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csdCenter for Sustainable Development

Advanced Tools for Building Simulation:

Energy and AirflowGreg Arcangeli

Editor

Werner LangAurora McClain

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II-Strategies Analysis

2.17 Advanced Tools for Building Simulation

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Introduction

A number of architecture schools have integrated aspects of sustainability into their curricula, and many design studios require students to incorporate sustainable strategies in their projects. However, even programs at the forefront of this movement have not provided their students with a means to empiri-cally measure and evaluate the behaviors of passive and active building environmental systems. In the studio setting, and in the pro-fessional office for that matter, the most logical means for such testing is building simulation software. Simulations performed during the de-sign process allow for meaningful comparison of the effects of different building orientations, material choices, glazing systems, shading strategies and HVAC systems on a building’s energy consumption.

This educational lacuna is understandable, since accurate building simulation requires a certain depth of specific technical knowledge normally reserved for engineering or computer science departments. To accurately model building performance, one must understand the physical principals of heat transfer and thermodynamics, be able to intelligently specify HVAC systems, and know how to properly configure and interpret a simulation in software that often has a fairly opaque inter-face. However, without such a tool, it becomes

practically impossible to quantitatively test a range of design solutions.

This paper will provide an introduction to the practice of advanced building simulation. It will begin with a discussion of the motivations for building modeling before moving on to the theory and practice of advanced energy simu-lation. The final section will touch on the more complex topic of airflow modeling, and high-light the state-of-the art in building modeling: coupled energy and airflow simulations. Even for the user of simpler, more accessible model-ing packages that fall outside the category of “advanced,” much of the information in this article is pertinent, and may even improve the usefulness of future simulations.

Motivations for building modeling

Building simulation software can facilitate and improve the design of an energy efficient building. Buildings account for roughly 40% of US energy consumption, and by extension, are major emitters of CO2. This is one clear motivation for designing an energy efficient building. Another, which speaks directly to most clients, is simply to reduce a building’s operating costs in the face of rising energy prices. Any building design that aims to im-prove energy performance will benefit from energy and/or airflow simulations. Typically, the numerical model is used to find

Advanced Tools for Build-ing Simulation: Energy and Airflow

Greg Arcangeli

Based on a presentation by Atila Novoselac

Figure 1: Airflow analysis of a porous double-curved brick assembly

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There are many serious efforts underway to quantify the effects of poor IAQ. For example, the federally-funded Indoor Air Quality (IAQ) Scientific Findings Resource Bank at Law-rence Berkeley National Laboratory measures economic effects of IAQ and occupant health (see Figure 2).

In the case of both energy analysis and issues of IAQ, simulation software can model building performance to a degree that is accurate-enough to estimate up-front capital costs and operating costs. These simulation tools are a crucial component in the complex task of quantifying benefits, such as better employee health and CO2 reduction that result from a better designed building that may present higher initial costs to the client.

Modeling energy performance

Capabilities of energy modeling softwareSimply put, energy modeling tools examine two aspects of building efficiency. First, they measure the efficiency of the building enve-lope, including skin, structure, and lighting. Second, it tests the efficiency of various solu-tions for the HVAC system. The relationship between envelope efficiency and HVAC ef-ficiency determines the total energy consump-tion of the building for a given comfort level. Since the benefits of an efficient envelope can be negated by a cheap and inefficient HVAC system, or vice-versa, finding an appropriate match is a critical part of building simulation. All advanced energy modeling programs are built on a similar foundation. Since they all

an optimal balance between up-front capital investment in a building and the payback pe-riod of an energy efficient design due to lower operating expenses. This calculation requires an energy simulation for a given building and HVAC configuration, combined with a life-cycle analysis.

It often turns out that the midpoint between first costs and the payback period is still more than most clients are willing to pay. To make the proposition more convincing, the life-cycle analysis side of the equation needs to be set up to factor in the importance of CO2 emis-sion reduction and the productivity benefits of increased comfort for the occupants. Work on quantifying these “hidden” costs is well underway in academic and governmental departments.

Building simulation software can also help to ensure that the building environment and indoor air quality (IAQ) will be comfortable and healthy for occupants. We spend on average 86% of our time indoors, and buildings are the primary location of our exposure to toxins.1 Intuitively we understand that indoor air quality is important, but unlike energy or water con-servation, which are fairly predictable and can be directly measured, the relationship of health and productivity to indoor air quality is not as precisely understood and is far more difficult to accurately predict. In addition, the costs associated with poor IAQ are hard to measure and generally are “hidden” in sick days, lower productivity, unemployment insurance, and medical costs.2

simulate how energy moves through the set of rooms or building described in the model, they utilize the same basic equations for the different modes of heat transfer: conductivity, convection, long- and short-wave radiation, and also mass transfer. Energy modeling software solves the complex equations that describe these phenomena, but of course this computational prowess does not guarantee an accuracy of solutions. A good building simula-tion program still requires a knowledgeable user to properly set up the conditions for the software to analyze.

Setting up a model

The basic steps for setting up any energy simulation model are:

1. Define the simulation domain:

The model domain could be a single room, one floor of a building, or an entire building. It is up to the person running the simulation to choose the area that makes the most sense for the model. Smaller domains allow for a level of modeling detail (including furniture, partitions, etc.) that might overwhelm comput-ing resources if simulated at the scale of a building. A whole-building simulation might be useful for testing a building skin strategy.

2. Identify the most important phenomena and define the most important elements:

This includes identifying the sources that are likely to play a significant role in the heat trans-

Source of Productivity Gain

Reduced sick building syndrome

Reduced allergies and asthma

Reduced respiratory disease

Better thermal environment and lighting

Annual Health Benefits

20% to 50% reduction in symptoms experienced frequently by 15 million workers

8% to 25% decrease in symptoms in 53 million people with allergies and 16 million people with asthma

16 to 37 million avoided illnesses

not applicable

Annual Savings

$10 to $30 billion

~ $300 per office worker

$1 to $4 billion

$20 to $80 per person (with allergies)

$6 to $14 billion

$23 to $54 per person

$20 to $160 billion

Figure: 2 Potential Annual Healthcare Savings and Productivity Gains from Improved Indoor Environments


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3. Discretize elements and define connections

In this step, the important phenomena and elements are sorted into unique entities, or calculation nodes, and the energy exchanges between them are identified. In order to prop-erly navigate this part of the simulation set-up, users must have a precise understanding of the space they are modeling, and the physical phenomena likely to be present within.

fers within the domain. It is also necessary to identify significant energy flows between the domain and exterior adjacencies, which could be neighboring rooms or outdoor environment.

Phenomena and elements within the domain typically include people, computers, light fixtures, or any other object in the space that emits and/or absorbs energy. This step requires judgements about which nodes are significant to the model. For example, furniture might not be included if it is expected to mini-mally affect the simulation results, because it would increase modeling time, and drain com-puting resources during the simulation. Also, one must account for the effects of the HVAC system, which is designed to keep the indoor environment at a predetermined set points for temperature and in some cases relative hu-midity. Some of these internal energy modeling factors are position dependent, while others are not. Energy transfers involving people and computers are typically modeled independent of position, while the position of light fixtures and their proximity to windows is important to consider.

The basic external boundary conditions that may need to be factored into the simulation include:

• Diffuse and direct solar radiation.

• Wind velocity, which drives convection through the external envelope, and can affect mass transfer by increasing air infiltration.

• Sources of long-wave radiation, which all bodies with temperature differentials ex-change. A person exchanges long-wave radiation with a desk or a wall, and the outside wall of a building exchanges with the parking lot, plants, and the sky.

• External temperature, either of a neigh-boring room or outside.

To properly factor in the external meteorologi-cal conditions specific to the building’s loca-tion, a good data set is required. Fortunately, there are on-line sources for this. NREL main-tains 30-year databases of typical meteorologi-cal years from 1960-1990, and 1978-2008.2 A typical meteorological year selects the most characteristic months from the data set and merges them into a composite year. The NREL databases track temperature, wind velocity, and diffuse and direct radiation at hourly reso-lution. A valid energy model must simulate the domain throughout the entire typical meteoro-logical year.

The accuracy of this accounting is crucial to the usefulness of the simulation. In order to carry it out properly, one must identify all of the significant external and internal energy sourc-es that will affect the space being modeled, and understand the ways in which heat moves between them, whether through convection, conduction, radiation, or some combina-tion of these. If any key node of the model is overlooked, or if one of its mechanisms of heat exchange is left out, the results of the simula-tion will not be accurate.

Figure 3: Energy modeling software solves complex equations behind the scenes, but model accuracy is dependant on user inputs

Interface for resultpresentation

Interface for input data Solver

Preprocessor Engine Postprocessor

Graphical User Interface(GUI)

Graphical User Interface(GUI)

asciifile

asciifile

lamps

ventilation system

Inter-zoneair�ow

Long wave radiation

Convection

Convection

(For all surfaces)

Short wave radiation

Radiation

Di�use sun radiation

Direct s

un radiatio

n

In�ltration

internalsources

In�uence of Surrounding

Zones

ExternalBoundary Conditions

Figure 4: Diagram of important phenomena and elements in a single room model

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Interpreting results

Assuming that the simulation has been properly set up, the resultant data should be accurate and useful–but what do the results of an energy simulation describe, exactly?

The data generated by an energy simula-tion model should not be taken as absolute. Instead the simulation yields comparative figures, which can be placed side-by-side with a model using different parameters for construction of the building envelope or the HVAC system, to see how they perform rela-tive to one another. As a result, one should never promise precise energy benefits based on the results of an energy simulation model. It is quite possible, due to the unpredictability of precise meteorological conditions or difference between constructions and design that a solu-tion based on the model will cost more or less to operate in certain years (in absolute dollars) than the simulated building.

Iterative modeling

The ultimate reason for energy modeling is to find better design solutions. The role of energy simulation in this endeavor is to function as a comparative tool, checking the performance

Modes of heat transfer

What follows is a description of the modes of heat transfer, with some of their typical roles in a building’s energy flows.

Conduction is the transfer of energy through solid matter. Through-wall conduction plays a major role in heat transfer through building envelopes. Changing the characteristics of a wall, for example increasing or decreasing its density and thickness can affect the speed at which heat moves through the wall. For a climate that experiences large diurnal tem-perature swings, the simulation model could be used to find the proper wall construction to create the necessary isolation from outdoor environment and appropriate thermal mass that store heat from the sun to warm a building at night, and keep it cool by day.

Convection is the transfer of heat energy in a gas or liquid by movement of air or liquid flow. Inside a building, the velocity and volume of air movement can have a significant effect on the thermal comfort of occupants, and it affects the heat transfer between the internal surfaces and air. On the exterior building surfaces, outdoor airflow can drive convective energy exchange between the building and the outdoors.

Typically, we distinguish two types of radiative transfers that occur in buildings, short-wave and long-wave. One difference between the two is that short-wave radiation can pass through glass, while long-wave radiation cannot. Short-wave radiation heats up the exterior walls of a building and the surfaces surrounding the building, while also passing through windows to heat interior surfaces and objects. In turn, the objects emit long-wave radiation, which is unable to escape back through windows and remains largely within the space. This is the basic principle of work in a greenhouse. An understanding of the differ-ences between the two types allows for control over the interior environment. For example, coatings can be applied to windows that allow visible light to pass through, while blocking shortwave radiation in the infrared spectrum. Low-e coatings are a way to control radiative heat exchange via long-wave radiation. Ap-plied to the internal surface of window glass, it can reduce the exchange between internal objects and the window glass or between the two window glass pans. Such a strategy is very useful in cold climates, where it is advan-tageous to retain thermal gains from the sun and reduce the heat loss through the window surfaces due to the temperature difference.

of different configurations of parameters. By maintaining the same internal conditions and typical building schedule while modifying parameters related to the configuration of the building envelope and HVAC system, different scenarios can be meaningfully compared to one another.

Through experimentation, one may discover a configuration for a room, or a whole-building strategy, that yields optimal efficiency. In order to efficiently work through this phase of design, one needs to understand the basic principles of the heat transfer mechanisms (conductiv-ity, convection, and radiation) and have some knowledge about the heat transfer character-istics of building materials. This knowledge allows for the formation of hypotheses about how energy transfer in a space might be improved by, for example, using low-E glass in the windows, which can then be tested through a new round of simulation.

Modeling air and pollutant flow with airflow simulation software

Computational fluid dynamics (CFD) software can be used to model airflow inside the build-ings. Its common applications include testing potential for natural ventilation, examining

casual gains

air point node

internal convection

window conduction

mechanical ventilation

door conduction

radiativecomponents

internal longwave radiation

solar to furnishings

transmitted diffuse solar

re-radiated

radiant and convectiveplant

adjacent zone convection

adjacent zone radiation

external convection

external longwaveradiation

internal surface node

transparent surfacesolar radiation

outside air node

convectivecomponents

latent gain

opaque surfacesolar radiation

adjacent zone air node

construction node

infiltration and/ornatural ventilation

conv.

transmitteddirect solar radiation

Figure 5: Diagram of discrete elements in a typical room and their connections.


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of temperatures. It can also provide data about air velocity and humidity distribution, and mod-el how potential contaminants such as gasses and particulates will be transported through-out a building or particular space. This more nuanced and accurate picture of the building environment, relative to energy simulations, requires more computational power. The software used for CFD is less accessible than that used for energy analysis, both because it requires broader technical competence, and because the cost of the software is relatively high.

thermal comfort factors, and simulating con-taminant distribution and potential for human exposure. CFD software can simulate airflow rates under different conditions, in order to examine the potential for natural ventilation based on fluctuating pressure difference on building openings (in combination with wind tunnels), and analyze the Stack effect for dif-ferent shape of a building. When it comes to examining occupant comfort, CFD can provide a more nuanced picture of conditions than en-ergy simulation software. While energy models assume even temperatures within the model domain, CFD can examine spatial distribution

CFD simulations are carried out with different software packages depending on the type and scale of the simulation. A whole building simu-lation will require substantial modeling effort and computational power. However a single room airflow analysis or simulation of an occu-pant vicinity, such as an employee workstation can be conducted with moderate computation-al power such as a personal computer.

CFD results, even from a competently executed simulation, should not be used for granted. This is due to the assumptions used in turbulence modeling and the abstraction and approximation that simplify a highly complex indoor or outdoor environment. Traditionally, CFD models are checked against real-world experimental data. The need to conduct costly verification testing obviously reduces the degree of affordability of CFD simulation in the building design projects. However, CFD modeling of the building environment is still a young discipline, and there are many efforts afoot to improve the trustworthiness of building simulation models, particularly of the indoor environment. Chen and Zhai (2003) outline a CFD workflow that can be used to produce trustworthy results.

State of the art

Energy transfer, airflow, and comfort are all interrelated in many buildings. The most ad-vanced building simulations couple airflow and energy analysis. The integration of building energy simulation and computational fluid dy-namics programs can provide more accurate predictions about building energy use and indoor environment due to the complementary information provided by the two programs. The comparison of simulated results with experimental data has revealed advantages of the integrated building simulation over the separated energy simulation and computation-al-fluid-dynamics applications.

This type of software can be used to accurate-ly predict thermal comfort with different HVAC systems, at the spatial resolution achievable with airflow simulation software. A coupled solution is also able to examine buildings or spaces that strategically exploit wind pressure on the façade and stack pressure due to tem-perature difference to drive natural ventilation. Naturally, while this type of software combines all the power of Energy and airflow simulation, it also retains all the complexities of both.

Figure 6: Contaminant concentration distribution within a room, with spatial and temporal resolution, modeled in CFD software

Figure 7: State-of-the-art coupled energy and airflow anlaysis, is required to model systems that exploit heat to drive airflow

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Conclusion

It is becoming more widely recognized in universities that building simulation is a vital tool for current and future generations of build-ing industry professionals. Advanced building simulation is not a topic with clear disciplinary boundaries. Cross-domain knowledge of build-ing physics, human behavior and environment, architectural design, building and HVAC engi-neering, computer science, risk analysis, and policy development is required in order to use building simulation tools to their greatest ef-fect. Spurred by the current movement to build more sustainably, many simulation consultants are emerging to provide services to architects. Professionals in the emerging field of building simulation will have no shortage of work in the foreseeable future. Of course architects can, and should, consult with these specialists. In order to maintain control over their designs, however–to create beauty and psychological atmosphere while also paying attention to a

building’s effect on our planets atmosphere–a certain level of technical proficiency and under-standing of these programs will be required.

Energy Simulation ProgramsThe Department of Energy’s website includes a comprehensive directory of programs: http://www.eere.energy.gov/buildings/tools_directory

Sofware tools recommended by Atila Novoselac

Name What it does best Limitations User Friendly? Cost

DOE2 + eQuest Life-cycle and Limited HVAC choices. Yes, with eQuest GUI. $300-$2000 parametric analysis. Cannot handle detailed models.

ESPr Detailed models. Designed for UNIX. Requires educated user. Free Conduction analysis. Very few limitations for modelling. Simulation of actual physical systems. Integrated performance assessments.

TRNSYS Modular model building. User must provide detailed Yes, when used with GUI. $2100 (Edu.) Large component library, information about the building including HVAC and systems. Renewable energy and emerging technologies.

EnergyPlus Accurate and detailed Very modest interface, unless user Requires educated user. Free(DOE2 + BLAST) models. purchases costly GUI. (EnergyPlus Complex models. w/o GUI) Many HVAC options. Some CFD and zonal integration for airflow.


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Notes

1. Wallace, L. “Source strengths of ultrafine and fine particles due to cooking with a gas stove”. Environmental Science Technology, 2004, p. 2304.

2. A.M. Malkawi, “Immersive building simula-tion”. In: A.M. Malkawi and G. Augenbroe, Editors, Advanced Building Simulation, Taylor & Francis, UK (2004), pp. 217–247.

3. Zhai, Zhiqiang (John) and Qingyan (Yan)Chen, “Performance of coupled building energy and CFD simulations”. Energy and buildings. 2005, vol. 37, no4, pp. 333-344.

4. Zhai, Zhiqiang (John) and Qingyan (Yan)Chen, “The use of Computational Fluid Dy-namics tools for indoor environmental design”. In: A.M. Malkawi and G. Augenbroe, Editors, Advanced Building Simulation, Taylor & Fran-cis, UK (2004), pp. 119–140.

5. Addington, Michelle, “New Perspectives on Computational Fluid Dynamics Simulation”. In: A.M. Malkawi and G. Augenbroe, Editors, Ad-vanced Building Simulation, Taylor & Francis, UK (2004), pp. 141–158.

6. Kelly, Abigail, “Understanding HVAC eco-nomics”. Building Operating Management, October, 2001.

7. Aerias Air Quality Sciences, IAQ Resource Center. “Economics of IAQ: A tough sell... or is it?” http://www.aerias.org/DesktopDefault.aspx?tabindex=5&tabid=97 (accessed Novem-ber 14, 2008).

8. Indoor Air Quality Scientific Findings Resource Bank, Lawrence Berkeley National Laboratory. http://www.iaqscience.lbl.gov/

Figures

Figure 1: © Defne Sunguroğlu, AA London, 2006. Appeared in AD, Vol 78, No 2, 2008.

Figure 2: Fisk, WJ, Rosenfeld AH. 1997. Estimates of improved productivity and health from better indoor environments. Indoor Air ‘97 7: 158-172.

Figure 3: Atila Novoselec, 2008. Redrawn by Gregory Arcangeli.



Figure 6: Atila Novoselec, 2008.

Figure 7: Pelli Clarke Pelli Architects, Transbay Transit Center, http://www.pcparch.com/trans-bay/citypark.swf, 2008.

Biography

Atila Novoselac, Ph.D., is an Assistant Profes-sor in the Department of Civil, Architectural, and Environmental Engineering at the Univer-sity of Texas at Austin. His research interests include ventilation and indoor air quality, computations and measurements of airflows in buildings, pollutants transport modeling, and building energy analysis.

Atila received his B.S. in Mechanical Engineer-ing from the University of Belgrade, Serbia (1994), an M.S. in Mechanical Engineering from the same university (2000) He received his Ph.D. in Architectural Engineering from Pennsylvania State University (2004), for his dissertation “Coupled Airflow and Energy Simulation Program for Building Mechanical System Design.”

He teaches several architectural engineer-ing courses, including Building Environmen-tal Systems, Energy Simulation in Building Design, Modeling of Air and Pollutant Flows in Buildings, and HVAC Design.

He has written, or collaborated on, a number of articles for peer-reviewed journals and conference papers, including, most recently, “On-Site Experimental Validation of a Coupled Multi-zone and CFD model,” in the Internation-al Journal of Ventilation with Jelena Srebric.

Atila developed Building Energy and Air Flow (BEAF) simulation software which couples energy simulation with computational fluid dynamics capabilities, and participated in the development of Building Environment Simula-tion and Testing (BEST) facility for indoor environment studies.

His home page is: http://www.ce.utexas.edu/prof/Novoselac/