indoor and built environment 2014 fung 1420326x14536189

XML Template (2014) [13.5.20145:55pm] [110]//blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/IBEJ/Vol00000/140047/APPFile/SG-IBEJ140047.3d (IBE) [INVALID Stage]

IIndoorndoor andand BuiltuiltEnvironmentOriginal Paper

Identifying the most influentialparameter affecting naturalventilation performance in high-risehigh-density residential buildings

Y. W. Fung and W. L. Lee

AbstractNatural ventilation contributes to comfort, reduction of energy use for air conditioning and improvingindoor air quality in high-rise high-density residential buildings. Measures to improve its performanceshould be of interest to the general public. Among factors that have an impact on natural ventilationperformance of residential units, openings configuration is perhaps the most investigated. Muchresearch work has been done on investigating the impact of different configuration parameters, butmost previous studies are focused on only one impact parameter. In this study, computational fluiddynamics simulations are conducted for evaluating the combined impact of different configurationparameters so as to identify the configuration parameter that has the highest impact on natural ven-tilation performance of residential units. Statistical analysis of simulation results revealed that amongstthe five studied parameters (i.e. ventilation mode, window type, orientation, window-to-wall ratio andliving room area), natural ventilation performance, represented by mean age of air, is most affected bythe ventilation mode adopted (i.e. cross or single-sided ventilation).

KeywordsResidential environment, natural ventilation, openings configuration, computational fluid dynamicssimulations, statistical analysis

Accepted: 27 April 2014

Introduction

Optimal use of natural ventilation contributes toachieve thermal comfort, reduction of energy use forair conditioning, and improving indoor environmentalquality (IEQ) in high-rise high-density residential envir-onment. As far as comfort and energy use are con-cerned, Zhu et al.1 investigated the optimization ofbuilding envelope design, including window type andglass characteristics, to minimize energy use for air-conditioning in Nantong City, Jiangsu China. Hiranoet al.2 studied the eects of natural ventilation perform-ance on reduction of the cooling load in hot and humidregions. Through computational uid dynamics (CFD)analysis and airow network analysis, it was found thatbetter natural ventilation performance could signi-cantly reduce the latent and total cooling loads. Wangand Wong3 studied indoor thermal environment fornaturally ventilated residential buildings in Singapore.

It was found that amongst the four ventilation strate-gies studied; full-day ventilation was the most preferredstrategy for achieving thermal comfort as compared tothe other three strategies, i.e. nighttime-only ventila-tion, daytime-only ventilation and no ventilation.Murakami et al.4 studied ventilation performance ofresidential buildings with and without voids. Theresults indicated that the model with voids improvednatural ventilation eectively and thus save 2.8% ofenergy use for air-conditioning compared with the

Department of Building Services Engineering, The Hong KongPolytechnic University, Hong Kong SAR, China

Corresponding author:W. L. Lee, Department of Building Services Engineering,The Hong Kong Polytechnic University, Hong Kong SAR,China.Email: [email protected]

Indoor and Built Environment

0(0) 110

! The Author(s) 2014Reprints and permissions:

sagepub.co.uk/

journalsPermissions.nav

DOI: 10.1177/1420326X14536189

ibe.sagepub.com

at Universitas Gadjah Mada on May 8, 2015ibe.sagepub.comDownloaded from


model without natural ventilation. Snell et al.5 pro-posed long time ago that the use of more functionalwindows could help conserve energy use for airconditioning.

While for the contribution of natural ventilation forimproving IEQ, Chao et al.6 studied the inuence ofventilation rates on indoor radon levels.Measurements at 12 residential sites in Hong Kongindicated that the ratio of indoor to outdoor radonlevel was 46.5 when the ventilation rate was around0.2 air change per hour (ACH), but was equalizedwhen the ventilation rate was increased to 3 ACH.Wong et al.7 investigated the reasons of the sick build-ing syndrome in individually air-conditioned house-holds in Hong Kong and found that it wasattributable to poor IEQ. Kolokotroni et al.8 foundthat indoor air quality of a school in the UK couldbe achieved simply by provision of natural ventilation.

Given the positive contribution of natural ventila-tion to thermal comfort, energy usage and IEQ, muchresearch work has been done on evaluating the inu-ence of dierent parameters on natural ventilation per-formance. Among previous studies, openingscongurations, commonly dened as the area, formand position of openings on wall surfaces,9 is themost investigated. However, most of them are onlyfocused on evaluating the inuence of one congur-ation parameter. Chou et al.10 conducted CFD simula-tions to investigate the inuence of window design onnatural ventilation performance of a bedroom inTaiwan. It was found that the degree of opening andthe ambient conditions introduced considerable inu-ence. Hassan et al.11 adopted similar method supple-mented with tunnel experiments to investigate theeects of window positions on ventilation characteris-tics of a simple single room. It was concluded thatsingle-sided ventilation with two distant openings (onefar left and one far right) performed better than twoadjacent openings (centre-located). Favarolo et al.12

also adopted CFD simulations and laboratory experi-ments to analyse the inuence of mode of ventilation onnatural ventilation performance, and concluded thatwhen dealing with single-sided ventilation, ventilationperformance was most aected by the vertical level andwidth of the openings. Several studies have been doneto investigate the inuence of inlets and outlets designs.Tantasavasdi et al.13 found that ventilation perform-ance was better when the inlet was larger than theoutlet. Li and Jones14 found that their geometricdesign would have a considerable eect. Yin et al.15

reported that ventilation performance was most inu-enced by the relative level of the inlets and outlets.

For the inuence of dierent conguration param-eters, a previous study by the authors16 identied thatnatural ventilation performance of a residential unit

was most aected by mode of ventilation (cross andsingle sided), orientation of the window and type ofwindow, but their relative inuence was not investi-gated. Thus based on the identied conguration par-ameters, the most inuential parameter, which is inabsence in extant literature, will be identied in thisstudy for the information of practitioners and the gen-eral public.

Methodology

To provide a basis for the study of the most inuentialparameter that aects natural ventilation performanceunder various conguration parameters, hypotheticalresidential units representing typical characteristics ofresidential units in Hong Kong were formulated,based on an extensive survey of oor areas, windowtypes, window areas and window orientations ofhomes at a representative residential estate inHong Kong.

Surveys were done through site visits and by refer-ence to layout plans available in public domain. Focuswas given to oor layouts of building blocks in residen-tial estates.

With respect to the inuence of surrounding build-ings on wind availability in a residential estate,Stathopoulos1721 conducted a series of studies in thepast two decades indicating that the inuence not onlyon the resultant wind speed, but also on the tempera-ture and relative humidity in the pedestrian zone. Forthe impact on pedestrian zones, there are studiesfocused on investigating the ground-level air qualityassociated with street canyons,22,23 heat islands24 andurban morphology25 eects.

A previous study was conducted by the authors26

to evaluate the inuence of surrounding buildings onnatural ventilation performance of dwellings. Thestudy took into account the combined inuence ofthe urban environment, prevailing wind conditions,and building levels. The results revealed that sur-rounding buildings lower the available wind poweradjacent to buildings; increase angular spread of pre-vailing winds; and adversely aect the natural venti-lation performance in residential dwellings. However,surrounding buildings did not aect the relativeimpact of dierent conguration parameters on thenatural ventilation performance in residential dwell-ings. Thus, the inuence of surrounding buildingswas not considered in this study to enable a focusedstudy on the relative inuence of dierent congur-ation parameters.

In this study, natural ventilation performance wasquantied by mean age of air (MAA). This concepthas been widely applied in similar studies.2729

A smaller MAA indicates better ventilation

2 Indoor and Built Environment 0(0)



performance. MAA at a sample point is dened byequation (1) as

MAAi 1C0

Z 10

Ci t dt 1

whereMAAi is the mean age of air at the sample point,C0 is the initial contaminant concentration at thesample point and Ci(t) is the contaminants concentra-tion at the sample point as a function of time, obtainedby regression of measured values over the decay period.

For determining the MAA at a sample point, CFDsimulations, being the most used method to assist build-ing design for better usage of natural wind,30 wereadopted. In this study, AIRPAK, embedded with amathematical module was adopted for calculation ofMAA. MAA relates the local air age spectrum to prop-erties of the air ow pattern in a domain. Under steady-state condition, the local air age at any positiondepends only on the ow characteristics. Upon check-ing the indoor air quality (IAQ)/thermal comfort itemin the basic parameter settings dialog box prior to run-ning the solver, MAA at any position in the domain canbe determined when the solution is converged.

Hypothetical residential units

Given the vast number of residential units in HongKong (2.05 million), it is almost impossible to accur-ately review the layout of each particular residentialunit. For more eective capturing of information, atypical residential estate was selected for formulationof the hypothetical residential units which were usedto facilitate investigation of the inuence of variousconguration parameters on ventilation performanceof residential premises.

The typical residential estate

One of the constituent estates of Centa-City Index ofHong Kong, Mei Foo Sun Chuen (MFSC) located atKowloon, Hong Kong, was identied as the represen-tative residential estate for further investigations, dueto four key features: (i) it consists of 99 blocks and isthe largest residential estate in Hong Kong; (ii) thehousehold distribution by oor area matches perfectlywith that of Hong Kong (i.e. 4069.9m2, 7099.9m2,100159.9m2 and over 160m2); (iii) the ventilationmode adopted includes both cross ventilation andsingle-sided ventilation types; and (iv) a wide varietyof window areas and window types are adopted.

MFSC is over 40 years old and consists of 13,500residential units accommodating approximately 60,000people. The 99 blocks in MFSC were developed in eightphases with large variations in oor layouts. A review

indicates that there are altogether 48 dierent types ofoor layouts. It is, therefore, almost impossible to con-duct simulations based on all the layouts. Accordingly,hypothetical layouts were formulated to represent typ-ical characteristics of all 48 oor layouts.

Living room is considered the most used area in ahousehold. Focus was, therefore, on living room lay-outs. A survey of their architectural characteristics wasconducted to identify typical living room oor areas,window types, orientations and window areas.

Floor area

According to the Rating and Valuation Department ofHong Kong,31 residential units are classied intoClasses A to E, according to the saleable area (ClassA: 160 m2). Figure 1shows private domestic units built in Hong Kong from2008 to 2011 by oor area;32 illustrating that Classes Ato D units account for most of the residential units inHong Kong. Based on the same classication, dimen-sions of living rooms of all 48 layouts were measured.The average dimensions of living rooms by area groupare summarized in Table 1 and these were used as thehypothetical living rooms for the present study.

Window type

A survey of the types of windows used in MFSC wasconducted, based on elevation photographs taken onsite in the 99 blocks. Two types of windows, whichdier only in the method of closing and opening, arecommonly used: i.e. side hung and end-slider types.Side hung windows are popular because of large oper-able area, while end-slider type is simple and spacesaving. Side hung windows, depending on the positionof the xed vision pane, can further be classied intohigh head and medium head types. They are, therefore,further classied as high head (Type A) and mediumhead (Type B) windows. Thus there are altogether threewindow types (Figure 2) where Type C refers to theend-slider type.

Among the three window types, Type A window ispredominantly found in MFSC (49%), followed by (indescending order) Type B (28%) and Type C (13%)windows. In the present study, the hypothetical livingrooms were therefore assumed to have these three typesof windows.

Orientation

Windows can be located at dierent positions in aliving room. A review of orientation of living roomwindows at MFSC, based upon layout plans available

Fung and Lee 3



in government records, was conducted. They were thenclassied into eight orientation groups, namely: south(S), southwest (SW), west (W), northwest (NW), north(N), northeast (NE), east (E) and southeast (SE). Thereview has identied that SW (25%), NW (24%), NE(20%) and SE (23%) were the four most frequentlyused orientations, while other orientations contributedonly 8%. Accordingly, the window in the hypotheticalliving room was assumed to have these four majororientations in our present study.

Window-to-wall ratio

Since MFSC is over 40 years old, elevation drawings ofbuilding blocks are not available in government recordsin public domain for ascertaining window areas. Givenwindow area is linked with wall area, a commonly usedparameter, window-to-wall area ratio (WWR), wasadopted in our present study instead.

Ascertaining WWR was accomplished on the basisof elevation photographs of the 99 blocks in MFSC.

Although a certain level of uncertainty could not beavoided, living room windows could still be classiedinto groups of WWR between 0.1 and 0.6; and the mostfrequently occurring WWRs were: 0.3 (46%), 0.4 (34%)and 0.5 (14%). These were, therefore, assumed for thehypothetical living rooms in our present study.

CFD simulations

After determining the oor areas, window types, orien-tations and WWR values for the hypothetical livingrooms, CFD software AIRPAK was used to simulateairow distributions.

Based upon characteristics of the hypothetical livingrooms, simulation cases were formulated (Table 2). Forcases with Type C window, only WWR of 0.5 was con-sidered because these windows also serve as the full-height full-width balcony access doors. Accordingly,cases generated by changing the conguration param-eters include 2 ventilation modes, 2 window types, 3WWR values, 4 orientations and 4 living room dimen-sions making a total of 192 cases. Adding the 32 casesgenerated based on Type C window, there are alto-gether 224 cases. Simulations were conducted by vary-ing only one input parameter for each simulation, withall other parameters retained.

Prevailing wind conditions

Natural ventilation performance of a residential unitcan change from time to time since wind directionand conditions change. Weather conditions insummer in Hong Kong were taken as the boundaryconditions in the simulations, i.e. south-westerly windof 2.65m/s. The wind direction was assumed on the

Figure 1. Floor area of new private residential units.

Table 1. Average dimensions and area range of livingrooms.

Average living room dimensions

ClassUnit floorarea range (m2)

Length(m)

Width(m)

Floorarea (m2)

A


basis of a review of meteorological conditions in HongKong during the months of July and August, between1961 and 1990; during this period wind directions weresouthwest for over 67% of the time,33 while the windspeed was with reference to that of the TypicalMeteorological Year (TMY) of Hong Kong (i.e.1989). The inuence of ambient temperature wasignored because temperature dierence betweenindoor and outdoor environments is small during ven-tilation seasons. According to previous measurementresults,27 this was particularly prominent when the nat-ural ventilation rate was stable. Ventilation was there-fore taken as isothermal to reduce requirements ofcomputing time and capacity, and to improve the e-ciency of solving other equations.

CFD model and settings

Three-dimensional models were constructed byAIRPAK for CFD simulations. The windows weremodelled as opening areas covering full width of theliving rooms. The adjustment of WWR was achievedby varying the height of the opening area. For model-ling the cross-ventilation mode, a xed opening ofdimensions ascertained on site (0.6m height and

0.78m width) was assumed perpendicular to thewindow as air outlet.

The renormalization group (RNG) k " turbu-lence model was used to simulate the steady statenatural ventilation. The SIMPLE (semi-implicitmethod for pressure-linked equations) pressurevelocity coupling algorithm was used to discretizethe controlling equations in AIRPAK. The solutionwas considered converged when residual ows wereless than or equal to their specied convergence cri-teria 103.

The calculation domain was set as 5L 5W 3H,where L, W and H are the length, width and height ofthe hypothetical living rooms, respectively. Toreect the geometric shapes of the objects in the build-ing, the unstructured hexahedral mesher was used tocreate the mesh; grid renement was adopted at theopenings to account for the large gradient that mightoccur. This follows the grid generation results of therst case and a grid independence test.

The above selected turbulence model, parameter set-tings, and calculation domain have been subjected tovalidations against measured MAA in a residentialbuilding in Hong Kong as reported earlier by theauthors.27

Table 2. Simulation cases.

Configuration parameters Abbreviation Details

Ventilation mode VENT Single-sided; Cross

Window type WIN A; B; C

Window-to-wall ratio WWR 0.3; 0.4; 0.5

Window orientation ORI SW; NW; NE; SE

Living room dimensions AREA LWH (m3) Area (m2)2.36 1.57 2.7 3.74.47 3.22 2.7 14.395.84 4.97 2.7 29.028.46 6.72 2.7 56.85

Figure 2. Three window types.

Fung and Lee 5



Results

Indoor air ow distributions of the 224 cases weresimulated by the use of AIRPAK. For each case,MAA was computed at 10 sample points, each at0.1m, 0.6m, 1.1m and 1.7m levels, at the hypotheticalliving room. This is according to ASHRAEs spacingrecommendations.34 Simple arithmetic mean of the g-ures was then used to devise the MAA gure for eachcase. Given the vast mass of data generated from thesimulations, the maximum (MAAmax), minimum(MAAmin) and average MAA (MAA) values wereadopted for evaluating the inuence of each congur-ation parameter on natural ventilation performance ofthe hypothetical units. Results are summarized inTable 3 and Figure 3 and discussed as follows.

Influence of ventilation mode

Figure 3 illustrates thatMAA, MAAmin andMAAmax ofsingle-sided ventilation cases are constantly higher thanthose of the cross-ventilation cases by 1.9 to 5.3 times.The results indicate thatMAA is signicantly aected bythe mode of ventilation. The result is consistent withconclusions reached by many other studies.35,36

Influence of window type

There were 96 cases each for Type A and Type B win-dows, and 32 cases for Type C windows. Considering

Type C window was assumed with WWR of 0.5 only,for a focused and fair comparison of the inuence ofwindow types, Type A and Type B windows with thesame WWR were extracted for a side-by-sidecomparison.

The computed MAA values (MAAmin, MAAmax andMAA) do not show a consistent trend for window TypesA, B and C. It is noted that MAAmin of Type B windowsare smaller than other window types, but a reverse forMAAmax and MAA, which are far higher than otherwindow types (85% and 51% higher than Type A win-dows). The comparison indicates that MAAmin,MAAmax and MAA values of Types A and C windowsare comparable, but if based on MAA values, Type Awindows performance is slightly better. Inconsistenttrend can be explained by the integrated inuence ofwindow types and other conguration parameters asreported in an earlier study by the authors.26

Nevertheless, the results on MAAmax and MAA valuesindicate that Type A window is the best performing, fol-lowed (in descending order) by Types C and B windows.

Influence of WWR

There were 64 cases each with WWR of 0.3 and 0.4 and96 cases with WWR of 0.5. As discussed in the previoussection, cases with Type C windows were assumed tohave WWR of 0.5 only. Thus, for a focused and faircomparison of the inuence of WWR, the 96 cases with

Table 3. MAA values by various configuration parameters.

Configuration parameters

MAA values

MAAmin (s) MAAmax (s) MAA (s)

VENT Single-sided 27.92 (+527%) 697.42 (+304%) 159.7 (+193%)

Cross 4.45 (0%) 172.58 (0%) 54.33 (0%)

WIN (WWR 0.5) A 6.52 (3%) 202.83 (+5%) 76.3 (11%)B 4.45 (33%) 375.51 (+94%) 114.89 (+35%)C 6.69 (0%) 193.42 (0%) 85.42 (0%)

WWR (WINC excluded) 0.3 6.84 (+53%) 697.42 (+86%) 124.64 (+30%)0.4 6.62 (+48%) 495.72 (+32%) 111.61 (+17%)

0.5 4.45 (0%) 375.51 (0%) 95.60 (0%)

ORI NE 6.52 (+47%) 697.42 (+147%) 142.64 (+118%)

NW 13.32 (+199%) 288.83 (+2%) 111.29 (+70%)

SE 12.61 (+183%) 283.42 (0%) 108.64 (+66%)

SW 4.45 (0%) 282.56 (0%) 65.49 (0%)

AREA 56.85 46.68 (+949%) 697.42 (+213%) 144.25 (+155%)

29.02 27.38 (+515%) 373.03 (+68%) 107.68 (+90%)

14.39 25.84 (+481%) 426.57 (+92%) 119.47 (+111%)

3.7 4.45 (0%) 222.53 (0%) 56.66 (0%)




WWR of 0.5 were excluded in the evaluations to avoidover-dominance of Type C windows. The computedMAA values show that MAAmax and MAA decreasewith an increase in WWR. The comparison indicatesthat MAAmin, MAAmax and MAA values of caseswith WWR of 0.5 are on average 50.5%, 59% and23.5% smaller than those with WWR of 0.3 and 0.4,indicating MAA improves with a bigger WWR. Theresult is consistent with studies by Heiselberget al.,3739 concluding that natural ventilation perform-ance is dependent on opening area.

Influence of window orientation

The 224 simulation cases were grouped by windoworientation (NE, NW, SE and SW). Figure 3 illustratesthat MAAmin, MAAmax and MAA of SW-facing win-dows are constantly smaller than those of other orien-tations by 2% to 199% to indicate that SW facingwindow is the best, followed (in descending order) by

SE-, NW- and NE-facing windows. The result is rea-sonable because south-westerly wind has been assumedas the boundary condition.

Influence of living room floor area

Comparison of MAAmin, MAAmax and MAA valuesindicates that living rooms in area class A would per-form much better than other area classes with a dier-ence in MAA (s) ranging between 90% and 155%,followed (in descending order) by area class C, classB and class D. The results show that MAA is not cor-relating increases with increase in oor area. This canbe explained by two ratios which are considered thearea factors aecting air movement and thus naturalventilation performance:13,3740 (i) window and oorarea ratio (WFR); and (ii) oor aspect ratio (lengthand width ratio). The computed ratios are summarizedin Table 4, illustrating that area class A has the highestWFR for dierent WWR values, and area class C has

Figure 3. Simulated MAA by different configuration parameters.MAA: mean age of air.

Table 4. Physical characteristics of various area classes.

Class

Dimensions WFR

Aspect ratioArea Length Width WWR 0.3 WWR 0.4 WWR 0.5

A 3.7 2.36 1.57 0.34 0.46 0.57 1.50

B 14.39 4.47 3.22 0.18 0.24 0.30 1.39

C 29.02 5.84 4.97 0.14 0.18 0.23 1.18

D 56.85 8.46 6.72 0.10 0.13 0.16 1.26

WFR: window and floor area; WWR: window-to-wall area.

Fung and Lee 7



the smallest oor aspect ratio to explain the smallerMAA values.

Most influential parameteridentification

The evidence from the above shows that each of thestudied parameter exerts a certain level of inuenceon natural ventilation performance of the hypotheticalliving rooms. For identifying the most inuential par-ameter aecting natural ventilation performance of aresidential unit, regression analysis was conducted.

The studied parameters aecting natural ventilationperformance of a residential unit were assumed to beindependent variables for inclusion into a regressionmodel, as shown in equation (2). Linear regression ana-lysis was conducted using the statistical package SPSS.41

MAA a1VENT a2WINA a3WINB a4ORINE a5ORINW a6ORISE a7WWR a8AREA b 2

where MAA is the mean age of air (s), VENT, the ven-tilation mode (0 for single-sided ventilation, and 1 forcross ventilation), WINA, the window type A (0 for notavailable, 1 for available),WINB, the window type B (0for not available, 1 for available), ORINE, the livingroom window orientation NE (0 for not available, 1for available), ORINW, the living room window orienta-tion NW (0 for not available, 1 for available), ORISE,the living roomwindow orientation SE (0 for not avail-able, 1 for available), WWR, the window-to-wall ratio(dimensionless), AREA, the living room area (m2), n,the coecient (n 18) and is the constant.

Equation (1) consists of a mix of quantitative (WWRand AREA) and qualitative variables (VENT,WIN andORI). To deal with the qualitative variables, thedummy variable method was adopted to assign thevalue 0 or 1 to indicate the absence or presence of aparticular condition. No dummy variable has beenassigned to cross-ventilation mode (VENTCROSS),living room window of south-west orientation(ORISW) and window type C (WINC) because theseare chosen as the base group against which the com-parisons are made.

With regard to the regression method, the enterapproach was adopted for determining the standar-dized coecients for the variables. The standardizedcoecients are used to reect the level of signicance.A higher value indicates an increase in signicance. Anegative coecient indicates that MAA decreases withan increase in the variable, and vice versa.

The model constructed by regression analysis has acoecient of determination (r2) of 0.55. Considering

there are 224 sets of data, the resultant r2 indicates asignicant correlation to conrm the inuence of thestudied parameters on natural ventilation performanceof resident units.42 The resultant model is representedby equation (3):

MAA 134:44 105:37VENT 9:23WINA 30:58WINB 77:15ORINE 43:15ORINW 45:8ORISE 1:45WWR 1:34AREA 3

Standardized coecients determined by regressionanalysis are compared in Table 5. Amongst all studiedparameters, ventilation mode (VENT) with the highestcoecient (0.565) is the most signicant parameteraecting natural ventilation performance of a residen-tial unit, followed (descending order) by window orien-tation (ORINE, ORINW and ORISE), living room area(AREA), window-to-wall ratio (WWR) and windowtype (WINA and WINB).

Furthermore, based upon the inuence of the stu-died parameters on theMAA as discussed earlier, nega-tive coecients for VENT, WINA and WWR, andpositive coecients for WINB, ORINE, ORINW,ORISE and AREA are judged to be reasonable.

It is therefore evident that the mode of ventilation(VENT) is the most inuential parameter aecting nat-ural ventilation performance of residential units.

Conclusions

CFD simulations using AIRPAK were used to evaluatethe inuence of various conguration parameters onnatural ventilation performance of residential dwell-ings. Hypothetical living rooms were formulated tofacilitate the analysis. They were formulated on thebasis of an extensive survey of living room area,window type, window area and window orientation ofresidential units in a representative estate in HongKong. Cases generated by varying the parametersinclude two ventilation modes, three window types,

Table 5. Summary of standardized coefficients.

Independent variables Standardized coefficients

VENT 0.565WIN WINA 0.049

WINB 0.162

ORI ORINE 0.358

ORINW 0.200

ORISE 0.213

WWR 0.130AREA 0.286




three window areas, four orientations and four oorareas. The inuence of the parameters was evaluatedby regression analysis. Based on the determined stan-dardized coecients, the natural ventilation perform-ance represented by the mean age of air (MAA) wasmost aected by the ventilation mode adopted(VENT). The ndings of this paper should be of inter-est to architectural practitioners, building managersand the general public.

Declaration of conflicting interests

The authors declared no potential conicts of interest with

respect to the research, authorship, and/or publication of thisarticle.

Funding

This research received no specic grant from any fundingagency in the public, commercial, or not-for-prot sectors.

References

1. Zhu J, Chew DAS, Lv S and Wu W. Optimization method for

building envelope design to minimize carbon emissions of build-

ing operational energy consumption using orthogonal experimen-

tal design (OED). Habitat Int 2013; 37: 148154.

2. Hirano T, Kato S, Murakami S, Ikaga T and Shiraishi Y. A

study on a porous residential building model in hot and humid

regions: Part 1 the natural ventilation performance and the

cooling load reduction effect of the building model. Build

Environ 2006; 41(1): 2132.

3. Wang L and Wong NH. The impacts of ventilation strategies and

facade on indoor thermal environment for naturally ventilated

residential buildings in Singapore. Build Environ 2007; 42(12):

40064015.

4. Murakami S, Kato S, Ooka R and Shiraishi Y. Design of a

porous-type residential building model with low environmental

load in hot and humid Asia. Energy Build 2004; 36: 11811189.

5. Snell JE. Achenbach PR and Petersen SR: Energy conservation

in new housing design. Habitat Int 1977; 2(1/2): 5972.

6. Chao YH, Tung CW and Burnett J. Influence of ventilation on

indoor radon level. Build Environ 1997; 32(6): 527534.

7. Wong S, La LW, Ho DC, Chau KW, Lam CL and Ng CH. Sick

building syndrome and perceived indoor environmental quality: a

survey of apartment buildings in Hong Kong. Habitat Int 2009;

33: 463471.

8. Kolokotroni M, Ge YT and Katsoulas D. Monitoring and mod-

elling indoor air quality and ventilation in classrooms within a

purpose-designed naturally ventilated school. Indoor Built

Environ 2002; 11: 316326.

9. Lukkunaprasit P, Ruangrassamee A and Thanasisathit N.

Tsunami loading on buildings with openings. Sci Tsunami

Hazard 2009; 28(5): 303310.

10. Chou P, Chiang C, Li Y, Lee C and Chang K. Natural ventila-

tion efficiency in a bedroom with a central-pivoting window.

Indoor Built Environ 2008; 17(2): 164172.

11. Hassan MA, Guirguis NM, Shaalan MR and El-Shazly KM.

Investigation of effects of window combination on ventilation

characteristics for thermal comfort in buildings. Desalination

2007; 209: 251260.

12. Favarolo PA and Manz H. Temperature-driven single-sided ven-

tilation through a large rectangular opening. Build Environ 2005;

40(5): 689699.

13. Tantasavasdi C, Srebric J and Chen QY. Natural ventilation

design for houses in Thailand. Energy Build 2001; 33(8): 815824.

14. Li AG and Jones PJ. Developments in strategies used for natural

and mechanical ventilation in China. Indoor Built Environ 2000;

9: 6574.

15. Yin W, Zhang GQ, Yang W and Wang X. Natural ventilation

potential model considering solution multiplicity, window open-

ing percentage, air velocity and humidity in China. Build Environ

2010; 45(2): 338344.

16. Gao CF and Lee WL. Evaluating the influence of window types

on natural ventilation performance of residential buildings in

Hong Kong. Int J Vent 2011; 10(3): 227238.

17. Stathopoulos T and Storms R. Wind environmental conditions in

passages between buildings. J Wind Eng Ind Aerod 1986; 24(1):

1931.

18. Stathopoulos T and Wu H. Generic models for pedestrian-level

winds in built-up regions. J Wind Eng Ind Aerod 1995; 5455:

515525.

19. Stathopoulos T and Baskaran BA. Computer simulation of wind

environmental conditions around buildings. Eng Struct 1996;

18(11): 876885.

20. Stathopoulos T, Wu H and Zacharias J. Outdoor human comfort

in an urban climate. Build Environ 2004; 39(3): 297305.

21. Stathopoulos T. Pedestrian level winds and outdoor human com-

fort. J Wind Eng Ind Aerod 2006; 94(11): 769780.

22. Zhang Y, Gu Z, Lee S, Fu T and Ho K. Numerical simulation

and in situ investigation of fine particle dispersion in an actual

deep street canyon in Hong Kong. Indoor Built Environ 2011;

20(2): 206216.

23. Liu XP, Niu JL, Perino M and Heiselberg P. Numerical simula-

tion of inter-flat air-contamination under the condition of single-

sided natural ventilation. JPBS 2008; 1(2): 133147.

24. Giridharan R, Ganesan S and Lau SSY. Daytime urban heat

island effect in high-rise and high-density residential develop-

ments in Hong Kong. Energy Build 2004; 36: 525534.

25. Nelson MA, Pardyjak ER, Klewicki JC, Pol SU and Brown MJ.

Properties of the Wind field within the Oklahoma city park

avenue street canyon. Part I: mean flow and turbulence statistics.

J Appl Meteorol Clim 2007; 46: 20382054.

26. Gao CF and Lee WL. The Influence of surrounding buildings on

the natural ventilation performance of residential dwellings in

Hong Kong. Int J Vent 2012; 11(3): 297310.

27. Gao CF and Lee WL. Evaluating the influence of opening con-

figuration on natural ventilation performance of residential units

in Hong Kong. Build Environ 2011; 46: 961969.

28. Walker RR and White MK. Single-sided natural ventila-

tionhow deep an office. Build Serv Eng Res T 1992; 13: 231236.

29. Gan G. Effective depth of fresh air distribution in rooms with

single-sided natural ventilation. Energy Build 2000; 31: 6573.

30. Zhai Z. Application of computational fluid dynamics in building

design: aspects and trends. Indoor Built Environ 2006; 15(4):

305313.

31. RVD. Hong Kong Property Review. Rating and Valuation

Department, Hong Kong SAR Government, 2004.

32. CSD. Hong Kong Monthly Digest of Statistics 2012 Edition.

Census and Statistics Department, Hong Kong SAR

Government, Hong Kong, 2012.

33. HKO. Summary of meteorological observations in Hong Kong.

Hong Kong Observatory, Hong Kong SAR Government, Hong

Kong, 2005.

34. ANSI/ASHRAE Standard 129-1997 (RA 2002). Measuring air-

change effectiveness. Atlanta: ASHRAE, 2002.

35. El-Agouz SA. The effect of internal heat source and opening

locations on environmental natural ventilation. Energy Build

2008; 40(4): 409418.

Fung and Lee 9



36. Evola G and Popov V. Computational analysis of wind driven

natural ventilation in buildings. Energy Build 2006; 38(5):

491501.

37. Heiselberg P and Sandberg M. Evaluation of discharge coeffi-

cients for window openings in wind driven natural ventilation. Int

J Vent 2006; 5(1): 4352.

38. Heiselberg P, Bjrn E and Nielsen PV. Impact of open windows

on room air flow and thermal comfort. Int J Vent 2002; 1(2):

91100.

39. Heiselberg P, Svidt K and Nielsen PV. Characteristics of airflow

from open windows. Build Environ 2001; 36: 859869.

40. Lee SR and Ryou HS. A numerical study on smoke movement in

longitudinal ventilation tunnel Eres for different aspect ratio.Build Environ 2006; 41: 719725.

41. SPSS: IBM SPSS Statistics. Version 20. IBM Corp., USA, 2011.

42. Milton JS and Arnold SC. Probability and statistics in the engin-

eering and computing sciences. New York: McGraw-Hill

International Editions, 1987.



indoor and built environment 2014 fung 1420326x14536189

Documents