Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation [1 ed.] 9781617283994, 9781607410492

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Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved. Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved. Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

ENERGY AND BUILDINGS: EFFICIENCY, AIR QUALITY AND CONSERVATION

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved. Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

ENERGY AND BUILDINGS: EFFICIENCY, AIR QUALITY AND CONSERVATION

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

JOSEPH B. UTRICK EDITOR

Nova Science Publishers, Inc. New York

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

Copyright © 2009 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Energy and buildings : efficiency, air quality, and conservation / editor, Joseph B. Utrick. p. cm. Includes bibliographical references and index. ISBN 978-1-61728-399-4 (E-Book) 1. Buildings--Energy conservation. 2. Buildings--Environmental engineering. I. Utrick, Joseph B. TJ163.5.B84E237 2009 696--dc22 2009002419

Published by Nova Science Publishers, Inc.    New York

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

CONTENTS

Preface Chapter 1

Chapter 2

Chapter 3

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

Chapter 4

Chapter 5

Chapter 6

Chapter 7

vii On the Numerical Study of Particle Dispersion in Indoor Airflow: Modelling Challenges J. Y. Tu, G. H. Yeoh and Z. F. Tian Evaluation of Modelling Techniques to Predict Indoor Temperature in Intelligent Buildings: A Literature Review G. J. Ríos-Moreno, M. Trejo-Perea, R. Castañeda-Miranda, D. Vargas-Vázquez, E. A. Rivas-Araiza and G. Herrera-Ruiz Methodology Developed for the Analysis of the Energy-Productive Buildings Modules in the Health Network Irene Martini, Carlos Discoli, and Elías Rosenfeld Designing Energy Efficient Buildings: A Methodological Approach Based on Computer Simulation Paulo Filipe de Almeida Ferreira Tavares and António Manuel de Oliveira Gomes Martins Architecture, Thermal Design and Energy Performance of Solar Schools in Argentina Celina Filippín, Silvana Flores Larsen and Alicia Beascochea

1

41

69

83

107

The Role of Buildings Energy Efficiency in Mitigating Carbon Emissions in China Jun Li

135

Energy Use in Canadian Buildings: What Have We Learned from Recent Data? David L. Ryan and Denise Young

177

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

vi Chapter 8

Building Integrated Renewable Energy Technologies: Embodied Energy, Economic Analysis and Potential of CO2 Emission Mitigation Arvind Chel

209

Chapter 9

Reducing the Cooling Load by Evaporative Cooling of the Roof Sahar Najib Kharrufa

Chapter 10

Life Cycle Considerations in Energy Conservation for Design of Low Income Housing E. M. Pearce and J. M. Pearce

257

Application of Building Energy Simulation in the Sizing and Design Optimization of an Office Building and its HVAC Equipment Olympia Zogou and Anastassios Stamatelos

279

New Strategies of HVAC System Design Based on Climatic Effect on Indoor Air Conditions in Buildings from Northwest of Spain José Antonio Orosa García

325

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

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Contents

Chapter 16

The Physics of Vented Roofs in Hot and Temperate Climates: Analysis of Different Strategies for the Reduction of Energy Consumption and the Improvement of Environmental Comfort M. D’Orazio, A. Stazi, C. Di Perna and E. Di Giuseppe

235

349

Suggestions for Increasing Energy Efficiency in Existing Building, Turkey Example Tülay Esin

369

Thermal Perception and Energy Consumption: A Comparative Study between Radiant Panel and Portable Convective Heaters Ahmed Hamza H. Ali and Mahmoud Gaber Morsy

381

Performance Analysis of Geothermal District Heating and Geothermal Heat Pump Applications in Buildings Leyla Ozgener and Onder Ozgener

409

Short Communication: Energy Conservation and Efficiency in the Service Sector Ramon Farreny, Jordi Oliver-Solà, Joan Rieradevall and Xavier Gabarrell

421

Index

433

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

PREFACE Buildings represent sites of both enormous energy usage but also offer equally enormous opportunities for energy savings. This book presents a wide-ranging discussion on efficiency, air quality and conservation of energy in buildings. Current applications of temperature predicting models for buildings are discussed, including their association with thermal comfort, heating, ventilating and air-conditioning (HVAC) control systems, and energy savings. Owing to the global energy crisis, energy conservation through energy efficient buildings has acquired prime importance. This book deals with various renewable energy technologies that can be integrated with building. Since low income housing is often designed primarily to minimize costs without consideration of operating costs, ways to integrate energy conservation in this setting are also presented. Chapter 1 - In this article, we provide an in-depth discussion on the modeling challenges for the computational simulation of the dispersion of contaminant particle in indoor air flow. The Eulerian-Lagrangian framework is adopted where the air flow is solved by the Eulerian equations and then integrated with the Lagrangian equations for particle motion thereby tracking individual particle through the flow field. Indoor air flow is always characterized by low-Reynolds-number turbulence and the application of turbulence models is usually used in the context of computational fluid dynamics. Application of three turbulence models – standard k-ε, RNG k-ε and the RNG-based large eddy simulation model – to simulate the indoor particle dispersion and concentration distribution is investigated. The flow of contaminant particles (with diameters of 1 and 10 μm) is considered. Measured data from Posnet et al. [Energy and Buildings 2003;35:515-26] and Lu et al. [Building and Environment 1996;31:417-23] are used to validate the simulation results. Comparing the three turbulence models for particle flow prediction, the RNG-based large eddy simulation model through better accommodating unsteady low-Reynolds-number turbulent flow structure has shown to provide more realistic particle dispersion and concentration distribution than the other two conventional turbulence models. The particle-wall impact model has also a considerable effect on the particle concentration prediction. A simple collision model which prescribes the normal and tangential restitution coefficients may not be adequately applied for all particle cases in accurately predicting the indoor particle deposition for the Lagrangian particle tracking model. More realistic particle-wall impact models are proposed within this article to improve the indoor particle prediction. Chapter 2 - Nowadays energy-efficient buildings are required, these structures are known as Intelligent Buildings, to provide user’s comfort and interaction with nature. The indoor

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Joseph B. Utrick

thermal comfort and air quality are significant factors determining the occupant’s productivity, health and welfare. It is quite clear that improving energy efficiency and reducing energy cost in buildings represents a positive impact towards sustainability. Current applications of temperature predicting models for buildings are associated with thermal comfort, heating, ventilating and air-conditioning (HVAC) control systems, and energy savings. Research focused on this issue has highlighted the potential usefulness of modelling to estimate the correct behavior or the optimal temperature inside the buildings. An exhaustive literature review on the existing modelling techniques is necessary to determine future research needs and direction. As a contribution to this need, we present a literature review and an analysis of various modelling techniques applied to predict the temperature behavior inside buildings. The models used to approximate the output of the system employ both non-statistical and statistical methods to formulate the relationship between inputs and outputs. In this work, both methods are reviewed and discussed in order to determine the best alternative to predict indoor-building air temperature. Neural networks models are non statistical, while statistical methods include linear and multiple regression, and least squares. Autoregressive models with external input (ARX) and autoregressive models with external input of moving average (ARMAX) models are often used in these cases. All the methodologies mentioned above represent a practical alternative for the implementation of an intelligent control system that will allow increasing the comfort of occupants while reducing energy consumption of the HVAC systems. Based on a detailed literature review, the paper also summarizes the future research directions in this recent and important area. Chapter 3 - The public health network in Argentina consists of a wide variety of buildings representing a complex system of services and structures. Such a system presents various problems, particularly of hygrothermal habitability and non-conscious use of energy, which have a great impact on the quality of services provided. To identify the energy consumption of each area within the different health facilities, a detailed methodology was developed that enabled the relation of energy variables of each health specialty through a differential analysis construction. This methodology involves analysing the buildings from a construction typology catalogue which modulates the representative units of various hospitals. A database of Energy-Productive Building Modules (Módulos Edilicios Energéticos Productivos: MEEP) was built in order to evaluate the interactions among physical spaces, building envelope, infrastructure, and equipment usage with the energy consumption, for each specialty service provided in the most common buildings present in the health service network. This database allows us to classify, describe, compare and design different health facilities using representative typology units that characterize the energy and productive needs of each health facility unit (laboratory, surgery, intensive care, etc.). Once we defined the MEEP´s values, a methodology was developed, in order to quantify and to discriminate the energy requirements by means of the analysis of characteristic sectors of each basic health service, in each integration level. These levels are identified as: MEEP, Functional Units, Services, Areas and Building. The analysis of the different levels allows us to contrast, to validate and to adjust the results obtained in each one. The methodology developed in this paper proposes alternatives and tools to identify and measure variables, in different integration levels, with the aim of improving energy efficiency in health buildings. Technological, energy-related, productive and behaviour variables were considered. A high complex hospital is analysed, defining the different Modules that

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ix

constitute it with its respective Functional Unity, Services and Areas. The first results are presented, in which the comfort conditioning requirements, lighting and equipment consumption are analysed for the different levels proposed. This methodology enables investigators: i. to obtain detailed information on each facility; ii. to identify variables critical to an energy consumption perspective; iii. to detect areas of over consumption and/or inadequate infrastructure; iv. to gather essential reference material for the design of health facilities and other similar sectors. Chapter 4 - The professionals involved in building design have a crucial influence on the contribution of buildings to sustainable development. In fact, the options taken at the very early stages of design are, to a great extent, determinant of a building's behavior during its useful life. Factors such as orientation or the specification of materials used in construction influence the intensity of use of the available natural resources in buildings operation. This chapter presents a methodology resulting of a work developed in the area of the efficient use of energy in buildings with the basic concern of sustainability of the design, using a concrete building as a case study. A systematic study of the building's behavior throughout one year is used, based on computational simulation, through which a good compromise may be found between comfort and energy consumption, addressing aspects such as volume and shape, internal organization of space, envelope and active systems. The use of computational simulations in the evaluation of building performance at the design stage reveals to be essential to assess different alternatives, from both points of view, comfort and sustainability. The proposed methodology corresponds to the need of providing a simple and yet powerful and detailed instrument for building designers, in order to guarantee a good receptivity on their part. The simulation tool must be friendly and versatile enough to avoid long learning periods. Chapter 5 - In Argentina the percentage of energy consumption for space heating in buildings is around 30%. In the school buildings in the province of La Pampa (in the central region of Argentina, betweeen 35 and 39º of south latitude) the percentage of heating energy is around 90% (10% = electricity consumption = lighting and appliances). The measured energy consumption is higher than the value estimated according to the Volumetric Heat Loss Coefficients (G-value) and Base Temperature (18ºC), indicating that the students and teachers work with temperatures higher than that value. The cost of the consumed energy corresponds to 17% of the total spent by students. In this context two solar schools (Promoter Entity: Ministry of Culture and Education) were built in different regions, one of great agricultural potential l— cattleman (Catriló); the other, a semi-arid region with low population density (Algarrobo del Águila). Both buildings were designed with passive solar design strategies that exploit the building's orientation, shape, materials, windows, and external landscaping, in combination with passive solar heating systems (direct solar gains through transparent areas and ventilated facade panels) and cooling (natural ventilation and earth-to-air heat exchange through buried pipes), to create a pleasant environment which is less dependent on fossilfuels-based energy. The schools were evaluated through the results of thermal monitoring and surveys, carried out to analyze the behaviour of the occupants, who explicitly control and affect the internal environment. In the Algarrobo’s school the occupants (mostly with two or three garments inside the classroom — Tshirt, sweater and jacket) were comfortable with temperatures around 17ºC. The heating practically doesn't light during the day and goes out at night. The energy saving in heating is around 90%. In Catriló’s solar school, the average daily temperatures in the classrooms are around 21ºC in winter. Indoors, the students wear only two

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Joseph B. Utrick

garments — T-shirt and sweater — or only a pinafore. The heaters work during the entire night and the energy saving is 50%. The results show that the area of comfort doesn't have clearly defined limits; instead, they are defined by those conditions in which a feeling of discomfort doesn't take place. The implementation of passive solar design strategies allowed an energy saving in heating between 50 and 90%, which in turn means a reduction of CO2 emissions. In the case of non-residential buildings, through the technology applied to the envelope design in solar school buildings, the G value was reduced by 35 %, decreasing from 1.40 W/m3ºC (average value for conventional school buildings in La Pampa) to 0.97 and 0.90 W/m3ºC at schools in Catriló and Algarrobo del Águila, respectively. The use of solar technology allowed a noticeable reduction of natural gas consumption in heating, constituting a major contribution in areas of temperate to cold climate, where the greatest energy consumption in buildings corresponds to heating. In view of these G values and direct gain values oscillating between 11 and 14 % of the building's useful area, a solar school building may consume at least 50 % less energy in heating to meet occupants' comfort standards. In the case of the school built in Algarrobo, with an average temperature of 17ºC, the consumption was equivalent to the 15% of the average energy consumed by other schools located in different places of the province. The qualitative and quantitative analyses carried out showed promising results related to the buildings’ energy behaviours, yet it cannot be denied that there are certain factors that have direct incidence on the optimal energy behaviour of buildings and their thermal comfort: (a) exogenous factors (the high variability of the external physical environment), (b) endogenous factors (construction characteristics) and (c) socio-environmental factors (life-styles and behaviour of dwellers, associated with the active energy contribution and use of heating ). The true challenge for solar energy architecture is to simplify technology and construction processes so that costs would not be higher than those for conventional buildings, assuring users' pro-active behaviour towards the correct building's thermal management. To involve users in these improved practices is yet another challenge. Chapter 6 - We use a qualitative analysis approach to assess the role of energy efficiency improvement of large urban infrastructure (buildings) in addressing the energy and climate security in China from a prospective analysis viewpoint. It is demonstrated that buildings efficiency improvement plays a critical role in shaping sustainable infrastructure to avoid carbon locking. Various aspects influencing buildings energy consumption such as building design and construction, urban planning, building materials are discussed in detail. A comparative study based on building efficiency standards benchmarking of China and foreign buildings standards and codes are undertaken. It is argued that the current Chinese codes can be improved further by 30% to 50%. The major barriers to building energy efficiency (BEE) implementation are identified and the effectiveness of a variety of policy instruments is compared to clarify the combination pattern. We suggest that a comprehensive policy framework needs to be established to facilitate the uptake of more stringent efficiency standards and transformation towards low carbon buildings construction practices, market-based economic and financial instruments can play essential role in complementing regulatory instruments to strengthen BEE implementation. Policy recommendations are provided based on comprehensive analyses. We use a qualitative analysis approach to assess the role of energy efficiency improvement of large urban infrastructure (buildings) in addressing the energy and climate security in China from a prospective analysis viewpoint. It is demonstrated that buildings

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

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Preface

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efficiency improvement plays a critical role in shaping sustainable infrastructure to avoid carbon locking. Various aspects influencing buildings energy consumption such as building design and construction, urban planning, building materials are discussed in detail. A comparative study based on building efficiency standards benchmarking of China and foreign buildings standards and codes are undertaken. It is argued that the current Chinese codes can be improved further by 30% to 50%. The major barriers to building energy efficiency (BEE) implementation are identified and the effectiveness of a variety of policy instruments is compared to clarify the combination pattern. We suggest that a comprehensive policy framework needs to be established to facilitate the uptake of more stringent efficiency standards and transformation towards low carbon buildings construction practices, market-based economic and financial instruments can play essential role in complementing regulatory instruments to strengthen BEE implementation. Policy recommendations are provided based on comprehensive analyses. We use a qualitative analysis approach to assess the role of energy efficiency improvement of large urban infrastructure (buildings) in addressing the energy and climate security in China from a prospective analysis viewpoint. It is demonstrated that buildings efficiency improvement plays a critical role in shaping sustainable infrastructure to avoid carbon locking. Various aspects influencing buildings energy consumption such as building design and construction, urban planning, building materials are discussed in detail. A comparative study based on building efficiency standards benchmarking of China and foreign buildings standards and codes are undertaken. It is argued that the current Chinese codes can be improved further by 30% to 50%. The major barriers to building energy efficiency (BEE) implementation are identified and the effectiveness of a variety of policy instruments is compared to clarify the combination pattern. We suggest that a comprehensive policy framework needs to be established to facilitate the uptake of more stringent efficiency standards and transformation towards low carbon buildings construction practices, market-based economic and financial instruments can play essential role in complementing regulatory instruments to strengthen BEE implementation. Policy recommendations are provided based on comprehensive analyses. Chapter 7 - In recent years, detailed surveys such as the Commercial and Institutional Building Energy Use Survey (CIBEUS) and the Survey of Household Energy Use (SHEU) have been conducted in order to collect data on energy use in residential and commercial buildings in Canada. The information contained in these surveys, as well as data on house energy characteristics from energy audits conducted pre- and post-retrofit, available in the Canadian EnerGuide for Houses Database, have provided researchers at the Canadian Building Energy End-Use Data and Analysis Centre (CBEEDAC) the opportunity to study several aspects of the determinants of energy use and energy efficiency in a variety of Canadian building types. Among other things, these studies have examined the roles of technologies (such as heating systems, thermostats, lighting systems, water heaters, home appliances) on the intensity of energy use by Canadian businesses and households. Additional studies have looked at the roles of private versus public building ownership and the types of activities undertaken (such as food retail, non-food retail, administration, etc.) on the demand for various types of energy in commercial buildings. In this chapter, we provide an overview of data sources available for the study of energy efficiency in Canadian buildings. We then summarize the analysis and findings from a

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number of studies undertaken by CBEEDAC, with a focus on the factors that have had a significant impact on energy consumption. These findings are discussed in the context of energy policy programs and initiatives in Canada. Chapter 8 - Owing to global energy crisis, energy conservation through energy efficient buildings has acquired prime importance. Hence, this chapter deals with various renewable energy technologies that can be integrated with building. The three main aspects which should be given consideration for energy saving in a building include first and foremost the building design, secondly the low energy building materials in building construction and lastly the use of renewable energy technologies for various applications. The solar passive heating/cooling design features like sunspace, Trombe walls, earth to air heat exchangers, adobe house, day-light etc can lead to considerable energy saving at the stage of building design. Further, low embodied energy building materials should be chosen like fly ash bricks; fiber reinforced bricks; and stabilized mud blocks etc. which reduce energy consumption at building construction stage. The use of such low embodied energy materials has gained importance nowadays. The energy requirements of building can be cut down to minimum by use of renewable energy technologies like solar photovoltaic, wind turbines, solar thermal devices etc. These technologies are discussed in this chapter with their economic analysis and environmental impacts. In the present alarming situation of global warming, it is known that about 1 kg of CO2 gets emitted in to the environment corresponding to generation of 1 kWh unit of electrical energy from the coal thermal power plants. Hence, for sustainable future of the world, there is urgent need for use of renewable energy sources to mitigate CO2 emissions. Today, alternative renewable energy technologies are becoming prime important to meet at least 10 % of the total building energy requirements in European countries like U.K. Chapter 9 - Air conditioners consume large amounts of power to cool a building. The purpose of this paper is to test a method using evaporative cooling to reduce this load. An evaporative air cooler is used to cool the roof of a building. This should result in a lower temperature inside which would consequently lead to a drop in the cooling load as well. The tests were conducted in the hot dry weather of Baghdad, Iraq. Evaporative cooling is cheap and only uses a fraction of the power that compressor units do. Furthermore its cooling effect increases with the rise in temperature and drop in humidity, conditions that prevail in large parts of Iraq. The test included setting up a thermally insulated compartment above an isolated room. The compartment was cooled by a small evaporative cooler. The walls of the room itself were insulated by adding a 2cm layer of Styrofoam as well as a 10cm air cavity. The results showed that the combination of roof cooling and wall insulation resulted in an average 5.5oC drop in interior temperature, compared to an ordinary room. A computer simulation showed that this would reduce the cooling load on a compressor air conditioner utilized to further cool the interior by around 38%. These figures are based on the assumption that the temperature inside the room is cooled to acceptable comfort levels. Chapter 10 - As the threat of climate destabilization becomes more acute and energy prices continue to escalate the public has become more informed and active in energy conservation. Although many energy conservation measures (ECMs) can be accomplished as retrofits that home owners can make themselves, the initial design and construction of a building plays an enormous role in its energy consumption over its life cycle. Unfortunately, low income housing is often designed primarily to minimize first costs without consideration

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

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Preface

xiii

of operating costs. This often leads to decreased energy efficiency and high utility bills for those least able to pay. Careful design, however, can improve operating costs by reducing energy consumption often without increasing first costs. This chapter presents the findings of a life cycle energy analysis for low income housing in Savannah Georgia. The housing model studied was House Plan #281120, which is a one story building of slab on grade construction. This chapter considers several ECMs including: the whole house orientation, orientation of the windows only, window placement, high performance windows, insulated window shades, high efficiency lighting, above code attic insulation and a cool roof. eQUEST, a comprehensive energy modeling software that uses the department of energy’s most advanced modeling programming (DOE2) and integrates graphic data reporting and an advanced simulation engine, was used to simulate modifications to the basic building design. Significant energy and cost savings were found for many of the ECMs and recommendations are made for improving general design practices for low income housing. As the threat of climate destabilization becomes more acute and energy prices continue to escalate the public has become more informed and active in energy conservation. Although many energy conservation measures (ECMs) can be accomplished as retrofits that home owners can make themselves, the initial design and construction of a building plays an enormous role in its energy consumption over its life cycle. Unfortunately, low income housing is often designed primarily to minimize first costs without consideration of operating costs. This often leads to decreased energy efficiency and high utility bills for those least able to pay. Careful design, however, can improve operating costs by reducing energy consumption often without increasing first costs. This chapter presents the findings of a life cycle energy analysis for low income housing in Savannah Georgia. The housing model studied was House Plan #281120, which is a one story building of slab on grade construction. This chapter considers several ECMs including: the whole house orientation, orientation of the windows only, window placement, high performance windows, insulated window shades, high efficiency lighting, above code attic insulation and a cool roof. eQUEST, a comprehensive energy modeling software that uses the department of energy’s most advanced modeling programming (DOE2) and integrates graphic data reporting and an advanced simulation engine, was used to simulate modifications to the basic building design. Significant energy and cost savings were found for many of the ECMs and recommendations are made for improving general design practices for low income housing. Chapter 11 - The reduction of energy consumption in buildings is of central importance in the North America’s and European Union’s energy policy. Following the adoption of legislative measures, methodologies are developed and employed to calculate the year-round energy performance of buildings. A number of computational tools are already available, however, the specific procedures of incorporating their use in the building shell and HVAC system’s design process are not yet agreed. Energy performance standards for new buildings and large existing buildings subject to major renovation, are set at national level, but the process of translating these standards to shell and HVAC system sizing and design is not yet clear. This paper demonstrates the application of TRNSYS building energy simulation to produce design optimization directions for a new Department building under study contract. Sensitivity runs are employed to assess the effect of certain design parameters on the transient building’s energy performance. The simulation process is demonstrated for two different levels of detail, with the higher level involving detailed simulation of the main HVAC equipment. The results of energy simulation runs are presented in the form of designer

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friendly diagrams, with special emphasis on the transient performance during critical periods of the year. The role of building shell insulation and design, HVAC system size, (heating and cooling), ventilation strategy, equipment performance characteristics and climatic conditions is studied. Based on the results, a methodology can be assembled for the incorporation of building energy simulation on the building shell and HVAC design process. Reasonable criteria for the degree of modeling complexity to be implemented, based on the available manpower, computing and other resources are discussed. Chapter 12 - This paper suggests new strategies for HVAC system design and operative parameters based on simulation, real sampled data, testing, questionnaires and climatic conditions. This investigation is based on real data measured in different kinds of typical Spanish buildings, such as a set of flats, office buildings, museums and schools located in the area of A Coruña, Spain. The purpose of these investigations was to determine the relationship between indoor comfort, energy saving, health effects, material conservation and work risk prevention as a consequence of parameters like HVAC adjustment, wall materials, indoor air renovation and occupants’ habits. The general conclusion obtained in flats was that ventilation procedures, under the current levels of occupation, should be modified to keep the relative humidity lower than the maximum recommendation of 60%. In spite of this, natural ventilation could be effectively used in keeping both indoor temperature and humidity, as well as the microbiological load, within acceptable limits. In office buildings, wall coverings showed a clear effect on indoor air conditions as a passive climate control method during the unoccupied period. Buildings with permeable coverings present better indoor comfort conditions and acceptability. Furthermore, this effect improves energy saving, especially during the first hours of occupation. Old schools and museums, with higher thermal inertia, showed better indoor temperature conditions on store zone than new buildings. This effect is a consequence of the clear thermal inertia in old museums, despite the fact that these now incorporate modern air conditioning systems. Chapter 13 - The application of the new EU directives on the reduction of CO2 emission and energy consumption increases the importance of energy efficiency for building envelopes, as well as for high quality indoor conditions. The reduction of energy consumption in winter is achieved by using very low U-value [W/m2°K] building envelopes, but there are still open discussions concerning how to obtain good energy performances and adjust interior conditions in summer, especially for pitched or flat roofs. Different strategies could be considered: (1) decreasing the periodic thermal transmittance Y [W/m2°K] to noticeably reduce the incoming thermal flux, (2) increasing the thermal mass to obtain a relatively large thermal lag, or (3) introducing battle cavities into the roofs to reduce the thermal flux crossing the slab below the covering. The first strategy is a relatively new concept related to the application of the EU directives, the others have been traditionally employed in Italy, Greece, Spain and southern France. The effects of the different solutions were considered by analyzing the thermal behaviour of a significant number of roofs. During the last 10 years we have carried out many experiments on small and large mock-ups and thermal measurements on real roofs. In this paper we report experimental data and results in order to describe the thermal behaviour of roof systems and the effectiveness of the three different strategies (low periodical transmittance, high mass, battle cavity) on the indoor thermal conditions.

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Preface

xv

The Results show how the increasing of insulation thickness in roofs reduces the effectiveness of other solutions. In particular high ventilation cavities do not seem to lead to significant improvements for indoor summer comfort. Chapter 14 - As is the case around the world, Turkey has experienced a rise in energy related Carbon dioxide (C02 ) emissions during the recent 20 year period. According to data from British Petroleum (BP)’s annual World Energy Report, global energy consumption rose by 2,4% during 2007. According to the said report, an increase of 7,7 % on energy consumption has been registered in China, when compared with a decrease of 2,2% in Europe. In Turkey, where the energy consumption accounts for 0,9% of the energy consumed globally, the consumption of the petroleum has been set as 31,1 megaton within last year with an increase of 1,5%. And thus, the energy consumption in this year has been increased by 5% when compared with year 2006. Assessing on these figures, Turkey is considered to be accounting for 0,8 % of the petroleum consumed globally. Approximately 70% of the total energy consumed in Turkey is imported and this portion is increasing. According to the World Energy Report, Turkey’s energy import is expected to be rising to 73% in the year 2010 and to 78% in 2020. Energy is used during every stage of the building life-cycle. According to Worldwatch Institute (WWE) data, building activities account for 40% of the energy consumed globally each year. In Turkey, energy consumption in buildings is considerably higher than the European Union (EU) average and energy use in the residential/service sector grew by an average of 2.7% annually between 1990 and 2000, reaching 34.5% in 2001. Furthermore, this energy is largely derived from fossil fuels, which causes a great deal of environmental damage and imposes significant liability upon the construction sector. There are no legal arrangements for energy save in existing buildings. Therefore, even if energy efficiency precautions for future constructions are taken, these problems will continue for the existing buildings. On the other hand, solutions for increasing energy efficiency can also be produced in the existing buildings. For producing these solutions, factors that increase the energy consumption should be well known. The aim of this study is to discuss proper solutions to increase energy efficiency in buildings considering the problems in Turkey. In Turkey, high energy load for heating and cooling comes in the first place in the factors which increase the energy consumption. Reasons like lack of enough insulation on the building shell, usage of building structures with improper thermal properties, not considering heat loss and gain through the windows increases the heat loss and this causes the energy consumption to increase. The solar energy is not benefited enough even the geographical location is proper for this. Another reason for increase in energy consumption is not considering the climatic data while designing the buildings. In this article, precautions for these problems and solutions to increase the energy efficiency will also be noted. As is the case around the world, Turkey has experienced a rise in energy related Carbon dioxide (C02 ) emissions during the recent 20 year period. According to data from British Petroleum (BP)’s annual World Energy Report, global energy consumption rose by 2,4% during 2007. According to the said report, an increase of 7,7 % on energy consumption has been registered in China, when compared with a decrease of 2,2% in Europe. In Turkey, where the energy consumption accounts for 0,9% of the energy consumed globally, the consumption of the petroleum has been set as 31,1 megaton within last year with an increase of 1,5%. And thus, the energy consumption in this year has been increased by 5% when compared with year 2006. Assessing on these figures, Turkey is considered to be accounting

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xvi

Joseph B. Utrick

for 0,8 % of the petroleum consumed globally. Approximately 70% of the total energy consumed in Turkey is imported and this portion is increasing. According to the World Energy Report, Turkey’s energy import is expected to be rising to 73% in the year 2010 and to 78% in 2020. Energy is used during every stage of the building life-cycle. According to Worldwatch Institute (WWE) data, building activities account for 40% of the energy consumed globally each year. In Turkey, energy consumption in buildings is considerably higher than the European Union (EU) average and energy use in the residential/service sector grew by an average of 2.7% annually between 1990 and 2000, reaching 34.5% in 2001. Furthermore, this energy is largely derived from fossil fuels, which causes a great deal of environmental damage and imposes significant liability upon the construction sector. There are no legal arrangements for energy save in existing buildings. Therefore, even if energy efficiency precautions for future constructions are taken, these problems will continue for the existing buildings. On the other hand, solutions for increasing energy efficiency can also be produced in the existing buildings. For producing these solutions, factors that increase the energy consumption should be well known. The aim of this study is to discuss proper solutions to increase energy efficiency in buildings considering the problems in Turkey. In Turkey, high energy load for heating and cooling comes in the first place in the factors which increase the energy consumption. Reasons like lack of enough insulation on the building shell, usage of building structures with improper thermal properties, not considering heat loss and gain through the windows increases the heat loss and this causes the energy consumption to increase. The solar energy is not benefited enough even the geographical location is proper for this. Another reason for increase in energy consumption is not considering the climatic data while designing the buildings. In this article, precautions for these problems and solutions to increase the energy efficiency will also be noted. Chapter 15 - This study investigates experimentally the thermal perception of indoor environment for evaluating the ability of radiant panel heaters to produce thermal comfort for space occupants as well as the energy consumption in comparison with conventional portable natural convective heaters. To compare the thermal perception of both heating systems, human subjects’ vote by the use of rating scales of thermal comfort was applied in an environmentally controlled test room, as well as measurements on a thermal dummy head, and the temperature distribution inside an office room was monitored. Based on the experimental results, an analytical study was carried out on radiant panel heaters to find their best distribution inside an office room that would provide a uniform operative temperature inside the office. The thermal perception results show that, compared to conventional convection heater, a radiantly heated office room maintains a lower ambient air temperature while providing equal levels of thermal perception on the thermal dummy head as the convective heater, and saves up to 39.1% of the energy consumption per day. However, for human subjects’ vote experiments, the results show that for an environmentally-controlled test room at outdoor environment temperatures of 0 ºС and 5 ºС, using two radiant panel heaters with a total capacity of 580W leads to a better comfort sensation than the conventional portable natural convective heater with a 670W capacity, with an energy saving of about 13.4%. In addition, for an outdoor environment temperature of 10 ºС, using one radiant panel heater with a capacity of 290W leads to a better comfort sensation than the conventional convection heater with a 670W capacity, with an energy saving of about 56.7%. The experimental results clearly indicate that the radiant panel heaters provide an equal or

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Preface

xvii

higher level of indoor environment thermal perception with energy saving values that are mainly dependent on the outdoor environment temperature. The analytical model results of wall temperature distribution were validated by the measured values, and a reasonable agreement prevailed. From the analytical results it is found that distributing the radiant panel heater inside the office room, one on the wall facing the window and the other on the wall close to the window, provides the best operative temperature distribution within the room. Chapter 16 - This study describes Geothermal District Heating Systems (GDHSs) and Geothermal Heat Pumps (GHPs) and their energetic and exergetic performance analysis. The case studies cover the actual system data taken from the systems, Turkey. General energy and exergy efficiencies of the GDHSs and GHP are introduced. Then this analysis applied to selected GDHS and GHP system using actual thermodynamic data for theirs recent performance evaluations in terms of energy and exergy efficiencies. Short Communication - Urban areas and environments are expanding worldwide and this ever-increasing urban population is likely to become larger still. Despite representing only two percent of the world’s surface area, world’s cities are responsible for 75 percent of the world’s energy consumption, and they emit almost 80 percent of global carbon dioxide mainly related to fossil energy carriers. Therefore, cities are a cornerstone in the implementation of strategies for energy conservation and its efficiency, establishing an intrinsic union between the concepts “cities” and “sustainability.” Within the city, energy is consumed in different sectors in order to satisfy the needs of the citizens. For this reason, we propose an approach based on the functional unit concept linked to the activities undertaken in cities. This commentary focuses on the service sector, without any doubt the most representative of cities and the western countries. However, this sector requires an important material and energy base for its correct operation, not only to maintain itself but also coming from the inputs of the other sectors. To analyze the energetic and environmental costs of the service sector, we propose integrating analysis tools such as Energy Flow Analysis (EFA) and Life Cycle Assessment (LCA) and communication instruments like the energy footprint. These have been applied to two case studies in the metropolitan area of Barcelona: Montjuïc Urban Park (including cultural, educational and sport services) and Sant Boi Retail Park. The results indicate that the average energy consumed in buildings and common services (street lighting and gardening practices) per visitor in Montjuïc is 0.35 kilograms of oil equivalent (Koe); meanwhile, the purchases in the retail park amount to a similar value of 0.37 Koe of energy. When transportation activities are considered in the analysis of the system, this energy consumption is heavily increased, up to four times in the case of the retail park. The greenhouse gas emissions are also indicated for both case studies. The results show the relevance of the environmental impacts of the service sector, which up to the moment has been forgotten from the environmental perspective.

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved. Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

In: Energy and Buildings: Efficiency, Air Quality … Editor: Joseph B. Utrick

ISBN 978-1-60741-049-2 © 2009 Nova Science Publishers, Inc.

Chapter 1

ON THE NUMERICAL STUDY OF PARTICLE DISPERSION IN INDOOR AIRFLOW: MODELLING CHALLENGES J. Y. Tu∗1, G. H. Yeoh2 and Z. F. Tian3 1

School of Aerospace, Mechanical and Manufacturing Engineering RMIT University, Vic. 3083, Australia 2 Australian Nuclear Science and Technology Organisation (ANSTO) PMB 1, Menai, NSW 2234, Australia 3 Minerals, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Clayton South, Vic. 3169, Australia

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ABSTRACT In this article, we provide an in-depth discussion on the modeling challenges for the computational simulation of the dispersion of contaminant particle in indoor air flow. The Eulerian-Lagrangian framework is adopted where the air flow is solved by the Eulerian equations and then integrated with the Lagrangian equations for particle motion thereby tracking individual particle through the flow field. Indoor air flow is always characterized by low-Reynolds-number turbulence and the application of turbulence models is usually used in the context of computational fluid dynamics. Application of three turbulence models – standard k-ε, RNG k-ε and the RNG-based large eddy simulation model – to simulate the indoor particle dispersion and concentration distribution is investigated. The flow of contaminant particles (with diameters of 1 and 10 μm) is considered. Measured data from Posnet et al. [Energy and Buildings 2003;35:515-26] and Lu et al. [Building and Environment 1996;31:417-23] are used to validate the simulation results. Comparing the three turbulence models for particle flow prediction, the RNG-based large eddy simulation model through better accommodating unsteady low-Reynolds-number turbulent flow structure has shown to provide more realistic particle dispersion and concentration distribution than the other two conventional turbulence models. The ∗

Mailing address: Professor Jiyuan Tu, School of Aerospace, Mechanical and Manufacturing Engineering RMIT University Vic. 3083,AUSTRALIA, Email: [email protected]; Tel:+61-3-9925-6191; Fax:+61-3-99256108

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

2

J. Y. Tu, G. H. Yeoh and Z. F. Tian particle-wall impact model has also a considerable effect on the particle concentration prediction. A simple collision model which prescribes the normal and tangential restitution coefficients may not be adequately applied for all particle cases in accurately predicting the indoor particle deposition for the Lagrangian particle tracking model. More realistic particle-wall impact models are proposed within this article to improve the indoor particle prediction.

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INTRODUCTION The fundamental function of a ventilation system in a building is to maintain a healthy and comfortable indoor air environment with acceptable energy consumption. Two major factors concerning the ventilation system design, operation and malfunction diagnosis have been identified: (i) indoor air quality (IAQ) and thermal comfort, (ii) energy consumption and efficiency. The issue of IAQ is gaining significant interest. It has a direct correlation with the occupants’ health since nowadays people are spending longer hours staying indoors such as in residential homes, working offices, mechanical workshops, schools, etc. In Australia, more than 90% of the workforce works in an indoor environment and more than 50% of the workforce is employed in office environments (Jamiriska et al., 2003). Within these environments, the presence of contaminant particles may cause discomfort to the eyes, irritation of the respiratory system, and may even spread diseases (Holmberg and Chen, 2003). In addition, an unhealthy indoor environment may decrease employees’ productivity. It is estimated that lost productivity in the USA is nearly five times than that of direct medical costs (DTIR, 1995). To keep the indoor air healthy and clean, it is necessary to maintain a sufficient air exchange rate (ASHRAE, 2001). Here, the energy consumption and efficiency need to be taken into consideration. In modern society, energy consumption is increasing in buildings and it is predicted that this escalating trend may continue (OECD, 2003). In developed countries, it is estimated that 30~40% of energy is consumed in buildings and between 10% and 60% of this is used for air-conditioning and ventilation (Ellis and Mathews, 2002). For instance, ventilation and air-conditioning account for about 43% of energy consumed in commercial buildings in Australia (Langston, 1997). To maintain a healthy IAQ and low energy consumption, the detailed information of indoor airflow and particles’ concentration is essential for engineers’ and scientists’ study. Several approaches have been employed to study indoor airflows and the motion of particles: experimental investigation, the zonal model and computational fluid dynamics (CFD). The experimental approach provides reliable information but it is rather expensive and impractical as a design tool. The zonal model approach gives a quick approximation of the overall flow but fails to provide the required detailed information. With increasing computational resources and the widespread availability of commercial code, CFD techniques are gaining in popularity and being used to predict indoor airflows and contaminant concentration. CFD has the capacity to provide “microscopic” information on the indoor air environment, like the air velocity, pressure, temperature and pollutants’ concentration distribution which are useful to derive the relevant “macroscopic” parameters for engineering purposes (Chow, 1996). Furthermore, CFD produces the graphical presentation of the building configuration, velocity, pressure and contaminant concentration fields (Chow, 1996). Coupled with other building

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On the Numerical Study of Particle Dispersion in Indoor Airflow

3

design tools such as Energy Simulation (ES), CFD helps engineers to modify the design towards achieving an optimal solution (Zhai and Chen, 2005). There are basically two categories of computational approaches that are currently used to predict the gas-particle flows: the Eulerian-Eulerian model and the Eulerian-Lagrangian model. In the Eulerian-Eulerian model, both the gas and particle flows are treated as continuous fluid flow and regarded as interacting with each other. It is also found that in most of the CFD studies of indoor air-particle flows, the drift-flux multiphase model, which is within the framework of Eulerian-Eulerian approach, has been used rather than the fully coupled Eulerian model (Holmberg and Li, 1998). Murakami et al. (1993) studied the diffusion characteristics of airborne particles in a conventional flow-type clean room via the drift-flux model and the standard k-ε model. Validations against the measurements showed that the drift-flux model reasonably reproduced the concentration distribution of small airborne particles. By employing the drift-flux model and a Low-Reynolds-Number k-ε model, Chen et al. (1992) investigated the influence of location of airborne particle source, ventilation rate, air inlet size, air velocity, air outlet location, and heat source on the particle concentration distribution. They have found that the particle concentration distributions in the recirculating zone are very sensitive to the location of the particle resource and airflow patterns. Shimada et al. (1996) studied the transport and dispersion of contaminant particles in a ventilated room via the standard k-ε model and the drift-flux model. From their calculations the concentrations were found to be in qualitative agreement with the measured results except in regions near the room floor and the walls. Holmberg and Li (1998) also utilized the standard k-ε model and the drift-flux model to simulate the particle transportation for indoor environment. It has been shown that the particle deposition on indoor surfaces in an indoor environment plays a significant role towards the total pollutant balance even in the presence of low or no ventilation flow rate. For the Eulerian-Lagrangian approach, the gas phase is usually solved by Eulerian equations and then integrated with the Lagrangian equations for particle motion thereby tracking individual particle through the flow field. Chung (1999) investigated the air movement and contaminant transport in a partitioned enclosure provided with ventilation using the standard k-ε and Lagrangian particle-tracking model. Computed results of temperature and velocity fields agreed well with the measured data. The results also show that the predicted data of contaminant particle’s trajectories gave valuable information to better evaluate the indoor air quality design procedure. Lu et al. (1996) simulated the air movement and aerosol particle deposition and distribution in a ventilated two-zone chamber consisting of a small opening between two compartments. Satisfactory agreement of the average particle concentrations between the Lagrangian predictions and measurements were obtained in both zones. Buchanan and Dunn-Rankin (1998) investigated the particle transport characteristics in a hospital operating room using two of the commonly available cross-flow and impingingflow ventilation configurations. From the analysis they concluded that numerical simulations can be a valuable design tool to control the transport of airborne contaminant particles. Recently, Zhao et al. (2004) numerically studied the air movement, aerosol particle concentration and its deposition in a displacing and mixing ventilation room using the Lagrangian model. Despite the many encouraging results, some uncertainties still prevail in particular the approximation of turbulence models that requires further resolution (Chen, 1997). The indoor air flows are always characterized by low-Reynolds-number turbulence. The improper

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J. Y. Tu, G. H. Yeoh and Z. F. Tian

handling of LRN turbulence can contribute to inaccurate calculations of the airflows and consequently the contaminant concentration, since the particle dispersion and distribution are strongly affected by the air phase velocity and turbulent fluctuations. There are three techniques currently available to numerically solve the turbulence flow which is the dominant flow pattern in indoor airflow: direct numerical simulation (DNS), Reynolds-Averaged Navier-Stokes (RANS) and large eddy simulation (LES). DNS solves directly the NavierStokes equations of the gas phase. It provides the most accurate solutions. However, extremely fine meshes is required to solve the smallest eddies that are within the size of 0.1 to 1 mm in an indoor flow (Spengler et al., 2001). Hence, the DNS approach can be rather expensive in view of the computational standing for engineering flow and is only useful as a basic research tool for flows with low Reynolds number and simple geometry. In the RANS approach for modeling turbulence, an approximation is introduced that all the flow unsteadiness is averaged out and the nonlinearity of the Navier-Stokes equations gives rise to terms that must be modeled. Although RANS model probably is the least accurate, it is quick, simple and possesses good numerical stability. When the model is properly applied, it provides reasonable results. The two-equation k-ε model is the most popular turbulence model for the prediction of room air movement (Sorensen and Nielsen, 2003). It is easy to program and modify whilst giving reasonable results in many applications (Chen, 1997). In contrast to RANS, LES takes advantage of the fact that larger eddies transport most of the momentum or thermal energy. In LES, large eddies are solved directly and the small eddies are modeled by the subgrid-scale (SGS) model, so it requires much less computing expense than DNS and it provides better results than RANS models in many cases. A SGS model based on the RNG theory has also been formulated through Yakhot et al. (1989). This RNG-based LES model is able to provide description of the low-Reynolds-number and near wall flow that are always encountered in indoor air flows. LES is being increasingly employed as a tool to study turbulent flow for configurations in which RANS equations are not sufficiently accurate. Davidson and Nielsen (1996) employed Smagorinsky SGS LES model and a dynamic SGS model to simulate the flow in a threedimensional ventilated room. They found that results of the dynamic SGS model were in good agreement with experimental data while Smagorinsky model gave poor performance. Emmerith and McGrattan (1998) utilized Smagorinsky SGS model to the ventilation airflow in a three-dimensional room. Their results were in good agreement with both experimental data and the LES results of Davidson and Nielsen (1996) except for the region near the floor and ceiling. Interaction between Smagorinsky constant and grid resolution has also been reported. Zhang and Chen (2000) have applied a dynamic SGS model to calculate natural, forced and mixed convection air flows in enclosures. They have obtained reasonable agreement between the calculated air velocity, temperature and turbulence distribution and corresponding experimental data. Recently, Jiang et al. (2003) used LES models to study the nature ventilation in buildings and found good agreement between the numerical results and experimental data. Kato et al. (2003) further employed the LES model to analyze the visitation frequency through particle tracking method and their simulation results were validated against a model experiment. Nevertheless, the in-depth investigations of the prediction of the indoor contaminant particle dispersion and concentration distribution using LES model are still lacking.

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

On the Numerical Study of Particle Dispersion in Indoor Airflow

5

For the purpose of assessing issues concerning indoor airflow and contaminant particle concentration, two geometrically different rooms are investigated using the EulerianLagrangian model in this chapter. For the first room configuration, the performances of three turbulence models, standard k-ε, RNG k-ε and the RNG-based LES model, for simulating indoor airflow are evaluated. The measured air phase velocity data obtained by Posner et al. (2003) are employed to validate the simulation results. In another other two-zone ventilated room configuration (Lu et al. 1996), contaminant particle dispersion and distribution within the room are simulated using the RNG-based LES model together with a Lagrangian particle tracking model. Corresponding experimental data of particle concentration decay from (Lu et al. 1996) are used to validate the simulation results. Several factors that may lead to the discrepancy between the CFD predicted results and the measured particle concentration are discussed.

CONSERVATION EQUATIONS General Conservation Equations for Gas Phase CFD is fundamentally based on the governing equations of fluid dynamics. They represent mathematical statements of the conservation laws of physics. These laws have been derived from the fact that certain measures must be conserved in a particular volume, which is called control volume. The gas phase conservation equations of a scalar φ can be cast in a general form:

(

∂ ρ gφ ∂t Copyright © 2009. Nova Science Publishers, Incorporated. All rights reserved.

(i)

) ∂(ρ +

g g ui

∂x j (ii)

)=

∂ ∂x j

⎛ ∂φ ⎜Γ ⎜ ∂x j ⎝ (iii)

⎞ ⎟ + qφ ⎟ ⎠ (iv)

(1)

where t is time and u ig represents gas velocity. Γ is the diffusivity of the scalar and qφ is a general source term. The term (i) in equation (1) is the local acceleration term and term (ii) is the advection term. The term (iii) on the right hand side is the diffusion term and term (iv) is the source term. Equation (1) is usually used as the starting point for development of numerical procedures in either applying the finite difference or finite volume methods. Algebraic expressions of this equation for the various transport properties are formulated and hereafter solved. By setting the transport property φ equal to 1, u g , T, and selecting appropriate values for the diffusion coefficient Γ and source terms qφ , the special forms for each of the partial differential equations for the conservation of mass, momentum and energy can be obtained in the form: Conservation of mass (φ = 1)

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

6

J. Y. Tu, G. H. Yeoh and Z. F. Tian

∂ρ g ∂t

+

(

∂ ρ g u gj xj

)=0

(2)

g Conservation of momentum (φ = ui )

(

∂ ρ g u ig ∂t

) + ∂(ρ

g g g u j ui

∂x j

)=

∂ ∂x j

g ⎞ ⎛ ∂p ⎜ μ g ∂u i ⎟ − g ⎜ ∂x j ⎟⎠ ∂xi ⎝

(3)

Conservation of energy (φ = T)

(

∂ ρ gT

) ∂(ρ +

∂t

g gu j T

∂x j

)=

∂ ∂x j

⎛ μ g ∂T ⎞ ⎜ ⎟ ⎜ Pr ∂x j ⎟ ⎝ ⎠

(4)

Standard and Re-Normalization Group (RNG) K-ε Model

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Many engineering applications require computational procedure that can supply adequate information about the time-averaged properties of the flow (such as mean velocities, mean pressures, mean stresses etc.), but which avoid the need to predict all the effects associated with each and every eddy in the flow. Therefore, by adopting a suitable time-averaging operation on the momentum equations, one is able to discard all details concerning the state of the flow contained in the instantaneous fluctuations. Osborne Reynolds first introduced the notation of splitting the instantaneous flow variables into their mean and fluctuating components (Hinze, 1975):

φ ( xi , t ) = φ (x i ) + φ ′( xi , t )

(5)

The overbar in equation (5) denotes the time-averaged qualities. By applying this process on the conservation forms of mass equation (1) and momentum equation (2) produces the time-averaged or more popularly known as the RANS equations:

∂ρ g ∂t

+

(

∂ ρ g u ig ∂t

(

∂ ρ g u jg xj

) + ∂(ρ

)=0 g g g u j ui

∂x j

(6)

)=

∂ ∂x j

(

)

g ⎞ ⎛ ∂p ∂ ρ g u i′u ′j ∂τ ij ⎜ μ g ∂u i ⎟ − g − + ⎜ ⎟ ∂x j ⎠ ∂x i xj ∂x j ⎝

(7)

g ′ where u i and u i are gas phase mean velocity and gas phase fluctuating velocity,

respectively. The mean viscous stress tensor

τ ij

is given by:

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

On the Numerical Study of Particle Dispersion in Indoor Airflow

⎛ ∂u ig ∂u jg + τ ij = μ g ⎜ ⎜ ∂x j ∂xi ⎝

⎞ ⎟ ⎟ ⎠

7

(8)

Time-averaged equations can be solved if the unknown Reynolds stresses, ρ g u i′u ′j in equation (7) can be related to the mean flow quantities. According to Hinze (1975), it was proposed that the Reynolds stresses could be linked to the mean rates of deformation:

⎛ ∂u ig

ρ g u i′u ′j = μ g ,t ⎜

⎜ ∂x j ⎝

+

∂u jg ⎞ 2 ⎟− ρ σ k g ij g ∂x i ⎟⎠ 3

(9)

where μ g ,t is the eddy viscosity or turbulent viscosity.

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Since the complexity of turbulence in most engineering flow problems precludes the use of any simple formulae, it is possible to develop similar transport equations to accommodate the turbulent quantity kg and other turbulent quantities such as the rate of dissipation of turbulent energy εg. Here, kg and εg can be defined and expressed in Cartesian tensor notation as:

kg =

1 u i′u i′ 2

εg =

μ g ,t ⎛ ∂u ' i ⎜ ρ g ⎜⎝ ∂x j

(10)

⎞⎛ ∂u ' i ⎟⎜ ⎟⎜ ∂x j ⎠⎝

⎞ ⎟ ⎟ ⎠

(11)

From the local values of kg and εg, a local turbulent viscosity

μ g ,t =

μ g ,t can be evaluated as

C μ ρ g k g2

(12)

εg

By substituting the Reynolds stress expressions in equation (9) into the governing equations (6) and (7), the following transport equations for the gas phase are:

∂ρ g ∂t

+

(

∂ ρ g u ig ∂t

(

∂ ρ g u jg xj

)+

∂ ∂x j

)=0 g ⎛ ⎛ g ⎜ ρ u g u g − ( μ + μ )⎜ ∂u i + ∂u j g ,t g ⎜ g j i ⎜ ∂x j ∂xi ⎝ ⎝

(13)

⎞⎞ ∂p ⎟⎟ = − g ⎟⎟ ∂xi ⎠⎠

Energy and Buildings: Efficiency, Air Quality, and Conservation : Efficiency, Air Quality, and Conservation, edited by Joseph B. Utrick, Nova Science

(14)

8

J. Y. Tu, G. H. Yeoh and Z. F. Tian The additional differential transport equations for the standard k-ε model are thus:

(

∂ ρg kg

) ∂(ρ +

∂t

(

∂ ρ gε g

∂x j

) ∂(ρ +

∂t

g gu j kg

g gu j

εg

∂x j

)=

)=

∂ ∂x j

⎛ ∂k μ ⎜ ( μ g + g ,t ) g ⎜ σ k ∂x j ⎝

⎞ ⎟ + Pk − ρ g ε g ⎟ ⎠

(15)

∂ ∂x j

⎛ μ ∂ε ⎜ ( μ g + g ,t ) g ⎜ σ ε ∂x j ⎝

⎞ ε ε2 ⎟ + C1ε g Pk − ρ g C 2ε g ⎟ kg kg ⎠

(16)

where the rate of production of turbulent kinetic energy Pk = − ρ g u i′u ′j

∂u ig ∂x j

can be

modelled by

⎛ ∂u g ∂u jg ∂u ig ≈ μ g ,t ⎜ i + ⎜ ∂x j ∂xi ∂x j ⎝

− ρ g u i′u ′j

⎞ ∂u ig ⎟ ⎟ ∂x j ⎠

(17)

The standard k-ε model contain five adjustable constants Cμ, σk, σε, C1ε and C2ε. These constants have been arrived at by comprehensive data fitting for a wide range of turbulent flows (Launder and Spalding, 1974): Cμ = 0.09,

σk = 1.0,

σε = 1.3,

C1ε = 1.44,

C2ε = 1.92.

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Yakhot et al. (1986) developed a k-ε model based on the RNG theory. The transport equations for k and ε are given in the following:

(

∂ ρgkg ∂t

(

∂ ρ gε g ∂t

) ∂(ρ +

) ∂(ρ +

g gu j kg

∂x j g gu j

∂x j

εg

)=

)=

∂ ∂x j

⎛ ∂k g ⎞ ⎟ + Pk − ρ g ε g ⎜ σ k μ eff ⎟ ⎜ x ∂ j ⎠ ⎝

(18)

∂ ∂x j

⎛ ∂ε g ⎞ ε ε2 ⎜ σ ε μ eff ⎟ − C1ε g Pk − ρ g C 2ε g − R ⎜ ∂x j ⎟⎠ kg kg ⎝

(19)

One difference between the standard and RNG turbulence models is the definition of the turbulent viscosity. The scale elimination procedure in RNG theory results in a differential equation for turbulent viscosity μ eff :

⎛ ρ 2k ⎞ vˆ g g ⎟ d⎜ dvˆ = 1.72 ⎜ ε μ ⎟ 3 ˆ 1 v C − + v ⎝ g g ⎠

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(20)

On the Numerical Study of Particle Dispersion in Indoor Airflow

9

where vˆ = μ eff μ g and Cv = 100. Another difference between the standard and RNG k-ε models is the presence of an additional strain rate term R in the ε-equation (19) for the RNG k-ε model. The term is modeled as:

R=

C μη 3 (1 − η η o ) ε g2 1 + βη 3

(21)

kg

where β and ηo are constants with values of 0.015 and 4.38. The significance of the inclusion of this term is its responsiveness towards the effects of rapid rate strain and streamlines curvature, which cannot be properly represented by the standard k-ε model. According to the RNG theory, the constants in the turbulent transport equations are given by σk = 0.718, σε = 0.718, C1ε = 1.42 and C2ε = 1.68 respectively (Yakhot et al., 1986).

RNG-BASED LES MODEL In LES models, the small eddies are separated by filters from large eddies that contain most of the energy. The resulting equations thus resolve only the dynamics of large eddies and these large-scale variables that can be achieved by the filtering operation

f ( x) =

1 ΔV

∫ f ( x ′)dx ′

(22)

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Here, ΔV is the control volume (the finite-volume cell). Applying the filtering operation to the conservation equations, the governing equations for the large-scale variables are:

∂ρ g ∂t

(

+

(

)

∂ ρ g u ig = 0 ∂xi

)

(

(23)

)

∂ ∂ ∂ ρ g u ig + ρ g u jg u ig = ∂x j ∂t ∂x j

g ⎞ ⎛ ∂p ∂τ ⎜ μ g ∂u i ⎟ − g - ij ⎟ ⎜ ∂x j ⎠ ∂xi ∂x j ⎝

(24)

where u g is the resolved gas phase velocity and is different from the overbar gas phase velocity in RANS modeling techniques where it represents Reynolds-averaged quantity. The effect of the small scales upon the resolved part of turbulence appears in the SGS stress term:

τ ij = ρ g u gj u ig − ρ g u jg u ig . Yakhot et al. (1989) derived a subgrid model by applying the RNG theory to the SGS eddy viscosity. In this RNG-based SGS model, the stress is modelled according to:

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J. Y. Tu, G. H. Yeoh and Z. F. Tian

τ ij −

δ ij 3

s τ kk = 2 μ eff S ij

(25)

s Here, μ eff is the SGS turbulent viscosity given as

s μ eff = μ g [1 + H (x)]

1 3

(26)

where H(x) is the ramp function defined by

⎧x, x ≥ 0 H ( x) = ⎨ ⎩0, x < 0

in which x equals to

(27)

s μ s2 μ eff

μ

3 g

- C and μ s = (C RNG ΔV

1

3 )2

2 S ij S ij . On the basis of the

RNG theory, the constants CRNG and C are 0.157 and 100, respectively

PARTICLE PHASE MODEL

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A Lagrangian-formulated particle of motion is solved. The trajectory of a discrete particle phase is determined by integrating the force balance on the particle. This force balance equates the particle inertia with the forces acting on the particle. Appropriate forces such as the drag and gravitational forces have been incorporated into the equation of motion. The equation can be written as:

g (ρ p − ρ g ) du ip = FD (u ig − u ip ) + dt ρp

(

(28)

)

where FD u ig − u ip is the drag force per unit particle mass, and FD is given by

FD =

18μ g C D Re p 24 ρ p d p2

(29)

In equation (29), ρp denotes the density of particle material, dp is the particle diameter,

u p presents the particle velocity and Rep is the relative Reynolds number which is defined as:

Re p =

ρ g d p u ip − u ig μg

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On the Numerical Study of Particle Dispersion in Indoor Airflow

11

The drag coefficient (CD) is correlated as a function of the Rep:

⎧0.44 ⎪ C D = ⎨ 24 ⎛ 1 0.66 ⎞ ⎪ Re ⎜1.0 + 6 Re p ⎟ ⎠ ⎩ p⎝

Re p > 1000 Re p < 1000

(31)

For the k-ε model, uig in equation (30) is calculated by using the instantaneous gas phase velocity, u ig + u i′ , along the particle path during the integration process. Here, the fluctuating velocity components u i′ that prevails during the lifetime of the turbulent eddy are sampled by assuming that they obey a Gaussian probability distribution, so that

u i′ = ζ u i′

2

(31)

where ζ is a normally distributed random number, and the remaining right-hand side is the local root mean square (RMS) velocity fluctuations can be obtained (assuming isotropy) by 2

u i′ = 2k g 3

(32)

The interaction time between the particles and eddies is smaller of the eddy lifetime τ e and the particle eddy crossing time τ cross . For the characteristic lifetime of the eddy, it can be defined as

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τ e = −TL log(r )

(33)

where TL is the fluid Lagrangian integral time, TL = 0.15 k g ε g , and the variable r in equation (33) is a uniform random number between 0 and 1 while for the particle eddy crossing time, it is given by

⎡

⎞⎤ ⎟⎥ p g ⎟⎥ ⎜ τ ui − ui ⎟ ⎝ ⎠⎦ ⎛

τ cross = −τ log ⎢1 − ⎜⎜ ⎢ ⎣

Le

(34)

where τ is the particle relaxation time and Le is the eddy length scale. The particle interacts with the fluid eddy over the interaction time. When the eddy lifetime is reached, a new value of the instantaneous velocity is obtained by applying a new value of ζ. For LES model, the resolved velocity of the particle location is used as the instantaneous fluid velocity in equation (29) as well as in equation (34). The characteristic lifetime of eddy is equivalent to the time scale of this LES calculation.

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J. Y. Tu, G. H. Yeoh and Z. F. Tian

The process of particle impacting on wall is taken into consideration by a particle-wall impact model. When reaching a wall surface, a contaminant particle either deposits or bounces depending on the presumed particle-wall impact type, i.e. being “trapped” or “reflected”. For the case of particle bouncing off the wall, the normal and tangential coefficients of restitutions at wall boundaries, defining the amount of momentum in the directions normal and parallel to the wall is retained by the particle after collision with the wall boundary are determined as:

en = u rnp u inp

(35)

et = u rtp u itp

where the subscripts i and r denote the incident and rebound components while n and t represent the normal and tangential directions, respectively. In this study, both en and et have been assumed to have values of 0.9.

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NUMERICAL PROCEDURE A generic CFD code, FLUENT, is utilized to predict the air and particle flows under unsteady-state conditions. The Lagrangian particle tracking model is used to predict contaminant particle dispersion and concentration. Two-way coupling between the air and particle is achieved through the momentum exchange that appears as a sink term in the air phase momentum equations. The transport equations are discretised using the finite-volume method. Within the equations, the time-dependent terms are handled through an implicit second order backward differencing in time. The QUICK scheme is used to approximate the convective terms at the faces of the control volumes for the k-ε turbulence models. For the LES model, it has been reported that the truncation error overwhelms the contribution of SGS force for Upwind and Upwind-biased schemes (Mittal and Moin, 1997). Central differencing scheme is thus employed instead for the LES model. The SIMPLE algorithm is employed as the pressurevelocity coupling method. An enhanced wall treatment, a near-wall modeling method combining a two-layer model with enhanced wall functions, is adopted to bridge the wall with the bulk air flow. For the LES model, a wall model is also adopted to reduce the computational expense. The convergence criteria for the air phase properties (resolved velocities and pressure) are set to a value of 10-5 and convergence of the solution is deemed to be reached when the residual is below the default convergence criteria (Sorensen and Nielsen, 2003). However, since the manner in which the non-linear equations approach the final solution is problem dependent, the default convergence criteria may not ensure the numerical simulation is close to the final solution as indicated in Sorensen and Nielsen (2003). The sufficiency of the convergence criteria is further checked by comparing the LES simulation of airflows with the convergence criteria values of 10-5 and 10-7. The difference of air velocity profile between the two simulations has been ascertained to be negligible.

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On the Numerical Study of Particle Dispersion in Indoor Airflow

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NUMERICAL PREDICTIONS Figure 2 illustrates the geometrical structure of room 1 for the assessment of the standard k-ε, RNG k-ε and the RNG-based LES model.

a

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b

c Figure 2. (a) Configuration of room 1, (b) mesh for the mid-plane, (c) mesh for the room top.

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J. Y. Tu, G. H. Yeoh and Z. F. Tian

The geometry has a floor area of 91.4 cm × 45.7 cm with a height of 30.5 cm. A partition with a height of 15 cm is located in the middle of the room. The inlet has the same size as the outlet, 10 cm × 10 cm. According to the experimental boundary condition (Posner et al., 2003), the inlet velocity (Uinlet) is 0.235 m/s with a uniform profile. Since the majority of the inlet air velocity profile is a laminar plug flow (Posner et al., 2003), the turbulent intensity is expected to be very low and assumed to be 1%. Based on the inlet velocity and the inlet width, the Reynolds number of the inlet airflow is determined to be 1500. The numerical simulations are obtained from a mesh density of 118 × 58 × 38 grids (structured mesh) for the whole domain. The grid-independency is achieved by refining the mesh to have a mesh density of 180 × 58 × 38 grids with the difference of the air phase velocity less than 1% predicted by RNG k-ε model. 12 × 12 grid points are allocated in the inlet and the outlet. The initial condition of the flow field in the room is assumed to be randomly perturbed about the mean velocity Uinlet. A non-dimensional time step of 0.0385 is used, which is defined by t’ = Uinlet t / H where Uinlet is the inlet air velocity, t is the physical time step (0.05 seconds) and H is the room height. To ensure that the solution achieved sufficient statistical independence from the initial state, time-averaged results are obtained from the instantaneous values after the airflow simulation is marched for 2000 non-dimensional time steps, representing 100 seconds in physical time. After this time, the instantaneous values such as the airflow velocities are averaged over 10000 non-dimensional time steps, or 500 seconds in physical time. No visible difference is found between the time-averaged velocities when the simulation is continued after 500 seconds. The particle concentration decay within a two-zone ventilated room (Lu et al. 1996) is investigated using the RNG LES model with Lagrangian particle-tracking. Figure 3 shows the geometry configuration of this room with a size of width × depth × height = 5 m × 3 m × 2.4 m. In the middle of the room, a partition with a large opening of height and width of 0.95 m × 0.70 m divides the room into two zones. The inlet (with size of 1 m in y direction and 0.5 m in z direction) is 1.6 m above the room floor and 1.5 m from the front wall. An outlet with the same size as the inlet is 0.3 m above the floor and 0.5 m from the front wall. Two air exchange rates are tested in this study: (i) air change per hour (ACH) 10.26 with inlet velocity of 0.1026m/s; (ii) air change per hour (ACH) 9.216 with inlet velocity of 0.09216m/s.

Figure 3. Configuration of room 2.

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On the Numerical Study of Particle Dispersion in Indoor Airflow

15

The grid independence is checked by comparing the computed particle concentration decay obtained by three different mesh densities of 50 × 30 × 24, 63 × 38 × 30 and 83 × 50 × 40 (all are structured meshes) for the 9.216 ACH. The difference between predicted particle concentration decay from the 63 × 38 × 30 and 83 × 50 × 40 mesh densities is negligible. The mesh density of 63 × 38 × 30 is used in this study in order to embrace the increase of computational efficiency towards achieving the final results.

NUMERICAL RESULTS OF THE CONFIGURATION OF ROOM 1 The comparison of the vertical air velocity along the vertical inlet jet axis (line 1 in Figure 2) predicted through the three turbulence models against the experimental data is presented in Figure 4(a). It can be seen from the figure that good agreement was achieved between all three turbulence model predictions and measurements. The result from the RNGbased LES model provided however the best agreement in the middle region from the distance of 0.15 m to 0.25 m. Herein, the velocities determined through the present standard k-ε model simulations were slightly under-predicted from those of RNG k-ε model, which were consistent with the computer investigations performed by Posner et al. (2003). The standard k-ε model generally over predicts the gas turbulence kinetic energy in the flow, which subsequently leads to a high turbulent viscosity ν g ,t

defined as

ν g ,t = C μ k g2 ε g Figure 5(a) shows the predicted turbulent kinetic energy normalised by 0.005 kg/ms along the vertical inlet jet axial (line 2 in Figure 2) and Figure 5(b) demonstrates the profile of the turbulent dissipation rate ε normalised by 0.01m2/s3. It is evidently clear that the standard k-ε model yielded excessive normalised turbulent kinetic energy values while the predicted normalised turbulent dissipation rate values at the same locations were almost

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identical to the RNG k-ε model. This therefore resulted in the over prediction of ν g ,t and the production of excessive mixing in the standard k-ε model, which subsequently reduced the jet velocity. Another possible cause could be the turbulent dissipation transport equation of the standard k-ε model. Figures 6(a) and 6(b) illustrate the turbulent viscosity profiles obtained by the standard k-ε and RNG k-ε models at the mid-plane of zone 1. The standard k-ε model predicted higher turbulent viscosity values than those of the RNG k-ε model in the central region. Figure 4(b) shows the comparison between the predicted and measured vertical air velocity component along the horizontal line at mid-partition height. From the location x = 0 m to the wall partition, all three turbulence models yielded almost similar results. RNG-based LES model gave slightly better prediction in the region from x = 0.2 m to the partition. In the near-wall regions about the locations x = 0.46 m and x = 0.9 m, the RNG-based LES model was seen to successfully captured the highest positive vertical velocities while the k-ε models significantly under-predicted the velocities. From these results, we can conclude that better prediction was achieved through the RNG-based LES model as demonstrated through the excellent agreement with the experimental results in the region from the partition to x = 0.6 m.

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16

J. Y. Tu, G. H. Yeoh and Z. F. Tian

Figure 4. Comparison between simulation and measured results of the vertical velocity component: (a) along the vertical inlet jet axis and (b) along the horizontal line at mid-partition.

Significant under-prediction of the negative vertical velocity by k-ε models, found in the region right beneath the inlet, could be possibly attributed to the over-diffusion caused the eddy-viscosity modelling. Marginal discrepancy between the measured data and the simulation results could nonetheless be found in the region about the location x = 0.85 m where the k-ε models results were marginally better than the RNG-based data. Overall, all the three turbulence models performed well; good agreement has been achieved between the predictions and measured data and the flow trends have been successfully captured through the three turbulence models. One important finding in this investigation was that the RNG-

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On the Numerical Study of Particle Dispersion in Indoor Airflow

17

based LES model has shown to provide significantly better results especially in zone 2 because the model better accommodated the flow behaviour within the model room. Based on the parametric study performed above, there are three principle advantages that RNG-based LES model has demonstrated to perform better over the two-equation k-ε models. Firstly, RNG-based LES model explicitly solves the large eddies that transport the momentum energy and turbulence, and only models the small subgrid-scale eddies. As a result, RNG-based LES model is less sensitive to the modelling errors than the k-ε models (see Figures 5.2(a) and (b)). Secondly, RNG-based LES can be considered to be more suited for LRN turbulent flows which are of importance for indoor airflows.

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a

b Figure 5. (a) Computed turbulent kinetic energy (normalized by 0.005 kg/ms) and (b) Computedturbulent dissipation rate ε (normalized by 0.01m2/s3) along the vertical inlet jet axis.

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18

J. Y. Tu, G. H. Yeoh and Z. F. Tian

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a

b

Figure 6. Turbulent viscosity profiles predicted: (a) Standard k-ε model and (b) RNG k-ε model.

It is well known that the Smagorinsky SGS LES model, which is based on high Reynolds number flow, predicts non-zero turbulence viscosity in laminar flows. RNG-based LES model remedies this problem by introducing a ramp function in equation (26). In low turbulent flow regions (relaminarization of turbulent flows), the argument of the ramp function becomes negative and the effective viscosity recovers the molecular viscosity. This enables the RNGbased SGS eddy viscosity to model the low-Reynolds-number effects encountered in transitional, laminar flows and near-wall regions that are always encountered in indoor airflows. Semi-empirical models such as in the standard and RNG k-ε models tend to better handle high-Reynolds-number flows as their constants have been specifically tuned via experimental data of fully developed turbulent flows. Thirdly, RNG-based LES has the capacity of capturing instantaneous turbulences, i.e. it can properly account for the history

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On the Numerical Study of Particle Dispersion in Indoor Airflow

19

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and transport effects on turbulence; the k-ε models are incapable because of the inherent timeaveraging modelling approach. This can be clearly seen in Figure 7 which compares the statistic root-mean-square (RMS) velocity profiles in the room middle-plane obtained by the three turbulence models. RNG-based LES model predicted significantly higher RMS velocities than the others. With the injection of contaminant particle into the enclosed environment, it is anticipated that the RNG-based LES model is expected to provide better prediction of the contaminant particle dispersion and concentration distribution that are strongly affected by the time-dependant air flow. The time-mean velocity fields simulated by the three turbulence models at the mid-plane of the model room are compared in Figure 8. All the three models successfully predicted the strong recirculation cell in the region about the location x = 0.85 m. The two k-ε models predicted larger recirculation zone than the RNG-based LES model indicating a larger dispersion of the airflow.

Figure 7. Comparison of predicted RMS velocity in the mid-plane of the room.

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20

J. Y. Tu, G. H. Yeoh and Z. F. Tian

Figure 8. Comparison of the predicted time-mean velocity field in the mid-plane of room 1.

As mentioned from above, one of many advantages of the LES model is that it can account for the historical and the transport effects on turbulence, i.e. it captures the instantaneous turbulences. Figure 9(a) illustrates the instantaneous velocity profile at the mid plane. Four recirculation structures are depicted in zone 1, which indicate the strong mixing of the air and particles there. However, these circulation zones are not shown in the timemean velocity profile in Figure 8. It is noted that the lack of instantaneous information may lead to inappropriate evaluation and design of the ventilation systems. This has been confirmed through the LES investigation of natural ventilation in a building with a large opening (Jiang et al., 2003). In that study, it was found that fresh-air streamlines reached the wall opposite to the opening in the instantaneous prediction. Nevertheless, the streamlines in the mean flow field presented a recirculation zone indicating that the fresh air could not have penetrated so profoundly into the enclosure. The instantaneous total pressure profile along the

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On the Numerical Study of Particle Dispersion in Indoor Airflow

21

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mid plane is given in Figure 9(b). Here, higher total pressure is found in the inlet jet region and the low pressure is predicted in zone 1.

Figure 9. LES prediction: (a) Instantaneous velocity field of the mid-plane and (b) Instantaneous total pressure profile of the mid-plane.

For indoor air-pollution problems, significant concerns are mainly focused on the dispersion of fine particles (aerodynamic diameter that is smaller than 2.5 µm). In this study, the prediction of particle (with diameter of 1 µm having a density of 800 kg/m3) concentration is investigated through the use of the three turbulence models. From an elapsed time of 70 seconds, 144 particles, evenly-spaced, were injected at the inlet vent, with the same velocity as the inlet air, until the simulation time has reached 100 seconds. In total, there were 86400 particles introduced in the room until the elapsed time of 100 seconds. The particle injections were subsequently stopped, and only the sample particles remained in room were tracked until the time reached 160 seconds. Indoor airflows are normally characterized by low characteristic velocities and the contaminant particles are with small diameters. So in most cases, the Stokes number for the contaminant particles flow indoor is far less than unity and the particles thereby act more like gas tracers. Figure 10 presents the comparison of tracked sample particles numbers obtained from the different turbulence models for the particle size, dp = 1 µm.

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22

J. Y. Tu, G. H. Yeoh and Z. F. Tian

Tracked sample particles(1000)

80 70 60 50 40 30 20 70

85

100 115 130 145 160 175

Simulation time (seconds)(1um) Standard k-e model

RNG k-e model

LES model

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Figure 10. Comparison of tracked sample small particles utilizing the various turbulence models.

After an elapsed time of 85 seconds, the three turbulence models tracked almost the same number of contaminant particles. Here, the contaminant particles were found just entering the room. Majority of the particles were concentrated in zone 1 of the model room with very few particles flowing out through the outlet vent. At times t = 100 seconds and t = 115 seconds, the RNG-based LES model were seen to track more contaminant particles than the k-ε models, i.e. more contaminant particles were predicted to be suspended in the room through the RNG-based LES model. Here again, since the size of the particles were small (St 500,000 sq ft # of Floors 1 2 3 4-9 10 or more Principal Building Activity (Commercial) Accommodation Entertainment and Recreation Offices: Private, Financial or Professional Retail Services Shopping Centres Transportation and Maintenance Facilities Warehouse / Wholesale

% of Buildings 28 19 23 11 8 5 4 2 37 34 15 12 3

2 4 12 14 18 10 3

Category Year of Construction 1919 or before 1920-1945 1946-1959 1960-1969 1970-1979 1980-1989 1990-1994 1995-2000 Energy Source Electricity Natural Gas Oil District Steam or District Heat Principal Building Activity (Institutional) Accommodation Administration Education Health Care: Hospital / Inpatient Care Health Care: Outpatient Care Public Assembly Other Uses

% of Buildings 8 9 9 12 19 20 9 13 100 68 13 3

1 5 10 2 2 5 1

10

Source: Hughes (2003).

Few of the buildings were high-rise, with most buildings in the sample having 3 or fewer storeys. There is a wide range of building ages. The median building was constructed in the 1970s, approximately one quarter of the buildings were constructed before 1960, and approximately 20% were 10 years old or less at the time of the survey. The sources of energy vary across the buildings, with all buildings using electricity, about 2/3 using natural gas, and approximately 1 building in 8 using heating oil. The activities undertaken in the buildings vary widely. Three quarters of the buildings in the survey are ‘commercial’ buildings with principal activities ranging from retail and wholesale trade to services. The remaining quarter of the buildings house institutional activities, with over half of these being educational or health care facilities. Table 2, based on charts presented in Hughes (2003), provides energy intensity values (gigajoules of energy used per sq ft) for the buildings in the CIBEUS data set.

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184

David L. Ryan and Denise Young Table 2. Energy Intensity by Principal Building Activity (Gigajoules per sq ft)

Category Principal Building Activity (Commercial) Accommodation Entertainment and Recreation Offices: Private, Financial or Professional Retail

Intensity

Services Shopping Centres Transportation and Maintenance Facilities Warehouse / Wholesale

0.25 0.13 0.13

0.15 0.15 0.12 0.14

Category Principal Building Activity (Institutional) Accommodation Administration Education Health Care: Hospital / Inpatient Care Health Care: Outpatient Care Public Assembly Other Uses

Intensity

0.15 0.15 0.10 0.22 0.12 0.14 0.12

0.10

Source: Hughes (2003).

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For the most part, energy intensities are reasonably similar across building activities. The major exception in the case of institutional buildings is found in in-patient hospitals. This is not surprising given the need for 24 hour per day space heating (or cooling) and the energy requirements of specialized health care equipment. Hospitals, however, are not the most energy intensive buildings. The most energy intensive buildings are commercial buildings whose principal activity is in the service sector.

3.1.1. Physical Building Characteristics and Energy Use The role of physical building characteristics on energy use in Canadian commercial and institutional buildings has been examined in Hughes (2003), Ryan et al. (2003), and Buck and Young (2007). An examination of the raw data by Hughes (2003) shows that buildings with triple glazed windows with low-e coating tended to also be the buildings with the highest intensity of energy use. This is, of course, counterintuitive since these windows are more energy efficient than the types of windows used in other buildings. And since there are several factors which taken together determine total energy use for a building, it is necessary to turn to regression models whereby the effect of one characteristic holding other characteristics constant can be determined.37 Hughes (2003) uses separate regression models to examine the determinants of total energy use, electricity use and natural gas use. The results indicate that total energy intensity (total energy use per sq ft of building area) falls as building area increases. A multitude of conservation features and retrofit types were included in the regression model including lighting and HVAC conservation features, window types, insulation types, and wall types. As opposed to what was found by simply looking at the raw data, in the regression model there is no evidence that window type affects overall total energy efficiency in a building. The only significant impact of a conservation feature was found for temperature setback features. 37

All regression models in CBEEDAC studies control for a variety of factors including location, as climate and energy input prices vary greatly across the country.

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Energy Use in Canadian Buildings

185

Surprisingly, however, the sign of the impact is counterintuitive, with buildings using temperature setback features tending to exhibit higher total energy intensity. Finally, as a larger percentage of building area is cooled, total energy use increases, but this impact is less pronounced for buildings that use heat pump technology for cooling purposes. When total energy use is disaggregated, it is found that the intensity of electricity use falls when manual dimmer switches or daylight controls that detect natural light are installed. Variable air volume systems and regularly scheduled maintenance for HVAC systems reduced natural gas consumption.38 Ryan et al. (2003) focus on the determinants and consequences of retrofit activities in Canadian commercial buildings. Selected summary statistics showing the types of retrofits made in various buildings and across regions are presented in Table 3. We see, for example, that slightly more than 3% of all buildings undertook lighting retrofits in 2000, and that lighting retrofits were much more likely to be made in large buildings than in smaller ones. Note that there are also differences in retrofit patterns across regions, building age and the type of building ownership.

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Table 3. Percentage of Specified Buildings undertaking Particular Types of Retrofits in 2000

All Size (sq ft) 1K-5K 10K-50K 100K-500K >500K Region Atlantic Quebec Ontario B.C. Ownership Private Non-profit Government Year of Construction 1995 or later 1975-1994 Before 1975

Light 3.17

Heat 4.57

Vent 3.30

Base 0.32

Roof(s)1 1.84

Roof(i) 1.75

Wall(s) 0.79

Wall(i) 0.92

Other 3.01

1.61 3.57 8.38 23.53

2.46 6.24 6.70 17.65

1.50 4.37 6.15 29.41

0.21 0.54 0 0

1.28 1.87 3.35 17.65

0.86 1.87 3.35 17.65

0.64 0.89 0.56 0

0.32 1.43 0 0

2.78 3.48 3.91 11.76

3.22 3.50 3.01 3.17

5.06 4.76 4.39 2.64

2.07 4.34 3.12 1.85

0.23 0.84 0.23 0

2.99 1.26 2.31 1.06

2.76 1.96 1.85 0.53

1.61 1.12 0.46 0.53

1.84 1.12 0.58 0.79

2.99 2.80 3.24 1.58

2.58 5.03 5.41

3.52 10.06 6.27

2.70 6.15 4.56

0.33 0.56 0

1.45 5.31 1.71

1.68 2.23 1.71

0.57 2.79 0.28

0.86 1.68 0.57

2.46 5.59 4.27

0.91 3.16 3.63

1.82 4.29 5.30

1.52 3.42 3.57

0 0.26 0.42

0.30 1.40 2.44

0.91 1.84 1.85

0 0.61 1.07

0.30 0.79 1.13

1.82 2.28 3.75

1(s) indicates a structural retrofit; (i) indicates an insulation retrofit. Source: Ryan et al (2003).

Although in some buildings only a single type of retrofit was undertaken, in others there was a package of retrofits. Figure 1 depicts the 10 most frequently adopted retrofit packages from 2000.

38

The impacts of building activity variables on energy use found in CBEEDAC studies will be discussed separately below.

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Source: Ryan et al (2003).

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Figure 1. Main Retrofit Packages or Combinations – 2000.

These cover about 75% of all retrofits made. The 4 most common retrofits each involved a single building component, with heating equipment topping the list. Fifth on the list is a combination of both heating and ventilation systems, corresponding to about 7% of all buildings with retrofits that year. A statistical model of the decision to retrofit indicates that physical building characteristics play an important role, with older and larger buildings being more likely to be retrofitted. While it is intuitively obvious why older buildings, which are more likely to embody older less energy-efficienct features, are more likely to be retrofitted, it is not obvious why this is also the case for larger buildings. It is possible that larger buildings house different sorts of establishments and with a larger tenant base may be in a better cash-flow situation. It could also be that larger buildings are more susceptible to maintenance problems. The other major physical factor affecting the retrofit decision is the type of fuel used for heating. Buildings that use oil as the main heating fuel are more likely to be retrofitted, possibly due to relative prices of oil, electricity and natural gas. Ryan et al. also examine the impact that retrofits have on various categories of energy use. Only observations where energy usage data were not imputed by Statistics Canada were used for this analysis. Although there is little evidence of an impact of retrofits on total energy use, they do find that heat and insulation retrofits tend to decrease electricity use, while ventilation retrofits (which often lead to an increased use of fans and air-conditioning) increase the use of electricity. These ventilation retrofits do, however, lead to lower consumption of heating oil. These types of offsetting effects on various components of energy may help to explain the lack of an observed impact of retrofits on total energy consumption. As with the results in Hughes (2003), some results are counterintuitive, with retrofits to heating and walls leading to higher oil usage. The CIBEUS data set also provides information regarding retrofits undertaken before 2000. Ventilation retrofits undertaken in 1999 led to lower natural gas consumption in 2000, while structural renovations led to higher natural gas use. Insulation retrofits over the 1995-199 period tended to increase oil use in 2000, while retrofits in the ‘other’ category had the opposite effect. The occasional counterintuitive results may be at least partially due to rebound effects whereby more efficient technologies are used

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more intensely than those that were replaced. (See, for example, Khazoom (1986) and Sorrell and Dimitropoulos (2008)). Buck and Young (2007) examine a subset of the building types contained in the CIBEUS data set in an effort to determine which factors tend to lead to more efficient use of energy. Applying a stochastic frontier model to a sub-sample of about 1,100 buildings for which energy use was not imputed by Statistics Canada, they find that in comparison to a theoretical minimum possible inefficiency rating of 1.0 the inefficiencies implied by the model range from a very efficient 1.13 to a highly inefficient rating of 51.94, with more than two-thirds of the sample having inefficiency ratings below an average rating of 2.70. What determines whether a building has a good or a bad inefficiency rating? Some physical characteristics matter. Smaller buildings use energy more intensely. The window-towall ratio in a building has an impact on the intensity of energy use, but the type of window used does not. Wall type, however, does matter, with concrete and curtain walls being associated with an increased intensity of energy use. Buildings with walls adjoined to other buildings tend to use energy less intensely, with the increased need for lighting being offset by savings in the amount of energy needed for heating purposes. When district hot water or packaged units are used to provide heat, energy is used more intensely than for comparable buildings using conventional furnaces. Fuel choice also matters. The lowest energy intensity values are found in buildings that use electricity, liquid petroleum gas or propane. The highest intensity is found when natural gas is used. These differences may possibly be related to relative fuel prices that allow for improved thermal comfort when heating and cooling are more affordable due to the use of less expensive fuels. Other factors such as HVAC and lighting conservation features do not appear to have any major impact on energy efficiency. Some types of buildings have unusual design and occupancy characteristics and were therefore not included in Buck and Young (2007). One such category is Shopping Centres and Malls. Buck and Young (2006) examine the energy use patterns of 28 enclosed shopping centres and 125 strip malls. The average age of a strip mall in the sample was approximately 19 years with an average age for enclosed malls of about 30 years. Along with being larger than strip malls, enclosed malls also tended to have larger numbers of people working in them and to be open longer hours. Furthermore, enclosed malls, more so than strip malls, are likely to have a supermarket as a major tenant. While some enclosed malls have indoor parking, only one mall in the sample provided heated indoor parking. All enclosed malls provide airconditioning to at least 50% of the building area. Although the small sample does not allow for as detailed a regression model as for other types of buildings, it was found that building design features such as size, window-type, wall-type and roof-type matter for energy efficiency.

3.1.2. Building Activity and Ownership In the CBEEDAC studies discussed above, building activities and the characteristics of building owners were also considered as potential determinants of energy use and/or retrofit activities. From Table 2, we saw that energy intensities vary across the main activities undertaken in buildings. This is expected since factors such as the energy-intensity of activity-specific equipment, the demand for thermal comfort, and the hours of operation will vary across activity types. The regression models in Hughes (2003) indicate significant impacts of the type of major building activity on energy use, with buildings providing

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accommodations, services and health care exhibiting higher total energy consumption per square foot. Ryan et al (2003) find that buildings that house private for-profit enterprises are less likely to undertake retrofits than those operated by non-profit groups or by one of the levels of government. More specifically, when a building housed a government or non-profit establishment there was a probability of about 16% of undertaking a retrofit. A building with similar characteristics that housed a private enterprise had a less than 10% probability of being retrofitted. A possible explanation for this is that cash-flow circumstances of non-profit and government agencies make it less likely for them to require a quick payback for energy efficiency improvements. And, as a result, they may be more willing to undertake retrofits that increase the environmental friendliness of their buildings. In Buck and Young (2007), a relationship was found between building activity and having an above-average inefficiency rating. High average inefficiencies were found primarily in buildings where customers remain on the premises for prolonged periods and may therefore demand higher levels of thermal comfort. These include food-based businesses (retail or service), indoor entertainment and recreation establishments, and meeting places such as cultural centres, public halls, and public worship venues. The least efficient building in the sample considered is from the Services – Food classification. Surprisingly, warehouses and storage facilities also exhibited higher than average inefficiency, possibly due to refrigeration requirements for some of these operations. Furthermore, building ownership has an impact on efficiency. Buildings owned by the government or by a non-profit group tended to be less efficient than those owned by a private sector enterprise, with this difference being statistically significant when a building was owned by a non-profit organization. This would imply that in spite of the fact that retrofits are less likely when a building is privately owned, a higher level of energy efficiency is achieved, holding other factors constant. This suggests that behaviour and how technologies are used may play important roles in achieving energy efficiency. In the shopping centre and mall study of Buck and Young (2006), it was found that the type of tenants that occupy space within a mall can have a significant impact on energy use. In particular, having a restaurant as a tenant significantly impacts energy use as a result of the energy intensity of the equipment required for food storage and preparation procedures. A further study by Fish and Young (2006) focuses on a subset of approximately 600 buildings from the CIBEUS data set whose occupants include one or more non-food retailers. Regression analysis indicates that, among these buildings, there is a weak association between (i) average total energy usage per sq ft and average electricity usage per sq ft of building area (not including indoor parking); and (ii) whether or not the floor space in the building is exclusively or primarily devoted to retail activities. Although specialization does seem to lead to a lower intensity of energy use, it is not obvious whether this is due to the fact that it is easier to manage energy in a situation where the activities are homogeneous or to the fact that the other activities housed in the particular buildings in the sample that were not devoted primarily to retail activities happened to be ones with high energy requirements. An important aspect of building activity that can’t be captured from the CIBEUS dataset is the impact of equipment use on energy consumption. As a result, no CBEEDAC studies look directly at this effect, although one study touches on the issue. As part of a general study of standby power energy use of appliances, Fish et al. (2006) report measurements of energy requirements for a variety of types of office equipment (such as adding machines, computing

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equipment, staplers, pencil sharpeners). Table 4 shows, as an example, the power used under various settings by CRT monitors of different vintages. Table 4. CRT Monitors – Power Usage (W) by Manufacturing Date and Power Mode Year of Manufacture 1999 2000 2001 2002 2003 2004

#

1 6 7 7 7 4

Off

On and Idle

On and Active

2.6 2.5 1.0 1.5 0.6 0.5

46.3 34.8 66.2 1.1 2.0 1.1

56.5 64.2 69.3 58.9 59.1 63.7

POWER MODE Min. Max. Automatic/ Settings Settings Manual Standby 1 48.8 67.5 5.2 47.0 78.3 4.3 52.9 75.0 4.9 42.2 68.1 1.5 52.4 75.9 3.1 48.2 69.4 1.0

Automatic /Manual Standby 2

Manual Standby

2.7

2.6 0.0 0.7

Source: Fish et al (2006).

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The average consumption in the off and on and idle modes for earlier models are substantially higher than for more recently manufactured monitors, with the most striking difference being found in the significant drop in power consumption for the on and idle mode that occurred between 2001 and 2002. In other modes, power use does not vary much across vintages. Other types of equipment were also analyzed, and it was found that desktop computers, printer, and laptops also use relatively large amounts of power when running in idle mode. Power consumption for selected pieces of office equipment in off and on and idle modes is illustrated in Figures 2 and 3.

Source: Fish et al (2006). Figure 2. Computers and Selected Home Office Equipment - Average Power Consumption in Off Mode.

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Source: Fish et al (2006). Figure 3. Computers and Selected office Equipment – Average Power Consumption in On and Idle Mode.

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Studies such as this highlight the fact that the equipment used in a commercial or institutional building, which will be a function of the types of activities housed, will affect energy use. Furthermore, given the standby energy use of most equipment, its effect is not limited to the hours of operation of a building, as power is drawn by many devices even when they are not being used.

3.2. Residential Buildings Activities and energy requirements in the residential sector are more homogeneous than those in the commercial sector. Energy is required for space heating / cooling, lighting, household production activities such as cooking and laundry, and at-home leisure activities such as watching television and playing video games. Many Canadian households have made strides towards reducing their energy consumption by making investments in energy-efficient practices and technologies. Several CBEEDAC studies have investigated the extent to which new technologies have been adopted and the impacts of technology choice on household energy demand.

3.2.1. Heating Given the harsh winter climate throughout much of the country, space heating accounts for the bulk (over 60%) of energy use in Canadian homes (Natural Resources Canada, 2007). A variety of technologies are used in Canadian homes. Table 5, drawn from Ryan and Cherniwchan (2007), provides an overview of these technologies. Given differences in energy prices and the availability of natural gas, we see that there are distinct regional patterns, with forced air furnaces being the norm in Ontario and the Prairies, while electric baseboards tend

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to be used in Quebec. In other parts of the country, a wider mixture of technologies are poular across households. Table 5. Space Heating Equipment in Canadian Residential Buildings (SHEU-2003) Heating Equipment

Maritimes

Quebec

Ontario

Prairies

BC

Canada

Forced Air Furnace Electric Baseboards Heating Stove (wood, coal, etc.) Boiler Furnace (Hot Water or Steam Radiators) Electric Radiant Heating Other

30.93% 33.60% 8.68%

12.92% 65.73% 6.41%

78.91% 8.52% 1.01%

83.97% 4.60% 0.63%

51.34% 24.34% 6.24%

54.95% 26.90% 3.69%

% with PT (Canada) 37.9% 14.6% 11.9%

21.26%

11.21%

8.33%

9.87%

12.04%

10.92%

22.7%

1.48%

1.30%

0.44%

0.00%

1.44%

0.81%

10.0%

4.05%

2.43%

2.80%

0.92%

4.61%

2.73%

15.0%

Source: Ryan and Cherniwchan (2007).

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One commonly touted technology for reducing the demand for energy used for space heating purposes is the programmable thermostat (PT). PTs allow a household to select preset temperatures for various times of day, thereby automatically reducing the temperature during times of the day when nobody is at home or when people are sleeping. As can be seen in Figure 4, less than one third of Canadian households have installed a PT. From Table 5, we see that they are most common in houses that use hot air furnaces. As a result, there is a higher tendency for households in Ontario and on the Prairies to have these devices installed.

Source: Ryan and Cherniwchan (2007). Figure 4. Percentage of Households with Programmable Thermostats by Province.

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Ryan and Cherniwchan (2007) and Ryan and Young (2008) investigate the use of PTs in Canadian households. Aside from regional differences and differences across heating technologies, Ryan and Cherniwchan find that PT ownership varies across the size and type and age of the dwelling, the main fuel used for heating, and the size of the household. PT ownership is also more common in higher income households and in households that have central air-conditioning. One important fact to note, however, is that ownership of a PT does not necessarily imply that a household actually uses more than one temperature setting during a typical day. Ryan and Cherniwchan find that only about one third of households who have a PT actually use it effectively in terms of having it set for different temperatures during the day, evening and night. Whether or not ownership and use of a PT leads to lower energy use is unclear. In terms of energy use per sq ft, Ryan and Cherniwchan find that in residences where there is no PT, the intensity of energy use is generally higher than in those with a PT (with the exception of the small sub-sample of households who heat with propane). This holds regardless of whether or not the PT is actually programmed to change temperatures at least once per day (except for households that heat with heating oil). These results, however, do not control for other factors such as the prices of natural gas and electricity, region, income, education, household size, and the age of a house. When controlling for these factors in regression analysis it is important to keep in mind that it may be the case that households who decide to install PTs tend to be ‘energy aware’. That is, they would have manually chosen temperature settings that vary throughout the day in the absence of a PT. Using a two-step statistical procedure that takes into account this ‘sample selectivity’ effect, and focusing on the subset of households who use natural gas forced air furnaces (for whom the expected gains from a PT are the best), Ryan and Young find that there is no statistically significant impact of PT ownership on energy use. Aside from sample selectivity effects, other possible explanations for this result include ineffective use of the device (such as not programming any setting changes across times of day), and rebound effects whereby the money saved from lower temperatures during one part of the day is used to increase thermal comfort during another time of day.

3.2.2. Appliances and Home Electronics After home heating, the next biggest source of energy demand in Canadian residential buildings is for the use of appliances and home electronics. Including the hot water needed for dishwashing and laundry, appliances and electronics account for approximately 30% of residential energy use (Natural Resources Canada, 2007). Several CBEEDAC studies have looked at various aspects of this component of household energy demand. The standby power study of Fish et al. (2006), that was discussed above in the context of commercial and institutional buildings, also looked at the power requirements for a wide range of kitchen appliances and home electronics under various modes of operation. Table 6 provides examples of the power requirements for one specific type of home electronics product (television). From a total of 27 televisions that were metered in the off and on and idle power modes, the average power consumption for a television that was off was 4.6W, while that for a television that was on and idle was 110.8W. Note, however, the variation in energy consumption in these modes, with ranges of 54.4 and 321.6W, respectively. Of the 27 televisions, 7 were metered with all display features set to their minimum and maximum settings.

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Table 6. Televisions – Power Usage (W) by Metered Mode Mode

Number of Measurements

Mean (W)

Minimum (W)

Maximum (W)

Range (W)

Standard Deviation

Off On and Idle Minimum Settings Maximum Settings

27 27 7 7

4.6 110.8 131.0 172.6

0.0 4.1 83.6 111.9

54.4 325.7 175.7 318.7

54.4 321.6 92.1 206.7

11.8 64.8 34.4 67.9

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Source: Fish et al (2006).

Again, a wide range of energy consumption is observed across models. For typical kitchen appliances, which were for the most part only metered in an off mode for this study, standby power requirements were generally low. For ranges, for example, the standby power requirements of the 12 models tested ranged from 0.0W to 5W, with an average value of 1.2W. The major determining factor appeared to be the presence or absence of digital clock displays. The total amount of energy used by appliances depends on a variety of factors. For dishwashing and laundry, both the efficiency of the appliance and the efficiency of the hot water heating technology used will matter. A study by Liu (2007a) examines hot water heating in Canadian households. Information from SHEU-2003 indicates that most (more than 9 out of 10) Canadian households have a hot water heater, with ownership rates varying somewhat across provinces. About half of these are fueled by electricity, with natural gas being the 2nd most commonly used fuel. The split across fuels varies by location, with 90% of hot water heaters in Alberta (Quebec), for example, being heated with natural gas (electricity). Liu reports that, according to 1996 United States Department of Energy (USDOE) figures, electric water heaters tend to be more efficient than those fueled by natural gas or propane. Factors influencing the observed regional differences in the choice of water heaters will include price, with electricity prices in Quebec and natural gas prices in Alberta being below the national average, respectively. Over half of the households surveyed in SHEU-2003 had a dishwasher. In terms of hot water requirements, Liu reports that using a dishwasher leads to lower energy demand than hand-washing, especially if the dishwasher has an internal booster heater. Table 7 illustrates the energy requirements for a typical dishwasher cycle for various models and fuel sources for hot water. Further calculations by Liu that take into consideration the relative fuel prices across Canadian provinces indicate that electric hot water use for a dishwasher is preferred in terms of overall energy costs in British Columbia and Manitoba, while gas water heating proves to be less expensive in other locations including Alberta and Quebec. Ownership rates for clothes washers are also high, with over 90% of households surveyed in SHEU-2003 owning one. With clothes washers, households have a choice of using hot or cold water. While 80% of households use cold water for rinsing, fewer than 40% use cold water for washing. For most major household appliances, technological advances have created a situation where newer models tend to be more efficient than older ones. Natural Resources Canada (2005) points out that most major household appliances, with the exception of electric ranges, experienced major improvements in energy efficiency over the 1990-2003 period. Therefore,

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as households replace older model dishwashers, clothes washers, dryers and refrigerators, it is expected that energy savings will be realized. Young (2008a and 2008b) uses SHEU-2003 data to investigate when and why Canadian households replace their appliances. Table 7. Energy Use per Cycle for Standard Dishwasher Efficiency Level

DOE Standard Energy Star CEE Tier 1 2007 Energy Star CEE Tier 2 Gap Fill Gap Fill Max Available

Machine

Standby Power

Water Heating

kWh/cycle

kWh

Electricity (kWh)

Natural Gas (GJ)

Oil (GJ)

0.80 0.70 0.68 0.66 0.65 0.63 0.60 0.52

0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002

0.37 1.02 0.93 0.88 0.82 0.76 0.65 0.38

0.0066 0.0049 0.0045 0.0042 0.0039 0.0036 0.0031 0.0018

0.0061 0.0048 0.0041 0.0039 0.0037 0.0034 0.0029 0.0017

DOE: USDOE baseline model; Energy Star: meets minimal “Energy Star” program requirements; CEE (Consortium for Energy Efficiency); Gap Fill (models with energy efficiency ratings between those of CEE 2 and the maximally efficient model available on the market (Max Available)). Source: Liu (2007a).

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Table 8. Ages of Appliances at Replacement in Canada Approximate age

Refrigerators*

Freezers

Dishwashers

Clothes Washers

Dryers

3 years or less 4 to 5 years 6 to 10 years 11 to 15 years 16 to 20 years 21 to 25 years 26 years or more

4.3% 2.6% 9.7% 23.0% 28.2% 17.4% 14.8%

2.8% 4.7% 11.0% 13.9% 27.7% 18.9% 21.0%

6.7% 5.9% 22.7% 29.1% 23.0% 9.5% 3.1%

3.6% 3.3% 14.3% 27.4% 29.1% 13.2% 9.1%

4.6% 3.2% 10.9% 28.0% 26.5% 15.8% 11.0%

* Only refrigerators replaced due to failure are included. Source: Young (2008a).

In the SHEU-2003 survey, information on the ages at which appliances were replaced was gathered for refrigerators, freezers, dishwashers, clothes washers and dryers. Respondents were also asked whether or not an appliance was still working when it was replaced. Some summary information regarding the ages of appliances that were replaced due to failure is provided in Table 8. The ages at which appliances are replaced in Canadian homes varies across appliances. Dishwashers are relatively more likely to be replaced within 5 years of purchase and are unlikely to be used for more than 20 years. As a result, new dishwasher technologies make their way into common household use relatively quickly. On the other hand, more than 3 out of 10 of all refrigerators that were replaced due to failure and more than 4 out of 10 freezers were used for over 20 years.

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A statistical examination of the factors that influence replacement ages of appliances showed that large families tend to replace appliances at younger ages (likely due to an increased intensity of use). Only for clothes washers were replacement ages income-sensitive. Although all households indicated that clothes washers were replaced due to failure, it may be that some mechanical problems that a high income household might perceive to be a 'failure' of the appliance are actually repairable problems that a low-income family would opt to fix (Young, 2008a). One obstacle to realizing overall energy savings as new appliances are purchased is the fact that many Canadian households opt to keep their old refrigerator when they buy a new one. Other owners of secondary refrigerators either purchase new models or obtain an older model as a gift or on the ‘used’ market. Use of an older model can be a very energy inefficient option for obtaining increased refrigeration capacity in a home given the changes in technology that have occurred in recent decades. Figures published by the Canadian Appliance Manufacturers Association (CAMA) indicate that a 1975 (1985) vintage refrigerator consumes about 1580 (1060) kWh of energy per year (CAMA, 2005). Today's Energy Star® models can use as little as 380 kwH for a full sized refrigerator and under 300 kWh for smaller models.39 Table 9 provides some basic information about the age distribution of secondary refrigerators from SHEU-2003. We see that while about one third of households with two or more refrigerators had fairly new models, another 3 in 10 of these households were using very energy inefficient secondary refrigerators. An examination of the factors that influence a household’s decision to continue to use a refrigerator when a new model is purchased revealed that higher income families (who are more easily able to afford the electricity costs of running an inefficient appliance) are more likely to keep an old refrigerator. Other factors include whether or not a household lives in an apartment or other rented accommodation, as space constraints and the fact that renters are less likely to own their own appliances will impact the feasibility of keeping a second refrigerator. Finally, in Quebec, where electricity costs are relatively low, there is an increased probability that a household will continue to use an old refrigerator once it has been ‘replaced (Young 2008b). Table 9. Age Distribution of ‘Secondary Refrigerators’ (SHEU-2003) Age in Years 1-5 6-10 11-20 21 or more Total

Former Primary Refrigerators

All (including former primary refrigerators

Number

Percent

Number

Percent

3 16 80 119 218

1.4 7.3 36.7 54.6 100.0

168 221 382 334 1105

15.2 20.0 34.6 30.2 100.0

Source: Young (2008b).

39

Information on energy usage for current models is available on the Office of Energy Efficiency (Natural Resources Canada) website: http://www.oee.nrcan.gc.ca/energystar/english/consumers/refrigerator-search.cfm

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The most recent CBEEDAC study that looks at the impact of appliances on residential energy use focuses on the influence of a household’s decision to adopt time-saving technologies such as microwave ovens, dishwashers and clothes dryers. These technologies require energy inputs and may or may not be more energy intensive than alternative technologies that require greater time commitments. Furthermore, the use of time-saving appliances frees up time that can be allocated to a variety of activities, some of which entail the consumption of energy. Such energy-using activities might include increased household production (such as cooking and laundry) and/or home leisure activities (such as computer gaming and watching television). Overall, it is difficult to predict whether or not an increased use of time-saving devices will lead to higher or lower energy use. Using information on the ownership and intensity of use of a variety of time-saving devices from SHEU-2003, it was found that if anything, households with more time-saving appliances consume less electricity per sq ft of heated dwelling area. Statistical analysis indicates that the adoption of microwave ovens by high income households and the adoption of a washing machine by low income households are negatively associated with residential electricity use when other factors are controlled for. None of the time-saving appliances has a significant impact on residential natural gas use (Brenčič and Young, 2008).

3.2.3. Lighting Lighting accounts for about 5% of residential energy consumption in Canada (Natural Resources Canada, 2007). According to SHEU-2003, most lighting in Canadian homes is produced from incandescent bulbs. These bulbs produce light by heating a filament to the point where it produces light. Compact fluorescent light (CFL) bulbs, on the other hand, do not rely on heat creation to produce light. Instead, electricity interacts with the gas contained in the bulb to produce ultraviolet (UV) light. This UV light becomes visible when it hits the phosphor coating on the inside of the bulb. As a result, CFL technology requires less than a third of the energy needed to produce light with an incandescent bulb (Wesley and Ryan, 2006). Table 10 shows that, despite the energy savings that could potentially be gained by using CFLs, the use of CFLs ranked fourth out of all available light bulb types in SHEU-2003. In fact, only 3 out of 10 Canadian households had any CFL bulbs at all. And within the subset of households using CFL bulbs, these bulbs accounted for only about 15% of lighting in the dwelling, on average. Households with one or more CFL bulbs tend to use a greater number of light bulbs than other Canadians, but they also tend to have fewer lights that are left turned on over prolonged periods. Figure 5 shows that the rates at which CFLs are used vary across Canadian regions, with CFLs being more popular in the western provinces and Ontario and less popular in Quebec and the Atlantic region. One reason for the low rate of penetration of CFLs in Canadian households may be the fixed cost of purchasing a CFL. Even so, calculations by Wesley and Ryan show that even with electricity prices that are lower than any observed in Canada and a high discount rate, the savings in operating (i.e., electricity purchase) costs associated with operating a CFL bulb for 3 hours per day over the its useful life (about 10,000 hours) years swamp the fixed purchase cost of CFL bulbs in Canada. For lights that are used less frequently, the savings would be less; i.e., for lights that are not left on for prolonged periods of time each day, there may not be sufficient cost savings to justify the purchase of CFL bulbs when replacing incandescent ones.

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Source: Wesley and Ryan (2006). Figure 5. CFL Use in Canada.

Table 10. Types of Light Bulbs Present in Dwellings (SHEU-2003) Type of Light Bulb

Average Number of Light Bulbs per Household

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All Households Incandescent Fluorescent Halogen CFL Security ALL

20.86 2.39 2.25 1.38 0.63 27.51

Households with CFLs 20.74 3.08 2.69 4.38 0.90 31.79

Switched on for 3 hours or more daily

2.24

2.02

Households with no CFLs 20.92 2.08 2.05

2.34

0.51 25.56

Source: Wesley and Ryan (2006).

A preliminary look at the data shows that one of the factors that may affect the purchase of CFLs is household income. Figure 6 shows that the percentage of households with CFLs, the average number of CFLs per household, and the average number of CFLs per CFL-using household all increase as income increases. A statistical analysis was undertaken to see if income and/or other factors could be seen to have a significant impact on lighting choice. The statistical analysis of Wesley and Ryan focuses on the decision regarding whether or not to purchase any CFLs. When other factors are taken into consideration, income does not have an impact on the decision to use CFLs.

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Source: Wesley and Ryan (2006).

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Figure 6. CFL Use and Income.

This decision does however appear to be dependent on several factors. Households that have more lights are more likely to use at least one CFL, possibly in an effort to control the operating costs of their lighting systems. However, there is evidence of consumer inertia in terms of lighting choice as households that are the biggest users of incandescent are less likely to use any CFLs. Contrary to what may have been expected, in spite of the fact that they may have the most to gain, households who leave more lights on for prolonged periods are less likely to use CFLs. CFL use is, however, more common in households that use more electricity. There was a marginally significant impact of dwelling size on CFL use, with CFL use being more common in smaller dwellings. CFL use is also more common in older houses and in row housing.

3.2.4. EnerGuide for Houses The studies based on SHEU-2003 data indicate that most Canadian households are not big users of simple low-cost energy-saving technologies such as programmable thermostats and CFL lighting. CBEEDAC studies using the EnerGuide for Houses database attempt to look at what sorts of households undertake retrofits and what sorts of retrofits they select. Gamtessa (2006) and Prasol and Ryan (2007) provide an overview of participation rates in the program and of the types of retrofits that were made by households who obtained second audits under the EGH program (which was described in Section 2). As can be seen from Table 11, the number of first audits varied across regions, and these variations do not always mirror relative populations. This is especially evident in the case of Quebec which is home to almost one quarter of Canada’s population but only accounts for 12% of first audits under the EGH program. The highest participation rates for the 1st audit portion of the program were in the western and northern regions of the country where the climate is generally more severe.

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Table 11. EGH participation by region

Region

British Columbia Alberta Saskatchewan Manitoba Ontario Quebec New Brunswick Nova Scotia Prince Edward Island Newfoundland and Labrador Territories Total

6461 7927 3756 2782 11344 1580 334 696

2 Audit: % of Houses in Region 0.38% 0.63% 0.93% 0.60% 0.24% 0.05% 0.11% 0.18%

2nd Audit: % of 1st Audit Participants Region 19.79% 22.78% 27.17% 21.96% 18.77% 7.15% 18.75% 13.07%

0.98%

41

0.08%

7.69%

2089

0.97%

187

0.09%

8.95%

2143 188368

6.28% 1.45%

181 35289

0.53% 0.27%

8.45% 18.73%

st

# of Houses in Region

1 Audit: # of Houses

1716206 1252144 402357 465709 4781743 3348495 307445 389385

st

nd

2 Audit: # of Houses

32647 34798 13823 12668 60448 22112 1781 5326

1 Audit: % of Houses in Region 1.90% 2.78% 3.44% 2.72% 1.26% 0.66% 0.58% 1.37%

54433

533

214799 34147 12966863

nd

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Source: Gamtessa (2006).

The highest uptake of 2nd audits was seen in the Prairie provinces, with 27% of EGH participants opting for a 2nd audit in Saskatchewan, and 22% in Manitoba and Alberta. The lowest rate of second audits was found in Quebec. Prasol and Ryan also find that, as might be expected, participation rates for older houses were higher than for more recently built homes for both the 1st and 2nd audits. There are many possible reasons for the lack of a second audit for houses in the EGH program. Some homeowners will have decided against undertaking (m)any of the suggested retrofits and would therefore have no reason to consider a second audit. Also, recall that in early years of the program, there was no grant system in place. For houses with early first audits where retrofits had been undertaken, there was no financial incentive to incur the cost of a second audit. Even when the grant system was in place, the first audit may have revealed that the home was already quite energy efficient, so that any retrofits undertaken would not yield sufficient energy savings to warrant a second audit. Finally, for some homes with significant retrofits, there could have been a planned 2nd audit that hadn’t yet been performed at the time the EGH data were made available. For those houses that did undergo a 2nd audit, the most common types of retrofits undertaken are shown in Figure 7. Heating system upgrades and retrofitting of windows and doors are by far the most popular changes made, which is not surprising given the improvements in heating technologies from which older homes can benefit and the cold Canadian winters. These are followed by structural / insulation improvements. For those who did have 2nd audits, Gamtessa finds that the difference between the engineering model estimates of energy consumption before and after the upgrades vary with the age of the

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building. This is depicted in Figure 8. Evidently, some EGH participants who retrofitted their homes and obtained a 2nd audit generated more (potential) energy savings than others.40

Source: Gamtessa (2006).

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Figure 7. Number of Homes with Specific Upgrades.

Source: Gamtessa (2006). Figure 8. Pre and Post-Upgrade Energy Consumption (MJ) by Vintage.

Prasol and Ryan break down (potential) energy cost savings by region and find that highest average energy cost savings were realized in the Atlantic region and the lowest in the 40

Since the energy consumption figures are based on engineering calculations and not on actual observed consumption, actual changes in energy use will not necessarily match up with the values in the EGH database.

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Prairies. Among other possibilities, this could reflect differences in the average state of energy efficiency of participating homes, differences in fuel costs, or differences in the costs of obtaining retrofits in the various regions. A question of particular interest is whether or not those who would benefit most from retrofits undertake them. Gamtessa and Ryan (2007) note that when a comparison is made between the characteristics of houses that only participated in the 1st audit and those that were retrofitted and underwent a 2nd audit, houses in the second group had energy inefficiency problems that were more severe. Furthermore, when more energy savings were available according to the initial 1st audit evaluation, those who retrofitted tended to make more changes. These characteristics of the EGH data indicate that energy savings provide a major motivation for making energy-saving improvements to a home. However, the low participation rate in the 2nd audit invites further investigation into the determinants of retrofit activities in Canadian households. Gamtessa and Ryan (2007) therefore augment the EGH database with socio-demographic information from the 2001 Canadian census. These data are matched by the first 3 postal code digits with EGH participant dwelling locations. After estimating the expected cost savings and capital costs associated with recommended retrofits from a household’s 1st audit, they model the household decision to undertake at least one retrofit as an investment decision based on these two factors. Controlling for other a wide set of other factors, they find that aside from the expected cost savings (which are a function of the extent of inefficiencies observed on the 1st audit), the cost of making appropriate retrofits matter in the decision to undertake at least one upgrade. As the required capital costs increase, the probability of undertaking retrofits falls. However, this can be at least partially offset through financial incentives. As would be expected, retrofits are more likely to be undertaken on older homes. Household demographics such as household size, the number of children, education and possibly income also play roles in the decision to make substantial retrofits.

3.2.5. Energy Use in Apartment Buildings According to the Canadian Mortgage and Housing Corporation (CMHC), almost 3 out of 10 Canadians lived in apartment buildings in 2001 (CMHC, 2001a). There is however only limited information available regarding energy use by residents of apartment buildings. The coverage of SHEU-2003 extends only to apartment complexes with 3 or fewer storeys. And a mere 0.01% of dwellings in the EGH database correspond to apartment units. The HiStar database collected by CMHC is the most comprehensive available for Canadian apartment buildings. It contains data on the location, type, construction characteristics, mechanical system, and energy consumption for 81 Canadian apartment buildings with 4 or more storeys (CMHC, 2001b). Slightly over two-thirds (55) of these buildings are located in Ontario. Given the small sample size and the fact that the buildings included in the HiStar database were not selected with an aim of collecting a representative sample for Canada, the analysis of energy use in apartment buildings is not as detailed as for other Canadian dwellings. Table 12 provides a summary of the basic energy consumption data for the 81 buildings included in the HiStar data. Liu (2007b) notes that there are striking regional differences in energy consumption. While an average suite uses about 88 GJ of energy, this average falls to 58 in British Columbia (where winters are milder than in most other parts of the country).

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David L. Ryan and Denise Young Table 12. Average Annual Energy Use by Apartment Complex Size and Location Location Canada

British Columbia

Prairies

Central

Atlantic

Number of Storeys 4 to 6 7 to 20 > 20 Total 4 to 6 7 to 20 > 20 Total 4 to 6 7 to 20 > 20 Total 4 to 6 7 to 20 > 20 Total 4 to 6 7 to 20 > 20 Total

Number of Buildings 15 52 14 81 1 4 0 5 1 7 0 8 10 36 14 60 3 5 0 8

Number of Suites 1102 8115 3768 12985 62 400 0 462 106 746 0 852 719 6274 3768 10761 215 685 0 910

Energy per Suite (GJ) 87.87 85.51 93.00 87.88 62.80 56.83 -57.64 82.54 82.15 -82.20 81.64 88.36 93.00 89.54 118.55 79.90 -89.03

Energy Intensity (GJ per metre2) 0.87 1.00 0.94 0.96 0.65 0.64 -0.64 1.17 1.31 -1.29 0.89 1.01 0.94 0.97 0.78 0.86 -0.83

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Source: Liu (2007b).

In terms of energy intensity, buildings on the Prairies, where the winters are more severe, use more energy per square metre than those in the rest of the country. Liu notes that the relationship between building age and energy use is unclear. While buildings built before 1960 had higher energy intensity ratings on average, energy use per suite is higher in newer buildings. When categorized by building size and region, there is no consistent trend in energy use according to building age. One noteworthy trend, though, was that newer high-rise (more than 20 storeys) buildings tend to use more energy than older ones. Trends are more obvious when it comes to building size and energy use. While larger buildings use less energy per square metre, they use more energy per suite as the average unit size tends to be larger in these buildings. Given the lack of data on other factors related to energy use (such as the number of appliances in use, household size, etc.) it is difficult to compare energy use in apartment buildings to that of other dwellings or to that of commercial buildings of a similar size. Liu does note, however, that the energy intensity values from the HiStar data are comparable, and sometimes lower, than values cited by Natural Resources Canada (2007) for the residential sector as a whole and for most types of commercial buildings.

4. IMPLICATIONS OF RESULTS FOR POLICIES / INITIATIVES A reduction in energy use and corresponding greenhouse gas emissions is of concern to many individuals, as well as to federal and provincial policy makers in Canada. Better

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management of energy use in Canadian buildings provides a potential source of aggregate energy savings for Canada, especially given that about 7 in 10 commercial buildings and 6 in 10 residential buildings had been built before 1980 according to the CIBEUS and SHEU2003 surveys, respectively (NRCan 2003, 2006b). This provides a large pool of buildings that could potentially benefit from structural retrofits and/or the installation of updated technologies. As a result, there are numerous programs in place across Canada that are aimed at improving the energy efficiency of residential, commercial and institutional buildings. An on-line directory of these programs is maintained by NRCan.41 The programs listed range from initiatives undertaken by various levels of government to programs put in place by energy utilities and industry associations to educational initiatives sponsored by non-profit organizations. The approaches taken in these various programs include minimum standards for structural materials and equipment, the provision of information through awareness and ‘labeling’ programs (such as EnerGuide© and Energy Star©), the provision of subsidized building energy audits, and financial assistance for making energy-efficiency improvements. What do the data from the data sets used in CBEEDAC studies tell us about the potential for the success of various programs? For some programs, it is possible to comment directly. The EGH data discussed above are promising in some respects, but not in others. While older less energy efficient homes tend to be the ones that undergo sufficient retrofits to justify a second audit, the low (18%) rate of second audits from the pool of first audit participants indicates that, on aggregate, programs such as the EGH program may not generate large energy savings. Also of concern is the varying participation rates across regions, with low overall participation in some areas of the country where large energy savings from retrofits might be expected (Gamtessa and Ryan, 2007). Gamtessa and Ryan point out that the costs associated with installing retrofits specified in the dwelling-specific EGH first audit report may not appear to be a good financial investment in the eyes of home owners. Given that grants available under the program increase with the age of the dwelling, the fact that house age was an important determinant of whether or not a first-round participant obtained a second audit in their empirical work is indicative of a positive impact on financial incentives on energy improvements for residential dwellings. This points to the possibility that the grants available under the EGH program may not have been sufficient given the costs associated with paying for two separate audits (incurred over and above the actual retrofit costs), but that other grant programs providing financial incentives could be effective in inducing retrofits in more energy-inefficient Canadian dwellings. This relationship between financial incentives and residential energy improvements is borne out in the responses to the one question on the SHEU-2003 questionnaire that looks at the relationship between finances and home improvements. Those who didn’t undertake improvements in 2003 and had no plans for retrofits in 2004 were asked about the main reason underlying this decision. Over half of this subset of survey participants indicated that no improvements were needed. The most common stated reasons for not making improvements for the remainder of this group involved financial considerations, with almost 30% citing the costs of improvements or insufficient government financial aid or assistance. Costs also appear to be a major factor affecting retrofits in commercial buildings. The CIBEUS survey also included one question regarding the rationale underlying retrofits. In the 41

http://www.oee.nrcan.gc.ca/corporate/statistics/neud/dpa/policy_e/programs.cfm

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CIBEUS survey, all respondents were asked if any or all of a set of specific factors (economic competitiveness, concern for the environment, access to special funding) play a role in the decision to undertake energy-related retrofits. Over half of the respondents indicated that economic competitiveness mattered, and 4 in 10 were or would be influenced by special funding programs. Furthermore, approximately 3 in 10 respondents who cited ‘other’ specific factors, indicated an economic rationale such as costs, energy prices or profitability was important in their decision regarding whether or not to retrofit. Ryan et al (2003) use the responses to this CIBEUS question in a statistical model that examines the impacts of competitiveness and grant/program incentives on retrofit decisions. They find impacts on some, but not all, categories of retrofits. This is not surprising given the wide range of possible retrofits that might be undertaken. They find, for example, that owners of relatively large (50,000 to 100,000 sq ft) buildings were more likely to make retrofits to heating and ventilation systems if they foresaw a positive impact on their competitiveness or were able to access grants for these modifications. The low rates of 2nd audits under the EGH program and low rates of take-up for simple inexpensive technologies such as programmable thermostats and CFL lighting according to SHEU-2003 are only one source of concern for policy makers. Even if retrofits are made or new technologies are adopted, there is no guarantee that energy use will fall. The most striking example is the fact that a sizeable proportion of Canadian households will continue to use an old inefficient refrigerator alongside a new energy efficient model that has been purchased. As a result, in many households, there is an increase instead of a decrease in energy use as a new technology is adopted. For other technologies such as programmable thermostats, energy use tends to be lower in households that use them, but this may be largely due to the fact that these households would have manually adjusted temperatures in order to save energy in the absence of a programmable thermostat. For such households, they may be more of a time-saving or convenience device than an energy saver. It is only if these devices are installed and properly programmed by households who would otherwise maintain higher temperatures during part of the day that aggregate energy savings will be observed. Obviously, the physical features of a building and the technologies that are in place are only one facet of a building’s energy efficiency. How the building and equipment are used are also important. For commercial buildings, Buck and Young (2007) find that for a given set of physical characteristics and main activity type in a building, ownership type makes a significant difference in terms of how intensely energy is used. Buildings with private owners, who may pay more attention to the ‘bottom-line’, tend to be more efficient than those owned and operated by non-profit organizations. Furthermore, buildings whose activities involve customers being onsite for prolonged periods tend to use more energy, possibly in order to ensure the ‘thermal comfort’ of their customers. Many of the CBEEDAC studies discussed above point to a need for more effective education regarding energy use in the home and workplace. It is unlikely, for example, that many households are aware of the amount of power that they are using to run an old secondary refrigerator or to keep their home appliances and electronics plugged in and either off or in an on and idle mode. On aggregate, considerable amounts of energy could be saved by unplugging appliances when not in use or by selecting home electronics settings that do not provide an ‘instant on’ when a piece of equipment is activated. Even though basic information on energy consumption is generally provided by the manufacturer on a specification sheet and/or is available on the internet, most users are unaware of the amount

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of power that is drawn in various modes of operation, including ‘off’ mode. Generalized reminders of standby energy use and a centralized source of information for power use in various modes of operation could be useful. This could be included as a supplement to ‘EnerGuide’ information on annual energy use for many appliances that is already provided online by NRCan.42 Education could also play an important role in convincing more Canadians to try simple inexpensive technologies such as programmable thermostats or CFL lighting. In 2003, fewer than 3 in 10 Canadian households used programmable thermostats, and many of those who did use one neglected to take advantage of their programmable features. Only about 3 out of 10 Canadians households used at least one CFL bulb in 2003. Holding other factors constant, it was found that households who have more lights on for 3 or more hours per day were less likely to use any CFLs at all. Given that these households are also the ones who are most likely to realize noticeable energy savings from switching technologies, educational efforts informing them of the benefits of CFL use could lead to more widespread adoption of these (or other energy saving) lighting technologies. Overall, analysis based on the various available Canadian data sources suggests that energy improvements in Canadian residential and commercial sectors can be extended beyond what has currently been achieved by building owners. It is not clear, however, what the best strategy would be to attain energy efficiency gains. Modifications to the attractiveness of financial incentives and to the amount and accessibility of information on available strategies for saving energy could provide a starting point to achieving noticeable aggregate energy use reductions in Canadian buildings.

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CONCLUSION The CIBEUS and SHEU-2003 surveys of Canadian commercial and residential buildings that were conducted in the early years of this decade have provided CBEEDAC researchers with detailed information on many aspects of the physical characteristics and technologies adopted in these types of buildings and their impacts on energy use. Data on a much larger set of Canadian dwellings have been gathered through the EnerGuide for Houses database, although the energy use data for these buildings is based on engineering models, not on actual consumption. Furthermore, socioeconomic and demographic information on occupants are not included in the EGH database, requiring researchers to match up census information to the EGH data in order to perform more detailed analysis than is possible based simply on the physical characteristics of the buildings. Other available data include direct measurements by CBEEDAC researchers of appliance and office equipment energy use in various modes of operation and data from Canada Mortgage and Housing Corporation’s HiStar database of energy use in apartment buildings. CBEEDAC studies have focused mainly on retrofit activities undertaken by building owners and the use of various technologies such as programmable thermostats and CFL lighting. One outcome that is seen frequently across the studies is that building and equipment characteristics only tell part of the story when it comes to energy consumption. Human behaviour is also important. Commercial building energy use varies substantially across 42

http://oee.rncan.gc.ca/publications/infosource/pub/appliances/2007/index.cfm

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activities housed within a structure, and across the type of ownership, possibly reflecting that a profit-motivated ‘bottom line’ makes a difference for how efficiently energy is used. Similarly, household energy use depends on how equipment is used, which can be a function of many factors such as family size and income. As more up-to-date data become available, it will be possible to ascertain whether or not new programs and technologies that have been introduced in the years following the CIBEUS and SHEU-2003 surveys have led to major energy-efficiency improvements in Canadian commercial and residential buildings.

ACKNOWLEDGEMENT The authors gratefully acknowledge funding from Natural Resources Canada through the Canadian Building Energy End-Use Data and Analysis Centre (CBEEDAC). DISCLAIMER: The views and analysis contained in this paper are the sole responsibility of the authors, and should not be attributed to any agency associated with CBEEDAC, including Natural Resources Canada.

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REFERENCES Blais, S., Parekh, A. and L. Roux (2005) “Energuide for Houses Database – An Innovative Approach to Track Residential Energy Evaluations and Measure Benefits” Proceedings of the Ninth International IBPSA Conference, Montréal, Canada, August 15-18, 2005. Brenčič, V. and D. Young (2008) “Time-Saving Innovations and Canadian Household Energy Use” CBEEDAC Report 2008-RP-02. Buck, J. and D. Young (2006) “Energy Use in Malls and Shopping Centres: Evidence from Canada” CBEEDAC Report 2006-RP-01. Buck, J. and D. Young (2007) “The Potential for Energy Efficiency Gains in the Canadian Commercial Building Sector: A Stochastic Frontier Study” Energy – The International Journal 32, 1769-1780. Canada Mortgage and Housing Corporation (2001a) “Healthy High-Rise: A Guide to Innovation in the Design and Construction of High-Rise Residential Buildings” Ottawa: Canada Mortgage and Housing Corporation Canada Mortgage and Housing Corporation (2001b) “HiStar Database Manual” Ottawa: Canada Mortgage and Housing Corporation Canadian Appliance Manufacturers Association (2005) “2005 Major Appliance Industry Trends and Forecast” Mississauga: Electro Federation Canada. Fish, N. and D. Young (2006) “Energy Use Patterns According to Main and Subsidiary Activities: Evidence from Buildings Housing Non-Food Retailers” CBEEDAC Report 2006-RP-02. Fish, N. and D.J. White and D.L. Ryan (2006) “Standby Power Energy Use of Common Household Appliances” CBEEDAC Report 2006-RP-04. Gamtessa, S. (2006) “Trends in Retrofit Activity in the EnerGuide For Houses Program” CBEEDAC Research Report 2006-RP-08.

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Gamtessa, S. and D.L. Ryan (2007) “Utilization of Residential Energy-Saving Retrofit Programs in Canada: Who, What, and Why?” CBEEDAC Research Report 2006-RP-07. Hughes, C. (2003) “Analysis of Energy Efficiency in Commercial Buildings in Canada,” unpublished M.A. research project, Department of Economics, University of Alberta. Khazzoom, J.D. (1986) An Econometric Model Integrating Conservation Measures in the Residential Demand for Electricity, JAI Press: Greenwich, Connecticut. Liu, R. (2007a) “Energy Use for Residential Water Heating for Dishwashers and Clothes Washers” CBEEDAC Research Report 2007-RP-02. Liu, R. (2007b) “Energy Consumption and Energy Intensity in Multi-Unit Residential Buildings (MURBs) in Canada” CBEEDAC Research Report 2007-RP-04. Natural Resources Canada (2003) “Commercial and Institutional Building Energy Use Survey 2000 Detailed Statistical Report – December 2002.” Ottawa: Natural Resources Canada Office of Energy Efficiency. Natural Resources Canada (2005) “Energy Consumption of Major Household Appliances Shipped in Canada: Trends for 1990-2003.” Ottawa: Natural Resources Canada Office of Energy Efficiency. Natural Resources Canada (2006a) “Energy Efficiency Trends in Canada, 1990 to 2004.” Ottawa: Natural Resources Canada Office of Energy Efficiency. Natural Resources Canada (2006b) “Survey of Household Energy Use (SHEU): Detailed Statistical Report.” Ottawa: Natural Resources Canada Office of Energy Efficiency. Natural Resources Canada (2007) “Energy Use Data Handbook” Ottawa: Natural Resources Canada Office of Energy Efficiency. http://oee.rncan.gc.ca/Publications/ statistics/handbook07/ Prasol, E. and D.L. Ryan (2007) “Participation Rates and Energy Cost and Consumption Savings in the EnerGuide for Houses Retrofit Incentive Program” CBEEDAC Report 2007-RP-06. Ryan, D.L. and J. Cherniwchan (2007) “Ownership and Use of Programmable Thermostats in Canada in 2003” CBEEDAC Report 2007-RP-09. Ryan, D.L. and D. Young (2008) “Modelling Energy Savings and Environmental Benefits from Energy Policies and New Technologies” CBEEDAC Report 2008-RP-09. Ryan, D.L., D. Young and A. Plourde (2003) “Changing Panes/Changing Pains: The Determinants of Commercial Retrofit Decisions in Canada” Proceedings of the 23rd IAEE North American Conference, Mexico City, Mexico, October 19-21, 2003. Sorrell, S. and J. Dimitropoulos, (2008) “The Rebound Effect: Microeconomic Definitions, Limitations and Extensions”. Ecological Economics 65, 636-649. Wesley, A. and D.L. Ryan (2006) “Compact Fluorescent Lights (CFLs) in Canada” CBEEDAC Report 2006-RP-03. Young, D. (2008a), ‘When do Energy Efficient Appliances Generate Energy Savings? Some Evidence from Canada’, Energy Policy 36, 34-46 Young, D. (2008b), ‘Who Pays for the ‘Beer Fridge’? Evidence from Canada’, Energy Policy 36, 553-560.

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Chapter 8

BUILDING INTEGRATED RENEWABLE ENERGY TECHNOLOGIES: EMBODIED ENERGY, ECONOMIC ANALYSIS AND POTENTIAL OF CO2 EMISSION MITIGATION Arvind Chel∗ Centre for Energy Studies, Indian Institute of Technology (IIT) Delhi, New Delhi (India)

ABSTRACT

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Owing to global energy crisis, energy conservation through energy efficient buildings has acquired prime importance. Hence, this chapter deals with various renewable energy technologies that can be integrated with building. The three main aspects which should be given consideration for energy saving in a building include first and foremost the building design, secondly the low energy building materials in building construction and lastly the use of renewable energy technologies for various applications. The solar passive heating/cooling design features like sunspace, Trombe walls, earth to air heat exchangers, adobe house, day-light etc can lead to considerable energy saving at the stage of building design. Further, low embodied energy building materials should be chosen like fly ash bricks; fiber reinforced bricks; and stabilized mud blocks etc. which reduce energy consumption at building construction stage. The use of such low embodied energy materials has gained importance nowadays. The energy requirements of building can be cut down to minimum by use of renewable energy technologies like solar photovoltaic, wind turbines, solar thermal devices etc. These technologies are discussed in this chapter with their economic analysis and environmental impacts. In the present alarming situation of global warming, it is known that about 1 kg of CO2 gets emitted in to the environment corresponding to generation of 1 kWh unit of electrical energy from the coal thermal power plants. Hence, for sustainable future of the world, there is urgent need for use of renewable energy sources to mitigate CO2 emissions. Today, alternative

∗

Corresponding author’s e-mail: [email protected], Tel.: +91-9968144689 (India)

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Arvind Chel renewable energy technologies are becoming prime important to meet at least 10 % of the total building energy requirements in European countries like U.K.

1. INTRODUCTION The dwelling and habitat are the most proximate and visible symbols of present day lifestyles. About 30-40 % of total global basic resources are invested in building sector, which is growing at a rapid pace. The common aspirants are invariably linked to making buildings as comfortable and convenient as possible. It is in this pursuit that the present day buildings have become the third largest consumer of fossil energy after industry and agriculture. The Asia-Link Programme is an initiative by the European Commission to promote and spread the knowledge on Sustainable Built Environment quickly and widely for people at all levels in general, all the reading materials can be viewed and downloaded from website (www.ChinaSBE.com). Energy consumed in the building sector constitutes a significant proportion of total energy consumption. Sources place the amount of energy expended in the building sector in Europe to about 40–45% of total energy consumption [1]; about two thirds of this amount is used in private buildings. Other sources claim, that in industrialized countries, energy usage in buildings is responsible for approximately 50% of carbon dioxide emissions [2-4]. Hence there exists a tremendous potential to conserve energy in buildings. Energy conservation measures are developed for newly constructed buildings and for buildings under refurbishment. However, to achieve a significant reduction in energy consumption apart from the standard energy-efficiency methods, innovative technologies should be implemented, including renewable energy [5,6].

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2. ENERGY CONSERVATION IN BUILDING There are three broad ways to reduce the energy consumption of building to mitigate CO2 emissions. These are described as follows: a) Passive building design b) Low embodied energy construction materials and c) Building integrated with renewable energy technologies

2.1. Passive Building Design The most sustainable energy technique is to conserve energy as much as possible. Passive solar building design can aid energy conservation efforts because building design is directly related to energy use. Buildings with passive solar building designs naturally use the sun’s energy for free of charge heating, cooling and day-lighting.

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This reduces the need to consume energy from other sources and provides a comfortable environment inside. The principles of passive solar design are compatible with diverse architectural styles and building techniques [7].

2.1.1. Passive Solar Design Principles Passive solar design integrates a combination of building features to reduce or even eliminate the need for mechanical cooling and heating and daytime artificial lighting [8]. Designers and builders pay particular attention to the sun to minimize heating and cooling needs. The design does not need to be complex, but it should involve knowledge of solar geometry, window technology, and local climate. Given the proper building site, virtually any type of architecture can integrate passive solar design [8]. The basic natural processes that are used in passive solar energy are the thermal energy flows associated with radiation, conduction, and natural convection. When sunlight strikes a building, the building materials can reflect, transmit, or absorb the solar radiation. Additionally, the heat produced by the sun causes air movement that can be predictable in designed spaces. These basic responses to solar heat lead to design elements, material choices and placements that can provide heating and cooling effects in a home. Passive solar energy means that mechanical means are not employed to utilize solar energy. There are some rules of thumb which must be considered for effective solar energy utilization through passive solar systems [9]. The building and safety regulation issues are also very important while designing the solar passive building [9].

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2.1.2. Passive Solar Heating The goal of all passive solar heating systems is to capture the sun's heat within the building's elements and release that heat during periods when the sun is not shining. At the same time that the building's elements (or materials) is absorbing heat for later use, solar heat is available for keeping the space comfortable (not overheated). Two primary elements of passive solar heating required are as follows: o o

South facing glass for northern region and vice versa Thermal mass to absorb, store, and distribute heat

There are three approaches to passive solar heating systems - direct gain, indirect gain, and isolated gain. i) Direct gain - In this system, the actual living space is a solar collector, heat absorber and distribution system. South facing glass admits solar energy into the house where it strikes directly and indirectly thermal mass materials in the house such as masonry floors and walls as shown in Figure 1. The direct gain system will utilize 60-75% of the sun's energy striking the windows. In a direct gain system, the thermal mass floors and walls are functional parts of the house. The thermal mass absorbs solar radiations during daytime and radiates the heat energy during night time into the living space [10] as shown in Figure 1. ii) Indirect gain - In an indirect gain of solar passive heating system, thermal mass is located between the sun and the living space. The thermal mass absorbs the sunlight that strikes it and transfers it to the living space by conduction. The indirect gain system will

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utilize 30-45% of the sun's energy striking the glass adjoining the thermal mass. There are three types of indirect gain systems: o o o

Thermal storage wall systems (or Trombe wall) Water wall Roof pond systems

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Figure 1. Direct gain: Thermal mass absorbs heat in day and radiates in night [10].

Figure 2. Thermal mass wall (or Trombe wall) operation in day and night [11].

In indirect gain solar passive heating system, Trombe wall absorbs and stores heat during the day. Excess heat is carried out by passage air between wall and glass through

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thermosyphon principle in to the interior space as shown in Figure 2. At night Trombe wall vents are closed and the storage wall radiates heat into the interior space as shown in Figure 2. In water wall indirect solar passive heating system, the wall is composed of water stored in the transparent/opaque containers. During daytime water absorbs solar heat and radiates heat during night as shown in Figure 3 [11].

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Figure 3. Water wall absorbs and stores heat during the day and radiates in night [11].

An indirect gain solar passive design which provides both heating and cooling is the thermal pond approach, which uses water encased in ultraviolet ray inhibiting plastic beds underlined with a dark color, that are placed on a roof. Hence, this system is known as roof pond solar passive heating/cooling system. In warm and temperate climates with low precipitation, the flat roof structure serves directly as a ceiling for the living spaces below (Figure 4) thereby facilitating heating and cooling for the spaces below. In colder climates, where heating is more desirable, attic ponds under pitched roof glazing are effective. Winter heating occurs when sunlight heats the water, which then radiates energy into the living space as well as absorbs heat within the water thermal mass for nighttime distribution. During nighttime in winter months, movable insulation is covered over the roof pond and hence it radiates heat in the interior living space as shown in Figure 4. During daytime in summer months, the movable insulation is covered over the roof pond so that solar heat gain is minimized and water absorbs heat from the room to provide the cooling effect inside the living space. While during nighttime in summer months, the movable insulation is removed and the water radiates heat outside the room by absorbing the heat from the interior living space [11]. One of the major advantages of this approach is that it allows all rooms to have their own radiant energy source with little concern about the orientation of the structure or optimal building form [11].

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iii) Isolated gain - An isolated passive solar heat gain system has its integral parts separate from the main living area of a house [10]. Isolated gain involves utilizing solar energy to passively move heat from or to the living space using a fluid, such as water or air by natural convection or forced convection [12]. Examples are sunroom and convective loop through flat plate air collector to a storage system in the house [10]. The ability to isolate the system from the primary living areas is the point of distinction for this type of system. The isolated gain system will utilize 15-30% of the sunlight striking the glazing toward heating the adjoining living areas. Solar energy is also retained in the sunroom itself.

Figure 4. Roof pond stores heat daytime and radiates in nighttime for winter [11].

a) Sunroom (or solar greenhouse): It integrates a combination of direct gain and indirect gain system features. Sunlight entering the sunroom is retained in the thermal mass and air of the room. Sunlight is brought into the house by means of conduction through a shared mass wall in the rear of the sunroom, or by vents that permit the air between the sunroom and living space to be exchanged by convection [10]. b) Flat plate solar collector: It is attached to the structure separately and uses a fluid (liquid or air) to collect solar heat gain. The heat is transferred through ducts or pipes by natural convection to a storage area - comprised of a bin (for air) or a tank (for liquid), where the collected cooler air or water is displaced and forced back to the

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collector. If air is used as the transfer medium in a convection loop, heated air coming from the collector is usually directed into a rock (or other masonry mass material) bin where heat is absorbed by the rocks from the air. As the air passes its heat to the rocks it cools, falls to the bottom of the bin and is returned to the collector completing the cycle. At night the interior space of the structure is heated by convection of the collected radiant energy from the rock bin. If water is the transfer medium, the process works in much the same way except that heat is stored in a tank, and as hot water is introduced, cooler water is circulated to the collector. In naturally occurring convection systems (non-mechanically assisted) collectors must be lower than storage units, which must be lower than the spaces to be heated [11].

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Figure 5. Day and night operation of a Sunroom isolated heat gain system [10].

Figure 6. Flat plate solar heat gain collector with water/air convection loop [11].

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2.1.3. Passive Solar Cooling: A combination of proper insulation, energy-efficient windows and doors, daylighting, shading, and ventilation will usually keep homes cool with a low amount of energy use [13]. The approaches include use of operable windows, wing walls and thermal chimney. Natural ventilation can be created by providing vents in the upper level of a building to allow warm air to rise by convection and escape to the outside. At the same time cooler air can be drawn in through vents at the lower level. This lower vent is provided where there are trees planted besides the building to provide shade for cooler outside air [14]. i) Ventilation and Operable Windows o o

o

Place operable windows on the south exposure. Casement windows offer the best airflow. Awning (or hopper) windows should be fully opened or air will be directed to ceiling. Awning windows offer the best rain protection and perform better than double hung windows. If a room can have windows on only one side, use two widely spaced windows instead of one window [15].

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ii) Wing Walls Wing walls are vertical solid panels placed alongside of windows perpendicular to the wall on the windward side of the house. Wing walls will accelerate the natural wind speed due to pressure differences created by the wing wall [15].

Figure 7. Top view of wing walls airflow pattern [15].

iii) Thermal Chimney A thermal chimney employs convective currents to draw air out of a building. By creating a warm or hot zone with an exterior exhaust outlet, air can be drawn into the house ventilating the structure. Thermal chimneys can be constructed in a narrow configuration (like a chimney) with an easily heated black metal absorber on the inside behind a glazed front that can reach high temperatures and be insulated from the house. The chimney must terminate above the roof

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level. A rotating metal scoop at the top which opens opposite the wind will allow heated air to exhaust without being overcome by the prevailing wind. Thermal chimney effects can be integrated into the house with open stairwells and atria as shown in Figures 8 and 9. This approach can add into the aesthetic of the home.

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Figure 8. Thermal chimney [15].

Figure 9. Thermal chimney effect built into home [15].

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iv) Other Ventilation Strategies o o

Make the outlet openings slightly larger than the inlet openings. Place the inlets at low to medium heights to provide airflow at occupant levels in the room.

Passive solar building cooling design is used to (1) slow the rate of heat transfer into a building in the summer, and (2) remove unwanted heat from a building. The principles of physics are holistically integrated into the exterior envelope. This is much easier to do in new construction. It involves a good understanding of the mechanisms of heat transfer: heat conduction, convective heat transfer, and thermal radiation (primarily from the sun) [16].

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2.2. Embodied Energy in Buildings An important goal for the building sector is to produce buildings with minimum environmental impacts. Energy use is a central issue as enormous energy is used in the construction of buildings [17]. Hence it is important to consider low embodied energy materials that reduce the energy in the building construction. Embodied energy is the energy consumed by all of the processes associated with the production of a building, from the acquisition of natural resources to product delivery, including mining, manufacturing of materials and equipment, transport and administrative functions [18]. Energy in buildings can be categorized into two types: (1) energy for the maintenance/servicing of a building during its useful life, and (2) energy capital that goes into production of a building (embodied energy) using various building materials. Study of both the types of energy consumption is required for complete understanding of building energy needs [19]. Presently the embodied energy of building materials contributes anywhere from 15 to 20% of the energy used by a building over a 50 year period. Homeowners have tremendous influence as to what material are used and can specify those materials with low embodied energy, thus reducing the amount of fossil-fuel energy used during production [20]. Use of low embodied energy materials in buildings can greatly reduce the energy consumption in buildings and also minimize the environmental impacts of building construction. Table 1 gives the embodied energy of the commonly used construction materials. The construction industry is one of the largest in terms of employing manpower and volume of materials produced (cement, brick, steel and other materials). Demand and supply gap for residential buildings is increasing every year. Cement (>75 million tones per annum), steel (>10 million tonnes per annum) and bricks (>70 billion per annum) are the largest and bulk consumption items in the Indian construction industry. Minimizing the consumption of the conventional materials by using alternative materials, methods and techniques can result in scope for considerable energy savings as well as reduction of CO2 emission [19]. The modern tendency is to use energy intensive material in buildings like Aluminium, steel and glass. Hence, the consumption of these metals should be kept to a minimum, in order to keep the energy in a building low. Varieties of materials are used for the construction of masonry walls.

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Table 1. Embodied primary energy of building materials [21] Ranking

Building Material

Primary Energy Input (MJ/kg)

Very high energy

Aluminium

200-250

High energy

Plastics Copper Stainless steel Steel

50-100 100+ 100 20-60

Lead, Zinc Glass Cement Plaster board Lime Clay bricks and tiles Gypsum plaster Concrete In situ Blocks Precast Sand-lime brick Timber (sawn) Sand, aggregate

25+ 12-25 5-8 8-10 3-5 2-7 1-4 0.8-1.5 0.8-3.5 1.5-8 0.8-1.2 0.1-5