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Research Paper

Analysing ventilation efficiency in a test chamberusing age-of-air concept and CFD technology

K.-S. Kwon a,d, I.-B. Lee a,*, H.-T. Han b, C.-Y. Shin b, H.-S. Hwang a, S.-W. Hong a,Jessie. P. Bitog a, I.-H. Seo a, C.-P. Han c

aDepartment of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences,

Seoul National University, 599, Gwanakno, Gwanakgu, Seoul 151-921, Republic of KoreabDepartment of Mechanical Engineering, Kookmin University, Jeongneung-Dong, Seongbukgu, Seoul 136-100, Republic of KoreacDepartment of Automobile Engineering, Seojeong College, Eunhyeon-myeon, Yangju-si, Gyeonggi-do 482-777, Republic of KoreadResearch Institute for Agricultural and Life Sciences, Seoul National University, South Korea

a r t i c l e i n f o

Article history:

Received 14 January 2011

Received in revised form

13 July 2011

Accepted 30 August 2011

Published online 24 October 2011

* Corresponding author. Tel.: þ82 2 880 4586E-mail address: iblee@snu.ac.kr (I.-B. Lee

1537-5110/$ e see front matter ª 2011 IAgrEdoi:10.1016/j.biosystemseng.2011.08.013

The age-of-air concept can be used to assess the ventilation efficiency of an agricultural

facility. However, experimental research has been limited due to the indirect method of

using unstable tracer-gas and limited instrument. To overcome limitations and increase

applicability, CFD technique was employed from established methodology by user-defined

functions. A three-dimensional chamber was designed to accurately implement and verify

the age-of-air through simulation and tracer-gas experiment under unsteady-state

conditions. In validating the computations of the local-mean-age and local-mean-

residual-lifetime, the results showed similar quantitative and qualitative distributions

with average errors of 9 and 13%, respectively. It could be concluded that the method of

realising the age-of-air via CFD was reasonably well designed and capable of estimating

ventilation efficiency of agricultural facilities under unsteady-state conditions. The results

also showed that when air exchange rate (AER) increased in the target structure, the

age-of-air values decreased, but when comparing air exchange efficiencies, the values had

an opposite tendency. Through the methodology presented in this study, the feasibility of

analysing ventilation efficiency using Age-of-air in agricultural facilities was confirmed

and it will be upgraded for actual application considering characteristics of ventilation

structure. Through the methodology presented in this study, the feasibility of analysing

ventilation efficiency using age-of-air in agricultural facilities was confirmed and it will be

upgraded for actual application considering characteristics of ventilation structure.

ª 2011 IAgrE. Published by Elsevier Ltd. All rights reserved.

1. Introduction level in order to maintain a stable and suitable thermal condi-

Overall understanding of ventilationperformance is important

to promote optimal environmental conditions in agricultural

facilities. During winter, ventilation is controlled at a minimal

; fax: þ82 2 873 2087.).. Published by Elsevier Lt

tion and to reduce energy loss. However,minimumventilation

is prone to create poor environmental conditions because of

the accumulation of moisture, dust and harmful gases and

decreased fresh air. These factors suggest the importance of

d. All rights reserved.

Nomenclature

Cpex tracer-gas concentration at the outlet, when

introduced at a designated point p, ppm

CsupP tracer-gas concentration at a designated point p

coming through the inlet, ppm

CN tracer-gas concentration when the space is filled

with a homogeneous concentration of tracer-gas,

ppm

LMA local-mean-age, s

LMAP local-mean-age at a designated point p, s

LMR local-mean-residual-lifetime, s

LMRP local-mean-residual-lifetime at a designate point

p, s

Q ventilation flow, m3s�1

RMA room-mean-age, s

RMR room-mean-residual-lifetime, s

V volume of structure, m3

< ε > air exchange efficiency

sn nominal time constant, s

< s > room-mean-ventilation-effectiveness

b i o s y s t em s e n g i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3422

assessing ventilation efficiency in a quantitative manner. One

of thewidelyusedandwell established concepts in agricultural

ventilation studies is the “ventilation rate,” which is defined as

the frequency of exchanged air volume per unit time. Base on

this idea, various methods have been studied such as energy

balance model (Kozai. Sase, & Nara, 1980; Chalabi & Bailey,

1989; Fernandez & Bailey, 1992; Boulard et al., 1993; Boulard &

Draoui, 1995; Lee & Short, 2000), tracer-gas method (Bot, 1983;

De Jong, 1990; Lawrence & Whittle, 1960; Okada & Takakura,

1973; Morris & Neale, 1954; Nederhoff, Van de Vooren, &

Udink Ten Cate, 1985), pressure difference model (Boulard,

Meneses, Mermier, & Papadakis, 1996; Kittas, Boulard,

Mermier, & Papadakis, 1996; Papadakis, Mermier, Meneses, &

Boulard, 1996) etc. However, these conventional methods are

poor at evaluating local ventilation. In the latest research by

Hong et al. (2008) and Seo et al. (2009), the tracer-gas decay

methodwas used to analyse local ventilation characteristics in

a multi-span greenhouse and broiler house through CFD,

respectively. However, this also has a weakness in not

guaranteeingventilationefficiency.Theage-of-air conceptwas

proposed by Sandberg (1981) to examine ventilation efficiency

using the terms of local-mean-age (LMA) and local-mean-

residual-lifetime (LMR). These physical parameters can be

used to evaluate fresh air distribution and contamination

evacuation, respectively, and allows local and overall ventila-

tion efficiency to be assessed. Experimental study of age-of-air

has already been reported. Han (1992 and 1999) suggested an

experimental method using Sulphur hexafluoride (SF6) using

a small scale model. However, this method has some limita-

tions, especially with regard to the acquisition of quantitative

and qualitative data and for uncertainties due to the unstable

gas. Nevertheless, these limitations can be overcome through

alternative simulation research like computational fluid

dynamics (CFD). Recently, CFD was employed to analyse

ventilation efficiency using age-of-air in naturally ventilated

livestock buildings (Norton, Grant, Fallon, & W Sun, 2009),

aircraft cabins (Wang, Zhang, Sun, & Wang, 2008) and forced-

ventilation food plants (Chanteloup & Mirade, 2009); however

these studies were restricted to steady-state conditions.

Therefore the technique needs to be upgraded for unsteady-

state conditions so as to be applicable to real situations such

aschangesofexternalweatherorofa forcedventilationsystem

by time-schedule operation.

The main objective of this study was to develop a method

for analysing and understanding ventilation efficiencies using

CFD technology based on the age-of-air under unsteady-state

conditions to improve applicability to analyse agricultural

ventilation. To emphasise the implementation of the age-of-

air concept and the validation of the simulation model,

a three-dimensional chamber with a narrow width was

designed as a preliminary to working with real agricultural

structures. The methodology to realise age-of-air was estab-

lished via user-defined-functions (UDF) coded by C language

in the CFDmodel, and a laboratory experiment was conducted

for validation, with the age-of-air values measured using

a tracer-gas. Using thismethod, the airflowpattern, the values

of LMA and LMR and the local ventilation efficiency of the

target structure were analysed.

2. Materials and methods

Based on the age-of-air concept, CFD technology was used to

compute LMA and LMR values for a three-dimensional test

chamber. FLUENT software (version 6. 3, Fluent Co., New

Hampshire, USA) was used to solve the governing equation in

themodel utilising theUDF through the application of the LMA

and LMR concepts. A tracer-gas experiment was also con-

ducted to validate the accuracies of the simulation model and

case studies for thequantitative computation of LMAand LMR.

2.1. Age-of-air concept

Sandberg and Sjoberg (1983) used the terms “age” and “resi-

dence time” in his comprehensive theoretical framework of

ventilation engineering. The age-of-air is generally defined as

the time it takes for freshair froman inlet to reachadesignated

point inside the structure. Theage-of-air concept assumes that

theageof air at the inlet is theoretically equal to zero, providing

a reflection of the airflow pattern in the ventilated space (Li, Li,

Yang, & Yang, 2003). The fresh air from the inlet flows to an

arbitrary point via various pathways; therefore, the airmust be

described statistically, and the mean value of age-of-air is

defined as the local-mean-age (LMA). The time for air or

a contaminant to move from an arbitrary point to the outlet is

defined as the residence time. The air or a contaminant can

also reach the outlet via various routes; the mean value of

residence time to reach the outlet from a designated point is

defined as the local-mean-residual-lifetime (LMR). The

concepts of age-of-air are presented in Fig. 1.

The advantages of the age-of-air concept can be illustrated

using the following example: in the recirculation or stagnant

region, a higher air velocity does not always result in an

increased net fresh air exchange. The conventional method of

Fig. 1 e The concepts of LMA and LMR based on the age-of-air (continuous line, statistical mean values of various courses

“age-of-air” or “resident-lifetime”; *, exemplary LMA and LMR of the arbitrary point “P” with length of the each line

indicating the time duration; dotted line, various courses of “age-of-air” or “resident-lifetime”).

b i o s y s t em s e ng i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3 423

evaluating ventilation rates cannot easily express these kinds

of scenarios, and they can be easily misunderstood. The age-

of-air values can be determined considering real behaviours

of air or the airflow pattern. Therefore, the LMA can be

adopted to quantify and evaluate the fresh air distribution

characteristics, while the LMR can be adopted to quantify and

evaluate the exhaust potential for contaminants. The average

LMA value of the whole designated space is called the room-

mean-age (RMA), while the average value of the LMR refers

to the room-mean-residual-lifetime (RMR); the values of RMR

and RMA should be mathematically identical and are called

the room-mean-ventilation-effectiveness. The ventilation

flow Q (m3s�1) over the volume of the structure V (m3) is

defined as a nominal time constant sn (s), and its reciprocal

value is defined as the ventilation rate (m3s�1)

sn ¼ VQ

(1)

To calculate LMA and LMR, three methods are available,

each with a different tracer gas injection technique; the pulse

method, step-upmethod, and step-downmethod (also known

Fig. 2 e Concentration curves measured at a designat

as the decaymethod (Sandberg& Sjoberg, 1983)). The LMA and

LMR values can be calculated based on the moments of

concentration versus the time curves of contaminants or

tracer gas. This study used the step-upmethod, and Eq. (2) and

(3) shows the formula used to calculate the LMA and LMR

values using a tracer gas approach respectively. Themeasured

tracer gas data were fitted as concentration history versus

time (Fig. 2), and the first moment of the concentration curve

was calculated according to the following equations.

LMAp ¼ZN0

1� Csup

p ðtÞCN

!dt (2)

LMRp ¼ZN0

�1� Cp

exðtÞCpexðNÞ

�dt (3)

In the Eqs. (2) and (3), Cpex is the tracer-gas concentration at

the outlet which is introduced at a designated point p; CsupP is

the tracer-gas concentration at a designated point p coming

through the inlet; CN is the tracer-gas concentration when the

space isfilledwithahomogeneous concentrationof tracer-gas;

ed point “p” after step-up injection of tracer gas.

Fig. 3 e The designated study points for the tracer-gas experiment and CFD simulation (C, points used in the tracer-gas

experiment; B, additional points used in the CFD simulation).

b i o s y s t em s e n g i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3424

LMAP is the local-mean-age at a designatedpoint P, s; and LMRP

is the local-mean-residual-lifetime at designated point P, s.

The RMA and RMR values are calculated using Eq. (4) when

the tracer gas is injected using the step-up method.

RMA ¼ RMR ¼ QV

ZN0

t$

�1� Csup

ex ðtÞCN

�dt (4)

Sandberg and Sjoberg (1983) defined the air exchange effi-

ciency< ε > (%) as the ratio of the nominal time constant sn (s)

to double the room-mean-ventilation-effectiveness < s > (s).

The air exchange efficiency is a barometer indicating the

efficiency and speed of fresh air exchange. This value is

described in Eq. (5) and has a value of 0.5 in the case of

complete mixing.

< ε >¼ sn2 < s >

(5)

2.2. CFD simulation tools

In this study, GAMBIT (version 2. 4, Fluent Co., New Hamp-

shire, USA) was used as a preprocessing tool to define the

computational domain, form the meshes of the control

volume, and establish the boundary conditions. The total

number of cells for the CFD model was approximately

1,280,000. To achieve an efficient and economic design,

Fig. 4 e Scenes of the installed tracer-gas experiment set; (a) des

detector and (b) shows the plastic ball installed at point 3-5 for

tetrahedral meshes were designed near the “42 designated

tracer gas exhausts” which were considered very critical in

influencing the result of the numerical computation. Hex-

ahedral meshes were used for the rest of the volume. With

regard to accuracy and calculation time, the inlet, outlet,

a nearby side wall, and the 42 designated tracer gas exhaust

points were designed with 20 mm sizes. The qualities of the

designed meshes based on the average equiangular senses

also had an effect on the calculation, convergence, and

accuracy of the result. In this study, the quality of the equi-

angle skewness (QEAS; FLUENT manual, 2009) value ranged

from 0.5 to 0.75, within the acceptable mesh quality range.

As a main module for numerical analysis, FLUENT (version

6. 3, Fluent Co., New Hampshire, USA) was used for the

calculations of the nonlinear differential NaviereStokes

Equation using the finite volume method, which was then

converted into an algebraic equation to analyse flow problems

using a numerical algorithm. The governing equations for

solving flowproblems consist of the conservation equations of

mass, momentum and energy.

2.3. Tracer-gas experiment for validation of CFD

The validation experiment for the designed simulation was

conducted in an acrylic test chamber whose shape and size

were the same as those in the CFD simulationmodel. In all the

cribes the monitoring of tracer-gas at each point using gas-

the measurement of LMR.

Fig. 5 e Flowcharts diagram of UDF code for computing LMA and LMR values.

b i o s y s t em s e ng i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3 425

three geometric cases, the inlet located at left-upper section

was a slot of 0.025 m high; whereas the location of outlet was

designed differently for each case (the height was the same

with inlet and thewholewidthwas 0.1m consideringminimal

space for experiment). The slot outlet for Case 1 was located in

the upper right section; that of Case 2 was in the lower right

section; and that of Case 3 was in the lower left section. The

designated points for the validation test are illustrated in

Fig. 3. As wall friction could influence the results, partial

points for validation located in the central section of the CFD

simulation model were chosen.

The airflowwas injected using a blower and flow-controller

system with the flow controlled using a variable resistor. The

velocity-magnitudes at the designated points were measured

to validate the accuracy of the CFD simulation model by mesh

formation through the multi-channel hot-wire anemometer

(Kanomax, Japan). The qualitative flow pattern was filmed

using a smoke generator (Fog machine, Rosco, USA) and laser

visualisation device (GAM-1700, Intech system, Korea). The

Table 1 e Constant input values for the CFD simulationmodel.

Content Value Unit

Operating temperature 288.16 K

Gravitation acceleration 9.81 m s�2

Specific heat of air 1006.43 J kg�1 K�1

Density of air 1.225 kg m�3

Viscosity of air 1.86 � 10�5 kg m�1 s�1

Molecular weight of air 28.966 g mol�1

Specific heat of CO2 840.37 J kg�1 K�1

Density of CO2 1.788 kg m�3

Viscosity of CO2 1.37 � 10�5 kg m�1 s�1

Molecular weight of CO2 44.01 g mol�1

tracer-gas experiment was performed in order to validate the

age-of-air values computed using the programmed UDF.

Carbon dioxide (CO2) was chosen as the tracer-gas and its

concentration was measured at each designated point every

secondusingagasdetector (Multi-RAE,Germany). Basedonthe

experimentofHan,Choi, and Jang (2001), in thecaseofLMA,the

tracer-gas was introduced at the inlet via the step-up method;

gas is injected to reach an arbitrary concentration from a base

concentration, and the concentration of CO2 was simulta-

neously measured at 20 points (Fig. 3). The LMA values were

finallycalculatedusing themomentsof theconcentration-time

curve using gas concentration data from the gas sampler

devices. In the case of LMR, the tracer gas was introduced at

each designated point. To assume two-dimensional flow and

reduce the influence of the width of the acrylic chamber,

a plastic ball with eight holes punched equidistantly around

a diametric plane was installed, and the tracer-gas was intro-

duced through the holes via the step-up method (Fig. 4). The

LMRvalueswere calculated in the samewayas the LMAvalues.

Themass of the tracer-gas was the same as that of the inhaled

quantity of gas at the detector, in order to retain the mass

balance in the acrylic chamber and reduce the influence on

airflow patterns. The validation experiment in the laboratory

was conducted based on 2.0 AER in the acrylic chamber.

Table 2 e Classification of cases according toconfiguration and AER.

Configuration Case

0.5 AER 1.0 AER 2.0 AER

Case 1 Case 1-1 Case 1-2 Case 1-3

Case 2 Case 2-1 Case 2-2 Case 2-3

Case 3 Case 3-1 Case 3-2 Case 3-3

b i o s y s t em s e n g i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3426

2.4. CFD simulation modelling procedures

The CFD simulation consisted of three steps. The airflow

pattern was computed first in order to reduce calculation

time. Second, once the airflow computation had converged,

the tracer-gas was introduced via the step-up method. CO2

was injected at the inlet to determine the LMA values as

a source term of the governing equation, while the each gas

was introduced at 42 designated internal points (Fig. 3) to

determine each LMR value. Third, LMA and LMR values were

computed employing UDF in the main module, FLUENT. The

user-defined-function (UDF) code allows for the custom-

isation of FLUENT and can strengthen its capabilities for

particular simulation modelling. Through the developed UDF

code, several commands were operated to determine age-of-

air values. The concentration of tracer-gas at each cell of the

computational domain was monitored, and numerical

calculation was carried out with the monitored data. After

monitoring the concentration data, FLUENT allocated the

data to the user-defined memory (UDM) embedded in

FLUENT after every iteration according to a command in the

UDF code. After completing these steps and applying the

custom-field-function, the method of computing the values

of LMA was finally established based on the measured

concentration of tracer gas in the UDM. In the case of LMR,

the tracer gases were introduced at the 42 designated points,

Fig. 6 e Results of the visualisation test, computed qualitative a

distribution of velocity magnitudes for (a) Cases 1-3, (b) Case 2-

simulation result). (For interpretation of the references to colou

version of this article.)

and the concentrations were monitored at the outlet.

Numerical calculation was again performed by allocating the

monitored values to the UDM and applying the custom field

function. The computed values through the mentioned three

steps were continuously written in the text-file during

simulation until the concentration of tracer gas at each point

reached a particular gas concentration; i.e., when the

concentration reached equilibrium. If the monitored values

converged, the numerical calculation was stopped to avoid

loop operation. This technical method for the computation

of LMA and LMR values in the CFD model is described in

Fig. 5.

The turbulence model employed in this study was the

standard k-ε model. It has been used widely for practical

engineering flow calculation since it was first introduced by

Launder and Spalding (1974). The robustness, economy and

reasonable accuracy for a wide range of turbulent flows

explain its popularity in industrial flow and heat transfer

simulations (FLUENT manual, 2009). The time step size was

chosen to be 1 s based on the results of preliminary grid

independence tests. To analyse the changes in ventilation

efficiencies, the inlet conditions were set to a 0.5 air exchange

rate (internal volume) per minute (AER); 0.36m s�1 of velocity-

magnitude at the inlet part, 1.0 and 2.0 AERs at each structure

were entered as designed quantity of flow. The constant input

values for the CFD simulationmodel are shown in Table 1, and

irflow pattern in the CFD simulation model, and the

3 and (c) Case 3-3 (grey, experiment result; red, CFD

r in this figure legend, the reader is referred to the web

b i o s y s t em s e ng i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3 427

the classification of each case based on the configuration and

AER value is shown in Table 2.

3. Results and discussion

3.1. Validation of the accuracy of the mesh structure inthe CFD simulation model

Fig. 6 shows the results of the visualisation test, the computed

qualitative flow pattern created in the CFD simulation model,

Fig. 7 e Validation of the computed values in CFD and the mea

experiment result; red, CFD simulation result). (For interpretatio

is referred to the web version of this article.)

and the distribution tendency of the measured velocity

magnitudes of Cases 1-3, 2-3, and 3-3 (when AER was 2.0).

In Case 1-3, the smoke from the inlet passed straight

through to the outlet, while the rest of the flow formed eddies

in the central section of the acrylic chamber, although they

were smaller than those in Cases 2-3 and 3-3. The smoke

travelled to the right-lower outlet in the form of small and

slow eddies (Case 2-3) but formed larger eddies at the left-

lower outlet. In the CFD simulation of Case 1-3, fresh air

flowed straight through the upper side wall from the inlet. In

Case 2-3, fresh air from the inlet moved to the right-lower

sured experimental values for LMA and LMR. (grey,

n of the references to colour in this figure legend, the reader

b i o s y s t em s e n g i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3428

exhaust in the form of small and slow eddies. In Case 3-3,

a relatively large eddy was generated in the internal structure

compared to those in the Case 2 series, with fresh air flowing

through the sidewalls towards the left-lower exhaust.

A relatively slow air stream influenced by the large eddy in the

centre of the internal structure was also observed. These

visualisation results from the test chamber showed that the

flow pattern of each case was similar to the flow pattern of the

simulation model. Therefore, it was concluded that the CFD

simulationmodel can efficiently describe the flow pattern and

tendency in a qualitative manner.

The experiment to measure velocity-magnitude was con-

ducted for 10 min and the values were calculated on the basis

of mean velocity. The average differences in velocity-

magnitude at each point between CFD and experiment, were

0.03m s�1 (Case 1-3), 0.06m s�1 (Case 2-3), and 0.07m s�1 (Case

3-3). Comparing the correlation coefficient, Case 1-3 was 0.92,

Case 2-3 was 0.85, and Case 3-3 was 0.96, showing that the

experimental and CFD simulation results were similar, indi-

cating that the accuracy of CFD simulation for the designed

mesh structure (size 20 mm) was appropriate. This suggests

that the simulation could accurately describe theflowpatterns

and physical characteristics in a quantitative manner.

Fig. 8 e Computed CFD results of the

3.2. Validation of the CFD simulation forimplementation of the age-of-air concept

The results of the computed LMA and LMR values from the

CFD simulation were compared with the measured results of

the tracer-gas experiment when the AER was 2.0. The distri-

butions of LMAs and LMRs at designated points (Fig. 7) were

well related except for those for the point at which the airflow

was stagnated such point 3-3, 3-4, 3-5 to point 4-3, 4-4, 4-5. The

average error in each case was 30.0 s (Case 1-3), 21.8 s (Case

2-3), and 26.0 s (Case 3-3) for LMA and 43.1 s (Case 1-3), 65.7 s

(Case 2-3), and 48.6 s (Case 3-3) for LMR. Correlation coefficient

values were 0.93 (Case 1-3), 0.91 (Case 2-3), and 0.92 (Case 3-3)

for LMA and 0.96 (Case 1-3), 0.84 (Case 2-3), and 0.93 (Case 3-3);

it could be concluded that mutual variances was statistically

explained well.

In considering the results from experiment and CFD, we

need to take note of the errors associated with the devices for

detecting tracer-gas, and the irregular inlet flow and CO2 from

the blower. In the tracer-gas experiment, the response of gas

detector was shown to be delayed by 5e10 s. The pressure of

CO2 tank also tended to drop and needed to be controlled by

manual operation to keep pressure equilibrium. In a previous

LMA distribution of each case.

Fig. 9 e Computed CFD results of the LMA distribution of each case.

b i o s y s t em s e ng i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3 429

study, Demmers, Burgess, Phillips, Clark, and Wathes (2000)

also indicated that the experimental measurement of tracer

gas is very complex because it requires conditions of complete

mixing within the ventilated space. He pointed out some

accuracy uncertainty exists with the tracer gas method, such

as when the internal volume is not perfectly mixed, causing

the results to become less accurate due to variations in the

measurement of internal concentration From this it can be

judged that the CFD simulation and its UDF code for realising

and computing LMA and LMR values performed reasonably

Table 3 e Computed LMA values (s) of some selected points.

P1e2 P1e6 P3e3 P3e5 P4

Case 1-1 174.1 134.0 264.8 338.1 28

Case 1-2 94.3 73.9 266.3 280.3 30

Case 1-3 49.8 37.6 250.7 256.9 23

Case 2-1 146.0 111.0 236.8 308.8 28

Case 2-2 80.0 61.0 234.3 264.5 26

Case 2-3 42.2 31.2 237.2 244.6 23

Case 3-1 115.6 90.3 233.1 305.6 30

Case 3-2 66.6 51.0 251.5 268.2 25

Case 3-3 32.7 24.4 206.1 206.6 19

with similar distributions and high correlation coefficient

between variances.

3.3. CFD computed LMA and LMR values

Fig. 8 and 9 describe the contours of the computed distribu-

tions of LMAs and LMRs, respectively. The distributions of

LMA and LMR were generally similar to the shape of the

computed airflow pattern in each case because the transport

of the tracer gas was physically dominated by the airflow

e4 P6e2 P6-6 Ave. Std. Max.

9.5 264.1 201.3 248.8 66.9 367.9

7.5 113.3 115.1 171.2 79.2 314.7

4.8 56.3 56.9 112.3 74.7 256.9

0.4 196.5 175.0 210.3 70.9 331.1

6.0 93.3 99.0 148.5 77.9 272.5

7.7 47.2 45.7 93.0 70.6 244.6

5.0 112.3 102.1 174.4 79.3 322.0

7.2 58.1 54.7 127.0 78.5 268.2

5.9 29.3 25.7 69.3 60.0 210.7

Table 4 e Computed LMR values (s) of some selected points.

P1e2 P1e6 P3e3 P3e5 P4e4 P6e2 P6-6 Ave. Std. Max.

Case 1-1 169.8 102.3 308.9 559.8 393.4 411.1 241.5 302.3 158.0 754.8

Case 1-2 92.5 79.6 533.5 394.8 591.6 94.6 114.8 205.0 155.8 659.1

Case 1-3 46.9 56.1 153.5 436.3 278.3 52.4 69.7 132.5 114.0 498.5

Case 2-1 154.4 71.1 316.4 694.4 727.0 241.2 193.5 262.5 200.8 771.1

Case 2-2 81.5 57.0 609.7 366.1 471.6 88.1 107.8 180.5 155.4 615.7

Case 2-3 49.8 41.9 221.1 457.0 590.7 59.5 68.0 119.9 130.8 590.7

Case 3-1 155.5 84.3 471.7 619.5 480.7 8.5 14.0 200.4 181.8 719.0

Case 3-2 80.1 57.1 587.4 369.0 405.1 2.0 22.0 149.1 144.5 587.4

Case 3-3 45.7 39.8 321.4 418.9 261.8 0.5 31.6 90.5 98.7 418.9

Table 6 e Computed AER (s) according to the age-of-airvalues.

DesignedAER

Nominaltime constant (s)

CFDcomputed AER

b i o s y s t em s e n g i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3430

pattern. The values of LMA for Case 1-1 were significantly

greater than for the other cases at every point. This was

because fresh air flowed from the inlet straight through the

upper side wall by the “short-circuiting” phenomenon, which

meant that the main air stream had limited influence on the

internal airflow. This situation decreased the contribution of

the fresh air from the inlet to the flow pattern and resulted in

the formation of stagnant air inside the structure. Compared

to Cases 2-3 and 3-3, the overall values of LMA of Case 1-3,

with the same inlet conditions, were relatively larger because

of short-circuiting. As mentioned above, the LMR values for

Case 1-1 were also significantly greater than those for the

other cases at every point. This obviously showed that the

distribution ability of the fresh air and its potential to elimi-

nate contaminants inside the structure was very poor. The

distributions of LMAs and LMRs showed that the results were

almost similar to shape of the airflow pattern in each case.

However, when the AERwas 2.0, the distributions of LMAs and

LMRs were very similar to each other. These situations arise

because the airflow which formed the internal part of the

simulation domain flowed straight and around the right wall,

producing relatively strong circulating flows. Based on the

qualitative visualisation results of the LMA and LMR distri-

butions, it was concluded that the ventilation efficiencies of

Cases 2 and 3 were superior to that of the Case 1 series;

however, when the AER was 2.0, the results were similar.

Tables 3 and 4 show the quantitative values of the

computed LMAs and LMRs of some selected points in the

corners andmiddle of the simulation structure respectively. It

was found that the LMA and LMR of the areas containing an

eddy were generally greater than those of the other sections

because the air trapped in the eddy disturbed the supply of

fresh air and the emission of contaminant air. This phenom-

enon is shown at representative points 3-3, 3-5, and 4-4 in

Tables 3 and 4. In the case of LMA value at point 3-5, the values

for Case 2-1 and 3-1 were bigger than that for Case 1-1 by 109%

and 110% and in the case of LMR, the values of Case 2-1 and 3-1

were smaller than that of Case 1-1 by 81% and 90%. The

Table 5 e Results of room mean ventilation effectiveness(s) for each case.

Room Mean Age

Case 1-1 252.9 Case 2-1 230.1 Case 3-1 190.4

Case 1-2 170.3 Case 2-2 149.5 Case 3-2 120.8

Case 1-3 99.2 Case 2-3 87.2 Case 3-3 63.5

average LMA and LMR values of each case were smallest for

Case 3-3, indicating that the configuration of Case 3-3 was

superior with regard to fresh air distribution and contaminant

exhaust. The standard deviation values of the LMAs and LMRs

were also found to be minimal in Case 3-3, implying that Case

3-3was also superior in terms of the homogeneity. In contrast,

the standard deviation values of LMAs and LMRs in Case 1-1

were considerably greater than those for the other cases,

illustrating a poor uniformity because of lower efficiency in

terms of fresh air distribution and elimination of contami-

nated air. In all cases, it was concluded that lower LMA and

LMR values were obtained at higher ventilation rates. It is

notable that the LMA and LMR values were lower along the

main air stream, easily observed in the distribution results of

LMAs and LMRs because the areas throughwhich themain air

stream flowed had comparatively greater velocity fields.

3.4. CFD computed RMA and RMR values

The results obtained in the present study are shown in

Table 5. The RMA values in the Case 1 series were relatively

higher than those in the Case 2 and 3 series. It is also observed

that as the ventilation rate increased, the room-mean-

ventilation-effectiveness value was decreased. If the room-

mean-ventilation-effectiveness value is the barometer of the

total distribution ability of fresh air or the ability to eliminate

contaminants, the values of Cases 2-1 and 3-1were superior to

that of Case 1-1 by 108% and 131%, respectively.When the AER

was 1.0, Case 2-2 had a value 114% greater than that of Case 1-

2, and Case 3-2 was 141% greater. When the AER was 2.0, Case

2-3 was 114% and Case 3-3 was 156% greater than was Case 1-

Case 1-1 0.5 122.0 0.49

Case 1-2 1.0 61.6 0.97

Case 1-3 2.0 31.2 1.92

Case 2-1 0.5 119.7 0.50

Case 2-2 1.0 61.7 0.97

Case 2-3 2.0 30.6 1.96

Case 3-1 0.5 117.4 0.51

Case 3-2 1.0 59.6 1.00

Case 3-3 2.0 29.9 2.01

Fig. 10 e Air exchange efficiency according to the air AER.

(>: Case 1, ,: Case 2, 6: Case 3).

Table 8 e Air exchange efficiencies (%) of LMRs of someselected points.

P1e2 P1e6 P3e3 P3e5 P4e4 P6e2 P6-6

Case 1-1 0.36 0.60 0.20 0.11 0.16 0.15 0.25

Case 1-2 0.33 0.39 0.06 0.08 0.05 0.33 0.27

Case 1-3 0.33 0.28 0.10 0.04 0.06 0.30 0.22

Case 2-1 0.39 0.84 0.19 0.09 0.08 0.25 0.31

Case 2-2 0.38 0.54 0.05 0.08 0.07 0.35 0.29

Case 2-3 0.31 0.37 0.07 0.03 0.03 0.26 0.23

Case 3-1 0.38 0.70 0.12 0.09 0.12 6.91 4.19

Case 3-2 0.37 0.52 0.05 0.08 0.07 14.90 1.35

Case 3-3 0.33 0.38 0.05 0.04 0.06 29.86 0.47

b i o s y s t em s e ng i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3 431

3. According to these results, Case 3 was the most favourable,

and Case 3-3 had the greatest distribution ability.

3.5. CFD computed AER based on the age-of-air concept

The nominal time constant (sn) is equal to the value of LMA at

the outlet and to the LMR value at the inlet (Han, 1992 and

1999). The results of computed AER according to the LMA

and LMR values are described in Table 6. The computed AER in

every case was similar to the designed ventilation rate. The

results proved that the local and overall ventilation efficien-

cies decreased in contrast to the general view that the venti-

lation efficiencywould improve if the designed AER increased.

Through comparison between the designed and computed

AER, the results were as follows; the difference between them

in the Case 1 series fell by 3% on average and the one in the

Case 2 series dropped by 2.5% exceptionally when the AERwas

1.0 and 2.0. And in the Case 3, the AER based on the Age-of-air

concept rose 2% and 0.5% when the AER was 0.5 and 2.0

respectively.

Due to the designed slot inlet and relatively high ventila-

tion rate, there was not a large difference in computed AER;

however, these rates could be calculated based on the real

airflow pattern. For different design AER values, the practical

AER results will be depend on the configuration.

Table 7 e Air exchange efficiencies (%) of LMAs of someselected points.

P1e2 P1e6 P3e3 P3e5 P4e4 P6e2 P6-6

Case 1-1 0.36 0.46 0.23 0.18 0.21 0.23 0.30

Case 1-2 0.33 0.42 0.12 0.11 0.10 0.27 0.27

Case 1-3 0.31 0.41 0.06 0.06 0.07 0.28 0.27

Case 2-1 0.41 0.54 0.25 0.19 0.21 0.30 0.34

Case 2-2 0.39 0.51 0.13 0.12 0.12 0.33 0.31

Case 2-3 0.36 0.49 0.06 0.06 0.06 0.32 0.33

Case 3-1 0.51 0.65 0.25 0.19 0.19 0.52 0.58

Case 3-2 0.45 0.58 0.12 0.11 0.12 0.51 0.54

Case 3-3 0.46 0.61 0.07 0.07 0.08 0.51 0.58

3.6. Air exchange efficiency

From the computed results of the LMA and LMR distributions,

it was concluded that, if the AERwas higher, the LMA and LMR

values in every section would be lower. However, if the

concept of air change efficiency was applied, the efficiency

had an opposite tendency. Fig. 10 shows the results of air

change efficiency in each case, where the ventilation effi-

ciencies of the Case 3 series were superior to those of Case 1

and Case 2, and the optimal efficiency was observed in the

condition of 0.5 AER. Tables 7 and 8 show the air change

efficiency results for the LMAs and LMRs of selected points.

According to Sandberg and Sjoberg (1983), if the ventilation

produces completely mixed air, the air exchange efficiency

value is 0.5. Like the preceding result, air exchange efficiencies

decreased when AER increased. In the case of LMA, a value

close to 0.5 was observed in the section near the inlet port and

in the path of the main air stream. In the stagnant air region,

there were lower efficiencies.

4. Conclusions

The age-of-air concept has the advantage of analysing venti-

lation efficiency in the real situation because it can be used

practically as a numerical barometer of the overall or local

ventilation efficiencies. The upgraded method to determine

not only local-mean-age but also local-mean-residual-lifetime

under the unsteady-state conditions was suggested to

emphasise its applicability to ventilation study of agricultural

facilities.

4.1. Validation of the CFD simulation model on twofactors

1) The accuracy of the model itself focused on realisation of

physical airflow; comparing the coefficient correlations of

the cases, the study found 0.92, 0.85 and 0.96 for Case 1-3,

Case 2-3 and Case 3-3, respectively. This result showed

a similar tendency between the experiments and the CFD

results.

2) Validations of the UDF for implementation of Age-of-air;

average correlation coefficient of the LMA was 0.92, and

0.91 for LMR. Considering possibility of errors, the Age-of-

air model was designed reasonably.

b i o s y s t em s e n g i n e e r i n g 1 1 0 ( 2 0 1 1 ) 4 2 1e4 3 3432

4.2. Overall and local ventilation analysis

Results of the comparisons among room-mean-ventilation-

effectiveness of each structure showed that the effective-

ness has improved as the AER increased in every case.

However, each case had a different trend when it was

observed locally. Air exchange efficiency of each point and

configuration varied widely. This is interpreted to result from

the airflow pattern such as stagnation of airflow and vortex-

flow. Focussing on the homogeneity of the ventilation, for

instance, when comparing the LMAs of the Case 1 series, the

study revealed that the total standard deviation (STD) of Case

1-3 was higher by 11% than that of Case 1-1 which had the

smallest AER. The total STD value of Case 1-2was higher by 5%

than that of Case 1-3. The result implies that it is worthwhile

computing the local ventilation efficiency of each case besides

of the overall structure.

4.3. Comparison of AER for conventional and computedconcept

According to the case studies, there was a difference of

approximately e3w þ2 %between the designed AER based on

conventional concept and the computed AER based on the

age-of-air. Since these results were limited to the experiments

conducted in this study, it will be necessary to expand the

experimental conditions like the AER or to apply to a full scale

agricultural facility.

Using the methodology introduced in this paper, it is

anticipated that the approach of analysing the performance of

the ventilation system can be enhanced. The efficiency and

expected impact of each ventilation opening on the overall

facilities and the appropriate plan and assessment of elimi-

nation of pollutants reflecting real changes in weather

condition or forced ventilation system by time-schedule

operation can be estimated. Considering the full range of

configuration of agricultural facilities, theoretical consider-

ation of the age-of-air concept with multi-inlets and outlets

will need to be conducted further, as the present concept of

age-of-air is a narrow ideawith simple structure of single inlet

and outlet.

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