PASSIVE SURVIVABILITY IN RESIDENTIAL BUILDINGS DURING A HEAT

WAVE UNDER DYNAMIC EXTERIOR CONDITIONS

A Thesis Presented

By

Timothy Timilehin Aduralere

to

The Department of Mechanical and Industrial Engineering

in partial fulfillment of the requirements for the degree of

Master of Science

in the field of

Mechanical Engineering

Northeastern University Boston, Massachusetts

December 2018

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ABSTRACT

The increased frequency of heat waves around the world has prompted numerous studies to improve building resilience and maintain in the face of extreme conditions. However, previous studies rely on the assumption of steady-state conditions, including external temperature, which limits real-world applicability, demanding a more practical perspective. This thesis presents the use of recorded temperatures for a specified location to simulate the effect of extreme outdoor temperatures on the interior temperature of buildings when air conditioning is not used.

The objective of this research is to study how a building becomes uninhabitable during extreme heat and to effectively compare the changes in internal temperature of different building types during heat waves and standard climatic conditions.

Residential buildings were modeled using OpenStudio and simulated using EnergyPlus

8.7.0. for modified weather data files using recorded historical heat wave events. The results obtained provide a method for dynamic simulation of extreme events, establish a framework for policies supporting passive survivability in construction and consequently, reduce heat-related mortality.

iii

ACKNOWLEDGMENTS

Many thanks to Professor David Fannon and Professor Jacqueline Isaacs for their unparalleled guidance and penchant for excellence throughout the pursuit of this research work and the writing of this thesis.

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TABLE OF CONTENTS

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

1. INTRODUCTION ...... 1

2. LITERATURE REVIEW ...... 7

2.1 Heat-Related Mortality ...... 7

2.2 Climate Change and Extreme Weather Events ...... 9

3. METHODS ...... 11

3.1 Software ...... 12

3.2 Location and Climate Data ...... 15

3.2.1 The 2006 Heat Wave ...... 15

3.3.2 Fresno Weather data ...... 18

3.3 Building Prototype ...... 19

3.3.1 Schedules ...... 20

3.3.2 ...... 21

3.3.3 Internal Gains ...... 23

3.3.4 Heating Ventilation and Air Conditioning (HVAC) System ...... 24

3.4 Experimental Design ...... 25

4. ANALYSIS OF RESULTS ...... 27 v

4.1 Steady-state Meteorological Conditions ...... 27

4.2 Dynamic Meteorological Conditions ...... 30

4.3 Power Outage During a Heat Wave ...... 33

4.4 Comparison of Weather Data ...... 36

5. CONCLUSIONS AND FUTURE WORK ...... 39

5.1 Conclusions ...... 39

5.2 Future Work ...... 39

REFERENCES ...... 41

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LIST OF TABLES

Table 3. 1 Comparison of building simulation software (Crawley et al., 2008)...... 13

Table 3. 2 All-time record high minimum temperature (oF) during the July 2006 heat wave

in California (CNRFC, 2007)...... 16

Table 3. 3 All-time record maximum temperature (oF) during the July 2006 heat wave in

California (CNRFC, 2007)...... 16

Table 3. 4 Summary of major features and characteristics of building prototypes ...... 19

Table 3. 5 2006 IECC requirements for building thermal envelopea...... 22

Table 3. 6 Summary of building envelope materials...... 22

Table 3. 7 Summary of EnergyPlus simulation runs ...... 26

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LIST OF FIGURES

Figure 1. 1 Hazardous weather statistics, showing the fatalities caused by heat-related

weather events (NWS Analyze, 2017)...... 1

Figure 1. 2 Buckled train tracks during a 2018 UK heat wave (Clark, 2018)...... 3

Figure 3. 1 Heat related deaths in the United States from 1999 – 2009 (CDC, 2013). .... 17

Figure 3. 2 Comparison of Dry Bulb Temperature, from July 15 – 24, between the Typical

Meteorological Year (TMY) and 2006 heat wave of Fresno, CA...... 17

Figure 3. 3 3D graphic representations of the Single-family (SF) detached residential

prototype and the Multi-family (MF) low-rise apartment residential prototype

buildings...... 20

Figure 3. 4 Residential Building Schedule for Lighting and Occupancy...... 21

Figure 3. 5 International Energy Conservation Code (International Code Consortium,

2006) climate regions...... 23

Figure 4. 1 The simulation of a single family building prototype using a constant extreme

temperature to represent a heat wave (Nahlik Matthew J. et al., 2017)...... 27

Figure 4. 2 Changes in the interior temperature of a typical MF residential building during

a constant extreme outdoor temperature of 44oC...... 29

Figure 4. 3 Changes in the interior temperature of a typical SF residential building during

a constant extreme outdoor temperature of 44oC...... 29

Figure 4. 4 Changes in the interior temperature of a typical SF and MF residential building

during the 2006 heat wave...... 30 viii

Figure 4. 5 Changes in the interior temperature of a typical SF and MF residential building

in the case of a power outage during the 2006 heat wave...... 33

Figure 4. 6 Comparison of a typical MF building indoor temperature during power outage

and the absence of an HVAC system during the 2006 heat wave...... 34

Figure 4. 7 Comparison of a typical single-family building indoor temperature during

power outage and the absence of an HVAC system during the 2006 heat wave.. 35

Figure 4. 8 Comparison between steady state and the 2006 heat wave simulation results,

showing the trends of the interior building temperature...... 36

Figure 4. 9 Comparison between TMY and the 2006 heat wave simulation results, showing

the trends of the interior building temperature...... 37

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1. INTRODUCTION

Heat waves are sometimes described as silent killers (Carroll, 2002). The 1995 heat wave in Chicago (739 heat-related deaths over a period of five days), the 2003 heat wave in Europe (nearly 15,000 deaths), and the 2006 heat wave in North America (over 220 deaths) are a few notable heat wave occurrences with significantly fatal aftermaths.

Following prior literature (Meehl and Tebaldi 2004; Grossman-Clarke et al. 2010), this research defines a heat wave as a period during which the daily maximum temperature exceeds a threshold of the 97.5th percentile of historical summer temperatures in that particular area.

Figure 1. 1 Hazardous weather statistics, showing that the fatalities caused by heat-related weather events are the greatest for the 30-year average (NWS Analyze, 2017). 2

According to the National Weather Service (Figure 1. 1), heat waves are responsible for more casualties on average than any other weather-related cause of death

(NWS Analyze, 2017).

Passive Survivability

Passive survivability refers to the ability of a building to maintain critical life- support conditions for its occupants if services such as power, heating fuel, or water are lost for an extended period (Wilson, 2005). In related terms, when a occurs that leads to a lack of comfort, a building built for passive survivability will maintain livable conditions, while other buildings may become dangerous to occupants. For instance, following the 2005 in New Orleans, it was reported that all the hospice patients died of heat exhaustion due to loss of power in the hospitals as temperatures rose above 100oF (Davis, 2005).

Building HVAC systems are designed to provide conditions of acceptable human comfort. While a complex and nuanced field, indoor human thermal comfort is generally held to occur in a range of operative temperatures from 67oF (19oC) to 82oF (28oC), assuming low air velocities, < 40 fpm (0.2032 m/s), and moderate relative humidity (<65%) for sedentary subjects wearing western-style clothing (ASHRAE, 2017; ASHRAE, 2016).

In older people, however, this range is narrower because their responses to changes in body temperature are altered (Güneş & Zaybak, 2008). In buildings without mechanical cooling, or where mechanical systems are not functioning, indoor conditions can exceed comfort conditions, and become dangerous. In these circumstances, providing passive survivability

—rather than comfort—is the objective. 3

Heat Wave as a Natural Disaster

In the wake of natural disasters, infrastructure and buildings suffer damage of varying severity, and heat waves are no exception. Transport infrastructure is designed to accommodate expansion and contraction caused by regular changes in temperature.

However, on days where extreme heat strikes, train tracks could buckle (Figure 1. 2) and slow or totally impede traffic flow. Similarly, the higher demand resulting from a greater use of air conditioners stresses the energy infrastructure. Generation and transmission systems are harder to cool during a heat wave and coupled with an increased demand, generate more heat. These factors inevitably lead to a lower efficiency and a propensity to break down, resulting in a power outage.

Figure 1. 2 Buckled train tracks during a 2018 UK heat wave (Clark, 2018). 4

In buildings, the same positive feedback loop can be observed during a heat wave.

By the law of conduction, heat is transferred from a region of higher to lower temperature.

During a heat wave, conduction ensures that a building cooler than ambient conditions will gain heat through the building envelope. Furthermore, the occupants and their activities produce additional internal heat gains, which can lead to a higher temperature than the outdoor temperature and reversing the flow of conduction. The complex and reciprocal relationship of temperatures are neither obvious nor easily predicted.

In the presence of mechanical cooling systems, occupants can cool off easily.

However, if the energy infrastructure is stretched to the point of failure, buildings can experience loss of power, as occurred in a brief 2018 heat wave in California (CNN, 2018).

Overheating in buildings occurs during a heat wave when there is a power outage, or the occupants cannot afford to install an air conditioning system.

Overheating in buildings could be dangerous to human health, possibly leading to dehydration, heat cramps, heat exhaustion, heat stroke, and ultimately, death. The most vulnerable to these conditions are the elderly, the infants and young children, people with chronic health problems/disabilities, and people living in low-income houses without air conditioning. The Center for Disease Control and Prevention, (2017) states why older adults are more prone to heat stress:

1. Older adults do not adjust as well as young people to sudden changes in

temperature.

2. They are more likely to have a chronic medical condition that changes normal body

responses to heat. 5

3. They are more likely to take prescription medicines that affect the body’s ability to

control its temperature or sweat.

During heat waves, overheating in buildings in the absence of air conditioning is a major concern. Under normal weather, the ambient temperature ought to become cooler at night, but during a heat wave, it tends to remain hot and this makes it impossible to lose heat to the exterior and reduce the interior temperature of buildings. Consequently, the occupants find it hard to regulate their body temperature and this can lead to discomfort and danger as mentioned previously.

Motivation

Researchers and policymakers are turning to building performance simulation to understand the performance of buildings in extreme conditions, for example, to estimate how quickly indoor air temperature of buildings increase during extreme heat. Nahlik et al.

(2017) simulated 39 building prototypes under steady-state conditions in Los Angeles and

Phoenix and found that newer buildings are more vulnerable to extreme heat than older buildings because they allow cool air exfiltration at slower rates.

Based on these data, the researchers proposed a new metric for building vulnerability to extreme outdoor heat called Building Vulnerability Index (BVI). However, these results used a constant outdoor air temperature, which is not a good representation of dynamic exterior conditions. The present research uses weather data records for a heat wave to present a method for simulation of dynamic exterior conditions.

The research work presented in this thesis is organized into five chapters. Chapter one is an introduction to heat waves and overheating in buildings. Chapter two is a brief 6 literature review of heat waves and climate change. Chapter three is a comprehensive explanation of the research methods. Chapter four contains the results obtained from the

EnergyPlus simulations and it is accompanied by a discussion of the results. Chapter five presents the conclusion of the thesis and recommendations for future work.

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2. LITERATURE REVIEW

2.1 Heat-Related Mortality

The advent of modern air conditioners in the 20th century reduced the adverse consequences of buildings’ vulnerability to heat waves. A careful study of 72,740 persons, from April 1980 through December 1985, revealed that during heat waves, the death rate of persons who had central air-conditioning was 42 percent lower than the rate for persons who did not have air conditioning (Rogot, Sorlie, & Backlund, 1992). However, there remains a high-risk population– consisting of the elderly, young children, the disabled and low-income housing inhabitants—who suffer the impact of heat waves in their homes, such as heatstroke, heat exhaustion, heat syncope and heat cramps (Kilbourne, 1997).

Significantly, the target individuals for preventing mortality associated with the effects of heat waves should be persons with critical illnesses, the elderly, infants, and economically marginalized people. This was proven by a study of 231,676 nonaccidental deaths in nine California counties from May through September of 1999–2003. It also revealed that gender or educational level does not affect mortality rate during heat waves

(Basu & Ostro, 2008).

Several researches have been devoted to the study of heat-related mortality and morbidity. The most common denominators amongst them are:

1. Extreme and prolonged exposure to heat is closely tied to death rates during a

heat wave.

2. Heat-related mortality is age-stratified – affecting mostly senior citizens and

young children.

3. An ailing person is more prone to suffering a heat-related death. 8

4. An absence of mechanical cooling during a heat wave significantly increases

the chances of occupants being affected by heat-related illnesses.

Some studies reveal more information such as the concept of mortality displacement. This is a speculation that an environmental exposure can cause a death to occur faster in a weak individual than it would have a few days later (Basu, 2009).

In addition to the vulnerable groups stated earlier, ongoing research constantly provides more information on heat vulnerability. For example, a study in California showed that a 5.6°C increase in weekly average temperature was linked to a rise in preterm birth, irrespective of the mothers’ racial/ethnic group, age, education, or sex of the infant

(Basu, Malig, & Ostro, 2010).

In developed countries, people spend 90% of their time indoors (Höppe &

Martinac, 1998). Therefore, the indoor thermal conditions and air quality must be comfortable and at an acceptable level to ensure good health of the occupants. During heat waves, special attention needs to be paid to the indoor air temperature because it depends on the outdoor temperature as well as internal gains. It is also influenced by differences in the behavior of the inhabitants and the building structures (Franck et al., 2013). The indoor temperature during heat waves tend to be hotter than the outdoor temperature, making living conditions worse for occupants when air conditioning isn’t used.

The increasing interest in heat-related mortality as a matter of public health concern prompted more research into projections of the future impact that climate change will have on global mean temperature and heat waves. A systematic review on this topic showed that climate change will create a substantial increase in heat-related mortality. However, a better understanding of socioeconomic development, adaptation strategies, land-use 9 patterns, air pollution, mortality displacement, future changes in climate, population, and acclimatization will create a better framework for making these projections (Huang Cunrui et al., 2011; Knowlton et al., 2007).

2.2 Climate Change and Extreme Weather Events

Nearly all extreme weather events are exacerbated by climate change. A report noted that “If an extreme event truly is rare in the current climate, then almost by definition it required some unusual meteorological situation to be present, and the effect of climate change is a contributing factor (National Academies of Sciences, 2016).” However, the influence of global warming on heat waves is particularly direct. It is very likely that human influence has contributed to observed global scale changes in the frequency and intensity of daily temperature extremes since the mid-20th century, and likely that human influence has more than doubled the probability of occurrence of heat waves in some locations

(Intergovernmental Panel on Climate Change (IPCC), 2013).”

Heatwaves are a dangerous natural hazard, and one that requires increased attention

(World Meteorological Organization & WHO, 2015), therefore a diverse body of prior research on heat waves includes exploring the risk and damage, providing recommendations for community adaptation, and identifying personal safety practices.

With the global average temperature rising at a rate of about 0.2 °C per decade for the past few decades and projected to rise even more rapidly, as is the trend

(Intergovernmental Panel on Climate Change (IPCC), 2013), the intensity, duration, and frequency of heat waves will rise accordingly. To better understand and respond to such disasters, modeling and numerical simulations promise to identify critical thresholds, 10 formulate parameters to estimate the damage, and evaluate possible design responses. The two-pronged approach to responding to climate change, as popularized by NASA (2018), are:

1. Mitigation: Reducing emissions of and stabilizing the levels of heat-trapping

greenhouse gases in the atmosphere; and

2. Adaptation: Adapting to climate change already in the pipeline.

This research focuses on adaptation to heat through the concept of passive survivability.

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3. METHODS

Building simulation is the process of using a model to study the performance and behavior of a building—proposed or existing—by manipulating variables that could be expensive, too dangerous, or time-consuming in a real-life study. The model compiles characteristics such as construction and thermal properties to produce a computable representation of a building. This study uses building energy simulation software to transcend three fundamental limitations of the prevailing conditions:

a. The study considers a heat wave, which is by-definition an anomaly, and therefore

may not occur when and where the study occurs. A simulation can be conducted as

needed, and as many times as desired using recorded meteorological data of a heat

wave.

b. Simulations provide a way to collect data and study the effects of anomalies on

buildings and conditions which may not have existed in the real event and may not

have been instrumented to record specific behavior. These precise-speculations

allow what-if testing of hypothetical conditions that could not be observed in the

field.

c. The study requires testing thermal conditions that are potentially fatal to human

health. It could be unethical to subject people to such conditions, especially

vulnerable populations.

It is common to describe simulation-based research methods by referencing an industry standard. This section is closely patterned after the Building Performance Rating

Methods in the ASHRAE 90.1 Appendix G (ASHRAE, 2016a). 12

3.1 Software

The choice of a simulation software depends largely on the purpose of the study, questions to be answered, and available inputs. The wide variety of building simulation software provides a broad range of choices for this research. Several readily-available building simulation tools commonly used in research were considered, and the features most relevant to the nature of this project compared before making selection.

A report by Crawley et al. (2008) compares the features and capabilities of twenty

(20) building energy simulation programs across fourteen (14) categories. Among the twenty programs listed in the report, three were compared in greater detail in Table 3. 1.

These three programs with modeling characteristics particularly relevant to the present study are:

• eQUEST:

eQUEST is an open-source building energy use simulation program that uses the

U.S. Department of Energy (DOE)-developed DOE-2 simulation engine 2, which

was first released in the late 1970's. It is popularly used by engineers and energy

modelers from the early stage to the final stages of building development. The

results of the simulations are presented in hourly reports (“eQUEST - the Quick

Energy Simulation Tool,” 2018).

• EnergyPlus:

EnergyPlus is an open-source, whole-building energy modeling engine developed

in 1997 by the US DOE as the next evolution of tools beyond the DOE-2 engine.

Widely-used in research and practice, it performs dynamic thermal simulations to

yield hourly results for an entire year or period thereof, in this case a heat wave. 13

EnergyPlus simulates sub-hourly timesteps through an iterative calculation

procedure to improve the accuracy of its results (“EnergyPlus | EnergyPlus,” 2018).

• TRNSYS:

TRNSYS is a graphically based open-source software environment used to simulate

the behavior of transient systems. The model uses a user-defined time-step, which

ranges from 0.01 seconds to 1 hour, and it can analyze a time-horizon of multiple

years. It has found extensive applicability in the simulation of solar-thermal

systems and the analysis of the thermal performance of buildings (“Welcome |

TRNSYS : Transient System Simulation Tool,” 2018).

Table 3. 1 Comparison of building simulation software (Crawley et al., 2008). Modeling Characteristics EnergyPlus eQUEST TRNSYS Interior surface convention: • Dependent on temperature X - X • Dependent on air flow P - E • User-defined coefficients X - X Internal X X X Automatic design day calculations for sizing: • Dry bulb temperature X X - • Dew point temp. or relative humidity X X - • User-specified minimum and maximum X X - • User-specified design conditions - - X Outside surface convection algorithm: • BLAST / TARP X - - • DOE-2 X X - • ASHRAE simple X - - • User selectable X - X Single zone X X X Natural ventilation (pressure, buoyancy driven) X P O Multizone airflow (via pressure network model) X - O Control window opening based on zone X - O Idealized HVAC systems X - X User-configurable HVAC systems X - X

14

Nomenclature: X: feature or capability available and in common use, P: feature or capability partially implemented, O: optional feature or capability, R: optional feature or capability for research use, E: feature or capability requires domain expertise, I: feature or capability with difficult to obtain input.

As shown in Table 3. 1, EnergyPlus possesses the greatest number of features desirable for the simulations required in this research. All simulations presented here were carried out using EnergyPlus v8.7.0 (EnergyPlus, 2018). Simulations were controlled and input files were edited using the graphical user interface of the Open-studio plugin

(OpenStudio, 2018) for Trimble SketchUp® (Trimble Sketchup, 2018) and also edited using the text-based EneryPlus IDF editor (EnergyPlus, 2018).

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3.2 Location and Climate Data

To conduct an annual simulation, data describing the exterior climate conditions must provide values for the exterior parameters needed by the simulation, for example temperature and humidity (ASHRAE, 2016a) at the required time-step. ASHRAE standards recommend that climate data be from a weather station close to the building site if possible. Most simulations use weather files made by selecting hourly data from years of historic records to yield an annual representative of the typical year’s climate conditions.

For example, EnergyPlus combines this TMY data with information about design days to enable system sizing, resulting in the EnergyPlus Weather (EPW) file format. This study, however, considers an extreme event of a heatwave, and so required first identifying the extreme event, and then obtaining an appropriate weather record in a format the simulation tool could read.

3.2.1 The 2006 Heat Wave

In the United States, mortality records from 1999 – 2009 (Figure 3. 1), revealed that heat exposure was the underlying cause of 7,233 deaths; an average of 658 per year (Center for Disease Control and Prevention, 2013). This peaked at 2006 with almost 1,200 deaths recorded. For that reason, this thesis employs the 2006 North-

American heat wave as the case study, to represent the prevailing weather conditions in a significantly deadly heat wave. The United States and parts of neighboring Canada were affected from July to August 2006. In California, this record-breaking summertime heat wave brought all-time high records for overnight minimum temperatures (Table 3. 2) and 16 daytime maximum temperature (Table 3. 3) in multiple counties, and resulted in 147 reported deaths (Ostro, Roth, Green, & Basu, 2009; CNRFC, 2007).

Table 3. 2 All-time record high minimum temperature (oF) during the July 2006 heat wave in California (CNRFC, 2007).

New Record (2006) Old Record Station Name Date Temp (oF) Date Temp (oF) Needles 07/22 100 06/30/2001 100 Imperial 07/22 93 08/10/1946 90 Fresno 07/23 90 08/01/1908 86 Sacramento 07/23 84 06/23/1909 78 Modesto 07/23 84 07/13/1990 80 Madera 07/23 83 07/13/1999 81 Stockton 07/23 82 07/25/1974 80 Riverside 07/23 79 08/14/1994 77 San Jose 07/22 74 07/25/1974 73 Campo 07/23 74 09/03/1950 73

Table 3. 3 All-time record maximum temperature (oF) during the July 2006 heat wave in California (CNRFC, 2007).

New Record (2006) Old Record Station Name Date Temp (oF) Date Temp (oF) Woodland Hills 07/22 119 08/24/1985 116 Stockton 07/23 115 07/14/1972 114 Wild Animal Park 07/22 114 08/29/1998 112 Modesto 07/24 113 06/15/1961 112 El Cajon 07/22 113 09/04/1988 109 Escondido 07/22 112 08/12/1994 109 La Mesa 07/22 109 09/03/1988 109

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Figure 3. 1 Heat related deaths in the United States from 1999 – 2009 (CDC, 2013).

Figure 3. 2 Comparison of Dry Bulb Temperature, from July 15 – 24, between the Typical Meteorological Year (TMY) and 2006 heat wave of Fresno, CA, showing the temperature increasing compared to typical conditions in late July, by the 24 daytime highs are more than 10°C higher than typical, and overnight temperatures nearly 15°C above typical. 18

Fresno county, located in the Central Valley region of California, had the greatest number of heat-related deaths among the nine counties evaluated by Ostro et al. (2009) and was selected for the present study. The mortality data and county statistics collected for the heat wave period from July 1 through July 31, 2006, which were determined to be the study period for the present work.

3.3.2 Fresno Weather data

A weather station located at Fresno Yosemite International Airport provides a local, historical data record, and a base Typical Meteorological Year (TMY) weather file is available from the National Renewable Energy Laboratory's (NREL's) Analytic Studies

Division. Each prototype was simulated with two different weather files: first the baseline

TMY; and the second one consisting of measured extreme weather in this location in 2006.

The second weather file contains the record of meteorological data for 2006, a year when this area experienced a major, multi-day heat event, and was purchased from White Box

Technologies.

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3.3 Building Prototype

To support replication, this study adapted residential prototype building models developed by the Pacific Northwest National Laboratory (PNNL) under the Department of

Energy’s Building Energy Codes Program. The present study considers the single-family detached house (SF), and the multi-family (MF) low-rise apartment building, both illustrated in Figure 3. 3. The prototype building models are intended to be representative of homes in the state of California (PNNL, 2006). The energy models are designed to satisfy the 2006 version of the International Energy Conservation Code (IECC). Major features and characteristics are summarized in Table 3. 4.

To support this research, the available prototype models were upgraded from

EnergyPlus version 5.0.0 to version 8.7.0 using the IDF Version Updater. This eliminates errors associated with discrepancies in the software and IDF (Input Data File).

Table 3. 4 Summary of major features and characteristics of building prototypes Features Single Family (SF) Multi Family (MF)

Building Area 1600 sq. ft (148.6 m2) 2205 sq. ft (204.9 m2) Window-Wall Ratio (%) 13.81 16.25 Construction Properties 2006 IECC for low-rise 2006 IECC for low-rise residential buildings residential buildings 20

Figure 3. 3 3D graphic representations of the Single-family (SF) detached residential prototype (left) and the Multi-family (MF) low-rise apartment residential prototype (right) showing basic form and fenestration of sample buildings.

3.3.1 Schedules

People drive building energy consumption, through their need for thermal comfort, the building systems they control, and the metabolic heat they produce. The presence of humans and their control of building systems are represented in simulation models using schedules. To simulate the thermal load conditions of the residential buildings, it is important to have a schedule that properly represents the pattern of typical home use. The default residential schedules for occupancy, lighting, appliances, and HVAC were taken from the Residential Building Prototype Models provided by PNNL. The schedules are based on IECC 2006.

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Figure 3. 4 Residential Building Schedule for Lighting and Occupancy, note that lighting generally tracks occupancy, and for example the low values in the middle of the day as residents are assumed to be away at work or school. Note also the morning and evening peaks which align with occupants awake but insufficient daylight.

In a typical home, lighting and occupancy are at a minimum in the middle of the day while occupants are away, and the sun is shining, and then peak at in the evening when residents are back at home from their various daily activities and the sun is down. The lighting use drops while occupancy remains high once occupants go to sleep.

3.3.2 Building Envelope

The building prototypes are designed to meet 2006 IECC requirements, therefore, the components of each building’s exterior envelope conform to the values in Table 402.1.1 found in Chapter 4: Residential Energy Efficiency of the 2006 IECC; this table is excerpted 22 in Table 3. 5. The requirements are climate-dependent, and Fresno is in Climate Zone 3B, as shown in the map in Figure 3. 5.

Table 3. 5 2006 IECC requirements for building thermal envelopea.

Climate Fenestration Glazed Ceiling Wood Frame Slab Edge Slab Zone U-Factor Fenestration R-Value Wall R-Value R-Value Depth SHGC 1 1.20 0.40 30 13 0 0 2 0.75 0.40 30 13 0 0 3 0.65 0.40 30 13 0 0 4 except 0.40 NRb 38 13 10 2 ft Marine 5 and 4 0.35 NRb 38 19 or 13+5c 10 2 ft Marine 6 0.35 NRb 49 19 or 13+5c 10 4 ft 7 and 8 0.35 NRb 49 21 10 4 ft a. R-values are minimums. U-factors and SHGC are maximums. b. NR: No Requirement. c. “13+5” means R-13 cavity insulation plus R-5 insulated sheathing.

Windows were defined in the EnergyPlus IDF Editor using the Fenestration U-

Factor and SHGC found in Table 3. 5 for climate zone 3. The Window-Wall ratio of the single family and multifamily residential prototypes are 13.81% and 16.25% respectively

(Table 3. 4).

The materials that make up the layers for the building envelope for both the single family and multifamily residential prototype buildings are summarized in Table 3. 6.

Table 3. 6 Summary of building envelope materials.

Exterior Wall Ceiling Exterior Roof Floor Outside Layer Stucco (1in.) Ceiling consol layer Asphalt shingle Plywood (3/4 in.) Layer 2 Building paper/Felt Drywall (1/2 in.) OSB (1/2 in.) Carpet Layer 3 Sheathing Layer 4 OSB (5/8 in.) Layer 5 Wall consol layer Layer 6 Drywall (1/2 in.)

23

Figure 3. 5 International Energy Conservation Code (International Code Consortium, 2006) climate regions.

3.3.3 Internal Gains

Internal heat gains in the building consists of the heat released from:

1. Occupants.

2. Domestic hot water use.

3. Interior lighting.

4. Electrical appliances which include a refrigerator, clothes washer, clothes dryer,

dishwasher, and an electric cooking range.

5. Miscellaneous electric loads (such as computers, televisions, stereos, and other

plug loads). 24

The total internal heat gains specified by 2006 IECC standard reference design specifications for residential energy efficiency are based on equation 1.

IGain = 17,900 + 23.8 x CFA + 4104 x Nbr (1)

(Btu/day per dwelling unit)

where:

CFA = conditioned floor area (Table 3. 4)

Nbr = number of bedrooms (3 bedrooms for SF and MF)

Total internal heat gains, IGain is 20.01 kWh/day (68,292 Btu/day) for the SF building prototype and 24.23 kWh/day (82,691Btu/day) for the MF building prototype.

3.3.4 Heating Ventilation and Air Conditioning (HVAC) System

The building has a single speed unitary system with an air conditioner with a

Coefficient of Performance (COP) of 3.97. When the simulation is being carried out for cases of power outage and an absence of HVAC systems, the HVAC settings are turned off in the IDF editor by removing all AirLoopHVAC objects, all HVAC-related Branch &

BranchList objects, heating source, and cooling source. The HVAC system details and operating conditions in the residential prototype buildings can be summarized as follows:

1. No dehumidification or economizer is provided in the system.

2. Heating is provided by electric resistance heaters, which are considered 100%

efficient.

3. The thermostat setpoints defined in the schedule are 22.22oC (72oF) for heating

and 23.88oC (75oF) for cooling.

4. There is no mechanical ventilation, only natural ventilation. 25

3.4 Experimental Design

The present study of passive survivability compares conditions (specifically operative temperature) inside a conventional building under normal conditions, with the same building under a heat event. It also speculates about the potential effects if the cooling system is no longer working (for example, in a power failure). Of course, a lack of electricity would also affect the lighting and other electrical appliances, thereby slightly reducing the internal loads.

The baseline model is the building prototype provided by the PNNL (Table 3. 4).

The HVAC system, lights and all electrical appliances are turned on. See Table 3. 7 for a description of the simulation runs that were conducted to make this comparison. The basic configuration of the building remains unchanged for all eight cases (SF and MF building), however, variations of the baseline model are used to simulate the case studies in this thesis.

To run the simulations in the EP-Launch interface for EnergyPlus, the following settings are made in the EnergyPlus IDF Editor, in addition to the preset settings in the residential prototype building model:

a. Timestep:

Number of timesteps per hour – 6.

This is used in the Zone Heat Balance Model calculation as the driving timestep for

heat transfer and load calculations.

b. Run period:

Begin month and day – 7/1.

End month and day – 8/31. 26

Table 3. 7 Summary of EnergyPlus simulation runs

Indoor Condition Weather Data Description

Baseline HVAC on TMY The thermostat controls of the HVAC systems regulate the indoor temperature to ensure maximum comfort of occupants.

Case 1 HVAC off TMY This reveals the indoor thermal condition of the buildings when the HVAC systems turned off, so-called free-running building

Case 2 HVAC off 2006 heat This reveals the indoor thermal wave condition of the buildings when the HVAC systems turned off during the 2006 heat wave.

Case 3 HVAC off Constant This reveals the indoor thermal condition of the buildings when the HVAC systems turned off, assuming a steady state exterior temperature, to compare with prior literature.

Case 4 Power outage 2006 heat This reveals the indoor thermal wave condition of the buildings when a power outage occurs during a heat wave, so internal loads as well as HVAC are not operating.

For each case, there were 2 runs (SF and MF prototype building), totaling eight case studies and 2 baseline simulations.

The data obtained from these simulations are used to compare the interior building temperatures listed in the description column of Table 3. 7. Comparison between indoor operating conditions, weather data, and the resultant indoor temperature are made.

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4. ANALYSIS OF RESULTS

4.1 Steady-state Meteorological Conditions

Nahlik et al., (2017), in an effort to develop a metric for the vulnerability of buildings to heat waves, simulated buildings under steady-state meteorological conditions

(DBT of 111°F (44°C) and 2% relative humidity). They stated that the indoor temperature of a building that doesn’t use air-conditioning increases and gradually approaches the value of the constant exterior temperature but never quite reaches it.

Figure 4. 1 The simulation of a single family building prototype using a constant extreme temperature to represent a heat wave (Nahlik et al., 2017).

The building was exposed to the exterior conditions starting with an initial temperature of 77°F (25°C) and the prototype simulation was recorded over 100 hours.

This, however, cannot accurately represent the interior conditions of a heat wave, as it assumes the only load is envelope conduction. Because the interior temperature of an 28 occupied building is still affected by internal loads and solar loads even in the absence of an HVAC system, the temperature would behave as shown in Figure 4. 2 and Figure 4. 3.

Although both building prototypes considered experience different maximum and minimum indoor temperatures, they both reveal a similar daily pattern. When the HVAC systems in a building are turned off and the external dry bulb temperature is held at a constant value, the building indoor temperature cannot drop below the external temperature, as it is not possible to lose those internal gains to a warmer outside via conduction. Also, the sinusoidal nature of the indoor temperature changes is due to the variations in occupancy schedule and accompanying internal heat gains, as well as solar gains through the windows and, to a lesser extent, exterior walls. This is a clear representation of the limitations of using a constant extreme outdoor temperature to represent a heat wave.

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Figure 4. 2 Changes in the interior temperature of a typical Multi-Family residential building during a constant extreme outdoor temperature of 44oC.

Figure 4. 3 Changes in the interior temperature of a typical Single-Family residential building during a constant extreme outdoor temperature of 44oC. 30

4.2 Dynamic Meteorological Conditions

An accurate model of a building interior temperature during a heat wave should reflect the natural undulation produced by variations in day and night conditions, as well as variation in solar gains and occupancy. Using EnergyPlus simulations as discussed in the methods section, changes in the interior temperature of a building with occupants in the absence of an HVAC system was observed to behave as shown in Figure 4.4. It is crucial to note that the simulation was done using the 2006 weather data and this is the climatic record of an actual heat wave.

Figure 4. 4 Changes in the interior temperature of a typical Single-Family and Multi- Family residential building during the 2006 heat wave. Delta-T is measured between interior and exterior temperature. Note that the interior is never cooler than the exterior; it only loses heat if the exterior conditions are also cooler.

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The diurnal patterns in absolute temperature are visible. More importantly, there is a significant day-on-day trend of increasing temperature over the course of the heatwave, meaning the home continues to get warmer. Finally, there is a declining difference (delta-

T) between interior and exterior temperatures as the home does indeed warm up.

Figure 4. 4 shows that the natural, diurnal variation of outdoor air temperature is approximately 15°C in July from warmest (midday) to coolest (predawn). Notably, the indoor temperature without HVAC closely follows the pattern of varying outdoor dry bulb temperature, with a slight time lag from thermal mass. There is significant heat gain from people, electrical equipment, and solar gains, therefore the indoor temperature always remains above the outdoor dry bulb temperature. Further, the relatively low-mass of the prototype buildings does little to damp the diurnal oscillation from dynamic thermal simulation.

The MF building exhibits longer lags and less variation, due to higher mass and a lower surface-to-volume ratio. In both the MF and SF cases, the rate of conductive heat loss to the exterior is, of course, dependent on the magnitude of the temperature difference between interior and exterior temperature. Because the dynamic exterior conditions drive the interior conditions, the behaviors cannot be well represented by assuming a constant/steady state outdoor temperature.

The largest magnitude of difference between interior and exterior temperatures

(ΔT) typically occurs at night, because the outdoors cools down after sunset, but the house cannot release all the heat it has gained. The nights are not cooling off as much during the heat wave, therefore the entire building remains warm and uncomfortable, and has less capacity to absorb heat the next day. The ΔT reduces the loss through envelope conduction, 32 so the home cannot shed heat as quickly and continues to grow warmer. Consequently, as the outdoor DB temperature increases with increasing severity of the heat wave, the indoor

SF and MF indoor temperature increases with decreasing ΔT. This indicates that a more severe heat wave, produces a more uninhabitable building in the absence of mechanical cooling or other strategies.

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4.3 Power Outage During a Heat Wave

To further investigate the possible range of thermal discomfort associated with heat waves, the event of a power outage is considered. The simulation results as presented in

Figure 4. 5 revealed that the indoor temperature follows the same pattern as the case of an absence of air-conditioning alone and highlights the importance of occupant and solar heat loads.

Figure 4. 5 Changes in the interior temperature of a typical Single-Family and Multi- Family residential building in the case of a power outage during the 2006 heat wave. Delta- T is shown for the difference between the interior and exterior temperature and follows a similar pattern with the HVAC turned off.

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It is obvious in Figure 4. 6 and Figure 4. 7 that the maximum indoor temperature attained during a power outage is relatively lower than it is when there is power supply but no active HVAC system. This notable difference can be ascribed to the reduction in internal heat gains from electrical equipment such as lighting and plug loads.

Figure 4. 6 Comparison of a typical multi-family building indoor temperature during power outage and the absence of an HVAC system during the 2006 heat wave. Delta-T shows the difference between the values from the power outage simulation and the HVAC-off case. 35

Figure 4. 7 Comparison of a typical single-family building indoor temperature during power outage and the absence of an HVAC system during the 2006 heat wave. Delta-T shows the difference between the values from the power outage simulation and the HVAC-off case.

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4.4 Comparison of Weather Data

As mentioned previously, the most effective representation of an extreme heat event is a dynamic meteorological condition with temperature that matches what happens in a heat wave. While comparing the results obtained from simulating the MF and SF building prototypes with the steady-state and 2006 heat wave dynamic exterior conditions in the absence of HVAC systems, a striking difference is the nature of progression of the interior temperature (Figure 4. 8).

Figure 4. 8 Comparison between steady state and the 2006 heat wave simulation results, showing the trends of the interior building temperature in MF and SF buildings. Delta-T declines with increasing temperature of the 2006 heat wave.

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The constant conditions create an interior temperature that is predictably stable throughout the period of the heat wave. The interior temperature of the MF and SF buildings obtained from the 2006 heat wave simulation, however, shows a continual increase in the daily maximum and daily minimum. This is typical of a heat wave and could potentially rise above the values obtained in the steady-state conditions, depending on the severity of the heat wave.

Figure 4. 9 Comparison between TMY and the 2006 heat wave simulation results, showing the trends of the interior building temperature in MF and SF buildings. Delta-T rises, showing how progressively intense the heat wave gets.

The continuous rise in the delta-T in Figure 4. 9 shows that attempting to simulate a heat wave period using the TMY weather data would grossly understate the extreme temperature attained during that period of the year. 38

The temperature difference between the interior temperature obtained from the

TMY and 2006 heat wave had a minimum of 1.93OC and 1.05OC, and a maximum of

10.73OC and 11.83OC for the MF and SF building prototypes respectively. With a steadily increasing trend, this difference which represents the intensity of the heat wave will rise.

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5. CONCLUSIONS AND FUTURE WORK

5.1 Conclusions

This thesis applied dynamic building energy simulation techniques to analyze the effect of heat waves on the indoor thermal conditions of residential buildings. The single- family and multi-family residential building prototypes provided by PNNL were used in this study and simulated using TMY, steady state, and the 2006 heat wave weather data for

Fresno California. The two major results compared were from simulations carried out when the HVAC systems are turned off and in the event of a power outage during a heat wave.

Results show that interior conditions of residential buildings are correlated and covariant with exterior dry bulb temperature. Simulations under steady-state exterior conditions are significantly different from those of the dynamic meteorological conditions measured in a heat wave. Steady-state simulation over-states the risks and under-values the possible mitigation strategies. The dynamic approach proposed here represents important complexity when designing buildings to ensure occupant safety in extreme heat events.

5.2 Future Work

The effect of alternative building materials and designs in construction could be taken into consideration. Varying these characteristics could give an insight into the choices of materials that have substantial impact on the comfort and safety of occupants.

While more complex, a measure for characterizing the vulnerability of a building to extreme heat based on dynamic simulation would replace current static metrics, and enable an interdisciplinary approach investigating the physiological impact of heat waves 40 on occupants. It would also serve as a reference point for policymakers when formulating new and safer standards for building codes and developing effective outreach strategies.

Building orientation and resulting solar heat gain, are crucial to changes in building indoor temperature. Although held constant in this work for ease of simulation, future research should investigate their effects. Similarly, humidity is an important component of human comfort and life safety, as sweating is a primary thermoregulatory mechanism whose effectiveness depends on ambient conditions. These conditions will change in future climate, but the effect of those changes warrants further study.

There are other system-scale effects that can affect heat gain and loss, such as urban heat island effect and the cooling effect from neighborhood vegetation. Additional building-occupant adaptations, such as operable windows could also be tested using models and increase the utility of this approach. Employing some of these strategies in a building simulation will reveal cost-effective and efficient methods to attain more livable indoor temperature conditions during adverse weather conditions.

As the dynamic building simulation approach becomes more widely accepted and used in the industry and academic research, the effects of heat waves on building can be predicted and palliated. This will reduce mortality of all demographics exposed to overheating in buildings.

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