749

Using CFD Capabilities of CONTAM 3.0 for 

Simulating Airflow and Contaminant Transpor

in and around Buildings

Liangzhu (Leon) Wang, PhD W. Stuart Dols Qingyan Chen, P

Associate Member ASHRAE Member ASHRAE Fellow ASHRAE

Received March 20, 2010; accepted August 9, 2010

CONTAM is a multizone building airflow and contaminant transport computer program o

used for ventilation and indoor air quality analysis. The program was recently enhance

incorporate CFD capabilities for both outdoor and indoor environmental analysis. This pa

introduces the CFD features implemented within the most recent version, CONTAM 3.0. 

outdoor or external CFD link predicts wind pressure coefficients and contaminant concen

tions for airflow paths at the building surface. A converter computer program translates 

wind pressure coefficients to the CONTAM data format. This external CFD link is usefu

parametric studies of the impact of outdoor air quality on indoor environment when conside

different wind directions or contaminant locations, especially simulations under transient 

ditions. The ability to embed a single CFD zone in a CONTAM network model has also b

implemented. This enables the detailed modeling of a zone when the well-mixed multiz

assumption is not appropriate and then uses the multizone approach for the rest of a build

thus capturing the local distribution of air and contaminant properties in a zone and t

impacts on other zones of a building. CFD capabilities are demonstrated using a generic r

dential house model to show how these two new CFD features enhance the existing CONT

capabilities for both indoor and outdoor air quality analysis.

INTRODUCTION

Building ventilation design and indoor air quality (IAQ) analysis involve evaluations of 

bulk and detailed air properties in buildings. One of the most commonly used building ven

tion tools for bulk airflow analysis is multizone network modeling. In a multizone buil

model, airflow and species transport are calculated between the rooms of a building and betw

the building and the outdoors. The so-called well-mixed assumption, in which each zone is c

acterized by a single contaminant concentration, is used to make the bulk analysis simpler 

faster. Under the well-mixed assumption, a building is subdivided into zones having hom

neous air properties and species concentrations. When air and species properties are hi

non-uniform, the well-mixed assumption is not valid. For these situations, a tool to calcu

detailed air properties, such as computational fluid dynamics (CFD), is needed. Compared 

multizone model, CFD is more computationally intensive and is less often used 

whole-building analysis or longer-term transient simulations. Table 1 lists some of the pros 

cons of multizone and CFD models. The multizone model is faster with broader assumpti

Liangzhu Wang is an assistant professor in the Department of Building, Civil and Environmental Engineering at 

cordia University, Montreal, Quebec. W. Stuart Dols is a mechanical engineer with the Indoor Air Quality and Ve

tion Group of the Building and Fire Research Laboratory at the National Institute of Standards and Techno

Gaithersburg, MD. Qingyan Chen is a professor in the School of Mechanical Engineering at Purdue University, 

Lafayette, IN.
whereas the CFD is more accurate (if used correctly) but with more computational cost. 

multizone model provides average characteristics of airflow and contaminant transport phen

ena, while the CFD method can predict spatial distributions of these parameters. For buil

ventilation design and IAQ analysis, the multizone and CFD models are not conflicting meth

but can be used as appropriate to take advantage of the benefits offered by each. However

until recently, the analyst had to choose between using one or the other modeling approach.

The idea of coupling multizone and CFD models was proposed by Schaelin et al. (1993)

the method of detailed flow path values. This method was meant to improve upon 

well-mixed assumption by using CFD to calculate pressures, velocities, and contaminant 

centrations near flow paths and then using them as inputs to multizone simulations. They d

onstrated the method through manual exchange of boundary conditions between two stand-a

programs and showed promising results. Soon thereafter, Clarke et al. (1995) and Negrao (1

started to implement an automatically coupled CFD and network model inside the Environm

tal Systems Performance, Research version (ESP-r) building simulation program (Clarke 19

Bartak et al. (2002) then adopted a conflation controller (Beausoleil-Morrison 2002) to imp

the coupled module of ESP-r. Their work provided one of the earliest coupled CFD and netw

models. Some applications of coupled CFD and multizone methods and some experime

studies were conducted by Srinivas (2001), Jayaraman et al. (2004), and Srebric et al. (20

Their studies showed that coupling a CFD model with a multizone model could obtain m

realistic predictions of airflow and contaminant transport in buildings with large spaces. H

ever, these studies were mostly research-oriented and did not provide a general, more wi

available design tool. Some commercial software is available with integrated building en

Table 1. Comparison of Multizone and CFD Models

Multizone CFD

Simulation assumptions

Uniform contaminant concentration in each room Yes No

Quiescent or still air in each room Yes No

Neglect air resistance in each room Yes No

Neglect inflow momentum effect, if any Yes No

Instantaneous contaminant transport inside room Yes No

Hydrostatic distribution of pressure inside room Yes No

Simulation capabilities

Whole-building and yearly dynamic simulations Better

Modeling building air infiltration Better

Computational speed Better

Modeling spatial airflow and contaminant concentration Better

Modeling wind pressure on buildings Better

Modeling large openings and spaces Better

Modeling spatial personal exposure Better
simulation and CFD capability, but none specifically developed for building ventilation 

IAQ analyses.

Aiming at developing a public-domain tool for building ventilation design and indoo

quality analyses for the general public, this study has coupled CONTAM (Walton and D

2008), a multizone network software program developed at the National Institute of Stand

and Technology (NIST), with CFD0, a CFD software tool with an indoor zero-turbulence m

(Chen and Xu 1998). CONTAM is a popular tool to determine building air infiltration, exfi

tion, and room-to-room airflows driven by wind pressures on building exteriors, buoya

effects related to the indoor and outdoor air temperature difference, and mechanical ventila

It also predicts the dispersal of airborne contaminants and can be used to calculate pers

exposure to contaminants. CONTAM includes a Microsoft Windows®-based front end, C

tamW, and a console solver program, ContamX. The first version of ContamW was release

1996, whereas ContamX evolved from AIRNET (Walton 1989). CFD0 was a CFD prog

originally developed for ASHRAE research project RP-927 (Chen et al. 1999), and improve

Wang (2007). Wang and Chen (2007a, 2007b) studied the solution characteristics of integr

multizone and CFD models, developed the coupling algorithm, and validated the coupled C

TAM and CFD0 program. In this paper, the coupling algorithm is implemented in a new ver

of CONTAM to be released as CONTAM 3.0. This paper first introduces the algorithm for 

methods of linking CONTAM and CFD0, the external link for performing external airflow a

ysis, and the internal link for embedding a CFD0 zone in a CONTAM airflow and contami

transport network. A demonstration case of a generic single-family house is presented for 

analysis of wind pressures on the building enclosure and indoor air quality.

METHODS OF CONTAM AND CFD0 INTEGRATION

Incorporation of CFD0 into CONTAM 3.0 provides two major capabilities: linking infor

tion on exterior wind pressures and outdoor contaminant concentrations to CONTAM’s ind

simulations, and embedding detailed CFD zones in the CONTAM airflow and contami

transport network. 

Linking of CFD and CONTAM for Building Exterior Simulations

Wind pressure is a major driving force for air infiltration through the building envelope. W

pressure, as a function of wind speed, wind direction, building configuration, and local ter

effects (ASHRAE 2009), can be accounted for by CONTAM with one of three options: spec

ing wind pressure to be constant, implementing surface averaged wind pressure profiles for 

envelope penetration, and using spatially and time-varying wind pressures based on an exte

wind pressure and contaminant (WPC) file. A general approach for handling the variable eff

of wind is to use a local wind pressure coefficient CP, which is a function of air density ρ, l

wind pressure, Pw, undisturbed wind speed UH, and wind pressure PH, at a reference height 

the far airflow field.

A rough estimate of the average wind pressure profile over each side of a building surface 

be obtained from empirical or experimental studies, such as Swami and Chandra (1988), 

function of wind direction relative to a building surface (i.e., relative wind direction). In fact

movement around buildings is a three-dimensional turbulent flow, so wind pressures could 

significantly even over a single building surface. Therefore, surface-averaged wind pres

coefficients may be insufficient for air infiltration predictions (Gao 2002). A more accu

approach is to address the variations of wind pressure coefficients at each location over a bu

ing surface. The most reliable means of determining Cp for a specific building are thro

on-site measurements or wind tunnel studies (Persily and Ivy 2001). However, these meas

ments can be expensive and technically difficult. CFD simulations of turbulent flows aro
buildings can be an alternative to obtain wind pressures, as demonstrated by Gao (2002) 

Jiang (2003). More importantly, using CFD for simulating external contaminant releases 

provide detailed profiles of contaminant concentration on building exteriors, which are usefu

study the impact of outdoor air quality on an indoor environment (Wang and Emmerich 201

The prior version of CONTAM, 2.4c, provides the capability of implementing spatially 

time-varying wind pressures and outdoor contaminant concentrations using the external W

file. However, a WPC file is created by a CFD analysis for a specific wind speed and direct

In comparison, the external CFD link in CONTAM 3.0 calculates a profile of wind pres

coefficients for each building leakage path and for a range of wind directions. This appro

avoids the need for an additional CFD run whenever the wind direction or speed changes. 

external CFD0 link is comprised of three steps, as shown in Figure 1. First, CFD0 calculates 

flow and contaminant transport outside a building and creates a file with all wind pressure c

ficients (CP) or contaminant concentrations (CC) over the building surfaces. A sepa

computer program then searches in the files for the values of CP or CC for each envelope air

path for which a location is defined in the CONTAM project. The program then converts 

values to a CONTAM library file with CP or CC values for all envelope airflow paths. Fin

when a CONTAM simulation is performed using a CONTAM weather file, the appropriate 

or CC for each envelope airflow path is utilized for each wind direction and simulation time s

The three-step procedure in Figure 1 provides a flexible external link between CONTAM 

CFD0 that can be used for the following types of studies:

• Wind pressure and/or outdoor contaminant concentrations that vary with wind di

tion. In the first step, different wind directions can be defined, for example, from 0° to 3

around the building. CFD0 calculates and automatically saves envelope CP or CC for all w

directions. When a CONTAM simulation needs to consider different wind direction, it 

the saved CP or CC values without having to run the CFD simulation again.

• Variable locations of outdoor contaminant sources. In the first step, CFD0 saves all ca

lated air properties and velocities, which can be reused if the locations of outdoor contami

Figure 1. Schematic of link between CONTAM and CFD0 for building external airf

and contaminant transport simulations.
sources change, assuming this change does not affect the airflow field. This feature is us

for parametric study of contaminant source locations on indoor concentration levels. 

• Transient contaminant transport outdoors using previously calculated external airfl

at steady state. Similar to the above feature, this option allows users to run a transient 

door contaminant transport simulation using steady-state outdoor airflow results alre

available without the need to rerun the external CFD airflow calculations.

The external CFD link in CONTAM 3.0 has been applied to the study of indoor exposur

carbon monoxide caused by operating gasoline-powered electric generators outside of an e

ing manufactured house and a hypothetical two-story house (Wang and Emmerich 2010; W

et al. 2010). This study involved an examination of many parameters, including contami

source location and weather conditions, and the external CFD link reduced the computati

cost significantly.

Embedding CFD Zone within a CONTAM Airflow Network

The well-mixed assumption of multizone models is less suitable for zones with nonunif

distribution of air properties and contaminant concentrations (Wang and Chen 2008). An ex

ple is a large atrium in a multistory building. One approach to this situation is for the large sp

to be handled by CFD0 and the rest of the zones in the building by CONTAM, with the two 

grams exchanging information at the boundaries formed by the airflow paths connecting 

CFD and CONTAM zones. Exchanged information at the interfaces include airflow rate

pressure, and contaminant concentration. To satisfy conservation equations on both sides o

boundaries, the process is often a two-way iterative procedure. This is different from the exte

link, where the values of CP or CC are passed one-way from CFD0 to CONTAM. Thus, em

ding the CFD zone within CONTAM is also called dynamic coupling of the two methods.

Because dynamic coupling of CONTAM and CFD0 integrates two separate programs, s

criteria need to be met to ensure a successful integration. Wang and Chen (2007a) studied 

solution characteristics of dynamic coupling and suggested two necessary conditions for a 

cessfully coupled airflow calculations:

• Mass airflow rate predicted by a multizone airflow network model must equal the integr

the mass airflow rate of all CFD grid cells for each interface airflow path to ensure ov

mass balance

• The resistance characteristic (i.e., the airflow rate as a function of pressure drop) mus

known for each multizone/CFD boundary grid cell to ensure closure of the problem

Given that the boundary conditions are exchanged iteratively between CONTAM and CF

the dynamic coupling procedure can be unstable if the boundary conditions are exchan

incorrectly (Negrao 1998; Bartak et al. 2002). Wang and Chen (2007a) suggested that 

exchange of pressure boundary conditions between the two programs can achieve an unco

tionally stable solution, whereas other types of information exchange, such as mass flow r

may lead to divergence. Moreover, to link two separate programs with distinct program m

ules and data structures, an appropriate programmatic coupling scheme is required. Figu

shows the two-step schematic for coupling CONTAM and CFD0 programs in CONTAM 

for a simulation at steady state: coupled airflow simulation and, if necessary, coupled conta

nant transport simulation. If an airflow result is available from a previous coupled calculat

it can be imported directly to save extra computational cost. Otherwise, the coupled prog

performs a fully coupled procedure for airflow prediction. The fully coupled procedure obt

initial values of air pressures and flow rates for all zones by running a CONTAM simula
for the whole building. With boundary conditions from CONTAM, CFD0 calculates the 

flow in the CFD zone and feeds boundary conditions back to CONTAM. The iteration con

ues until convergence is achieved for both sets of calculations. If a coupled simulatio

contaminant transport is needed, a similar iterative procedure is used except that the conta

nant concentration is exchanged at each interface airflow path as opposed to the pressures 

airflow rates. For a coupled transient simulation, the procedure in Figure 2 is repeated for e

time step.

The graphical user interface (GUI) of CONTAM was modified to provide inputs for 

CFD-related features. This includes the ability to define coupling methods, coupling converg

factors and iteration limits, and output options for CFD-related results. A separate GUI prog

Figure 2. Schematic of embedding a CFD zone in the CONTAM airflow network 

simulations at steady state in CONTAM 3.0.
was also developed to allow the user to graphically define the CFD domain and to review 

CFD-related results of the coupled simulation. These new GUI features were developed a

with a tutorial with an example problem to allow use of the program with minimal training.

DEMONSTRATION OF THE CFD CAPABILITIES

A low-rise residential house was modeled in order to demonstrate both the external and in

nal CFD modeling capabilities of CONTAM 3.0. Figure 3 provides CONTAM and CFD 

spectives of the model. Holmes (1986, 1994) measured the wind pressure distribution o

low-rise house model for various wind directions in an open-circuit, boundary-layer wind 

nel. For the purposes of this study, the house model in Holmes’ experiment was scaled u

15.5 m × 7.4 m × 3.0 m (50.9 ft × 24.3 ft × 9.8 ft) (L × W × H) (the height excluding the slo

roof), as illustrated in Figure 3a. The external CFD link with CONTAM is demonstrated 

using CFD0 to calculate wind pressure profiles for each airflow path of the house envel

Figure 3. (a) CFD mesh, (b) plan view of building in CFD, (c) CONTAM zone configu

tion with shaded zone of the utility room, and (d) internal view of utility room in CFD
Figure 3b shows the plan view of the house in CFD0. Infiltration rates were then calcul

using CONTAM with the zone configuration presented in Figure 3c. The internal CFD lin

demonstrated by simulating the transport of carbon monoxide (CO) in the house produced 

faulty furnace in the utility room, the CFD model for which is shown in Figure 3d.

Prediction of Wind Pressure Profiles and Infiltration Rates

The external CFD simulation of the house used a total grid number of 74 × 87 × 24 (X × Y × 

as shown in Figure 3a. The wind velocity profile at the domain boundary was selected to re

sent an “urban and suburban location” (ASHRAE 2009), similar to that in Holmes’ experim

CFD0 simulations of wind were performed for the relative wind direction θ between 0° and 3

with 30° increments. Figure 3b shows that the relative wind direction to a building surface 

specific envelope location is defined as the clockwise angle from the building surface azim

angle to the wind azimuth angle (Walton and Dols 2008). The simulation for each wind direc

took between 300 and 400 iterations to converge, with a convergence criterion of 1 × 10-3. 

total simulation time for all 13 wind directions was about 8 h on a desktop PC with an Intel 

tium 4 2.0 GHz microprocessor. The external CFD0 link calculates a wind pressure coeffic

for every relative wind direction at the location of each envelope airflow path.

Figure 4 shows the calculated wind pressure coefficients versus relative wind direction 

each path at the surface of the house as compared to the surface-averaged wind pressure co

cient (Swami and Chandra 1987) and the measurements by Holmes (1986, 1994). As see

these plots, wind pressure profiles show a strong dependence on path locations. They 

change dramatically with wind direction, especially for paths near the edge of the building 

face. The surface-averaged value of CP (dashed lines) does not exhibit this variation with 

location. The surface-averaged CPs are also symmetric about the relative wind direction of 1

which, in reality, would only be valid for the envelope locations on the vertical symmetry lin

a building façade. 

Compared to the measurements by Holmes (1986, 1994), the external CFD0 link gener

predicted the wind pressure profiles reasonably well, with some exceptions. The most sig

cant exception was observed for Path 28 for a relative wind direction of 90°, as marked by 

dot and arrow in Figure 4 (bottom left). The discrepancy could be caused by the inabilit

CFD0 to predict wind pressures correctly in the regions with strong turbulence separations 

recirculation flows near that path. In fact, the Reynolds Averaged Navier-Stokes (RANS) e

tions were found incapable of resolving the turbulence vortices and the curvature-sensitive s

ration flow features in wind flow around buildings (Cochran 2000), and are also not abl

capture the time-dependent nonhomogenous and/or anisotropic flows. Using a single turbule

model for each turbulence scale, RANS models focus on time-averaging flow features and t

interactions with turbulence effects (time-wise turbulence fluctuations). A better option ma

large eddy simulation (LES) models, which solve spatially filtered unsteady Navier-Sto

equations. LES is capable of accounting for the interactions between size-resolved large-s

eddies and unresolved smaller eddies (space-wise turbulence structures), for which turbule

effects are not averaged over time, so an unsteady calculation is performed. RANS models h

a lower computational cost than LES models, but they are not as good as LES for captu

time-dependent anisotropic large eddies, which occur in outdoor flows. As a RANS mo

CFD0 uses an indoor air zero-equation model for turbulence closure and has limited capabil

for outdoor airflows. Efforts to improve CFD capabilities in CONTAM for such airflows 

under development (Wang et al. 2010).

A primary use of the wind pressure profile prediction method presented here is calcula

building air infiltration rates. Therefore, it is useful to compare the air infiltration rate ca

lated using the various methods of obtaining wind pressure coefficients. The measured 
Figure 4. Comparison of calculated profiles of wind pressure coefficient by the exter

CFD link, measured data at each airflow path (Holmes 1986, 1994), and average data 

each building surface (Swami and Chandra 1987) of the house.
from Holmes (1986, 1994) were used as a baseline for comparison. Results of the compar

are shown in Figure 5, with the normalized mean square error (NMSE) (ASTM 2008

defined by Equation 1, which is a measure of the magnitude of difference relative to Fa and 

where

n = total number of envelope airflow paths

Fb,i = predicted airflow rate for path i by baseline method

= average absolute value of all envelope airflow rates by baseline method

= average absolute value of all envelope airflow rates by alternative method

Figure 5 illustrates that the external CFD link generally predicted airflows better than u

the surface-averaged wind pressure profiles: the NMSE was 14% compared to 22% when 

surface-averaged CPs were used. For wind out of the north (0° in Figure 5), the NMSE of 

dicted airflow rates using the surface-averaged CP was over 80%, whereas it was 57% using 

from the external CFD0 link. The worst case for the external CFD link was for the wind d

tion of 270°, for which comparisons are provided between the predicted airflows of the indi

ual envelope flow paths in Figure 6. Most of the airflows determined using the external 

were relatively close to those determined using the measured wind pressure data, except for 

28. When the wind direction was 270°, the relative wind angle for path 28 was 90°. As show

Figure 4, the predicted CP value (highlighted by the dot with arrow) determined using the ex

nal CFD link was three times higher than the measured value. These examples show how v

tion of wind pressures can affect infiltration predictions significantly. 

Figure 5. NMSE of predicted airflow rates under different wind directions using av

aged wind pressure coefficients (Swami and Chandra 1987) and predicted values fr

the external CFD link.

NMSE 1

Fa ( ) Fb ( ) ---------------------------- 1

n

-- Fa i , Fb i , ( ) – 2

i 1=

n

= ∑

Fa

Fb
Prediction of Indoor Contaminant Transport

The same low-rise residential house was used to demonstrate the embedding of a CFD z

within a CONTAM airflow network. For the purpose of this simulation, it was assumed tha

gas furnace in the utility room (the shaded zone in Figure 3c) was malfunctioning and relea

carbon monoxide (CO) into the building. In order to investigate the effects of nonuniformit

CO concentration in the utility room, it was simulated by CFD0, and the remaining rooms w

simulated as well-mixed zones.

The following describes the inputs used to establish the test case. The utility room is 3.2 

2.6 m × 2.7 m (10.5 ft × 8.5 ft × 8.9 ft) (L × W × H) with a total CFD grid of 70 × 70 × 90. Am

ent air temperature was set to 25°C, and the wind was set to 10 m/s (32.8 ft/s) from 30° rela

to the north. CO source strength was set to 126.6 g/h (4.5 ounce/h), which was determined f

a CO emission rate of 1 mg/kJ (3.7 × 10–5 ounce/Btu) by a previous experimental study (R

and McCrillis 1994) and a furnace power output of 35.2 kW (120 000 Btu/h) from another s

(Lee 1990). The furnace was run under a 10-minute on/off cycle. Figure 3d illustrates the 

source location, furnace, and the four airflow paths that were modeled as cracks located at ei

the top or bottom of the doors. Airflow was assumed to be steady, and the time-dependent tr

port of CO was simulated for 2 h with a one-minute time step. Typically, for coupled CO si

lations, only two exchanges of boundary conditions were required between the multizone 

CFD calculation regimes. The 2 h transient multizone/CFD simulation of CO distribution 

verged at each time step with a convergence criterion of 0.01 and required a total simula

time of about 13 h on a a desktop PC with an Intel 2.66 GHz microprocessor.

Table 2. Predicted Airflow Rates of Airflow Paths in Utility Room 

(Negative Value Shows Outflow from Room)

Airflow Path Path 3 Path 5 Path 6 Path 7

Airflow Rate × 103 m3/s (× 102 ft3/s) 5.5 (19.4) 5.6 (19.8) –0.9 (–3.2) –10.6 (–37

Figure 6. Comparison of calculated infiltration rates using wind pressure coefficie

predicted by the external CFD link, measured data from Holmes (1986, 1994), a

surface-averaged data from Swami (1987) when the wind is out of the west (270°).
Table 2 provides the calculated airflow rates through the door cracks of the utility ro

There were two air inflows to the room: from the storage room through path 3 and from the 

side through path 5. Most of the outflow goes to the kitchen through path 7, and the rest to 

garage through path 6.

With the calculated airflow rates as inputs, CFD0 calculated the time-dependent CO distr

tion in the utility room over 2 h. Local values of CO concentration were provided near the air

paths in the utility room for the CONTAM simulation of the rest of the rooms of the house

shown in Figure 7, at end of the two-hour simulation time the CO levels in the utility room w

non-uniform. The CO concentration was close to zero near paths 3 and 5 due to the inflow 

uncontaminated air. Because path 7 was closer to the CO source than path 6, the local CO l

near path 7 was higher. Moreover, the top portion of the room generally had higher CO le

than the lower, which also contributed to the higher CO concentration near path 7.

Figure 7 only shows one snapshot of the CO distribution at the end of the simulation. In 

the transient CO distribution in the whole house was determined by many of these snaps

from the CFD calculation. Figure 8 illustrates the time-dependent CO profiles in the ut

room, the kitchen and the living room, and the local CO levels near path 6 and 7 by using 

methods. One method was when the whole house was simulated by CONTAM (results tag

by “well mixed” in Figure 8) and the other was when the utility room was modeled by C

and the rest of the rooms by CONTAM (results tagged with “CFD” in Figure 8). When the 

ity room was simulated as a CFD zone, the CO level of the utility room was spatially avera

over the entire room. Figure 8 does not show the results of the bedrooms and the rest of 

house, which were much lower than the three rooms shown in the figure. It was shown that 

levels in the utility room fluctuated every 10 minutes with the on/off cycle of the furn

whether or not CFD was used for the utility room. The simulation using CFD predicted m

variable and higher CO concentration in the kitchen than that in the well-mixed case. This 

ference is due to the high local CO level near airflow path 7 connecting the kitchen and the 

ity room. Path 7 was very close to the CO source, so variations of CO generation rate c

Figure 7. Distribution of CO concentration (mg/m3) in utility room at end of 2 h sim

tion time (1 mg/m3 ≈ 9.9 × 10–7 oz/ft3).
easily affect the CO level in the kitchen. Moreover, the peak level of CO near path 7 was sig

icantly higher than that near path 6, which leads to CO concentrations in the utility room t

highly nonuniform. This example illustrates the importance of the source location and the l

concentration of CO near interzonal connections. 

The CO levels in the non-source zones were similar in magnitude between the CFD 

well-mixed case. However, the small difference in CO levels may be important with respe

occupant exposure. The lines in Figure 8 mark two critical CO levels for occupant expos

The bottom line (“Headache”) corresponds to 233 mg/m3 (2.3 × 10–4 ounce/ft3) (200 pp

Exposure to this level for 1 to 2 hours can cause flu-like symptoms such as headache, lighth

edness, nausea, and fatigue (NIOSH et al. 1996). The upper line (“Coma”) is locate

815 mg/m3 (8.1 × 10–4 ounce/ft3) (700 ppmv). Exposure to this level for one hour or more 

cause more severe CO poisoning, such as progressively worsening symptoms of vomiting, 

fusion, and coma (NIOSH et al. 1996). The well-mixed simulation (without CFD) is show

reach either of the two critical lines about 20 minutes later than that with CFD in both the li

room and the kitchen. A period of 20 minutes may be crucial in some situations relative to o

pant safety and evacuation. 

CONCLUSION

This paper introduced and demonstrated the new CFD features of CONTAM 3.0: the exte

CFD link for simulations of airflows around a building exterior, and the internal CFD link 

embedding a CFD zone within a CONTAM airflow network. The external CFD link predicts 

tributions of wind pressures and contaminant concentrations outside a building for simula

their effect on indoor contaminant concentrations. The internal CFD zone enables the deta

calculation of air and contaminant concentrations in a room or region of a building for which 

well-mixed assumption is insufficient, with the remaining rooms handled with the well-m

assumption that is typical of CONTAM. These new capabilities were demonstrated by mode

Figure 8. Comparison of CO levels in utility room, kitchen, and living room of the ho

with and without CFD simulations.
a generic low-rise residential house. It was shown that the external CFD link generally provid

better estimation of wind pressure profiles and air infiltration than the simulations without C

Moreover, the embedded CFD zone is very useful for analysis of short-time contaminant tr

port, especially for evaluation of occupant exposure. The lack of experimental validations fo

specific house studied in this paper limits the ability to fully quantify the improvements in pre

tive accuracy for the CFD analysis relative to a multizone model. Further experimental studie

definitely needed to obtain such data, which will presumably improve the understanding of t

calculation methods. Nevertheless, the new CFD feature of CONTAM 3.0 provides a us

capability for building ventilation design and indoor air quality analysis. 

ACKNOWLEDGMENTS

The authors would like to express their appreciation for the numerous suggestions and c

ments from George N. Walton and Steven J. Emmerich from the National Institute of Stand

and Technology throughout this study, and the invaluable contributions of Brian J. Polid

also of NIST, in developing the tools needed to perform this study.

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