Sust. Build. 2, 5 (2017)© S.F. Fatima and H.N. Chaudhry, published by EDP Sciences, 2017DOI: 10.1051/sbuild/2017001

Available online at:www.sustainable-buildings-journal.org

RESEARCH ARTICLE

Steady-state CFD modelling and experimental analysisof the local microclimate in Dubai (UAE)Syeda Firdaus Fatima and Hassam Nasarullah Chaudhry*

School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, P.O. Box 294 345, Dubai,United Arab Emirates

* e-mail: H

This is an O

Received: 17 March 2016 / Accepted: 14 February 2017

Abstract. Rapid urban growth and development over the past few years in Dubai has increased the rate atwhich the mean maximum temperatures are rising. Progressive soaring temperatures have greater effect of heatislands that add on to the high cooling demands. This work numerically explicated the effect of HIs in a tropicaldesert climate by adopting Heriot-Watt University Dubai Campus (HWUDC) as a case study. The studyanalysed thermal flow behaviour around the campus by using Computational Fluid Dynamics (CFD) as anumerical tool. The three dimensional Reynolds-Averaged Navier–Stokes (RANS) equations were solved underFLUENT commercial code to simulate temperature and wind flow parameters at each discretised locations.Field measurements were carried out to validate the results produced by CFD for closer approximation in therepresentation of the actual phenomenon. Results established that the air temperature is inversely proportionalto wind velocity. Hotspots were formed in the zone 1 and 3 region with a temperature rise of 9.1% that caused atemperature increase of 2.7 °C. Observations illustrated that the building configuration altered the wind flowpattern where the wind velocity was higher in the zone 2 region. Findings determined increase in the sensiblecooling load by 19.61% due to 1.22 °C temperature rise. This paper highlighted the application of CFD inmodelling an urban micro-climate and also shed light into future research development to quantify the HIs.

Keywords: CFD / urban heat island / hotspots / thermal flow / sensible load

1 Introduction

Over the last few years, the world has been rapidlydeveloping towards urbanisation that is negatively affect-ing the environment leading to a substantial increase in thecooling requirements. One of the consequences of thisphenomenon is the heat island effect. The increase in thesurface temperature of urban structures leads to alterationof urbanmicroclimate as a result of variations in convectiveheat exchanges from these surfaces [1]. This leads to anincrease in the air temperature, where metropolitan citieswith hard built-up structures have higher mean air temper-atures than the immediate rural areas surrounding it.

Oke [2] portrayed the heat island phenomenon as theheat being trapped within the densely built up urban areasleading to elevated ambient air temperatures in the centreof the city as compared to the surrounding outlying areas.Tamet al. [3]mentioned it as an anthropogenic phenomenondue to modifications of the urban surfaces by human

.N.Chaudhry@hw.ac.uk

pen Access article distributed under the terms of the Creative Comwhich permits unrestricted use, distribution, and reproduction

inhabitation that leads to a higher air temperature in a denseurban centre. The elevated temperatures due to thisphenomenon lead to higher energy demand for cooling,increased concentration of air pollutions and greenhousegases and reduced thermal comfort.

Meteorological conditions such as wind speed anddirection is one of the major factors affecting the intensityof heat islands within the built environment [4]. High windvelocity allows for a reduction in the phenomenon whereasstagnant conditions increase it, where the wind flowpattern and velocity is influenced by the urban configura-tion that in turn affects the intensity of the heat exchange[5]. Allegrini et al. [6] observed increase in the convectiveheat transfer with the building façade and heat removaldue to increased flow of wind within the street canyon.Brown [7] indicated that the temperature build-up variesaccording to the wind velocity, where the intensity issignificant during calm wind conditions and is low understrong wind conditions. Fisher et al. [8] classified calm air aswhen the wind velocity is less than 3m/s. As the windspeed exceeds 3m/s it drifts out the warm air from the citylowering the intensity of the heat island phenomenon.

mons Attribution License (http://creativecommons.org/licenses/by/4.0),in any medium, provided the original work is properly cited.

2 S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017)

This study carries out a steady state temperatureassessment in context of wind flow pattern, micro-climaticspeed, building geometry and urban surface temperatureusing the Heriot-Watt University Dubai Campus(HWUDC) as the case study. The thermal and aerody-namic parameters impacting heat islands were numericallysimulated using Computational Fluid Dynamics, theresults of which were validated through physical fieldmeasurements. Specific hotspot locations around thebuilding infrastructure were determined in response tothe external local climate to quantify the cooling loads dueto sensible conductive heat gains through external.

2 Previous related work

Previous studies [9–12] have reviewed the influence ofsteady state surface temperatures and heat islands as afunction of urban geometry/orientation, nature of urbansurfaces, meteorological conditions, air movement and airvelocity. The intensity of the heat island effect can bemitigated by modifying these parameters strategically.The assessment methodologies implemented in literatureprovides a fundamental basis in the approach of assessingthe temperature profile and cooling loads on the builtenvironment located in hot arid climates. A studyconducted by Radhi [13] has shown that the thermalenvironments are influenced by built environment andtemperature increase could be well within 2–4 °C. The use ofnumerical tools such as CFD in predicting both steady anddynamic thermal and flowdistribution around buildings hasalso been extensively investigated in previous literature[14–16] as described in the following sections.

2.1 Impact, causes and mitigation strategies of steadystate heat islands

Abid et al. [9] investigated the effect of solar radiation andpollution dispersion of the urban microclimate in MasdarCity, Abu Dhabi at three times of the day. CFDsimulations were carried out in ANSYS FLUENT usingk–e model with steady Navier–Stokes equations. Resultsfrom the simulation depict higher air temperatures on thewindows facing the sun. It was found that increase in theambient air temperature was due to higher surfacetemperatures of ground and roof, heated up through solarradiation. The work concluded that higher aspect ratioprevents direct solar radiation heating up urban surfacesand reducing negative effects on the urban micro-climate.

Cho et al. [10] analysed the thermal environment on alarge-scale basis of a surrounding area (5 km2 and 10 km2)of Tokyo’s waterfront bay to examine the heat islandphenomenon. CFD simulations were performed using k–eturbulence model and were validated against the observa-tional measurements. Results depicted that the airtemperature at 2m from ground was dropped by 1 °C to2 °C above large vegetative areas and rivers. The studyhighlighted that the upward momentum (buoyancy driven)of air velocity occurs in the region of higher air temperature.The research work concluded that the existence of upwardanddownwardmomentumpairs of air velocity influences thevariation in ambient air temperature.

Taleb and Abu-Hijleh [11] studied the impacts ofvarious urban configurations in Dubai on temperaturevariations. 27 simulations were carried out with differentcombinations of urban layout, wind speed (0.1m/s for nowind case scenario, 3.6m/s as preliminary wind speed and7m/s for summer only to comprehend the influence ofincreasing wind speed on temperature variation) and timeof the year (June, December and September) with fixedinitial temperature input of 32 °C. Results revealed thatthe Bastakiya configuration (Fig. 1) had lower temper-atures in the summer case. On comparison of results withpresence and absence of wind, there was a reduction intemperature fluctuations in the presence of wind leadingto a reduction in the existence of hot spots. The studyconcluded that the Bastakiya configuration is the best inthermal performance under all conditions in terms of thelocation of site and alliance of prevailing winds parallel toroad.

Priyadarshini et al. [12] highlighted the fundamentalfactors causing the heat island phenomenon in Singapore.The CFD simulations used k–e turbulence model equationunder finite volume technique. The boundary conditionswere acquired from meteorological weather data and fieldmeasurements. The experiment was validated throughphysical field measurements. It was found that thetemperature dropped by 0.7 °C with an increase incorresponding air velocity by 35% due to the presence ofhigh rise towers of optimum H/W ratio. The studyconcluded that by strategically locating the high risetowers of optimum H/W ratio, air flow can be enhancedwithin street canyons thereby reducing the intensity of theheat island.

2.2 Application of CFD in the assessment of thermaland flow profiles

Allegrini et al. [6] investigated the influence of urbanmicroclimate on building energy demands of an urbanneighbourhood. The computational domain comprised of atotal of 14 buildings arranged in varying aspect ratios,simulated using 3-D Reynolds-Averaged Navier–Stokes(RANS) equations and k–e turbulence model within theOpenFOAM numerical code. A total of four differentweather conditions were modelled with wind speedsranging from 0.3m/s to 4.7m/s. The findings determinedthat the difference in air temperature concerning theapproach flow and the air inside an urban canyon or thelocal heat island intensities can be as high as 2.5 °C causinga significant increase in cooling demands. The studyconcluded that the CFD approach is a useful tool forpredicting heat island intensities for urban areas.

Toparlar et al. [14] presented an analysis of urbanmicroclimate in the city of Rotterdam, Bergpolder Zuid byconducting CFD simulations using 3D unsteady RANSequation under k–e turbulence model on a high resolutiongrid. Results depict that under calm wind conditions, thesurface temperatures of urban elements were relativelyhigh. Streets perpendicular to thewind velocity had reducedventilative cooling effect. CFD produced similar urbansurface temperature in comparison to the satellite thermal

Fig. 1. Bastakiya (left), orthogonal (centre), volume ortho (right) modelled in ENVI-met [11].

S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017) 3

images with a 7.9% deviation. The study concluded thatCFD can be used as a numerical tool to assess urbanmicroclimate and identify problematic areas.

Song and Liu [15] evaluated the distribution of thermalhumidity and velocity towards the urban micro-climate inthe presence of solar radiation and humidity transporta-tion. The boundary conditions were set from the dataobtained through meteorological centre. Standard k–eturbulence equation was used as turbulence model. Resultsillustrate that the building configuration significantlymodified the air temperature and moisture content. Withincrease in building height the cooling effect of the waterfeature was reduced due to reduced exposure to the sunblocked by tall buildings. The investigation highlightedthat the CFD simulations can be used as an effective tool topredict thermal environment of an urban micro-climate.

Setaijh et al. [28] applied ANSYS FLUENT on a casestudy in the city of Madinah, Saudi Arabia to analyse thethermal comfort in the urban streets. The simulation usedRANS turbulence equation with k–e model and standardwall function. Results from the simulation were comparedagainst the field measurements, where the air velocityobtained from the simulation was 6.5m/s and 7m/s fromthe field measurements indicating less than 20% error. Thestudy stated that according to Wilkinson et al. [17],acceptable error for CFD results is up to 20% thusvalidating the simulation performed. Results depict anacceleration in wind velocity by 1.3m/s with tallerbuildings enhancing wind movement along the streets alsocausing a reduction in the air temperature by 2 °C. Thestudy concluded that the urban geometry and orientationinfluences the air velocity and temperature affecting thethermal comfort along the streets. The study alsosuggested that CFD can be used to evaluate complexmicro-climatic issues such as thermal comfort, pollutiondiffusion, quality of air and wind flow patterns along withthe effects of vegetation and relative humidity.

Previous investigations have reviewed the effects of asteady increase in built environment temperature on alarge scale basis where the intensities were quantified basedon the temperatures occurring in the rural and urban areas.The studies revolved around mitigation strategies and howdifferent configurations of wind directions would affect theheat island phenomenon. However, a numerical and far-field assessment of the temperature build up on buildings inUAE’s hot arid climates has not been extensively coveredin the literature. Therefore, this work assessed the thermaland flow parameters around the HWUDC to comprehend

the behaviour of rising temperatures locally in order tomitigate the phenomenon in the most effective way infuture works.

3 Physical domain

TheHeriot-Watt University Dubai Campus was selected asthe case study to critically analyse the occurrenceof thermal and flow parameters. The campus comprisesof Phase 1 and Phase 2 buildings with an overall area of28 000m2 located in the Dubai International AcademicCity. The building has a symmetrical geometry withflushed and recessed exterior walls in a uniform fashion.Phase 1 of the building measures 51m wide, spreadingacross a length of 132m reaching 20m in height. Phase 2 ofthe building comprises of three blocks consisting of fivefloors with length, width and height of 32m, 12m, 16mrespectively. One block for auditorium with dimensions as25m� 35m� 5m. The two phases are situated at adistance of 15m measured from the central auditoriumblock. Figure 2 displays the HWUDC modelled in three-dimension on a scale of 1:100.

The window to wall ratio (WWR) on the geometry was37%. Glazing areas with high WWR were mainlyconsidered to cause significant temperature increase forsimplification purposes. In order to set the inlet boundaryconditions an enclosure of 212� 152� 40m was createdaround the geometry such that it was 2.5 times the lengthof the physical domain in x, y and z direction to avoidrebound effect.

The study assessed the heat exchange activity amidstthe two building phases (transitional circulation space)that has relatively high level of pedestrian movement. Theresultant high temperatures due to the phenomenon wouldlead to increase in the thermal discomfort thus identifica-tion of activity in this area was considered to be crucial.The building had been divided into three zones forillustration purposes as displayed in Figure 3.

3.1 Experimental setup

Field measurements were conducted around the HWUDCrequired for the inlet boundary conditions and experimen-tal validation to illustrate a closer representation of the realcase scenario. The measurements were logged on 2ndFebruary 2015 at 16:00. As the wind was prevailing fromthe northwest (NW) direction, velocity and temperature

(a)

(b)

Fig. 2. (a) Heriot-Watt University Dubai Campus, (b) modelled version.

Phase 2Phase 1

Zone 1

Zone 2

Zone 3

N(a)

(b)

Fig. 3. (a) Division of zones and phases of modelled HWUDC plan, (b) three created zones as seen from north direction.

4 S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017)

parameters were set at the NW side of the symmetryenclosure as the inlet boundary conditions. In order toassess the effect of high temperature of the campus façade,surface temperature of the glazing and external wall werealso input as the boundary conditions. Extech heat stressmeter (HT30) having an accuracy of ±1% with a 0.1 °C

resolutionwas used to record the dry-bulb temperature. TheMeterman IR 608 infrared thermometer of ±2% accuracyrating having a resolution of 0.2 °Candhotwire anemometer(C.A 1226 Thermo-Anemo F) having ±3% accuracy with a0.01m/s resolution were used to record the surfacetemperature of the façade and air velocity respectively.

Glazing (south)Glazing (north)Glazing (east)

Glazing (west)

42 °C

37 °C

0.4 m/s

(b)(a)

Fig. 4. (a) Glazing boundary conditions, (b) surface temperature, ambient temperature and velocity measured on the south facingside for inlet boundary conditions.

Fig. 5. Points indicating physical field measurements conductedin HWUDC (dimensions in meters).

S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017) 5

Figure 4a displays the boundary conditions that werespecified on the glazing of the buildings. Windows facingsouth were grouped together and set at a surfacetemperature of 42 °C obtained from the field measure-ments. Figure 4b displays the real image of inlet conditionlocation.

In order to validate the results generated from the CFDmodel, 10 discrete measurement points were created alongthe circulation zone connecting the two phases across thethree zones. Figure 5 displays the points at which theinvestigated parameters were measured. The dry bulbtemperature and air velocity recorded at the designatedpoints were used as the measurement parameters tovalidate the CFD findings.

3.2 Data reduction

Readings obtained from the physical field measurementswere applied to calculate the sensible heat gain due toconductive heat transfer through glazing in order toanalyse the impact on the cooling load of the building. Heattransfer coefficient for double glazed glass of the buildingwas taken to be as 2.26W/m2K [18]. Surface area of theglazed area was estimated as 87.31m2. Temperaturedifference could be acquired through measured data orcomputational results. These variables were merged toobtain the degree of convective heat transfer due to glazingin equation (1).

Qglazing ¼ UAðDT Þ; ð1Þ

where Qglazing represents sensible load due to convectiveheat exchanges through glazing (W), U represents thethermal transmittance of the glazing (W/m2K), A denotesthe surface area of the external glazing (m2) and DTrepresents the temperature difference between the outdoorand indoor environment in °C.

4 Computational domain

4.1 Solution methods

According to Chaudhry [16] the three-dimensional Rey-nolds-Averaged Navier–Stokes (RANS) using the k–eturbulence model is one of the most suitable numericalmethods for built environment simulations. The modelsolves for two governing equations, the kinetic energy andturbulent dissipation. Abid et al. [9] used RANS along withthe turbulent kinetic energy model to simulate the effect ofsolar radiation in an urban microclimate. Priyadarshiniet al. [12] applied k–e turbulence model to simulate heatisland phenomenon in Singapore. Furthermore, Allegriniet al. [6], Song and Liu [15], Toparlar et al. [14], etc., haveapplied the same to analyse and simulate the urbanthermal environments as reviewed in Section 2.2. Thus thecurrent research study used 3D steady state Reynolds-

T: 27 °C, v: 0.4 m/s

N

Inlet

Fig. 6. Representation of the flow domain.

6 S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017)

Averaged Navier–Stokes (RANS) equation using the k–eturbulence model to resolve the discretised computationaldomain.

Therefore, the RANS equations along with themomentum and continuity equations were solved usingthe commercial CFD code for the velocity and pressure fieldsimulations. The model employs the control-volumetechnique and the Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) velocity–pressure couplingalgorithmwith the second order upwind discretisation. Theturbulence kinetic energy, k, and its rate of dissipation, e,are obtained from the following transport equationsformulated in equations (2) and (3).

∂∂t

ðrkÞ þ ∂∂xi

ðrkuiÞ ¼ ∂∂xj

mþ mt

sk

� �∂k∂xj

� �þGk

þGb � re� YM þ Sk; ð2Þ

∂∂tðreÞ þ ∂

∂xiðreuiÞ ¼ ∂

∂xjmþ mt

se

� �∂e∂xj

� �

þ C1ee

kðGk þ C3eGbÞ � C2er

e2

kþ Se; ð3Þ

where Gk represents the generation of turbulence kineticenergy due to the mean velocity gradients, Gb representsthe generation of turbulence kinetic energy due tobuoyancy. YM represents the contribution of fluctuatingdilatation in compressible turbulence to the overalldissipation rate. C1e, C2e and C3e are constants; sk andse are the turbulent Prandtl numbers for k and e.

4.2 Mesh generation

Previous studies by Shengwei [19], Dandgawal [20], Houdalet al. [21] have indicated that a tetrahedral mesh is able tocompute thenumerical solutionswithacceptableaccuracy inrelatively lowcost andcomputational time.Anunstructured

tetrahedral mesh was therefore applied due its complexgeometrical configuration having series of flushed andrecessed exterior walls. The relevance centre of theunstructured mesh was set to ‘fine’ with ‘high’ smoothingsizing in order to resolve the flow fields and temperaturefluxes around the corners of the recessed and flushed walls.The meshed model comprised of 113 040 nodes and 580 464elements to compute the numerical codes of the physicaldomain. Figure 6 displays the meshed model with the inletconditions.

4.3 Boundary conditions

The applied boundary conditions (Tab. 1) comprised of aninlet velocity of 0.4m/s approaching from the north-westdirection. The geometry was modelled as a solid zone whilethe enclosure was modelled as a fluid zone for the analyses.The boundary conditions were kept identical throughoutthe numerical investigation for all analysed models.

5 Results and analysis5.1 Model validation

The validation of the results is considered to be of utmostimportance as the computational programme can havenumerous sources of error that could fail to represent anymajor or critical parameters and behaviour in the real casescenario. Uncertainty in modelling errors due to approx-imations and assumptions in the physical geometry,mathematical equations, boundary conditions and numer-ical errors such as inappropriate grid discretisation andimproper grid convergence may lead to inaccurate results.Thus validation of the simulated results against physicalfield results is scientifically significant.

As observed from Figure 7, the error percentage for thetemperature variable ranged from 2% to 17% with anaverage error of 11% whereas the velocity parameterincorporated a mean variation in the range of 19%. Theerror bars are plotted at 5% for the CFD recordings.

Table 1. Boundary conditions.

Parameter Value

Inlet velocity 0.4m/sInlet temperature 27 °CWall temperature 37 °CGlazing (east) 25 °CGlazing (north) 26 °CGlazing (south) 42 °CGlazing (west) 30 °CGeometry Solid zoneEnclosure Fluid zoneOperating pressure AtmosphericViscous model k–e (Eq. (2))Near-wall treatment Standard wall functionsVelocity formulation Absolute

1 2 3 4 5 6 7 8 9 10 Texp ( C) 26.5 25.4 24.5 26.2 22.8 25 23.3 24.5 23.4 24

Tansys ( C) 27 27 28.7 27.5 27.3 27.1 27.8 27.6 27.3 27.8

0 5

10 15 20 25 30 35

Texp ( C) Tansys ( C)

1 2 3 4 5 6 7 8 9 10 Vexp (m/s) 0.27 1.16 0.35 0.32 0.32 0.32 0.42 0.35 0.22 0.42

Vansys (m/s) 0.23 0.63 0.2 0.34 0.4 0.32 0.25 0.32 0.24 0.45

0 0.2 0.4 0.6 0.8

1 1.2 1.4

Velo

city

(m/s

)

Vexp (m/s) Vansys (m/s)

(a)

(b)

Tem

pera

ture

(°C

)

Fig. 7. Comparison between CFD and experimental results for(a) temperature and (b) velocity.

S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017) 7

According to Wilkinson et al. [17], an acceptable error forCFD results is up to 20%.Thus it can be established that thenumerical model has been validated.

5.2 Flow and thermal profiles

Figure 8 displays the temperatures distribution in thedirection of the inlet conditions attaining an ambienttemperature of 27 °C. Higher temperatures were observedaround the immediate adjacent areas of the glazed façade.A section of zone 1 located in between the flushed andrecessed geometry of the phase 1 building as illustratedrevealed relatively warmer surface temperature (hotspot)leading to probable higher heat exchange intensity in thatarea. Lower surface temperature was observed in zone 2although higher convective heat fluxes that generally occurwith any glazing façade of a building would lead to rise inthe temperatures. The highest overall surface temperaturethat distributed across a relatively larger area was found tobe in zone 3.

Figure 9 shows the velocity levels of the flow fieldstirring around the 3 zones of the campus. With an inletvelocity of 0.4m/s, the contour levels in zone 1 depicts thatthe zone experienced highest air velocity in that region ofup to 0.64m/s. Following the contact between the buildingand the approaching wind flow, the speed adjacent to thebuildings reduced considerably. The flow profile around therecessed geometry displayed a significant decrease in thewind speed that can almost be considered as stagnant windconditions. However, the outdoor corridor between phase 1and phase 2 of the building had accelerated wind flow ascompared to the flow in between the building geometry.The approaching wind from zone 1 funnelled in to thenarrow corridor of zone 2. Furthermore, zone 3 experiencedrelatively higher air velocity than zone 2 section due to ahigher re-circulation of air in proportion to the zonal area.

The temperature variation between the structuralconfiguration of the buildings and the surroundingmicro-climate is displayed in Figure 10. A temperature

increase of 28% was observed on the external walls andglazing indicated by the increased surface temperature. Atemperature increase of 7% was observed at the immediatesurrounding areas to both building phases highlighting theeffect locally formed of heat islands.

In order to assess the findings quantitatively, a line wascreated intersecting through the three zones at a height of2m above ground level to simulate the conditionsexperienced by the pedestrians. Figure 11a displays thetemperature formation across the building geometry. Asobserves, the temperature reached its highest peak in theregion of zone 3 with a value of 28.62 °C. This trend wascontrary to the air velocity Figure 11b which indicated areduction to approximately stagnant conditions, thusconfirming the inverse proportionality between the twoparameters.

Two perpendicular lines (lines 3 and 4) in zone 1 and 3were plotted on a temperature graph connecting the twophases of the building. Figure 12a illustrated that line 4 inthe zone 3 region had the highest maximum temperature of33.8 °C on the glazing as compared to line 3 in zone 1. Theoverall trend of the graph also depicted that zone 3 hadhigher temperature intensity than zone 1. Furthermore thetemperature tends to rise in region close to the buildingfaçade and roughly remained constant in the central regionof the lines in both the zones. Lowest temperatures were

Fig. 8. Surface temperature in XY plane at 2m above ground level.

Fig. 9. Velocity distribution in XY plane at 2m above ground level.

8 S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017)

recorded in these central regions of line 3 and 4 as 26.8 °Cand 27.1 °C respectively with zone 1 having the lowestminimum temperature.

Figure 12b shows a lower air velocity near phase 1building than phase 2 (zone 1). Maximum air velocity of0.17m/s was found to be at line 4 which falls under zone 3close to the phase 2 building. However, the overall airvelocity was higher at line 3 in the zone 1 region. The leastminimum velocity was found at the phase 1 side of zone 3 at0.005m/s which can almost be considered as stagnant windcondition.

5.3 Sensible cooling loads

Probe points were plotted on all areas adjacent to windowswithin the outdoor corridor in the ANSYS results interfaceand were calculated using the average function calculatorwithin the software. Conductive heat gain due to glazingwith maximum detected temperature (28.22 °C) wascalculated to find the possible contribution of sensibleheat gain on the cooling load of the building as a worst casescenario. Figure 13 shows the graphical representation ofthe increase in cooling loads before and after the steady-state heat island effect. A 1.22 °C increase in the ambient

Fig. 10. Representation of the local temperature formations on the building geometry and the surrounding environment.

0.549

0.005

0

0.1

0.2

0.3

0.4

0.5

0.6

0 10 20 30 40 50 60 70 80 90 100

Velo

city

(m/s

)

Data points

Max Min Linear (velocity trend (m/s))

28.62

26.84 26.6

27.0

27.4

27.8

28.2

28.6

0 10 20 30 40 50 60 70 80 90 100

Tem

pera

ture

(C

)

Data points

Max Min Linear (Temperature [ C ])

(b)(a)

Fig. 11. Instantaneous variations in axial (a) temperature, (b) velocity parameters.

S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017) 9

air temperature adjacent to the glazing caused theconductive heat gain through glazing to increase by19.61%. This is further discussed in Section 6.3.

6 Discussions6.1 Hotspot existence and relationship betweentemperature & air velocity

The most common trend that could be analysed from theresults was the increase in local temperature in areas withlow wind speed. According to Yuan and Ng [22] wind speed

less than 0.3m/s is considered to be stagnant pedestrianlevel natural ventilation within a street canyon implyinginsufficient removal of heat which tends to build up overtime. Heat dissipated from urban surfaces (windows,ground, etc.) adds on to the rise in the ambienttemperature due to lower convective heat exchangesbecause of limited air movement. Hence higher tempera-ture intensity was experienced in these areas leading tohotspots. Due to the presence of these hotspots, nearby airtemperature interacts with the higher temperature of thehotspots. Zeroth law of thermodynamics states that higher

(a)

(b)

Line 3

Line 4

Fig. 12. Instantaneous variations in radial (a) temperature, (b) velocity parameters.

986 1227

0

200

400

600

800

1000

1200

1400

Before UHI After UHI

Q g

lazi

ng (W

)

Fig. 13. Sensible cooling load due to glazing before and after theheat island effect.

10 S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017)

temperature from a fluid or object transfers to a body or afluid with lower temperature in order to attain thermalequilibrium [23], therefore relatively lower ambient airtemperature (27 °C)undergoesa rise throughheat exchangeswithhotspots (29.7 °C)therebyspreadingacross thephysicaldomain. The relationship between temperature and windvelocityobservedconcludes that the temperature is inverselyproportional to air velocity.

Zone 2 experienceda temperature riseof0.74 °C(27.7 °C)as seen fromFigure 6. According Voogt [24], heat islands arecaused when the temperature rise is more than 1 °C than theactual ambient temperature. Figure 7 shows relativelyhigher air velocity (0.32–0.4m/s) that helps to flush out theheat. Although the presence of glazing would allow forincreased temperature due to increased convective heatexchange from relatively higher glazing surface temperaturein zone 2, the narrow corridor and staggered phase 2 centralbuilding allows for an increased streamline effect that helpsto increase air velocity allowing heat removal. To supportthis phenomenon, evidence from literature suggests thatincrease in the wind velocity increases the convective heat

transfer with the building façade and heat removal [6]. As aresult, zone 2 and inlet location displayed lower surfacetemperature indicating negligible heat transfer.

6.2 Relationship between building geometry and windflow speed/pattern

As observed from the findings, the wind exerted higherpressure on the windward side of the building (zone 1)causing the wind flow to move towards the leeward side(zone 3). The initial speed of the prevailing wind wasmeasured as 0.4m/s, however, upon impact, the windsheared laterally around the building causing the speed toreduce around the building symmetry due to frictional dragfrom surface roughness of the building. The wind speed wasfound to increase in the outdoor void due to theacceleration caused by the wind deflection upon hittingthe building. Thus, maximum air velocity was found to bein the zone 1 region reaching up to 0.64m/s.

HWUDCbeing a symmetrical building with continuousflushed and recessed walls has spaces through which winddoes not circulate resulting in low or no air movement inthese areas. As the prevailing wind is parallel to the longface of the building, the wind passes straight past withoutcirculating in the recessed regions causing the heat to retainwithin the space. In addition, large amount of glazinglocated within a small enclosed area added to the rise oftemperature by 2.7 °C, consequently becoming a dominantfactor for the occurrence of hotspots.

6.3 Consequences of the increased temperature on thecooling demand

The thermal profile in Figures 6 and 8 displayed highertemperatures adjacent to glazing. Maximum temperaturewas found to be in zone 2 of about 35.08 °Cbeing 23%higherthan the ambient temperature. The increased glazing

S.F. Fatima and H.N. Chaudhry: Sust. Build. 2, 5 (2017) 11

temperatureaffects the resultingair temperatureadjacent tothese glazed façades. This temperature rise caused thesensible cooling load through glazing to increase by 19.61%.Following the heat island effect, the ambient temperatureincreased by 4.32%. As the sensible cooling load depends onexternal temperature any change in the external tempera-ture would lead to changes in the sensible cooling loadsthrough glazing. Thus the cooling loads were observed toincrease from 986W to 1227W.

From a study conducted in Greece it could be seen thatas the mean steady state heat island exceeds 10 °C, thecooling load on the buildings doubled [25].

According to theEPA[26] for every0.6 °Cof rise in theairtemperature, electricalconsumptiondueto increasedcoolingdemand increases by1.5%to2%.Moreover, variations in theexternal ambient air temperature lead to significantconsequences on thermal performance of the building [13].Al-Sallal [27] estimated that due to UAE’s harsh climaticconditions, 60% of the building’s energy consumption isaccounted by high cooling demands. Thus even a smalldegree rise in air temperature could have a catastrophic riseonthetotal energydemandofabuilding.HenceHIsareoneofthe phenomenon that negatively affect the local microclimate leading to increase in the space cooling demandwhich in turn increases carbon emissions due to increasedpeak electricity load from air-conditioning.

7 Conclusion

This study analysed the wind flow and temperaturedistribution for the assessment of steady state temperatureand flowprofile around buildings in theUAE.The numericalinvestigation was carried out using CFD, the findings ofwhich were validated using far-field experimentation. Thefindings indicated those specific locations in the zone 1 and 3regionexperienced9.1%increase intheambienttemperaturewhich caused a temperature increase of 2.7 °C leading to theformation of hotspots in these regions. The findingsdetermined that wind flow pattern and speed was one ofthe major factors in the formation of higher temperaturesalong with the building geometry that altered the windvelocity and high convective heat exchanges from glazingfaçade with relatively high surface temperature.

Zones with high air temperatures had relatively lowervelocities in the corresponding regions. Thus it wasconcluded that the air temperature is inversely proportionalto the wind velocity around the campus. Highest maximumtemperature of 28.22 °C was found adjacent to the windownear zone 1 due to 4.32% increase in the adjacent surfacetemperature. The temperature rise caused the sensible loadto increase by 19.61% leading to adverse effect on theelectricity demand. Based on the extensive analysisconducted in this work, the study has established potentialfor assessing and quantifying the consequences of steadystate heat island in the context of the sensible coolingrequirements. The current study provides scope to befurthered by assessing the steady state heat islandphenomenon in terms of solar radiation which is one of themajor factors for the high temperatures in UAE.

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Cite this article as: S.F. Fatima and H.N. Chaudhry: Steady-state CFD modelling and experimental analysis of the localmicroclimate in Dubai (UAE). Sust. Build. 2, 5 (2017).