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Building and Environment 37 (2002) 865871

www.elsevier.com/locate/buildenv

Integrating CFD and building simulation

M. Bartaka, I. Beausoleil-Morrisonb, J.A. Clarkec; , J. Denevd, F. Drkala, M. Laina,I.A. Macdonaldc, A. Melikove, Z. Popiolekf, P. Stankovd

aTechnical University in Prague, Czech RepublicbCETC, Natural Resources Canada, Canada

cESRU, University of Strathclyde, Scotland, UKd Technical University of Soa, Bulgaria

eCIE Energy, Technical University of Denmark, DenmarkfSilesian Technical University at Gliwice, Poland

Abstract

To provide practitioners with the means to tackle problems related to poor indoor environments, building simulation and computational

uid dynamics can usefully be integrated within a single computational framework.

This paper describes the outcomes from a research project sponsored by the European Commission, which furthered the CFD modelling

aspects of the ESP-r system. The paper summarises the form of the CFD model, describes the method used to integrate the thermal and

ow domains and reports the outcome from an empirical validation exercise. ? 2002 Published by Elsevier Science Ltd.

Keywords: Building performance simulation; Computational uid dynamics; Integrated modelling; Empirical validation

1. Introduction

Within building energy simulation two modelling ap-

proaches are extant: zonal networks and computational uid

dynamics.

Within the former method, as implemented within the

ESP-r system [1], the building and its air handling plant are

treated as a collection of nodes representing rooms (or parts

of rooms), equipment connection points, ambient conditions

etc. Inter-nodal connections are then dened to represent

components such as cracks, doors, windows, fans, ducts and

pumps. Each component is assigned a model that gives itsmass ow rate as a function of the prevailing pressure dif-

ference. Consideration of the conservation of mass at each

node leads to a set of non-linear equations that can be inte-

grated over time to characterise the ow domain.

Although well adapted for building energy application,

the nodal network method is limited when it comes to

consideration of indoor comfort and air quality: because

momentum eects are neglected, intra-room air movement

cannot be studied; and, as a result of the low resolution, local

Corresponding author.

E-mail address: joe@esru.strath.ac.uk (J.A. Clarke).

surface convection heat transfer is poorly represented. To

overcome these limitations, it is necessary to conate CFD

and building simulation. This paper describes the approach

taken within the ESP-r system [2] and reports on the re-

sults from an empirical validation exercise utilising a testing

facility established at the Technical University in Prague.

2. CFD modelling

ESP-rs CFD model comprises seven coupled partial dif-

ferential equations. The velocity distribution of the roomow is obtained from the three momentum equations corre-

sponding to the three spatial axes. The continuity equation

is modied to become a pressure correction equation which

is then employed to obtain the pressure distribution by uti-

lizing the well-known SIMPLEC algorithm [3]. Turbulence

is accounted for by the standard version of the k turbu-

lence model [4] together with appropriate treatments for the

conditions at solid boundaries (as described later). A sep-

arate transport equation for temperature distribution in the

room is then introduced and is coupled with the velocity

eld by means of the Boussinesq approximation. To enable

equation solution, the room is subdivided into orthogonal

0360-1323/02/$ - see front matter? 2002 Published by Elsevier Science Ltd.

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866 M. Bartak et al./ Building and Environment 37 (2002) 865871

control volumestypically between 30,000 and 100,000 in

numberand the above-mentioned variables obtained nu-

merically for each volume.

The basic mathematical model has been rened in order

to account for a number of practical problems relating to

room indoor air quality and thermal comfort. A concentra-

tion equation, governing the distribution of water vapour inthe room, has been added to determine the spatial variation

of relative humidity as a function of any specied humid-

ity source. A further concentration equation has been estab-

lished to represent the distribution of carbon dioxide and is

able to model occupant respiration under dierent metabolic

rates.

The freshness of the air at each grid is obtained through a

local mean age of air parameter which denes the average

time required for air to reach a given point after it enters

the room [5]. The corresponding equation is derived from a

method proposed by Sandberg and Sjoberg [6] and Davidson

and Olsson [7]. In its nal form it is given by

@

@x

u

l+t

t

@

@x

+

@

@y

v

l+t

t

@

@y

+ @

@z

w

l+t

t

@

@z

= 1;

where is the local mean age of air, u; v and w are the

velocity components, the air density, ; t the physical

and turbulent viscosity, andl; t the laminar and turbulent

Schmidt numbers.

Since it is important to accurately calculate the tempera-

ture gradient in stratied ows arising, for example, in rooms

with displacement ventilation, and because the basic math-

ematical model has diculty in correctly accounting for the

interaction between buoyancy and turbulence, the general-

ized gradient diusion hypothesis (GGDH) model is used

as introduced by Daly and Harlow [8]. This requires the in-

troduction of a new production term for buoyancy, Pb, of

the turbulent kinetic energy,k:

Pb= 1

T+ 273:150:15g

k

t

@u

@z +

@w

@x

@T

@x

+t @v

@z

+@w

@y@T

@y

+ 2t@w

@z

2

3

k@T

@z ;

where g is the gravity acceleration acting along the z-axis

andTthe air temperature (

C).

3. Integrating CFD and building simulation

The solvers for the building thermal, HVAC, network

air ow and CFD equations act co-operatively in the man-

ner illustrated in Fig. 1. To ensure that the CFD turbu-

lence model is appropriately congured at each time-step, a

conation controller [9] is introduced in order to improve

the simulation accuracy [10]. At the start of a time-step,

the zero-equation turbulence model developed by Chen and

Xu [11] is employed in investigative mode to determine

the likely ow regimes at each surface. The eddy viscosity

distribution to result is then used to initialise the k and

elds and a second simulation performed for the time-step.

This process repeats at each computational time-step.The nature of the ow at each surface is evaluated from

the local Grashof (Gr) and Reynolds (Re) Numbers as de-

termined from the investigative simulation. The Grashof

Number (the ratio of the buoyancy and viscous forces) indi-

cates how buoyant the ow is adjacent to the surface, while

the Reynolds Number (the ratio of the inertial and viscous

forces) indicates how forced is the ow. The following con-

ditions are relevant:

Gr=Re21: forced convection eects overwhelm free

convection;

Gr=Re21: free convection eects dominate;

Gr Re2: both forced and free convection eects are

signicant.

Based on the outcome from the above condition test at each

surface in the CFD model, the following procedure is in-

voked.

Where buoyancy forces are insignicant overall, the

buoyancy term in the z-momentum equation is dropped to

improve solution convergence.

For a surface where free convection predominates, the

log-law wall functions are replaced by functions developed

by Yuan et al. [12] and a Dirichlet 1boundary condition im-

posed where the surface is vertical; otherwise a convectioncoecient correlation is prescribed and a Neumann (see

footnote 1) boundary condition imposed (this means that

the thermal domain will inuence the ow domain but not

the reverse).

For a surface where convection is mixed, the log-law wall

functions are replaced by a prescribed convection coecient

and a Robin (see footnote 1) boundary condition imposed.

For a surface where forced convection predominates, the

ratio of the eddy viscosity to the molecular viscosity (t=),

as determined from the investigative simulation, is examined

to determine how turbulent the ow is locally:

t=6 30: the ow is weakly turbulent; the log-law wall

functions are replaced by a prescribed convection coe-

cient and a Neumann boundary condition is imposed;

t= 30: the log-law wall functions are retained and a

Dirichlet boundary condition is imposed.

The iterative solution of the ow equations is initiated for

the current time-step. For surfaces where hc correlations are

1 Dirichlet conditionxed temperature = s; Neumann condition

xed heat ux k(@=@n) = q; Robin conditionheat ux proportional to

the local heat transferk(@=@n) = hc( s).

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868 M. Bartak et al./ Building and Environment 37 (2002) 865871

Fig. 2. Coupling network ow and CFD models.

also that iteration may be invoked to resolve problematic

couplings between domains.

It is also possible to operate on the basis of partially

matched schemes. For example, the building model might

comprise of several zones, with only a subset addressed by

CFD. At the same time, a ow network may be linked to one

or more CFD domains, have nodes in common with some,

but not all, of the other zones comprising the building model

and have extra nodes to represent zones and=or plant compo-

nents that are outwith the modelled building portion. Each

part of such a model would then operate on the basis of best

available information (e.g. a zone with no matched air ow

model would utilise its user-specied inltration=ventilation

rates).

4. Empirical validation

To test the new CFD model as installed within ESP-r,

a laboratory facility was designed and built. The test room

allows control of its boundaries via surface mounted heat-

ing and cooling panels and by encapsulating it within an

outer room whose environmental condition can be sepa-rately controlled. Finally, the size and=or position of the

room inlet=outlet openings can be modied and the inlet air

condition varied via a dedicated HVAC system.

A series of experiments were conducted correspond-

ing to isothermal mixing ventilation and non-isothermal

displacement ventilation and the following data

collected:

1. the distribution of the local mean age of air (isothermal

case);

2. the air temperature distribution (non-isothermal case);

3. room comfort parameters (non-isothermal case).

The distribution of the local mean age of air param-

eter was measured using the tracer-gas concentration

decay method, while the vertical air temperature pro-

les were measured with multi-point probes using NTC

thermistors. The mean air velocity, the standard de-

viation of velocity uctuations, the turbulence inten-

sity and the draught rating were all measured by a

multi-channel low velocity anemometer. Details on

the experimental set-up have been reported elsewhere

[1618].

4.1. LMA test result

Two 3D CFD models with a coarse (1089 control vol-

umes) and a ne grid (24,300 control volumes) were com-

pared with the experimental data. Fig. 3 shows the results.

As can be seen, the predictions are up to 20% higher than

the measurements: a result that is consistent with results

obtained by some researchers (e.g. [19]) and better than

those obtained by others (e.g. [7]). Possible reasons for

the discrepancy include the so-called repeatability deviation

(6.5%), the experimental accuracy (5%), the uncertainty in

determining the volume of the test room (5%) and the use

of mean velocities within the CFD model in place of instan-

taneous values.

The ne and course grids produce comparable results,

with the former, as expected, being closer to the experimen-

tal data. That said, the results from the coarse grid gave suf-

cient accuracy for the case studied (mixing ventilation, a

single air supply=extract and isothermal conditions).

4.2. Buoyancy test result

This test assessed the results corresponding to the use of

the standard and improved forms of the k model when

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M. Bartak et al./ Building and Environment 37 (2002) 865871 869

Fig. 3. Comparisons between predicted and measured local mean age of air.

Fig. 4. Comparisons between predicted and measured temperature proles.

applied to buoyant ows. The improved k model was

based on the generalized gradient diusion hypothesis. The

results from 3D computations are compared with the mea-

sured vertical temperature proles as shown in Fig. 4.

The numerical results show good agreement except for

locations close to the oor where the adiabatic condition

was not maintainable within the experimental set-up. For

all other locations the agreement is within the measurement

uncertainty (0:5

C). The improved k model performed

better than the standard model in terms of both convergence

speed and the prediction of the temperature distribution.

4.3. Comfort test result

Measurements were performed using a Dantec multi-

channel low velocity anemometer. The comparison with

predictions is given in Table 1. The air mean velocity and

temperatures show good agreement for all measured points,

with any dierences being within the measurement accu-

racy (0:03 m=s and 0:5

C, respectively). The standard

deviation of velocity uctuations and turbulence intensity

show disagreement at locations where the anemometer is

outwith its recommended range of operation (i.e. where the

mean velocity falls below 0:05 m=s). At these points, the

anemometer uncertainty is high, while the numerical model

is likely to overestimate the turbulence kinetic energy yield-

ing values of turbulence intensity that are too high. On the

other hand, at low mean velocities there will be no discom-

fort sensation (the draught rating is dened to be zero for

mean velocities below 0:05 m=s). At the locations where the

mean velocities are higher then 0:05 m=s (here close to the

oor), the agreement between the measured and simulated

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870 M. Bartak et al./ Building and Environment 37 (2002) 865871

Table 1

Predicted vs. measured parameters related to comfort

Parameter Measured Predicted

Measurement position: X = 1:340 m; Y= 1:8 m

h (m) 1.7 1.1 0.6 0.1 1.7 1.1 0.6 0.1

w (m=s) 0.04 0.03 0.04 0.57 0.05 0.03 0.04 0.54

STD (m=s) 0.007 0.005 0.012 0.109 0.044 0.051 0.058 0.100TI (%) 19.3 17.4 28.8 19.1 92.2 161.2 157.3 18.5

T (

C) 23.7 23.6 23.4 20.0 24.0 23.9 23.8 20.2

DR (%) 0 0 0 66.8 0 0 0 60.5

Measurement position: X = 2:818 m; Y= 1:8 m

h (m) 1.7 1.1 0.6 0.1 1.7 1.1 0.6 0.1

w (m=s) 0.04 0.03 0.05 0.30 0.03 0.03 0.03 0.27

STD (m=s) 0.003 0.003 0.011 0.091 0.052 0.061 0.075 0.086

TI (%) 9.5 9.7 21.8 30.3 159.5 180.7 280.5 32.3

T (

C) 23.8 23.6 23.0 21.2 23.9 23.8 23.4 21.7

DR (%) 0 0 0 35.1 0 0 0 30.0

h= height; w= mean velocity; STD = standard deviation of velocity uctuations; TI = turbulence intensity; T= temperature; DR = draught rating.

data is good after the measurement uncertainty is taken into

account [20].

The rened ESP-r system was also tested by comparing

predictions with physical measurements within an apartment

building located within a multi-storey block of ats in the

urban area of Gliwice in Poland. The apartment employed

a natural ventilating system via individual ducts connecting

each at with outlets on the roof. A system of four indepen-

dent electric heaters was used to heat the at. Power con-

sumption and indoor air temperatures were monitored along

with the local climate parameters (barometric pressure, out-

door air temperature, wind speed=direction and solar radia-tion).

In order to determine the air change rate, the tracer gas

concentration decay method was employed. The air change

rates were measured 15 times under two dierent air tight-

ness condition: for tight windows and for leaky windows.

During the experiment, the weather spanned a wide range

of conditions.

Comparison of the simulation results with the energy con-

sumption measurements showed good agreement in relation

to global heat demand and inltration: the relative error in

the former case not exceeding 14%. For the air inltration

prediction there was also a good agreement with the mea-

surements. Details are given in [21].

5. Output possibilities

On the basis of the multi-variate outputs from an inte-

grated simulation, the spatial and temporal variation of in-

door air quality and thermal discomfort may be assessed

according to relevant standards (e.g. ASHRAE 62-1989,

prENV 1752, ISO EN 7730 and ANSI=ASHRAE 55-1992).

Such assessments might typically be based on indicators

such as

the variation in vertical air temperature between oor and

head height;

the absolute temperature of the oor;

radiant temperature asymmetry;

unsatisfactory ventilation rate;

unsatisfactory CO2 level;

local draught rating index assessed from the coupled in-

uence of air speed, air temperature and turbulence inten-

sity distribution;

additional air speed required to o-set an elevated tem-perature;

thermal comfort assessment based on PMV, PPD and ef-

fective temperature indices;

local mean age of air.

Fig. 5 shows some typical CFD outputs showing the dis-

tribution of velocity, temperature, draught rating and air

mean age for dierent 2D room slices. Taken together, such

performance indicators allow indoor environments to be as-

sessed in terms of the spatial and temporal variation of the

quality and comfort.

6. Conclusions

The CFD module of the ESP-r integrated modelling pack-

age has been rened as part of a collaborative research

project funded by the European Commission with inputs

from related projects. These renements were concerned

with the treatment of complex geometries, blockages (fur-

nishings, equipment etc), buoyancy, ventilation openings,

surface heat transfer and the assessment of the spatial and

temporal variation of thermal comfort and indoor air quality.

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M. Bartak et al./ Building and Environment 37 (2002) 865871 871

Fig. 5. Some typical CFD outputs.

Acknowledgements

The authors are indebted to the European Commission for

their invaluable support.

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