance in cockpits. Build Environ 143:120–129 25. Li Y, Zhao B, Zhang S (2000) Experiment analysis of outdoor air temperature around a single building under the effect of solar radiation. In: Proceedings of international symposium on air conditioning in high rise building’ 2000 (ACHRB’ 2000), Shanghai, China, pp 360–369 26. Kumar S, Khan A, Bajpai A, Rao GS, Mathur J, Chamberlain L, Garg V (2011) User guide energy conservation building code. Retrieved from https://beeindia.gov.in/sites/default/files/ ECBC/User-Guide-V-0.2%28Public%29.pdf 27. Vijayalaxmi J (2009) Thermal comfort of a naturally ventilated house resulting from the evaporative cooling of a ceiling fan in the hot-humid climate. Int J Ventil Taylor Francis 15(3):234–256 28. Tyagi BK (2004) The invincible deadly mosquitoes: India’s health and economy enemy number 1. Scientific Publishers, India 29. Vijayalaxmi J, Sekar SP (2009) Indoor thermal performance of ventilated dwellings using fly screens in the hot-humid climate of Chennai, India. J Green Build College Publ USA 4(2):150–157

Study of Indoor Thermal Performance Due to Varying Ceiling Heights in a Hot-Humid Climate

Abstract This study explores the impact of changing ceiling height on the indoor thermal performance of a building for various combinations of room orientation and opening sizes. The methodology followed is building simulation and validation with field study data for some ceiling heights. This study uses the predictive model established in Chap. 7. Thermal performance of rooms along 8 different orientations for 11 opening conditions and 10 different ceiling heights is assessed using the predictive model. The results are validated by examining the indoor thermal performance of real-scale rooms with varying ceiling heights. For the first time, the indoor thermal performance due to varying ceiling height, opening size, and orientation is examined in naturally ventilated rooms in a hot-humid climate. It is found that for every 30 cm rise in ceiling height, there is a change of up to 0.1 °C. The indoor temperature at the working level increases by 0.5 °C when the ceiling height is increased from 3.0 to 6.0 m. The percentage of indoor air temperature difference reduces exponentially as ceiling height increases. For any ceiling height, the indoor temperature is the same two times a day. Conditioned rooms with large ceiling heights consume more energy to be cool. This study can direct the vent of the air conditioner at various levels for optimized cooling. In this manner, this study is useful in the design of air conditioner vents and the location of goods in warehouses and silos for minimizing energy use.

1 Introduction Houses with low ceilings are appropriate for colder climates, as warm air tends to rise upwards. Hence, having a low ceiling would ensure that the working zone is not devoid of warm air. In a hot-humid climate, a higher ceiling could encourage stratification of the air as hot air tends to rise and cooler air lingers in the working plane. The phenomenon of warm air rising while cool air descends is called air stratification, which causes vertical air temperature variations in a room [1]. At the same time, windows play an important role in enhancing air movement and replacing warm air. Several studies have been carried out to assess the impact of window size [2–5] and orientation [6–8] on indoor thermal performance. However, the thermal behavior of a room resulting from the combination of window size, orientation, and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_8

115

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Study of Indoor Thermal Performance Due to Varying Ceiling Heights …

ceiling height variation has not yet been studied. Hence, a study of the combined impact of opening size, room orientation, and ceiling height in a hot-humid climate needs to be taken up.

1.1 Background of the Study Research work on the impact of changing ceiling height has been carried out in three major areas, namely, perception studies, flame/smoke movement for ceiling profile, and thermal performance (Fig. 1). In the area of perception studies, research has been carried out on the response to reading-comprehension tasks and sense of presence by altering ceiling heights through the creation of virtual environments by Cha [9]. The main objective of this study was to measure spatial perception with varying ceiling heights and ceiling designs for an office environment. The study related to the psychology of consumption as a response to varying ceiling heights has been conducted by Meyers [10], suggesting that ceiling height is an important factor in the processing of information by consumers and reflects on their responses and choices. A study by Vartanian et al. [11] on the impact of ceiling height on the visual esthetic perception of spaces revealed that higher ceilings were attributed to openness and hence were perceived as esthetically pleasing. Studies have been carried out to understand the temperature changes below inclined ceilings during fire hazards by Chatterjee et al. [12] and Zhang et al. [13]. A similar study is done to understand the flame characteristics below a curved ceiling

Fig. 1 The broad areas of existing research on ceiling height variations

1 Introduction

117

by Liang et al. [14], while a study by Liu et al. [15] focused on how combustion behaviors occur below the ceiling for roofs with varying ceiling heights. Nguyen and Reiter [16] conducted a study using CFD to understand the wind flow pattern due to ceiling configuration for six ceiling shapes. Simulation studies were carried out and the results were compared with experimental results using a wind tunnel. This study explains the plausible reasons behind the discrepancies between the two results. Also, the study highlights that the configuration of the roof does not have much significance to the airflow rate. However, the ceiling height has a significant role in airflow. The airflow rate seems to increase when the ceiling height is reduced. Kindangen et al. [17] studied the impact of roof shapes and profiles on air movement inside buildings but did not direct their study to roof heights. Sharples and Bensalem [18] identified that roof height has a role in the extent of ventilation but did not address the impact it would have on indoor air temperature. A study by Guimarães et al. [19] used laboratory and mathematical models on the impact of indoor air temperature due to ceiling height variation suggesting that there is a difference of up to 4 °C between the upper and lower layers of the room. But this could be due to radiation from the roof. In any case, this study is limited to a completely enclosed room and suggests that openings would impact the indoor temperature. In a study by Zhang et al. [20], the experimental validation of a numerically simulated model of a full-scale office room showed that the predicted temperature in the room is underestimated. This could be because the numerical model did not sufficiently account for thermal buoyancy. The errors could have occurred because the radiation effects were not modeled in the simulation code. To reduce errors, the research suggests that evaluation must be conducted with the simplest case involving rooms of simple geometry with no internal heat load and no internal obstruction, and then, further complexities could be added. This study also carried out the effect in unobstructed rooms with no internal heat gain when the room is a simple cube geometry. Mofrad [21] studied the impact of ceiling height on indoor thermal comfort using a laboratory model with adjustable ceiling height, between 2.1 and 4.0 m, to understand the optimum height of urban residences in Iran. The findings suggest that a ceiling height of 2.7–2.8 m is appropriate. The study did not consider room heights above 4.0 m. A team led by Professor Shalon [22] conducted a study on the climatological performance of rooms with ceiling heights of 2.32, 2.50, 2.68, and 2.86 m and found that there is not much difference in the maximum temperature in these rooms with varying ceiling heights, but the rooms with more heights had their minimum temperature to be lower. But the difference is extremely minimal and therefore physiologically imperceptible. This study expressed that in real time, the results could vary. However, this study did not include considerable variation in ceiling height. Hashimoto and Yoneda [23] used a modeled room to carry out numerical simulations in rooms with ceiling heights of 2.5, 3, and 3.5 with a heat source placed in the center of the modeled room. This study investigates how the ceiling height of an office

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Study of Indoor Thermal Performance Due to Varying Ceiling Heights …

room influences thermal stratification for displacement ventilation. It also investigates the vertical temperature profiles for displacement ventilation with varying ceiling heights in an office room. The numerical analysis results are compared to the real-time data of a room with a 2.5 m ceiling height. A study by Ghafari et al. [24] in the cold climate of Tabriz, Iran, is carried out using EnergyPlus as a simulation tool to understand the relationship between ceiling height and heating energy consumption in a classroom. A standard classroom with a South facing window is modeled for analysis. The study found that for every 0.1 m reduction in ceiling height, the heating energy is also reduced by 1.0%. Ai et al. [25] examined the various types of a balcony that provide good ventilation with changes in balcony ceiling heights through field measurements and simulations. The results indicated that with an increase in balcony ceiling height, the ventilation performance is reduced. A similar study is carried out by Prianto and Depecker [26, 27] to understand the effect of changing ceiling height on cross ventilation. The results of the study align with the earlier findings that there is no major impact. In a hot-humid climate, comfort ventilation is the most effective way to enhance indoor thermal performance. At the same time, the ingress of hot breezes is also an important criterion of thermal performance, which depends upon room orientation and opening size. The impact of air stratification at the working plane depends on the ceiling height. However, no research has been carried out on the combined impact of opening size, room orientation, and ceiling height on indoor thermal performance. This research gap has been addressed in this study.

2 Ceiling Heights in Building Codes The National Building Code of India [28] prescribes that habitable rooms in naturally ventilated buildings should have a minimum height from the finished surface of the floor to the underside of the finished ceiling or a false ceiling of not less than 2.75 m. Most of the construction in India uses a reinforced cement concrete framing system of columns and beams. The beam depth usually adds up to around 0.4–0.6 m in height, thus, the total height achieved through beam adjustment is around 3 m. This also allows height for a ceiling fan, which is the most commonly used assisted device to enhance indoor thermal performance. Hence, in practice, the ceiling height of habitable spaces is 3.0 m.

3 Methodology Buildings are multi-cell assemblies within an envelope comprising fenestration and walls. The indoor temperature is directed by the local climate, macroclimatic conditions of the site, building envelope design, material data of the building, internal

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arrangements of partitions and furniture, acclimatization, clothing, and activity levels of inhabitants. The impact of the indoor thermal performance of all the factors cannot be studied simultaneously as a building’s disassembly is not practical and also because of the dynamism involved in natural ventilation [29]. For these reasons, a new relational model that helps reduce the problem of uncertainty has been adapted to enable the study of a few factors at a time when all other conditions remain the same.

3.1 Components of the Study Therefore, this study is focused on the assessment of indoor thermal performance with changes in (i) Orientation of the room (ii) Size of openings and (iii) Ceiling heights. These three factors are considered as ‘variables’ while the other affecting factors such as building design and material data are considered as ‘constants’. The conditions of the constants and the method to arrive at the same are described in Chap. 7.

3.1.1

Design of Relational Real-Scale Building

A simulation model based on a representative house unit (Fig. 2) is generated using DesignBuilder and Rhino as shown in Figs. 3 and 4, respectively. The entire process of generating this predictive model is described in Chap. 7.

4 Results and Discussion 4.1 Results of the Simulation for Varying Ceiling Heights Using DesignBuilder The indoor temperature due to the impact of varying ceiling heights is simulated for ten different ceiling heights with variations of 0.3 m between each and compared with the predictive model, which had a ceiling height of 3.0 m. Simulations to obtain indoor temperature changes in all eight rooms for 11 opening conditions are performed for varying ceiling heights of 3.3, 3.6, 3.9, 4.2, 4.5, 4.8, 5.1, 5.4, 5.7, and 6.0 m.

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Fig. 2 Plan, elevation, and view of the real-scale experimental building

Fig. 3 View and elevation of building model in DesignBuilder

Fig. 4 View of the building model in Rhino

The results of the difference in indoor temperature between rooms of 3.0 m height and other varying ceiling heights for 30% opening for all eight orientations are shown in Fig. 5.

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Fig. 5 Indoor air temperature difference in comparison with 3.0 m ceiling height

Similarly, the results of the other 10 opening sizes are analyzed and are found to be similar. It is found that, with an increase in ceiling height, the indoor temperature is high from 10.00 to 20.00 h and is lower from 20.00 to 10.00 h as compared to a room with a height of 3.00 m. There is a very small change in indoor temperature with an increase in every 30 cm ceiling height for all 11 opening conditions and all eight rooms. There

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is an increase in indoor temperature from 10.00 to 20.00 h and a decrease from 20.00 to 10.00 h.

4.2 Impact of Ceiling Height Variation in Rooms with Changing Orientation Of all the rooms, the South-West room showed more difference in indoor temperature change than the other rooms for the same opening size for every increase in ceiling height by 30 cm. This can be attributed to the combination of solar radiation from the West and also the predominant wind direction from the South and South-East.

4.3 Impact of Ceiling Height Variation in Rooms with Changing Sizes of Openings It is found that as the opening sizes became smaller, the difference in indoor temperature becomes more. When the opening size is 5%, the maximum difference in indoor air temperature between ceiling heights of 3.0 and 6.0 m is about 0.8 °C occurring at around 13.00 h. The indoor temperature difference in smaller openings is lower from 20.00 to 10.00 h as compared to larger opening sizes. The indoor temperature difference is negligible for 100% of openings.

4.4 Rate of Increase in Indoor Temperature with Increase in Ceiling Height Even though there is a change in indoor temperature for every 30 cm increase in ceiling height, the change is not linear. The percentage change in indoor temperature reduces exponentially as compared to the subsequent ceiling height, as shown in Fig. 6. There is a 50% change in indoor temperature difference when the ceiling height is increased from 3.3 to 3.6 m. There is a 30% change in indoor temperature difference between ceiling height of 3.9 and 3.6 m. This shows that, even if there is a slight difference in indoor temperature with an increase in ceiling heights, the difference ceases to be significant when ceiling heights are large. For example, the indoor temperature difference when the ceiling height is increased from 3.3 to 3.6 m is much higher than when the ceiling height is increased from 5.7 to 6.0 m.

Percentatge of indoor temp change

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60% 50%

50%

40% 30%

30% 23%

20%

18%

10% 0%

3.6-3.3

3.9-3.3

4.2-3.9

4.5-4.2

15%

4.8-4.5

12%

5.1-4.8

Ceiling height difference in meters

11% 5.4-5.1

10% 5.7-5.4

9% 6-5.7

increase in %

Fig. 6 Percentage change in temperature difference between two consecutive ceiling height differences

4.5 The Indoor Temperature for All Ceiling Heights Coincided Twice a Day It is found that for all the 11 opening sizes and in all the eight rooms oriented along the cardinal and semi-cardinal directions and for all the eleven ceiling height conditions, the indoor temperature is the same twice a day. As an example, Fig. 7 shows that the indoor temperature at 09.00 and 21.00 h is the same for all ceiling heights for a 5% opening in the NE room. This phenomenon is found to occur for all opening sizes, rooms with varying orientations, and various ceiling heights. The reason for this phenomenon could not be justified in the study. There is no correlation between the time of this phenomenon and the sunrise time, moonrise time, or the time when the rooms achieve the indoor average. The study of this phenomenon is a separate research and not within the scope of this study.

Fig. 7 The indoor temperatures in the NE room with 5% openings

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4.6 Validation of Simulation Results for Varying Ceiling Height Indoor temperature data is obtained from two real-scale adjacent rooms in the hothumid climate of Vijayawada city. The rooms have a ceiling height of 4.5 and 9.0 m, respectively. The plan and section of the real-scale room are shown in Fig. 8. Indoor temperature is collected using data loggers in a similar manner in which the data is collected from the experimental real-scale building for 24 h as shown in Fig. 9. The difference in indoor temperature between the two rooms of 9.0 m height and 4.5 m height is analyzed, and it is found that there is a difference of a maximum of 0.5 °C between the two rooms due to the change in ceiling height, which is not too significant. Also for two times a day, there is no difference in the indoor temperature in both rooms. This occurs approximately at an interval of 12 h (once in the morning and once in the evening), as shown in Fig. 10. This correlates with the earlier findings of this research.

Fig. 8 Plan and section of the real-scale rooms of 4.5 and 9.0 m ceiling heights

Fig. 9 Two adjacent rooms of 4.5 and 9.0 m ceiling height with data loggers

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Fig. 10 Difference in indoor temperature in rooms with 4.5 and 9.0 m ceiling height

5 Conclusion Very little research has been done on the impact of indoor temperature due to variations in opening size, room orientation, and ceiling heights, especially in real-scale buildings. This study gains significance because the indoor temperature of the predictive model and the indoor temperature for validation is obtained from real-scale buildings. An increase in ceiling height correlated with an increase in indoor air temperature during the day and a decrease in air temperature in the evening. But the increase is not linear. The increase in indoor temperature reduces exponentially as the ceiling height increases for every subsequent 30 cm. Also, the results show that irrespective of the ceiling height, size of openings, or room orientation, the indoor temperature two times a day is the same in all rooms. The reason for this phenomenon has not yet been explored. The indoor temperature data from the realscale model is collected on different days for various opening sizes. The differences in the outdoor data are not accounted for in this research, which is a limitation. Furthermore, all indoor air temperature measurements are taken at a height of 1.1 m from the floor level. Hence, an indication of air temperature at various heights due to stratification is not included in this research. Validation with real-scale buildings in a hot-humid climate is done with a building in a different city. Hence, the data cannot be compared to the simulation data. Nevertheless, this research allows designers to understand the impact of ceiling heights in naturally ventilated rooms at a working plane in enhancing indoor thermal performance in a hot-humid climate. This study can be useful in understanding the outlet temperature of air conditioning in highceiling buildings such as cold stores and silos. This study also contributes to the deficit literature in this domain of work. Acknowledgements The author is thankful to MASA 2020 of SPAV for their support.

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References 1. Armstrong M, Chihata B, MacDonald R (2009) Cold weather destratification energy savings of a warehousing facility. ASHRAE Trans 115:513–518 2. Adunola AO (2014) Evaluation of urban residential thermal comfort in relation to indoor and outdoor air temperatures in Ibadan, Nigeria. Build Environ 75(1):190–205 3. Aflaki A, Mahyuddin N, Mahmoud ZA (2015) A review on natural ventilation applications through building facade components and ventilation openings in tropical climates. Ener Build 101:153–162. https://doi.org/10.1016/j.enbuild.2015.04.033 4. Koranteng K, Essel C, Nkrumah J (2015) Passive analysis of the effect of window size and position on comfort for residential rooms in Kumasi, Ghana. Int Adv Res J Sci Eng Technol 2(10):114–115 5. Shaik S, Gorantla K, Setty ABTP (2016) Effect of window overhang shade on heat gain of various single glazing window glasses for passive cooling. Procedia Technol 23:439–446 6. Bin S, Al-Sudais AA (2010) The effect of glass windows, orientation, area, and types on the thermal performance of the internal spaces of the buildings in hot-dry regions. In: An experimental case study: test cells at the educational farm—KSU—Riyadh, King Suad University 7. Marincioni V, Marra G, Altamirano-Medina H (2018) Development of predictive models for the probabilistic moisture risk assessment of internal wall insulation. Build Environ 137:257–267. https://doi.org/10.1016/j.buildenv.2018.04.001 8. Vijayalaxmi J, Sekar SP (2013) Thermal performance of naturally ventilated residential buildings with various room orientations in the hot-humid climate of Chennai, India. J Archit Plann Res 30(1):1–22 9. Cha SH, Koo C, Kim TW, Hong T (2019) Spatial perception of ceiling height and type variation in immersive virtual environments. Build Environ. https://doi.org/10.1016/j.buildenv.2019. 106285 10. Meyers-Levy J, Zhu R (2007) The influence of ceiling height: the effect of priming on the type of processing that people use. J Consum Res 34(2):174–186. https://doi.org/10.1086/519146 11. Vartanian O, Navarrete G, Chatterjee A, Fich LB, Gonzalez-Mora JL, Leder H, Skov M (2015) Architectural design and the brain: effects of ceiling height and perceived enclosure on beauty judgments and approach-avoidance decisions. J Environ Psychol 41:10–18. https://doi.org/10. 1016/j.jenvp.2014.11.006 12. Chatterjee P, Meredith KV, Wang Y (2017) Temperature and velocity distributions from numerical simulations of ceiling jets under unconfined, inclined ceilings. Fire Saf J 91(February):461– 470. https://doi.org/10.1016/j.firesaf.2017.03.078 13. Zhang X, Hu L, Sun X (2019) Temperature profile of thermal flow underneath an inclined ceiling induced by a wall-attached fire. Int J Therm Sci 141(July 2018):133–140. https://doi. org/10.1016/j.ijthermalsci.2019.03.028 14. Liang ZH, Zhu GQ, Liu HN, Zhou X (2019) Flame characteristic and ceiling temperature distribution under the effect of curved sidewall. Case Stud Therm Eng 14(April):100453. https:// doi.org/10.1016/j.csite.2019.100453 15. Liu J, Chen M, Lin X, Yuen R, Wang J (2016) Impacts of ceiling height on the combustion behaviors of pool fires beneath a ceiling. J Therm Anal Calorim 126(2):881–889. https://doi. org/10.1007/s10973-016-5559-7 16. Nguyen AT, Reiter S (2011) The effect of ceiling configurations on indoor air motion and ventilation flow rates. Build Environ 46:1–12. https://doi.org/10.1016/j.buildenv.2010.12.016 17. Kindangen J, Krauss G, Depecker P (1997) Effects of roof shapes on wind-induced air motion inside buildings. Build Environ 32(1):1–11 18. Sharples S, Bensalem R (2001) Airflow in courtyard and atrium buildings in the urban environment: a wind tunnel study. Sol Ener 70(3):237–244 19. Guimarães RP, Carvalho MCR, Santos Department FA (2013) The influence of ceiling height in thermal comfort of buildings: a case study. Int J Hous Sci 37(2):75–85

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20. Zhang JS, Christianson LL, Wu GJ, Zhang RH (1993) Experimental evaluation of a numerical simulation model for predicting room air motion. Indoor Environ 2(5–6):331–336 21. Mofrad MN (2013) The impact of floor-to-ceiling height on human comfort. Asian J Civil Eng (BHRC) 14(5):277–287 22. Technion (1958) Israel Institute of technology a study of the influence of ceiling height in dwelling houses. Athens Center of Ekistics Stable, vol 5, no 33, pp 288–293 23. Hashimoto Y, Yoneda H (2009) Numerical study on the influence of a ceiling height for displacement ventilation. Polytechnic University, Sagamihara, Japan Takenaka Corporation, Osaka, Japan, pp 1045–1052 24. Ghafari F, Mirrahimi SZ, Heidari S (2018) Influence of Ceiling Height on Heating Energy Consumption in Educational Building. In: Proceedings of 15th International Conference on Civil and Architecture Engineering (ICCAE-2018) 25. Ai Z, Mak C, Niu J, Li Z (2011) The assessment of the performance of balconies using computational fluid dynamics. Build Serv Eng Res Technol 32(3):229–243 26. Prianto E, Depecker P (2002) Characteristic of airflow as the effect of balcony, opening design and internal division on indoor velocity: a case study of traditional dwelling in urban living quarter in tropical humid region. Ener Build 34(4):401–409 27. Prianto E, Depecker P (2003) Optimization of architectural design elements in tropical humid region with thermal comfort approach. Ener Build 35(3):273–280 28. NBC (2016) SP 7: 2016, National building code of India 2016. Bureau of Indian Standards, The National Standards Body of India 29. Germano M (2007) Assessing the natural ventilation potential of the Basel region. Ener Build 39(11):1159–1166. https://doi.org/10.1016/j.enbuild.2006.07.010

Optimization of the Integrated Daylighting and Natural Ventilation in a Commercial Building Harshita Sahu and Vijayalaxmi J.

Abstract Integrating a building with a more efficient natural ventilation and daylighting system reduces the dependency on artificial lighting and HVAC systems that account for more than 50% of the total building energy. As commercial buildings are one of the main typologies of buildings that are largely dependent on active systems, maximizing the natural ventilation and daylighting potential can make the building more resilient. For this study, the atrium space, which forms a central connectivity point in a commercial space, is selected and optimized for maximum natural ventilation and daylighting while maintaining occupant comfort. A field study of an existing commercial building, similar to the proposed case, is conducted and data is collected for validation. A quantitative analysis is done to study the impact of various natural ventilation and daylighting strategies on indoor thermal and visual comfort through simulations. It is found that among the 11 design variables selected, the window-to-wall ratio and the type of glazing have the most impact on the daylighting and thermal comfort of the space. The opening schedule, vent area, and the size of the opening have the maximum impact on natural ventilation.

1 Introduction Incorporating passive design systems in buildings at an early design stage or even postconstruction as a retrofitting solution can be beneficial not only for building energy use minimization but also as a resilience strategy [1]. Several green building rating systems promote resilience in buildings by suggesting the maximization of natural ventilation and daylighting potential to maintain thermal and visual comfort. This can be done by providing openings on the façade or components such as courtyards, light wells, and atriums. In the case of commercial buildings, atriums are the most commonly found design component as they provide both physical (lifts, escalators) and visual connection in planning while also facilitating the space with daylight and fresh air. The solution to this is through optimization of the building’s energy consumption by providing much more dependency on natural ventilation with some addition of hybrid ventilation systems and lighting control so that it can achieve 100% comfort hours.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_9

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A study by Galal [2] considered atrium types, orientation, climatic zone, well index, atrium materials, and glazing. It is found that among the atrium types an attached atrium should not be used for hot and dry climates (excessive heat gain through the glazing), atriums with external walls should not be oriented West or South, atriums are more efficient in cold and temperate climates and need to be properly designed for warmer climates to reduce heat gain. It is also found that the increase in well index increases stack ventilation but decreases the daylight factor. The wall materials do not have much impact on the ventilation or daylighting performance but the change in glazing materials affects both parameters. This difference in the amount of impact of the factors on the research parameters can help optimize the atrium space more efficiently. In another study, by Ahadi et al. [3], light wells are optimized for daylighting and stack ventilation. It is found that a square light well with 4 × 4 m minimum dimensions and a rectangular light well with 3 × 4 m dimensions are the most efficient with an effective decrease of 2 °C on the average inside temperature. In both cases, the base case is designed and one of the parameters (like ventilation) is investigated for acceptability. This is followed by the same analysis for the other parameter. In the case of acceptability, the first parameter is re-evaluated. If adequate, the conclusions can be drawn, if not then further alterations are done to achieve the required results. This study aims to optimize the natural ventilation and daylighting in the atrium of a commercial building through an increase in comfort hours. Instead of individual impacts, the combined effect of natural ventilation and daylighting on indoor comfort is studied for better optimization.

2 Methodology and Materials A commercial building similar to the proposed case is selected for the case study. Field measurements of daylight and air temperature are collected for validation. This field study building is modeled using DesignBuilder for validating air temperature and Radiance for validating Daylighting. To demonstrate the optimization of daylight and ventilation, another building is modeled and the process of optimization is shown. The methodology as shown in Fig. 1 is followed for the optimization study.

3 Study Area The area selected for this study is Raipur city in India (Fig. 2). It is the capital of Chhattisgarh and is located centrally in the state. The temperature typically varies from 13 to 41 °C and is rarely below 10 °C or above 44.5 °C. The hot season lasts from April to June, with an average daily high temperature above 38 °C.

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Fig. 1 Stepwise flowchart of the methodology used for the study

Fig. 2 Area selected for the study

4 Case Study The building selected for the case study and field study measurement is a commercial complex measuring 12,500 m2 and has seven floors of commercial space. Its front façade is oriented 15° NW. The building has 4 entries, one on each façade, making the building accessible from all sides. The entrance corridors end in the atrium with shops on the side. The shops have their HVAC systems while the common spaces are all naturally ventilated. The connectivity to each floor through escalators, elevators, and lifts is provided in the atrium space. The atrium is approximately 750 m2 with a glazed roof (Fig. 3). A 3D model was created in DesignBuilder for validation. Similar WWR and openings are input along with some measurements (surface temperature used for CFD) for the validation results.

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Fig. 3 Plan of the case study building and field data measurements points

Fig. 4 Light meter (left) and Temperature and humidity measuring instrument (right)

4.1 Measurement Tools Used The lux levels, air temperature, surface temperature, humidity, and wind speed are measured for the outside as well as inside of the building and atrium spaces from 10 am to 6 pm on the hot day of 12th April 2022. For measuring lux levels, Lutron LX-1102 Light Meter is used. It has 5 ranges from 0 to 40/400/4000/40000/400000 lx. The testo 635-2 temperature and humidity measuring instruments are used (Fig. 4). It has a wide selection of optional sensors that can be used to measure temperature, relative humidity, wind speed, and even absolute pressure. For the validation, a model of the case study building is created in DesignBuilder, and the Radiance (for daylighting) and CFD (for ventilation) simulations are done.

4.2 Measurements and Validation The temperature, humidity, indoor air speed, and lux levels from five points are measured during field measurements as shown in Table 1.

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Table 1 Lux, temperature, humidity, and wind speed measurements Time

Temperature at ground level (°C)

Humidity at ground level (%)

Indoor air velocity (m/s)

NE corner (lux)

SE corner (lux)

Off-center (lux)

NW corner (lux)

SW corner (lux)

10.00

34.2

36.9

0.1–0.36

365

405

4103

1386

982

11.00

35.6

37.5

0.06–0.25

1220

995

5290

5869

1550

12 noon

36.8

38.7

0.07–0.29

4900

1384

4250

6000

1496

13.00

37.6

39.2

0.06–0.16

2865

1093

8520

2065

1560

14.00

37.4

39.3

0.07–0.16

2750

3000

5300

940

935

15.00

36.3

41.7

0.06–0.21

2560

3215

3130

740

723

16.00

35.9

42.4

0.09–0.27

986

2660

840

173

208

17.00

34.8

39.3

0.07–0.16

275

300

430

67

61

18.00

33.1

44.8

0.08–0.19

37

55

39

95

102

4.2.1

Daylighting

The lux levels are taken for multiple points. Concerning the model, the values are checked for 5 points, one each on the NW, NE, SW, and SE corners and one point offcenter (Fig. 5). The values lie in the corresponding ranges. Through the daylighting analysis, the sun movement as well as the self-shading of the atrium space is evident. This should be taken into consideration during the base case modeling.

4.2.2

Ventilation

The indoor wind velocity is verified for alternate hours for 12th April, i.e., 2 P.M. (0.07–0.16 m/s), 4 P.M. (0.09–0.27 m/s), and 6 P.M. (0.08–0.19 m/s) as shown in Fig. 6. The temperature increased about 0.1–0.2 °C when measured on each floor, i.e., for every 3 m, and the airspeed did not exceed 0.4 m/s. The CFD analysis is done.

4.2.3

Indoor Temperature

For validation of indoor temperature, the energy plus simulation of the atrium is done (Fig. 7). The outside dry bulb temperature and radiant temperature are considered. For 12th April, the radiant temperature differed from the actual measurements by 0.8–1.4 °C.

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10 A.M. Range 825-4942 lux

12 Noon Range 1206-6018 lux

2 P.M. Range 717-5719 lux

4 P.M. RANGE 356-3189 LUX

11 A.M. Range 648-6450 lux at

13P.M. Range 790-9446 lux

3 P.M. on 12the April - Range 433-3871 lux

5 P.M. RANGE 40-472 LUX

6 P.M. RANGE 10-120 LUX Fig. 5 Illuminance levels from 10.00 am to 6.00 pm in lux

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0.07-0.16 m/s

0.09-0.27 m/s

135

0.08-0.19 m/s

Fig. 6 Indoor air velocity at 2 and 4.00 pm. on 12th April range

Fig. 7 Radiant temperature results

5 Base Case Modeling 5.1 Design Constants and Variables Some of the design factors are considered constants, while the others are set as variables as shown in Fig. 8.

Fig. 8 Design constants and variables assumed for the study

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5.2 Base Case Design A site is considered for demonstrating the optimization based on the field study validated model as shown in Fig. 9. For the base case simulation, the proposed plan by the site developer is taken into consideration. The atrium is analyzed, and it is found that the atrium dimensions are 48 m × 20 m × 16 m. This gives a ratio of 2.4:1 (L: B) as shown in Fig. 9. The longer side of the building is oriented N-S as per the sun path analysis. The occupant load is taken as 3.7 area (m2 ) per person or 10 occupants per 37 m2 (for commercial buildings as per NBC). As per the previous study, the more ideal dimensional ratio for an atrium with 16 m height is 2:1. Therefore, 3 base case options are generated as shown in Fig. 10. In Case 1, the atrium is exposed on both sides to the outside. In Case 2, it is not exposed on any side, and in Case 3, the atrium is exposed along one side to the external wall.

Fig. 9 Site plan and floor plan on proposed site

Fig. 10 Suggested alternatives to the base case plan

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5.3 Base Case Simulation All 3 atriums in all 3 cases are simulated with 100% rooftop glazing for overcast sky conditions. For Case 1, almost 50% of the area gets glare. In Case 3, the amount of area receiving glare is reduced but the Northern part of the atrium gets much less daylight than the rest of the space. Case 2 with the central atrium is the most ideal as the daylighting is the most uniformly distributed and predominantly between 620 and 2485 lx as shown in Fig. 11. As per ECBC, illuminance between 500 and 2500 lx is considered Autonomous useful daylight Illuminance (it requires no artificial lighting during daytime). The atriums are further simulated as naturally ventilated zones in energy plus. It is found that the radiant temperature of the Case 3 atrium (1 external façade) is the least followed by Case 2 (central atrium) as seen in Fig. 12. Case 1 had the highest radiant temp due to multiple glazing resulting in excess solar radiation and heat gain. Cases 2 and 3 are further simulated for wind speed before optimization. From the CFD analysis in Fig. 13, it can be seen that both the atriums have similar airflow patterns due to a similarity in dimension. Case 3 has a slightly higher indoor air speed as it has multiple inlets (façade and roof). On the other hand, in Case 2, the airflow on the ground floor level is better as the atrium is centrally located. Therefore, for optimization both of the cases can be simulated. Additionally, a combination of the plan with one small prism atrium and one small central atrium is also tested for efficiency.

Fig. 11 Daylight simulation results of all 3 base cases

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Fig. 12 Temperature simulation results of all 3 base cases

5.4 Inputs Used for Optimization The boundary conditions for optimization are given in Tables 2 and 3.

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Fig. 13 CFD simulation results of all 3 base cases Table 2 Design variables inputs used for the optimization Design variables

Min. value

Max. value

Step (optimization)

Window-to-wall ratio

0

100

5

Façade type (height of fenestration from slab level in m)

–

–

0.5

Façade glazing type

–

–

11 options

Roof glazing type

–

–

11 options

Roof window shading coefficient

0

1

0.05

% External window area opening

0

100

1

% Roof window area opening

0

100

1

Infiltration (acph)

6

10

0.5

Vent area (m)

0

10

0.5

Roof vent area (m)

0

10

0.5

Nat vent set-point temp. (°C)

10

30

–

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Table 3 The types of glazing used for optimization Glazing type

U-valuea (W/m2 -K)

VLT

ASHRAE compliant, all types

4.2

0.56

ASHRAE compliant, metal framing, operable

3.2

0.27

ASHRAE compliant, metal framing, fixed

3.7

0.27

Dbl Clr 3 mm/13 mm air

2.7

0.81

Dbl Clr 6 mm/13 mm air

2.6

0.78

Dbl LoE (e2 = 0.1) Clr 6 mm/13 mm air

1.7

0.74

Dbl LoE (e2 = 0.1) Tint 6 mm/13 mm air

1.7

0.44

Dbl LoE (e3 = 0.1) Clr 3 mm/13 mm air

1.8

0.76

Dbl LoE (e2 = 0.4) Clr 3 mm/13 mm air

2.3

0.77

Trp LoE (e2 = e5 = 0.1) Clr 3 mm/13 mm air

0.99

0.66

Trp LoE Film (55) Clr 6 mm/13 m air

1.2

0.45

a

Source ISO 10292:1994

6 Findings and Results 6.1 Optimization Case 1: Optimization simulations are done for Case 1, and the results are shown in Fig. 14. 11 design variables are input, and the model is simulated for minimum discomfort hours. 127 iterations are performed for Case 1, and 45.5% of the total hours are brought into the comfort zone through iterations of WWR, position, size, type of glazing, percentage of glazing area open, etc. The Daylighting, solar radiation, wind speed, relative humidity, and temperature are checked for the top 2 most efficient cases of Case 1 (Figs. 15, 16 and 17). As per ECBC, illuminance between 500 and 2500 lx is considered Autonomous useful daylight Illuminance. For Iteration A, the daylighting on an overcast day is in the Autonomous UDI range while for Iteration B there is glare for the same conditions. The maximum radiant temperature for Iteration A is slightly higher than Iteration B but the number of hours in the comfort zone is more. Therefore, for Case 1, Iteration A is the most suitable option (Table 4). Case 2: The daylighting, solar radiation, wind speed, relative humidity, and temperature are checked for the top 3 most efficient cases (Fig. 18). The comfort hours are maximum for Iteration A followed by B and C. With respect to daylighting, Iteration C is the most suitable as the daylighting on an overcast day is in the Autonomous UDI range while for Iteration B there is slight glare for the same conditions. Iteration A exhibits inadequate daylighting levels. The maximum radiant temperature for Iteration C is the lowest followed by Iteration B and Iteration A (Figs. 19, 20, 21, and 22). Therefore, for Case 2, Iteration C is the most suitable option (Table 5).

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Fig. 14 Optimization results of base Case 1

Fig. 15 Temperature and CFD results of Case 1 A

Case 3: The daylighting, solar radiation, wind speed, relative humidity, and temperature are checked for the top 3 most efficient cases (Fig. 23). The comfort hours are maximum for Iteration A followed by B and C. With respect to daylighting, Iteration A is the most suitable as the daylighting on an overcast day is in the Autonomous UDI range while for Iteration C some areas have inadequate daylighting levels for the same conditions. Iteration B exhibits excessive glare in the center and near the external façade (Figs. 24, 25, 26, and 27). The maximum radiant temperature for

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Fig. 16 Temperature and CFD results of Case 1 B

Fig. 17 Daylight results of Case 1 A and B Table 4 Comparative analysis of base Case 1 A and B Model

Total comfort hours

Daylight (lux)

Solar radiation (kWh/m2 )

Wind speed (m/s)

Indoor air velocity (m/s)

Relative humidity (%)

Radiant temperature (°C)

Case 1-A

3893 (44.5%)

617–2096

45–220

0–2.3

0–0.3

37.52 (Jan)–70.17 (Aug)

24 (Dec)–40 (May)

Case 1-B

3890 (44.4%)

1384–3023

45–220

0–2.3

0–0.25

37.53 (Jan)–70.42 (Aug)

24.5 (Dec)–39 (May)

Optimization of the Integrated Daylighting and Natural Ventilation …

Fig. 18 Optimization results of base Case 2

Fig. 19 Daylight and temperature results of Case 2 A and B

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Fig. 20 CFD results of Case 2 A and B

Fig. 21 Daylight and temperature results of Case 2 C

Fig. 22 CFD results of Case 2 C

Iteration C is the lowest closely followed by Iteration A and then last Iteration B. Therefore, for Case 3, Iteration A is the most suitable option (Table 6). Based on the results, Case 2 with Iteration C is the most efficient followed by Case 3 Iteration A, and lastly Case 1 Iteration A (Table 7).

Optimization of the Integrated Daylighting and Natural Ventilation …

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Table 5 Comparative analysis of base Case 2 A, B, and C Model

Total comfort hours

Daylight (lux)

Site solar radiation (kWh/m2 )

Site wind speed (m/s)

Relative Radiant humidity (%) temperature (°C)

Case 2-A

3993 (45.6%)

13–573

45–220

0–2.3

34.22 (Jan)–73.58 (Aug)

24.6 (Dec)–35.4 (May)

Case 2-B

3984 (45.4%)

153–3182

45–220

0–2.3

37.53 (Jan)–72.83 (Aug)

26.6 (Dec)–34.1 (May)

Case 2-C

3983 (45.4%)

292–2268

45–220

0–2.3

36.16 (Jan)–69.88 (Aug)

27.02 (Dec)–34.06 (May)

Fig. 23 Optimization results of base Case 3

Therefore, for the proposed base case design, a central atrium with 80% WWR, ASHRAE compliant, metal framing, operable (U-value—3.2) glazing placed centrally at 1 m height from the ground for the façade is the recommended optimization.

146

Fig. 24 Daylight and temperature results of Case 3 A and B

Fig. 25 CFD results of Case 3 A and B

Fig. 26 Daylight and temperature results of Case 3 C

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Fig. 27 CFD results of Case 3 C

Table 6 Comparative analysis of Base Case 3 A, B, and C Model

Total comfort hours

Daylight (lux)

Site solar radiation (kWh/m2 )

Site wind speed (m/s)

Relative humidity (%)

Radiant temperature (°C)

Case 3-A

3984 (44.4%)

291–2510

45–220

0–2.3

37.2 (Jan)–70.7 (Aug)

26.4 (Dec)–33.8 (May)

Case 3-B

3977 (44.3%)

556–4244

45–220

0–2.3

37.2 (Jan)–70.6 (Aug)

26.6 (Dec)–34.1 (May)

Case 3-C

3976 (44.3%)

202–2006

45–220

0–2.3

37.4 (Jan)–71.2 (Aug)

26.3 (Dec)–33.5 (May)

6.2 Scope and Limitations of the Study This study applies to both proposed buildings as well as existing ones of composite climatic zone. The interventions can be incorporated at the design stage or implemented as a retrofitting solution for pre-existing buildings. This study will help determine the number of hours that can be increased in the comfort zone without the use of active strategies. Furthermore, the correlation between daylighting and ventilation parameters can be found in the results. The limitation of this study is that the atrium space should have external access to outdoor air and daylight. Therefore, the space should have an external facade or have provisions for opening at the roof level. The study does not apply to any central space that is double in height or more.

80

50

ASHRAE compliant, metal framing, operable (U-value—3.2)

Trp LoE Film (55) Clr 6 mm/13 m air (U-value—1.2)

Case 2

Case 3

40

ASHRAE compliant, metal framing, operable (U-value—3.2)

Case 1

WWR (%)

Type of glazing

Case

Table 7 Suggested optimization for each base case option Type

Height—1 m External from the ground, windows centered and skylight

Height—1 m External from the ground, windows centered and skylight

Height—1.5 m External from the ground, windows centered and skylight

Position

6.5

8

6

ACPH

291–2510

292–2268

617–2096

Daylight (lux)

3984 (44.5%)

3993 (45.6%)

3893 (44.5%)

Total comfort hours

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7 Conclusion The results of the base case simulation show that increasing the number of external envelope elements such as facade or roof results in higher lux levels which can cause visual discomfort due to glare and increase the heat gain resulting in more thermal discomfort. All the cases are simulated to find the most suitable façade system to maintain the thermal and visual comfort of the atrium space for at least 45% of the hours. It is found that among the 11 design variables selected, the window-to-wall ratio and the type of glazing have the most impact on the daylighting and thermal comfort of the space. The opening schedule, vent area, and percentage of openings have the maximum impact on natural ventilation. In conclusion, multiple design variable needs to be taken into consideration for optimization on an atrium including external envelope and facade elements.

References 1. Vijayalaxmi J, Sekar SP (2013) Thermal performance of naturally ventilated residential buildings with various room orientations in the hot-humid climate of Chennai, India. J Archit Plann Res 1–22 2. Galal KS (2018) The impact of atriums on thermal and daylighting performance. Archit Plann J (APJ) 24(Iss. 1), Article 4 3. Ahadi AA, Saghafi MR, Tahbaz M, The optimization of light-wells with integrating daylight and stack natural ventilation systems in deep-plan residential buildings: a case study of Tehran. J Build Eng

A Methodology to Optimize Thermal Conditions of Built Forms for Humans and Birds in a Birds Sanctuary Sowmiya R. and Vijayalaxmi J.

Abstract Ecological sustainability should be a holistic approach, where we consider all the biotic and abiotic factors of an ecosystem. Due to climate change, changes in vegetation patterns, a rapid increase of urbanization, depletion of resources, etc., in many ecological cases, we have crossed the line of conservation and now we face the phase of rejuvenation or revival of an eco-sensitive space. For the benefit of flora and fauna, environment much research is done, but the consideration of other species is very less. Sanctuaries being home to 80% of the migratory and native birds has to be rejuvenated by making them a vital space for avian habitat. This paper deals with the impact of buildings in a bird sanctuary on the indoor thermal performance of humans and the impact on birds (breeding temperature) due to human intervention. The site chosen for this study is the Chitrangudi Birds Sanctuary in Tamil Nadu, India. To achieve the optimal thermal conditions for the birds in and around a bird sanctuary, many stages of analysis and strategies benefiting both humans and birds have to be considered. The strategies derived after analysis for the site considering human adaptive thermal comfort and bird breeding temperature of each species are—roof, WWR%, shading device, the shape of the building, and jaali. Quantification of different passive strategies that balance both indoor comfort for humans and outdoor comfort for birds is carried out. The various conditions for which iterations are carried out are Orientation and form—10 conditions, aspect ratio:11 ratio each for two perimeter conditions (22 conditions of aspect ratio), roof structure and material—15 conditions of various pitch angles and overhang, WWR and Height—20 Opening condition of WWR, building height—7 height conditions, 5 Sill Heights and 5 Window heights, level of openings—8 conditions, perforated screens—6 configurations, and shading devices—8 conditions for optimizing indoor air temperature as well as lighting levels. Future studies can include noise. This study establishes a methodology for optimizing indoor comfort for humans with minimum disturbance to the requirement of avian microclimate in a bird sanctuary. This methodology can be followed in other sanctuaries to ensure safe human interventions to assist flora and fauna.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_10

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1 Introduction Birds from prehistoric times have been recognized and valued for their ecological, economical, and religious importance. Different birds have their special role in the ecosystem that helps in maintaining balance [1]. Their ability to acclimatize and adapt to the change in environment has helped them to bear the climate change, urbanization, and other possible threats. However, many birds are extinct and many are on the verge of extinction (Fig. 1). When the birds migrate to different parts of the world, they fly through different built forms and land patterns. One of the threats posed to the birds and ecosystem is the change in the outdoor environment due to climate change, change in vegetation patterns, a rapid increase of urbanization, and exploitation of resources [2, 3]. Currently, the phase to conserve the endangered birds is changing to the phase to revive the eco-sensitive zones. Sanctuaries are being used as home to 80% of migratory and native birds. According to Rahamani et al. [4], nearly 50% of the bird’s life is in and around the sanctuary. It is a place for breeding, nesting, etc. Some birds reside in the sanctuaries of India (Fig. 2). When a built form is placed in the avian habitat, there is a change in microclimate [5]. It is important to make sure that the built form does not affect the outdoor thermal environment of birds and the indoor thermal comfort of humans. These built forms are necessary to carry out research, rehabilitate injured birds, tag migrate birds, etc., as these activities can assist and rejuvenate migratory birds in a sanctuary [6]. But this sensitivity to the environment is of utmost importance to the birds to continue a location for migration. Hence, there is a need to design structures that enhance indoor thermal performance for humans such that their impact on the outdoor environment Fig. 1 Indian statistical graph. Source IUCN

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Fig. 2 Hierarchy of bird’s habitat

must not deter the birds. This study focuses on the methodology to achieve the same with a case study of Chitrangudi Birds Sanctuary. In the further sections, the paper discusses the parameters that impact the thermal comfort of both humans and birds. The parameters studied are, 1. Orientation and form—10 conditions 2. Aspect ratio:11 ratio each for two perimeter conditions (22 conditions of aspect ratio) 3. Roof structure and material—15 conditions of various pitch angles and overhang 4. WWR and Height—20 Opening condition of WWR. (1) (2) (3) (4)

Building height—7 height conditions, 5 Sill Heights, and 5 Window heights Level of openings—8 conditions Perforated screens—6 configurations Shading devices—8 conditions.

A quantification of different advance passive strategies that balance both indoor comfort for humans and outdoor comfort for birds is carried out.

2 Site Location Chitrangudi Birds Sanctuary is located near Chitrangudi village, in Tamil Nadu state, India (Fig. 3). The study of this sanctuary is considered as it was once a haven for migratory birds. Due to the rapid growth of invasive species and less awareness of the habitat, there have been insensitive construction activities in the eco-sensitive zone (Fig. 2). This is impacting the ingress of migratory birds. The Chitrangudi Bird Sanctuary is a total 44 ha land area that is located within an eco-sensitive zone of 300 ha, which also consists of the Kanjirankulam sanctuary of 99 ha. There are a total of 124 species of birds, among which 43 species are migratory breeding birds. They mostly come in October, November, December, January, and February for breeding [7]. Certain birds come in March, April, May, June, July, August, and September. These birds mostly arrive here to breed on the Babul tree, and also, they feed on the other species within the sanctuary. To make it a better

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Fig. 3 Location of Chitrangudi birds sanctuary

space for the Avian species, vital interventions are needed in favor of both Avian and human beings. The following is a breakdown of the paper’s structure: A workflow followed is presented in Sect. 3. Study and assessment of the parameters for ideal design values are described and analyzed in Sect. 4, pertaining to Orientation and form, Aspect Ratio, Roof structure, and WWR with heights. In Sect. 4.4, different iterations of WWR% with respect to building height, level of openings, perforated screens, and shading devices are provided. The optimum iterations and design values are determined, and the results are discussed in Sect. 5.

3 Methodology A site study of the sanctuary is carried out to understand the conditions of the Chitrangudi bird sanctuary. Several iterations for optimum design to achieve thermal comfort are studied and simulated using DesignBuilder (Fig. 4). The aspects studied are Orientation and Form, Indoor Air Temperature and Humidity, Aspect Ratio, Roof structure, WWR, and Height. The obtained analysis is then used to conclude the optimized values for strategies studied to provide thermal comfort.

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Fig. 4 Process work flow

4 Study and Assessment of the Parameters for Ideal Design Values 4.1 Orientation and Form For warm and humid climates, 0° to 30° from the North toward the East would be the optimized orientation. But when the structure is the courtyard with cross ventilation, 45° and perpendicular to the predominant wind direction is the best. To find the optimized form and orientation with respect to the site nearly 16 different shapes or form of the same area is simulated in different orientations such as 0°–45° (Fig. 5). The parameters considered for optimization are Indoor Air temperature, Humidity, surface-to-volume ratio, perimeter-to-area ratio, wind direction, and air velocity in indoor spaces. The constant parameters for all the shapes are Table 1.

Fig. 5 16 different forms considered for simulation

156 Table 1 Boundary conditions of case building

Sowmiya R. and Vijayalaxmi J. Variable

Values

WWR

40%

Wall material

Cement plaster + brick + cement plaster

Roof material

Tiles + RCC slab + cement plaster

Height

3m

Area

45 m2

Fig. 6 Comparing the 2 cases for aspect ratios

The optimization of form and orientation are carried out through DesignBuilder simulation and analysis for 10 angles 0°, 5°, 10°, 15°, 20°, 25°, 30°, 35°, 40°, and 45°. According to the indoor air temperature and humidity, the rectangular shape with central courtyard oriented 45° to north is observed to perform well.

4.2 Aspect Ratio Aspect ratio is performed considering two cases: Case 1—Aspect ratio for both perimeter and courtyard for 11 ratios. Case 2—Aspect ratio only for the perimeter of the building for 11 ratios. The ratios are 1:1, 1:1:1, 1:1:2, 1:1:3, 1:1:4, 1:1:5, 1:1:6, 1:1:7, 1:1:8, 1:1:9, and 1:2 (Fig. 6).

4.3 Roof Structure Different angles of the sloping roof and overhang roof for the 1:2 aspect ratio rectangle with a central courtyard are considered for simulation. The various overhangs are combinations of the slope as shown in Table 2.

A Methodology to Optimize Thermal Conditions of Built Forms … Table 2 Slope and pitch roof for the 1:2 rectangle with central courtyard

Slope

Pitch

45°

1 m, 2 m, 3 m, 0.3 overhang, 0.45 overhang, 0.6 overhang

60°

1 m, 2 m, 3 m, 0.3 overhang, 0.45 overhang, 0.6 overhang

30°

0.3 overhang, 0.45 overhang, 0.6 overhang

157

Fig. 7 Comparing flat roof and slope with overhang

From the optimization, it is observed that rectangle with central courtyard of 1:2 aspect ratio along with pitch roof of 60° with 0.6 overhang performs well in terms of indoor air temperature (Fig. 7).

4.4 Window Wall Ratio 4.4.1

WWR and Height of the Space

Assessing the performance of space considering the WWR with respect to height will help in understanding the optimal combination of the two aspects mentioned. The iterations of WWR% for 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, and 100% for the whole building and different orientations, NorthEast, North-West, South-East, South-West, and different room heights for 3, 3.5, 4, 4.5, 5, 5.5, and 6 are studied and tabulated. The study addresses the temperature and humidity parameter and concludes with a result of better performing iteration.

4.4.2

WWR% with Respect to Window and Sill Height

Tables 3 and 4 discuss the iterations to study the WWR % and lux levels. When the window height of 1.5 m above 0.8 m of sill level runs throughout the wall, which is 42.8% of the WWR, nearly 78.14% of area receives 500 lx (Fig. 8). When the window height of 2.1 m above 0.2 m of sill level runs throughout the wall, which is 60.0% of the WWR, nearly 82.24% of area receives 500 lx (Fig. 9).

158 Table 3 Iterations showing WWR% wrt window and sill height

Sowmiya R. and Vijayalaxmi J. Iterations

WWR% of opening

0.8 m sill height and 1.5 window height 60%, 65%, 70%, 75% 0.6 m sill height and 1.7 window height 65%, 70%, 75%, 80% 0.4 m sill height and 1.9 window height 70%, 75%, 80% 0.2 m sill height and 2.1 window height 75%, 80% 0 m sill height and 2.3 window height

Table 4 Iterations for 26 m × 13 m

80%,85%,

For 26 m × 13 m Iterations

WWR% of opening max

0.8 m sill height and 1.5 window height

42.85

0.6 m sill height and 1.7 window height

48.57

0.4 m sill height and 1.9 window height

54.28

0.2 m sill height and 2.1 window height

60

0.0 m sill height and 2.3 window height

65.7

Fig. 8 WWR% wrt window and sill height

Fig. 9 WWR% wrt window and sill height

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4.4.3

159

WWR% with Respect to Different Levels of Opening

The cases studied to analyze the WWR% for different levels of opening are as follows: . . . . . . . .

Without window Window on one side Window on one side + 0.3 m lintel opening Window on one side + 0.5 m lintel opening Window on opposite sides Window on opposite sides + one side 0.5 m lintel opening Window on opposite sides + one side 0.5 m lintel opening leeward Window on opposite sides + one side 0.5 m lintel window.

For this iteration, a room size of 5 m × 6 m is considered. The clear height of the room is 3.5 m. The orientation considered is 45° from North toward East. The base case taken for the iteration is 0% opening to compare the indoor air temperature, natural lighting, and humidity. Here, the sill height is 0.8 m and the window height is 1.5 m. 42.8% WWR above the sill height of 0.8 m and the window height of 1.5 m for the full wall length on opposite sides along with 0.5 m lintel opening on one side has reduced the indoor air temperature by 1.7–2.3 °C with 85.96% area of 500 lx and have increased humidity by 3–5% (Fig. 10). 42.8% WWR above the sill height of 0.8 m and the window height of 1.5 m for the full wall length on opposite sides along with 0.5 m lintel opening has reduced the

Fig. 10 42.8% WWR wrt different levels of opening

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Fig. 11 42.8% WWR wrt different levels of opening

indoor air temperature by 1.7–2.6 °C with 87.84% area of 500 lx and have increased humidity by 1–1% (Fig. 11). For the next iteration, same room size of 5 m × 6 m is considered. The clear height of the room is 3.5 m. The orientation considered is 45° from North toward East. The base case taken for the iteration is 0% opening to compare the indoor air temperature, natural lighting, and humidity. Here, the sill height is 0 and the window height is 2.1 m. In Fig. 12, 60% WWR wrt different levels of opening is considered.

4.5 Perforated Screens Height of the perforated screen is set to 2.1 m height. The cases studied to analyze optimum perforated screen design are as follows: . . . . . .

0.05 × 0.05 jaali; 0.05 × 0.05 jaali + 0.5 m vent 0.10 × 0.10 jaali; 0.10 × 0.10 jaali + 0.5 m vent 0.15 × 0.15 jaali; 0.15 × 0.15 jaali + 0.5 m vent 0.20 × 0.20 jaali; 0.20 × 0.20 jaali + 0.5 m vent 0.25 × 0.25 jaali; 0.25 × 0.15 jaali + 0.5 m vent 0.30 × 0.30 jaali (one side and both side); 0.30 × 0.30 jaali + 0.5 m vent.

0.15 m × 0.15 m from plinth height to 2.1 m along with a 0.5 m vent above the lintel. The total number of punchers is 133 which is 14.25% of the wall and the vent is 28.53%. In this case, the indoor air temperature is reduced by 1.31 °C in May and

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Fig. 12 60% WWR wrt different levels of opening

1.56 °C in December and humidity increased by 0.5–2.9%. 34.16% of the floor area receives 500 lx of daylight (Fig. 13). 0.30 m × 0.30 m from plinth height to 2.1 m along with a 0.5 m vent above the lintel. The total number of apertures is 27, that is, 11.57% of the wall and along with the vent, it is 23.71%. The indoor air temperature is reduced by 2.32 °C in May and 2.11° in December and humidity decreased by 0.80–5.56%. 30.53% of the floor area receives 500 lx of daylight and 87.26% receives 150 lx (Fig. 14).

4.6 Shading Devices Shading device analysis is performed only for the horizontal projection, vertical fin of 45° angle at different distance, and rectangular vertical fin (Table 5).

5 Conclusions A number of studies and assessments were done to get an optimum design value to provide a comfortable indoor environment for human beings and a comfortable outdoor environment for birds. In terms of Indoor air temperature, 15° and 20° orientation performs well but along with predominant wind, 45° performs best. A

162

Fig. 13 2.1 m height 0.15 × 0.15 Jaali one side + 0.5 m Vent

Fig. 14 2.1 m height 0.30 × 0.30 Jaali two side + 0.5 m Vent

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Table 5 Comparison of shading devices Iteration

No. of puntures

WWR%

300 lx

500 lx

Without shading

19 + 0.5 m vent

40.6

83.12

46.99

0.6 m horizontal projection

19 + 0.5 m vent

40.6

82.49

46.65

0.8 m horizontal projection

19 + 0.5 m vent

40.6

80.32

41.604

1 m horizontal projection

19 + 0.5 m vent

40.6

79.32

29.9

1 mH + 45D V (5 mD)

19 + 0.5 m vent

40.6

76.56

26.91

1 mH + 1 m V (5 mD)

19 + 0.5 m vent

40.6

70.46

26.56

1 mH + 45D V (2.5 mD)

19 + 0.5 m vent

40.6

67.33

22.64

1 mH + 45D V (1.25 mD)

19 + 0.5 m vent

40.6

46.44

17.66

courtyard with all sides closed performs well for a naturally ventilated building. It is observed that the aspect ratio applied only to the perimeter performs better. 1:2 performs the best along with the increase in the courtyard opening percentage. The roof structure with 60° slope and 0.6 m is observed to perform better; there is a temperature difference of 0.13° to 1.5° compared to other roof structure. WWR and Height . Higher the WWR% along with higher the height performs better . When sill height is 0.8 m and window height is 1.5 m, 40–45% WWR along with 0.5 m vent opening on opposite side performs better . When sill height is 0 m and window height is 2.1 m, 60% WWR along with 0.5 m vent opening on opposite side performs better . Jaali on one wall—0.15 × 0.15 m performs well in indoor temperatures and in daylight 0.15 × 0.15 m + 0.5 m vent performs better . Jaali wall on opposite walls—0.30 × 0.30 m + 0.5 m vent performs well . Opposite wall with full stretch Horizontal and vertical jaali—0.15 × 0.15 m + 0.5 m vent performs better . Overall jaali—0.15 × 0.15 m + 0.5 m vent of Horizontal stretch on opposite wall performs better . Shading devices—1 m projection + 45° angle vertical fin at a distance of 5 m performs well. Understanding of different strategies that balance both indoor comfort for humans and outdoor comfort for birds is carried out. Aspects such as roof, WWR%, shading device, the shape of the building, and perforated screens are studied, and different iterations are made to find the optimum design values that will help create a desired thermal comfort level in the sanctuaries for the birds as well as humans.

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References 1. Mony M, Murali K, Dorai K (2018) Interactive phenomenon of plants and avian diversity in Vettangudi birds sanctuary, Southern India. Sci Int. https://doi.org/10.17311/sciintl.2018.65.70 2. Irannezhad M, Tahami MS, Ahmadi B, Liu J, Chen D (2022) Compound climate extreme events threaten migratory birds’ conservation in western U.S.. Sustain Horiz 3. https://doi.org/10.1016/ j.horiz.2022.100023 3. Buchan C, Franco AMA, Catry I, Gamero A, Klvaˇnová A, Gilroy JJ (2022) Spatially explicit risk mapping reveals direct anthropogenic impacts on migratory birds. Glob Ecol Biogeogr J Macro Ecol. https://doi.org/10.1111/geb.13551 4. Rahmani AR, Islam MZ, Kasambe RM (2016) Important bird and biodiversity areas in India: priority sites for conservation (revised and updated). Bombay Natural History Society, Indian Bird Conservation Network, Royal Society for the Protection of Birds and BirdLife International (U.K.) 5. Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl Geogr 29(3):390–401. https:// doi.org/10.1016/j.apgeog.2008.12.005 6. Moorman CE (2000) Designing and presenting avian research to facilitate integration with management. Stud Avian Biol 21 7. Krishnan M, Nagendran A, Pandiaraja D (2016) Survey of birds in Chitrangudi and Kanjirankulam village ponds in relation to vegetation: an avian paradise of South India. J Entomol Zool Stud 5:407–412

Applications of Smart Building Materials in Sustainable Architecture

Abstract With advances in material research, there is a growing interest in the knowledge of smart materials and their application in improving energy efficiency and the indoor environmental quality of a building. Smart materials can sense and react to their environment, and thus, they behave like living systems. Smart materials and technology produce useful effects in response to an external condition. They can be combined to provide changing and dynamic solutions for problems encountered while designing for energy efficiency. This paper is an introduction to the characteristics of smart materials and their application in the construction industry. Due to their small scale, smart materials enable us to design dynamic thermal environments. Smart materials are applied for façade systems, lighting systems, and energy systems. By focusing on the phenomena rather than the material artifact, the use of smart materials has the potential to dramatically increase the sustainability of buildings. We can save energy by operating discretely and locally only when necessary.

1 Introduction In the construction industry, the term ‘smart’ is used to define materials and structures that are capable of adapting to external stimuli. Smart architecture is dynamic— according to the need and the dynamic environmental conditions, it changes its performance-based parameters. The main characteristics of smart architecture are as follows: . Being dynamic and active . Being consistent with the environment . Being reactive and responsive. With the aid of electrical, mechanical, and chemical mechanisms, smart materials may adapt to a variety of applications and configurations by changing their characteristics, features, and functionalities in a context-dependent, repeatable manner. The following traits will be present in all smart materials and technologies, whether they are molecules, materials, composites, assemblies, or systems:

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_11

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. . . . .

Applications of Smart Building Materials in Sustainable Architecture

Response in real time They react to various environmental conditions The ‘material’ has its inherent intelligence Their response is predictable and discrete The response is restricted to the ‘activating’ event [1].

2 Classification of Smart Materials Smart materials are categorized into active and passive smart materials. Passive smart materials are those materials that can transfer energy without a change in their properties, e.g., optical fibers. Active smart materials are further divided into two types as shown in Fig. 1.

2.1 Type 1 These are those ‘that have the capability to change their properties—chemical, mechanical, electrical, magnetic or thermal, including electrochromic, magnetorheological, thermotropic, shape memory alloys. The energy input to a material affects the internal energy of the material by altering the material’s microstructure and the input results in a property change of the material’. Fig. 1 Types of smart materials. Source Author

2 Classification of Smart Materials

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2.2 Type 2 These have the capability to transform the energy from one form to another, including photovoltaic, thermoelectric, piezoelectric, photoluminescent, and electrostrictive. The energy input to a material changes the energy state of the material composition, but does not alter the material, it stays the same, but the energy undergoes a change [1]. Thermochromic, photochromic, mechanochromic, chemochromic, electrochromic—Input of thermal energy, UV radiation, deformation, chemical concentration, and electric tension brings about a change in color. Suspended particles/liquid crystals—Input of electrical tension brings about a change in color. Photocatalysts—Absorbs light and transforms it to a higher energy level and transfer the energy to a reacting substance. Phase change materials—Absorb or release latent heat through a change in their physical state. Thermotropic—Reversible temperature-dependent change in the properties of the materials. Shape memory—Shape memory alloys have a memory of their original shape and can deform or return to their original shape based on external stimuli. Photovoltaics—Radiation energy from the visible energy is used to produce an electrical current. Thermoelectric—Materials with the ability to generate electricity through temperature difference. Piezoelectric—Electrical current is generated by the input of elastic energy strain. Piezoelectric devices are bi-directional in nature; the mechanism may be reversed; and deformation can be created using an electric current. Electrochemical—Chemical energy is converted into electric energy and vice versa. Electrostrictive—Application of electrical current produces elastic energy strain. Electroluminescent, photoluminescent, chemiluminescent, and thermoluminescent—Materials that transform electricity, energy from chemical reactions, heat, and energy from the UV spectrum into radiation energy in the visible spectrum.

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3 Application of Smart Materials Smart materials and technology, with their ability to produce a useful effect in response to an external stimulus, can be combined to provide changing and dynamic solutions for problems encountered while designing for energy efficiency. The applications of smart materials to improve energy efficiency can be categorized as follows: 1. In building facades for dynamic shading and solar control (chromogenic materials) 2. In building elements to increase the thermal mass (PCMs) [4] 3. Nanocomposites 4. Photovoltaic (PV) and energy generation.

4 Chromogenic Materials Electrochromic devices are the preferred technology for large-area switching devices. Technology with chromogenic materials is being researched for building applications where daylight and energy use are balanced.

5 Suspended Particle Device Two conductors which are transparent sandwich a layer of active dipole particles (1 mm long) suspended in a polymer (Fig. 2). In the absence of electricity, these particles are in disarray and absorb light. When turned on, the particles align and boost transmission. Transmission levels of 6–75% and 15–60% are typical, with switching speeds of 100-200 ms. For operation, the device requires 100 V and a modest current [5].

6 Polymer Dispersed Liquid Crystals The PDLC film is created between two layers of transparent conductor-coated polyester or glass that act as electrodes (Fig. 3). The liquid crystal molecules are oriented toward the field when electricity is supplied to the ITO electrodes. This causes index matching between the droplets and the matrix and results in the transmission of light traveling in the direction of the applied field. When turned off, the device appears to be a translucent white. In the presence of an electric field, the liquid crystals align with the field, and the film turns fully transparent.

7 Electrochromic Glass

169

Fig. 2 Working principle of SPD system. Source ahnoyark.com

Fig. 3 Working principle of PLDC system. Source ahnoyark.com

The devices typically run between 24 and 120 V and consume less than 20 W/m2 . However, because the devices need constant electricity to function properly, they require more power than electrochromic devices [5].

7 Electrochromic Glass Electrochromic glass (also known as smart glass or dynamic glass) is a type of electronic tint glass that is widely employed for windows, facades, curtain walls, and skylights. The lithium ions (seen below as blue circles) travel from the innermost to the outermost electrode when a voltage is supplied to the outer contacts (conductors) (from left to right in Fig. 4). As the window now reflects lesser light and transmits less, it seems opaque (dark). The layers are built up by applying thin coatings onto a ‘substrate’ which is typically a heavy piece of glass or plastic.

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Applications of Smart Building Materials in Sustainable Architecture

Fig. 4 Electrochromic window system. Source tootsale.com

Electrochromic glass, which can be controlled by temperature sensors and manually by occupants to either improve the indoor environmental conditions or optimize energy use, provides architects with optimized solutions which can be integrated into design through daylighting [8].

8 Phase Change Materials (PCM) PCM are materials that use the phase transition phenomenon to collect or release latent heat without raising their internal temperature. When the temperature is above a set-point temperature during the day, the material experiences a phase change from solid to liquid, removing heat from the surrounding environment (Fig. 5). When the temperature falls below the set-point temperature at night, the material reverses phase to restore the collected heat to the environment. They can use natural convection to absorb either thermal energy or solar energy. Human comfort levels can be improved by increasing the thermal storage capacity of the building. This reduces the frequency of the internal air temperature changes, which results in indoor air temperatures that are closer to the ideal temperature for a longer period. Phase change materials are widely employed in direct gain or sunspace passive solar systems when space restrictions prevent the use of bigger thermal storage units. PCMs were traditionally employed to keep interior building temperatures stable. Therefore, internal building surfaces like walls, ceilings, or floors were the best locations for PCM. PCM is used as a component of the building’s thermal envelope. Microencapsulated paraffinic PCM is inserted as part of the attic insulation system or in the wall cavity. The benefits resulting from their use in the building envelope are: . Achieving a thinner building envelope . Reduction of internal temperature oscillations

9 Shape Memory Alloys

171

Fig. 5 Working of phase change materials. Source eliandelm.com

. Reduced energy use within buildings . Improved thermal comfort.

9 Shape Memory Alloys These materials can alter their shape in response to temperature changes and can revert to their former shape in reaction to external stress. Shape memory alloys are also called intelligent materials. When heated, an item in its low temperature (martensitic) condition that has been plastically deformed with all external pressures removed will restore its original (memory) shape (Fig. 6). Shape memory alloys come in a variety of forms, but nickel-titanium alloys, which include 45–50% titanium, are the most efficient and popular [2]. Intelligent reinforced concrete (IRC) has a range of characteristics, self-damping, including self-rehabilitation, and self-structural health monitoring (SHM). Piezoceramics and SMAs are the materials that tend to show these characteristics the most. They use SMA rods made of post-tensioned martensite for buildings that need to be retrofitted. When cracks develop in an RC element, SMA rods are annealed, enabling complete fuse and mend (Fig. 7). The origin and depth of structural cracks are identified via implanted activated piezoelectric sensors [7].

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Fig. 6 Shape memory alloy operation. Source Tabrizikahou et al. [7]

Fig. 7 Application of SMAs in construction. Source Tabrizikahou et al. [7]

10 Nanotechnology The use of nanosilica in a cement-based material’s composition can improve its compressive strength and durability. The inclusion of nanotubes or nanofibers in concrete can enhance its tensile and bending strength. Due to its sterilizing and antifouling qualities, titanium dioxide (TiO2 ) nanoparticles are utilized to cover glazing. Nanoparticle-based coatings can improve transparency, self-cleaning, adhesion, resistance to weathering, and fire resistance.

11 Nanotechnology-Based Thermal Insulation Materials One or more modes of heat transport are impeded or inhibited in thermal insulation materials developed with nanotechnology. They are able to reduce structures’ heat transfer coefficient as a result. Aerogel is a lightweight and porous synthetic material created by swapping out the liquid for gas in a gel. The solid as a result has a very low density and thermal conductivity. Aerogel insulation and vacuum insulation

12 Energy Generation

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panels are types of thermal insulation. They can significantly restrict various types of heat transmission. As a result, they are much more resistant to heat transfer than typical materials [3]. Aerogel insulation may become a practical substitute for current traditional building insulation materials if it can be produced in a more economic and environmentally feasible way, combining the advantages of most conventional building insulation materials. Aerogels’ tremendous potential is seen in their translucency and possible transparency, since they may one day result in large energy savings in windows and skylights.

12 Energy Generation 12.1 Photovoltaics Photovoltaics actively use solar radiation by converting it into electrical energy; they can also be used as a type of passive sun shading. Semiconductors like silicon, which are both bad conductors and insulators, are used to produce the photovoltaic effect. However, by adding ‘dopants’ which are small impurities, the flow of electrons through the material can be controlled. Between two layers of n-type silicon is a layer of p-type silicon that makes up a solar cell. As sunlight falls on a solar cell, silicon electrons are expelled, resulting in the formation of ‘holes’. The electrons will flow to the n-type layer and the holes to the p-type layer if this occurs in an electric field. When the layers are connected by a metallic wire, electrons will pass the depletion zone from the n-type layer to the p-type layer and then return through the external wire of the n-type layer. This creates a flow of electric current (Fig. 8). Factors that affect the performance of a photovoltaic system are: . Location, tilt, and orientation . Over shading—Topography, cloud cover, solar obstructions, self-shading, etc.

Fig. 8 Grid-connected PV system (left), working of a solar cell (right). Source Moussa [6]

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. Temperature—The PV modules efficiency reduces by 0.4–0.5% for every 10C increase in ambient temperature above 250 °C . Panel efficiency—It will depend on the type of solar cell, system design, and technology of the module. The efficiency for a crystalline silicon-based module is between 12 and 14%, and a thin film-based module has an efficiency of 10–11%.

12.2 Piezoelectric Material By applying stress or strain to a piezoelectric material, mechanical energy is converted to electric energy and vice versa. A primary cell misalignment produces this effect, which is a link between electron energy divergence and mechanical deformation (Fig. 9). By the same principle, piezoelectric actuators transform electricity into mechanical movements that are used to adjust mirrors, lenses, and other car components. Piezoelectric materials are more environmentally conscious because they dissipate energy from structural deformations and disturbances, resulting in lower energy consumption and CO2 emissions [7]. Piezoelectric tiles are made of particular materials, such as crystals and ceramics, in which an electric charge accumulates when mechanical stress, such as a foot pushing down, is applied (Fig. 10). According to a case study, 260 ‘Sustainable energy floor’ tiles may produce 1820 kW per day. The adoption of piezoelectric tiles will provide 83% of the total daily requirement for electricity [6].

Fig. 9 Working of a piezoelectric mechanism. [7] Source Tabrizikahou et al.

References

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Fig. 10 Piezoelectric tiles. Source Moussa [6]

13 Conclusion Architects and designers are in a unique position to choose and direct how new innovations will permeate our daily lives. Building requirements have changed over the years and will continue to evolve in the future as well. The design, utilization, and repurposing of the building stock are all still impacted by societal and technological advancements. There is a tradeoff with respect to cost and the environmental impact of technology used to achieve energy efficiency. Smart materials provide us with dynamic solutions with minimal environmental costs. Smart materials can be integrated into building facades to achieve dynamic solar control and to increase thermal inertia and energy generation. Smart materials are being developed with the help of nanotechnology to provide improved thermal solutions. All the technical advancements in this field equip architects with the right tools to design and integrate dynamic behavior into the assemblies of any component. Energy conserving technology of these smart materials thus enables increased energy efficiency of buildings and their components.

References 1. Addington DM, Schodek DL (2012) Smart materials and technologies in architecture. In: Smart materials and technologies in architecture. https://doi.org/10.4324/9780080480954 2. Bahl S, Nagar H, Singh I, Sehgal S (2020) Smart materials types, properties, and applications: a review. Mater Today Proc 28(xxxx):1302–1306. https://doi.org/10.1016/j.matpr.2020.04.505 3. Bozsaky D (2016) Application of nanotechnology-based thermal insulation materials in building construction. Slovak J Civ Eng 24(1):17–23. https://doi.org/10.1515/sjce-2016-0003 4. Casini M (2016) Advanced materials for architecture. In: Smart buildings. https://doi.org/10. 1016/b978-0-08-100635-1.00002-2 5. Lampert CM (2004) Chromogenic smart materials. Mater Today 7(3):28–35. https://doi.org/10. 1016/S1369-7021(04)00123-3 6. Moussa RR (2020) Installing piezoelectric tiles in children’s outdoor playing areas to create clean & healthy environment; case study of El-Shams sporting club, Cairo_Egypt. WSEAS Trans Environ Dev 16:471–479. https://doi.org/10.37394/232015.2020.16.48 7. Tabrizikahou A, Kuczma M, Nowotarski P, Kwiatek M, Javanmardi A (2021) Sustainability of civil structures through the application of smart materials: a review. Materials 14(17):1–29. https://doi.org/10.3390/ma14174824

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8. Tarfiei M (2015) Smart building materials in sustainable architecture: a case study in electrochromic glass. Eur Online J Natural Soc Sci 3(3):408–416. www.european-science.com

An Analytical Assessment and Retrofit Using Nanomaterials of Rural Houses in Heat Wave-Prone Region in India Vijayalaxmi J. and Hete Dhananjay

Abstract The Indian state of Andhra Pradesh experiences intense heat wave in the summer months. It is important to assess the indoor comfort hours of rural houses which are built with locally available materials because of economic constraints. This study aims to gauge the embodied energy and heat conductance of the houses in the heat wave-prone hot and humid climate of Vijayawada, Andhra Pradesh, and suggest retrofit to better the indoor thermal environment. A field study of four houses with different walling materials and the same roofing material of a typical village is carried out, and their embodied energy and thermal performance are compared with a conventional modern house from the same location. HTC-AMV06 Thermometer is used for field measurements of indoor dry bulb temperature and humidity, and the globe thermometer is used for outdoor temperature data on a summer day in April. Thermal energy models are simulated in energy plus and correlated with recorded data to validate the models. Validated models are used for computing indoor comfort hours. Embodied energy analysis shows that a house made with a reed wall and mud plaster with a reed roof has the lowest embodied energy (473.5 MJ/m2 ). It is only 9.47% of the conventional house which has very high embodied energy (5002.2 MJ/m2 ). Comfort hours for all the houses lie in a narrow range of 51.4– 47.18% irrespective of the variation in embodied energy. Aerogel, when used as an insulation material, reduces indoor temperature by 11.09 °C in cement block houses and 6.17 °C in random rubble houses.

1 Introduction Embodied Energy (EE) is the integrated exhaustible energy used in procuring raw material from their processing to the site transportation also considering the construction throughout the whole life cycle. Conventional building materials like burnt clay bricks, cement, and steel with high Embodied Energy (EE) are used in constructing buildings. Rural houses are built using locally available materials because of economic constraints, but these materials have very low embodied energy [1]. Andhra Pradesh experiences heat wave in the summer months which majorly affect indoor thermal conditions. The choice of materials that have higher thermal

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Vijayalaxmi J., Building Thermal Performance and Sustainability, Lecture Notes in Civil Engineering 316, https://doi.org/10.1007/978-981-19-9139-4_12

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resistance and air conditioning can mitigate the impact of the heat wave. But because of the poor financial condition of rural dwellers, both options become unaffordable. The embodied energy (EE) of rural dwellings across India, with only natural materials such as adobe brick or block masonry, wattle and daub walls, stone masonry, thatch roofs, traditional mud roofs, bamboo, and slate tiles ranges from 0.002 to 0.16 GJ/m2 . An amalgamation of natural material and conventional material such as brick, steel, asbestos sheets, and tin sheets ranges from 0.08 to 1.3 GJ/m2 . And if only conventional materials are used, embodied energy ranges from 2.34 to 2.8 GJ/m2 [2]. Whereas replacing rubble stone masonry with burnt clay brick masonry and stabilized soil block masonry would increase the Embodied Energy of the dwelling by 9.7 times (870%) and 2.8 times (182%), respectively, but the Operational Energy for varying wall configurations has a negligible variation (less than 1 kWh/m2 per year) for the traditional dwelling in warm–humid and moderate climatic zones [3]. A house constructed of a mud block wall was on average 0.7 °C (air temperature) cooler than the conventional brick wall and the maximum temperature difference between them was around 6 °C [4]. An increment in the indoor temperature range (7–10 °C) during summer months was found when RCC roof and red brick walls were adopted instead of traditional mud roof and rubble masonry walls [5]. A study by Shukla et al. [6] shows that embodied energy of the adobe house is less than a conventional fired clay brick with concrete by 245 GJ/100 m2 , which also reduces the amount of CO2 into the environment by 101 tons/year and the energy reprisal time of the adobe house is 1.54 years. Christoforou et al. [7] states that the Embodied energy of adobe block (0.17 MJ/kg) considering off-site production is still much lower than conventional building materials such as Common brick (3.0 MJ/kg) and Concrete block (0.9 MJ/kg) and also lower than other earthen building material such as Stabilized rammed earth (0.45 MJ/kg). A study by Sharma and Marwaha [8] shows that the embodied energy of rural houses (93,047.4 MJ) is only 15.6% of urban houses (595,359.8 MJ) in Hamirpur. The annual operational energy of a rural house (2057.9 kW/h) is 75.9% of an urban house (2711.5 kW/h). This study aims to investigate embodied energy of generic rural houses and its relation with indoor thermal performance in the heat wave-prone region of Andhra Pradesh, India. The study intents to assess which existing rural materials have better thermal performance and list down the suggestive majors for thermally nonperforming houses. This research can be used to create a framework to improve the thermal performance of rural houses made with local materials.

2 Materials and Method 2.1 Study Area Four houses with different walling materials and the same roofing material have been selected from the warm and humid climate of Vallabhapuram village of Vijayawada

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179

district of Andhra Pradesh (16.3485° N, 80.7195° E). Houses are H1-Reed and mud plaster, H2-Random rubble with mud plaster, H3-Brick with mud mortar and cement plaster, H4-Cement blocks with cement mortar (exposed), and all of them have thick local Reed grass roof. Embodied energy and thermal performance comparisons of these houses are carried out with a conventional brick house H5 with brick walls and cement mortar and cement plaster and RCC roof. The selected houses lie within 25 m distance from the water canal and share the same microclimate (Fig. 1). All houses are single-story structures with built-up areas within 26–37 m2 . Field studies are conducted with HTC-AVM-06 Anemometer to measure the indoor dry bulb temperature (DBT) and relative humidity (RH). A globe thermometer is used for measuring outdoor temperature. The study was conducted on 27th August 2019 for 16 h on a typical sunny day with clear sky conditions from 5.00 to 21.00 h. The data was recorded by keeping all the doors and windows open in the absence of mechanical ventilation. All five houses have been simulated in Rhino-energy plus software for 27th August 2019 and correlated with measured temperature and relative humidity data on the same day. Further models are simulated for one year to investigate the indoor comfort hours. The Indian Adaptive Thermal Comfort (IMAC) range of the hot and humid climates of Nellore, Andhra Pradesh, is used to determine the comfort hours [9]. Embodied energy is calculated and compared for all five houses. The five houses are located in Vallabhapuram, a typical rural village in Andhra Pradesh, located 26.9 km away from the city of Vijayawada at a latitude of 16.34° N and longitude of 80.7195° E. The months from May to September are very warm. The outdoor average maximum in May 2019 is 44 °C, while the outdoor maximum in August is 37 °C. The details of the outdoor climatic parameters are given in Table 1.

Fig. 1 Location of the five houses

30

78

98

46

Average high relative humidity %

Average low relative humidity %

Average temp. 29 °C

Average relative humidity %

98

24

76

29

17

100

23

35

Oct. 2018

78

27

40

95

21

33

Nov. 2018

79

25

35

98

19

31

Dec. 2018

78

24

26

98

17

30

Jan. 2019

77

27

33

98

20

34

Feb. 2019

74

29

38

95

22

35

Mar. 2019

Source climate.onebuilding.org/WMO_Region_2Asia/IND/index.html#IDAP_Andhra_Pradesh

79

46

24

Average low temp. °C

36

37

Average high temp. °C

Sept. 2018

Aug. 2018

Outdoor climate parameters

Table 1 Monthly outdoor climatic parameters for the year 2018–2019

74

31

41

95

26

38

Apr. 2019

75

32

23

95

24

43

May 2019

68

33

31

94

26

43

Jun. 2019

75

30

36

96

24

39

Jul. 2019

76

30

42

100

23

37

Aug. 2019

180 Vijayalaxmi J. and Hete D.

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181

2.2 Details of Houses The five houses selected for field studies are representative of typical rural houses of Andhra Pradesh. The basis for house selection is that the form, size, and microclimate of all five houses should be similar. The walling material of the four houses should be different from each other, but the roofing material should be the same. In this case, Palmyra leaves are abundantly available in rural areas and are widely used. The fifth house should be built with conventional materials—230 mm thick brick wall with cement mortar and roofing of reinforced cement concrete (RCC). House 1 has a 34.5 m2 built-up area and is made with 55 mm thick reed wall and 10 mm thick mud plaster on both sides, supported by 100 × 100 mm thick RCC Columns as shown in Fig. 2. The house has a hip roof, made of 250 mm thick local reed grass supported on a bamboo framework. Doors and windows are made with country wood with a WFR of 4.7%. The flooring is made of 24 mm Thick 600 mm × 600 mm Tandur Stone. House 2 has a 26.3 m2 built-up area and it is made with 270 mm thick random rubble masonry with mud for adhesion and plastering (7 mm) on both faces of the wall (Fig. 3). It has a pyramidal roof, made of 250 mm thick local Reed grass supported on the bamboo framework. Doors are made with reused teak wood, and windows are made of cement jaali with WFR 1.1%. The flooring is made of PCC 18 mm Red Oxide flooring. Figure 4 shows House 3 of 36.8 m2 built-up area and it is made with a 230 mm + 230 mm double brick wall with mud mortar and 10 mm thick cement plaster on both sides. It has a hip roof, made of 180 mm thick local reed grass supported on a bamboo framework. Doors and windows are made with teak wood and have metal grills with a WFR of 14.4%. The flooring is made of Kadappa stone fixed with cement mortar. House 4 has a 32.2 m2 built-up area, and it is made with a 150 mm thick cement block with cement mortar left exposed on both sides (Fig. 5). The house has a

Fig. 2 Plan and section of house 1

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Fig. 3 Plan and section of house 2

Fig. 4 Plan and section of house 3

pyramidal roof, made of 150 mm thick local Reed grass supported on the bamboo framework. Doors and windows are made of reused teak wood with a WFR of 11.1%. House 5 has a 32.2 m2 built-up area, and it is made with a 230 mm thick fire burnt brick with cement mortar plastered on one side and exposed on the outer side (Fig. 6). House has a flat roof, made with a 150 mm thick RCC roof column and beam structural system. Doors and windows are made of reused teak wood with a WFR of 26.5%. All the houses are oriented to 66° N.

2.3 Indoor Data Collection Indoor climatic data of indoor air temperature, wind speed, and relative humidity was recorded simultaneously in all five houses on 27th August 2019 from 5.00 am to 9.00 pm as the heat stress was calculated for the working period of the day.

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Fig. 5 Plan and section of house 4

Fig. 6 Plan and section of house 5

The thermo-anemometer HTC-AVM-06 was used to continuously monitor indoor climate parameters. Simultaneously, outdoor climatic data was also collected. The digital thermo-anemometer detects changes in temperature of up to 0.1 °C and wind speed of 0.01 m/s and relative humidity of 1%. The instruments were elevated at 1.1 m from the flooring as per the ASHRAE 55 Protocol (ANSI/ASHRAE 55, 2010).

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2.4 Simulation and Validation of the Model Field measurements of indoor air temperature, wind speed, and relative humidity were recorded simultaneously in all five houses on 27th August 2019 from 5.00 am to 9.00 pm. The digital thermo-anemometer HTC-AVM-06 with an accuracy of ± 0.1 °C and ± 3% RH, was used to continuously monitor indoor climate parameters. The instruments were kept in the center of the room at a height of 1.1 m from the floor level. Recorded internal DBT and RH for all 5 houses are correlated against simulated DBT and RH of respective houses.

3 Results and Findings 3.1 Correlation Between Measured and Simulated Temperature The regression analysis between the measured and simulated indoor temperature (R2 value) is between 0.83 and 0.91 (Fig. 7). Therefore, the thermal performance of the simulated models matches the actual thermal performance of the houses and the selected examples are affirmed. The models are further simulated to extract annual DBT and RH data.

3.2 Monthly Comfort Hours Based on the Indian Adaptive Thermal Comfort [9], House 4 is the most uncomfortable with the least comfort hours in all the months. For all the houses, the month of May shows the least comfort hours. House 2 shows the maximum comfort hours for all the months as compared to the rest of the houses. House 3 (Double brick wall) and House 4 (Cement block wall) perform better thermally with a reed roof as compared to the conventional house (Fig. 8).

3.3 Embodied Energy Analysis From onsite field studies, floor plans and sections are drafted from the measurement, observation, and details of the material. The embodied energy of each house is computed based on the Indian construction material database of embodied energy and global warming potential [10]. The embodied energy of each element such as the wall, roof, floor, door, and windows are compared among all selected houses, to

An Analytical Assessment and Retrofit Using Nanomaterials of Rural …

Fig. 7 Correlation between field study and simulated indoor temperature of the 5 houses

Fig. 8 Number of monthly comfort hours

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understand which element has the maximum contribution toward embodied energy in each house. The house built with conventional materials, H5 has the highest embodied energy of 5002.2 MJ/m2 , whereas H1 made of reed wall and mud plaster has the least Embodied energy, 473.5 MJ/m2 which is 9.5% of H5 house. H2 made of random rubble masonry and H4 made of cement block do not have much difference in embodied energy, 2060.9 MJ/m2 and 2002.3 MJ/m2 which is 41.2% and 40%, respectively, compared to the House 5. H3 Made of fired brick and cement mortar has 2743.0 MJ/m2 of EE which is 54.8% of House 5. It is also observed from Table 2 that there is no correlation between embodied energy and comfort hours. House 1 consumes only 9.5% of embodied energy /m2 of the house as compared to the conventional house, while House 2 and House 4 consume 41 and 40%, respectively. House 3 consumes 54.8% of the conventional house (Fig. 9). Thus, it is observed that the embodied energy of House 1 is extremely less, while the thermal performance is not the worst. In all the five houses, walling material remains a major contributor of embodied energy followed by flooring material, whereas the roof contributes very little toward total embodied energy except in House 5. House 3 and House 5 houses have higher walling embodied energy because of the use of fire burnt bricks. House 3 exhibits higher walling EE due to the use of two brick thick walls. Reed wall has the lowest embodied energy among the five walling materials (Fig. 10). Table 2 Embodied energy/m2 and comfort hours

House

Comfort hours EE (MJ/m2 ) % Comfort hours

House 1

4503

473.5

51.40

House 2

4576

2060.9

52.24

House 3

4361

2743.0

49.78

House 4

4133

2002.3

47.18

House 5

4403

5002.2

50.26

Correlation

Fig. 9 Percentage of embodied energy as compared to conventional building materials

0.0196

120.0

100

100.0 80.0 60.0

41.2

40.0 20.0 0.0

54.8

40.0

9.5 House 1

House 2

House 3

House 4

House 5

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187

Fig. 10 Component-wise embodied energy

3.4 Thermal Performance Monthly DBT simulation shows that summer months from April to June experience very few comfort hours and need more operational energy whereas the winter and rainy months from August to December experience higher comfort hours which was expected. There is very little variation in annual comfort hours among five houses, ranging from 51.4% to 47.18%. This proves that, annually, all five houses require similar operational energy to ensure the thermal comfort of the occupants.

4 Correctives for Enhanced Thermal Performance Having known that the houses do not perform optimally for comfortable indoor living in a heat wave-prone region, there needs to be an intervention that does not disturb the existing conditions due to sociocultural acceptance. Some studies have been carried out on the retrofit of houses internationally. A study by Han and Yang [11] shows that during the air conditioning season, the maximum load per unit area is 10.12 W/m2 and the maximum heating load per unit area of air conditioning season is 29.6 W/m2 for a typical Hongcun village household. It also states that the heating load can be brought down to 7.28 W/m2 with proper retrofit and the reprisal for the retrofit is only 3 years. Aleva et al. [12] concludes that the energy-saving potential of the insulated external wall is the largest (17 to 47% with 200 mm insulation and 20 mm sheathing) of the building envelope by renovation, and this can be best explained because of the high thermal transmittance and the large area of the external walls of the entire building sheath. In India, some studies on the retrofit of rural houses have been carried out. A study by [13] shows that the maximum energy saving of 54% (594 kWh/year) can

188 Table 3 Physical properties of the aerogel [15]

Vijayalaxmi J. and Hete D. Physical parameter

Symbol

Unit

Value

Density

ρ

kg/m3

70–150

Compressive strength

σc

kPa

2–100

Specific heat

c

kJ/kgK

750–840

Thermal conductivity

K

W/mK

0.015

Embodied energy

EE

MJ/kg

53.9

be achieved in mud houses by adding Mud dung slurry as an insulating material. It also states that the optimum insulation thickness of Mud dung slurry is 67 mm. Thermal insulation quality of traditional materials is inferior when compared to materials based on nanotechnology. Silica Aerogel is a component of the same and is a fabricated and pervious material that is ultralight in weight and is obtained from a gel having gas as a replacement for the liquid component. The resultant solid has an extremely low density and thermal conductivity [14] and its physical properties are shown in Table 3. Silica aerogel is proposed as an alternative to traditional thermal insulating material because of the near-translucent nature of the aerogel insulation sheet. Transparent aerogel will keep the physical appearance of the existing houses intact. The thermal conductivity value of aerogel is 0.015 W/mk which makes it a thermally high-performing insulation material. A 10 mm aerogel sheet (85 kg/m3 density) is applied on the exterior face of the walls of all five dwelling units. Then, the houses are simulated with and without aerogel for indoor temperature for worst case scenario keeping windows and doors shut. Application of 10 mm aerogel can reduce daytime temperature up to 2.43 °C in May in House 1, 6.17 °C in House 2, 3.23 °C in House 3, 11.06 °C in House 4, and 7.73 °C in House 5 (Fig. 11). House 1 made of a reed wall and thatch roof showed the least variation because the air gaps in between the reed act as insulation. House 3 made with 450 mm thick brick with mud mortar and cement plaster with a thatch roof has comparatively lower variation because of the existing insulation offered by the air gap between the double brick wall and the resultant thermal mass. House 4 made with cement block and thatch roof has the maximum temperature reduction among all the houses. There is no significant difference in indoor temperature if an aerogel sheet is applied on the thatch roof.

4.1 Embodied Energy with Aerogel The production embodied energy of aerogel is 53.9 MJ/kg [16]. Additional embodied energy due to the usage of 10 mm aerogel on external walls is added to the existing embodied energy of all five houses. There is not much variation in embodied energy of the houses because of the use of aerogel (Fig. 12).

An Analytical Assessment and Retrofit Using Nanomaterials of Rural …

Fig. 11 Indoor temperature with and without aerogel Fig. 12 Embodied energy comparison of five houses with and without aerogel

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5 Conclusion This paper assessed the thermal performance and embodied energy of representative five rural houses in the heat wave-prone hot and humid climate of Andhra Pradesh. 4 houses with different walling materials and the same roofing material and the house made with conventional materials exhibit comfort hours in the range of 51.4– 47.2%. Hence, none of the five houses ensures indoor thermal comfort throughout the year. The embodied energy of the house made with the reed is the least with 473.5 MJ/m2 . The highest embodied energy is of the house made with conventional building materials at 5002.2 MJ/m2 . Irrespective of the huge difference in embodied energy, all the houses have similar operational energy as there is no correlation between embodied energy and comfort hours. This is because of the extremely hot outdoor climate. The houses need to be insulated from the outside heat in order to make the indoors comfortable. Since better thermal insulation is provided using thermal insulation materials based on nanotechnology, silica aerogel is proposed as an insulation material to reduce indoor temperature. Aerogel has reduced daytime peak hour temperature in May (heat wave month) by 2.43 °C in House 1 (made of reed). This can be attributed to the natural insulation due to air pockets in between the reeds. Indoor temperature can be reduced by 11.09 °C in House 4 made with cement block and thatch roof. The addition of embodied energy due to the application of aerogel is negligible. This study concludes that it is important to insulate rural houses from the impact of outdoor heat since they are economically challenged to have active means of cooling. Since rural building materials use less embodied energy, it is not prudent to push them to use conventional building materials such as burnt brick, cement, and steel. Strategies such as insulation must be subsidized by the government to ensure comfortable indoor conditions.

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