CFD analysis of the impact of physical parameters onevaporative cooling by a mist spray systemCitation for published version (APA):Montazeri, H., Blocken, B., & Hensen, J. L. M. (2015). CFD analysis of the impact of physical parameters onevaporative cooling by a mist spray system. Applied Thermal Engineering, 75(1), 608-622.https://doi.org/10.1016/j.applthermaleng.2014.09.078

DOI:10.1016/j.applthermaleng.2014.09.078

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Applied Thermal Engineering 75 (2015) 608e622

Contents lists avai

Applied Thermal Engineering

journal homepage: www.elsevier .com/locate/apthermeng

CFD analysis of the impact of physical parameters on evaporativecooling by a mist spray system

Hamid Montazeri a, *, Bert Blocken a, b, Jan L.M. Hensen a

a Building Physics and Services, Department of the Built Environment, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, TheNetherlandsb Building Physics Section, Department of Civil Engineering, Leuven University, Kasteelpark Arenberg 40 e Bus 2447, 3001 Leuven, Belgium

h i g h l i g h t s

* Corresponding author. Tel.: þ31 (0)40 247 4374;E-mail address: h.montazeri@tue.nl (H. Montazeri

http://dx.doi.org/10.1016/j.applthermaleng.2014.09.071359-4311/© 2014 Elsevier Ltd. All rights reserved.

g r a p h i c a l a b s t r a c t

� CFD simulation of evaporative cool-ing by water spray systems.

� Grid-sensitivity analysis and valida-tionwithwind-tunnelmeasurements.

� Analysis of the impact of physicalparameters on the performance ofthe system.

� Injecting droplets with a temperaturehigher than DBT of air can providecooling.

� The cooling performance of the sys-tem is enhanced for wider drop-sizedistributions.

a r t i c l e i n f o

Article history:Received 22 July 2014Accepted 28 September 2014Available online 13 October 2014

Keywords:Computational Fluid Dynamics (CFD)ValidationParametric analysisHollow-cone sprayWater spray system

a b s t r a c t

The evaporation of droplets in a turbulent two-phase flow is of importance in many engineering ap-plications. Water droplet evaporation in spray systems, for example, is increasingly used in public spacesand near building surfaces to achieve immediate cooling and enhance the thermal comfort in indoor andoutdoor environments. The complex two-phase flow in such a system is influenced by many parameterssuch as continuous phase velocity, temperature and relative humidity, drop size distribution, velocity andtemperature of the droplets and continuous phaseedroplet and dropletedroplet interactions. Most ofthese parameters are not easily varied independently. To gain insight into the performance of the system,however, detailed knowledge of the impact of every parameter is important. Computational Fluid Dy-namics (CFD) is a useful tool for performing such parametric analyses. To the best of our knowledge, adetailed analysis of the cooling performance of a water spray system under different physical conditionshas not yet been performed. This paper provides a systematic parametric analysis of the evaporativecooling provided by a water spray system with a hollow-cone nozzle configuration. The analysis is basedon grid-sensitivity analysis and validation with wind-tunnel measurements. The impact of severalphysical parameters is investigated: inlet air temperature, inlet air humidity ratio, inlet air velocity, inletwater temperature and inlet droplet size distribution. The results show that for a given value of inletwater temperature (35.2 �C), as the temperature difference between the inlet air and the inlet waterdroplets increases from 0 �C to 8 �C, the sensible cooling capacity of the system improves by more than40%. In addition, injecting water droplets with a temperature higher than the dry-bulb temperature ofthe air can still provide cooling, although the amount of cooling reduces considerably compared to thecase with water at lower temperatures. It is also shown that as D, the mean of the RosineRammler

fax: þ31 (0)40 243 8595.).

8

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622 609

distribution, is reduced from 430 to 310 mm, the cooling performance of the system is improved by morethan 110%. For a given value of D, the cooling is enhanced for wider drop-size distributions.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The evaporation of spray droplets in a turbulent two-phase flowis important for many engineering applications, such as internalcombustion engines, spray drying, fire suppression and evaporativecooling. Evaporative cooling bywater spray systems, for example, isincreasingly used to achieve immediate cooling and to enhance thethermal comfort in outdoor and indoor environments (e.g.Refs. [1e6]). Compared to other micro-climate control techniques,evaporative cooling introduces a number of advantages. It is anenvironmentally-friendly and cost-effective technique to improvethe quality of indoor and outdoor environments because it makesuse of passive cooling with relatively simple system components[7]. Evaporative cooling systems are unobtrusive which givesbuilding designers and urban planners much flexibility for inno-vative system design concepts, and it allows easy integration inexisting city infrastructures or renovation projects. The effect iscontrollable and can be employed in a dynamic way to operate onlywhen cooling is desired. Most other climate change and/or urbanheat island (UHI) mitigation/adaptation approaches, such as high-albedo surfaces, have an effect all year long, which implies theyhave a positive effect in warm seasons, but also negative side-effects such as increased energy consumption in winter [8,9].

In a water spray system, a cloud of very fine water droplets isproduced using atomization nozzles. This enhances mixing andincreases the contact surface area between the air and the waterdroplets resulting in a higher rate of evaporation, yielding greatercooling of the air. The complex two-phase flow in a water spraysystem is influenced by many physical parameters such as contin-uous phase velocity, temperature and relative humidity, drop sizedistribution, velocity and temperature of droplets and continuousphase-droplet and dropletedroplet interactions [10e13]. To gaininsight into the performance of a water spray system, detailedknowledge of the impact of every aforementioned physicalparameter is important.

Research on the cooling performance of water spray systemswas mainly performed by full-scale measurements [15], wind-tunnel measurements [16] and Computational Fluid Dynamics(CFD) [13,17e19]. CFD is a useful tool for performing parametricstudies for complex flows. This is especially the case for the two-phase flow in water sprays as the evaporation process dependson several physical parameters that are not easily varied indepen-dently. CFD also offers the advantage that the latent heat andsensible heat fluxes during the evaporation process can separatelybe determined. CFD is capable of providing whole-flow field data,i.e. data on the relevant parameters of the two phases in all pointsof the computational domain. In addition, it provides a high level ofcontrol over the boundary conditions. For this reason, CFD isincreasingly used for basic and applied research in urban physicsand environmental wind engineering, as demonstrated by severalreview papers [20e28]. Example applications for which CFD isfrequently used are pollutant dispersion [21,27], natural ventilationof buildings and streets [25,29e35], wind-driven rain on buildings[20] and pedestrian-level wind comfort [22,36]. Although not inurban context, CFD has also been used on several occasions in thepast to evaluate the performance of spray systems for differentapplications (e.g. Refs. [14,18,37e39]). In the vast majority of these

studies, the LagrangianeEulerian (LE) approach has been used. Thisapproach, which has several advantages over the Euler-ianeEulerian approach [40], is also used in the present paper.Detailed knowledge of the impact of both computational andphysical parameters is important for the accuracy of CFD simula-tions and optimizing spray performance. Some previous studiesalready analyzed the impact of computational parameters for waterspray systems [13,19]. For example, a systematic evaluation of theLE approach for predicting evaporative cooling provided by a waterspray system was carried out by the present authors in Ref. [13].Some previous studies also already analyzed the impact of physicalparameters [12,18]. However, to the best of our knowledge, none ofthese studies included grid-sensitivity analysis, and none of themincluded a drop spectrum rather than a single drop size.

Therefore, this paper investigates the impact of physical pa-rameters, including drop spectra, based on grid-sensitivity analysisand detailed validation with wind-tunnel experiments. It evaluatesthe evaporative cooling process provided by a water spray systemwith a hollow-cone nozzle configuration. The impact of thefollowing physical parameters is investigated: inlet dry-bulb airtemperature, inlet humidity ratio, inlet air velocity, inlet watertemperature and inlet droplet size distribution.

In Section 2, thewind-tunnel experiments by Sureshkumar et al.[16] that are used for the validation study are briefly outlined.Section 3 presents an overview of the computational settings andparameters for the reference case. The validation of the CFD resultswith the wind-tunnel measurements is also presented in this sec-tion. In Section 4, the impact of the physical parameters is inves-tigated. The limitations of the study are discussed in Section 5. Themain conclusions are presented in Section 6.

2. Wind-tunnel measurements

The evaporative cooling performance of a hollow-cone nozzlespray system was investigated by Sureshkumar et al. [16] usingwind-tunnel measurements. The experiments were performed inan open-circuit wind tunnel with a uniform approach-flow meanwind speed. The test section of thewind tunnel was 1.9m longwitha cross section of 0.585 � 0.585 m2 (Fig. 1a). The inlet air dry-bulbtemperature (DBT) and wet-bulb temperature (WBT) weremeasured by two thermocouples placed upstream of the spraynozzle. The exact position of the thermocouples was not providedby Sureshkumar et al. [16]. Note that only two thermocouples wereused, but that the approach-flow is assumed to be of uniformcharacteristics due to upstream mixing in the wind-tunnel. Theoutlet DBTandWBT variations weremeasured with thermocouplesat nine positions across the outlet plane of the test section (Fig. 1b).

Electric heaters were employed upstream of the tunnel blowerto reduce the effect of the background air temperature fluctuations.These fluctuations were limited within ±0.3 �C during each set ofexperiments. A thermal probe installed upstream of the spraynozzle was used to measure the air stream velocity. The maximumexperimental uncertainty for the mean velocity was estimated tobe less than ±0.05 m/s for air velocity up to 2 m/s and ±0.2 m/s forair velocity between 2 and 4 m/s.

To avoid wetting of the thermocouples, the remaining waterdroplets in the air flow were collected by the use of a drift

Fig. 1. (a, b) Wind-tunnel measurement setup with measurement positions in the outlet plane (modified from Ref. [16]). Dimensions in meter.

Table 1List of some main parameters of the reference case. D is the nozzle dischargediameter and a is the half-cone angle.

Inlet air Water Spray nozzle

V (m/s) DBT(�C)

WBT(�C)

P (bar) Tin(�C)

Tout(�C)

_m ðlit=minÞ D(mm)

a

(deg.)

3 39.2 18.7 3 35.2 26.1 12.5 4.0 18.0

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622610

eliminator with z-shaped plates placed close to the tunnel outlet.The inlet and outlet water temperatures were measured using twothermocouples upstream of the nozzle and downstream of the drifteliminator, respectively. Water pressure was also measured by apressure gauge upstream of the nozzle.

Four identical nozzles but with different discharge openings of3, 4, 5 and 5.5 mm were used to evaluate the impact of nozzlecharacteristics on cooling performance of the spray system. Eachnozzle was installed in the middle of a cross-section of the testsection (Fig. 1a) and designed in a way that the exiting water formsa hollow-cone sheet disintegrating into droplets. The dropletdiameter distribution was determined using an image-analysingtechnique. The uncertainty of this technique for the mean dropletsize was estimated to be ±22%. The impact of the mean of thedroplet size distributionwill be investigated in subsection 4.5.1. Thehalf-cone angle was measured in still air and reported as a functionof discharge opening, water pressure and inlet air velocity. Nocorrelations between droplet size and velocity were given bySureshkumar et al. [16].

The experiments were conducted for 36 cases; four differentnozzle discharge diameters (i.e. 3, 4, 5 and 5.5 mm), three inletnozzle gauge pressures (1, 2 and 3 bar) and three inlet air velocityvalues (1, 2 and 3 m/s). In the present study, the case with nozzledischarge diameter of 4 mm, gauge pressure of 3 bar and inlet airspeed of 3m/s is withheld for the validation study since droplet sizedistribution data are also available for this case. A list of some mainparameters of the reference case is presented in Table 1.

3. CFD simulations: reference case

In this study the commercial CFD code ANSYS/Fluent 12.1 [41] isused in which the LagrangianeEulerian approach is implementedto simulate multi-phase flows in sprays and atomizers.

3.1. Computational geometry and grid

A computational model was made of the wind-tunnel test sec-tion with dimensions 0.585 � 0.585 � 1.9 m3 (Fig. 2a). Geometryand grid generation was executed with the pre-processor Gambit2.4.6, resulting in a grid with 1,018,725 hexahedral cells (Fig. 2b).The minimum and maximum cell volumes in the domain are

approximately 1.9 � 10�8 m3 and 5.9 � 10�6 m3, respectively. Thedistance from the centre point of the wall adjacent cell to the wallsis 0.006 m. This corresponds to y* values between 35 and 135 forthe case with the maximum inlet air velocity (i.e. 3 m/s). As stan-dard wall functions are used in this study, these values ensure thatthe centre point of the wall-adjacent cell is placed in the logarith-mic region of the boundary layer. The grid resolution resulted froma grid-sensitivity analysis. More information about the grid can befound in Ref. [13].

3.2. Boundary conditions

In the simulations, the mean velocity inlet boundary conditionfor the continuous phase is a uniform profile according to themeasured data (¼U∞). As the turbulence characteristics of the flowwere not reported by Sureshkumar et al. [16], a turbulence in-tensity, I, of 10% is assumed for the inlet flow, which is relevant forpractical applications and for the atmospheric boundary layer windflow. In addition, a sensitivity analysis done by the current authors(not shown in this paper) shows that the impact of the turbulenceintensity (TI < 10%) on the results is negligible. The main reason forthis could be related to the high inertia of droplets. The turbulentkinetic energy k is calculated from U∞ and I using Eq. (1). The tur-bulence dissipation rate, ε, is given by Eq. (2) where Cm is a constant(~0.09). The turbulence length scale, l, in this equation is taken asl¼ 0.07DH where DH is the hydraulic diameter of the domainwhichis equal to the width of the test section (¼0.585 m).

k ¼ ðU∞$IÞ2 (1)

ε ¼ C3=4m

k3=2

l(2)

Fig. 2. (a) Computational domain (dimensions in meter). (b) Computational grid (1,018,725 cells).

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622 611

The thermal boundary condition at the inlet is a constant tem-perature equal to the measured inlet DBT. A fixed vapour massfraction is also calculated based on the experimental data andimposed at the inlet as a boundary condition for the vapourtransport equation. The vapour mass fraction for the moist air canbe taken as x/(x þ 1) where x (kgvapour/kgdry-air) is the humidityratio of air. The walls of the computational domain are modelled asno-slip walls with zero equivalent sand-grain roughness heightkS ¼ 0 in the roughness modification of the wall functions [42]. Thestandard wall functions by Launder and Spalding [43] are applied.The adiabatic thermal boundary condition is used for these sur-faces. Zero static gauge pressure is applied at the outlet plane.

The “reflected” boundary condition is imposed to take the effectof the wind-tunnel walls on the impinging drops into account.Using this boundary condition, it is possible to define the amount ofmomentum in the directions normal and tangential to the wall thatis retained by the particle after the collision with the boundary. Inthis study, it is assumed that after impingement the normal mo-mentum component is zero while the tangential component re-mains the same. In this case, therewill be awater film on thewall inthe lower part of the domain. For the top of the domain, however,droplets rebound and move downward under influence of gravity.When droplets impinge on solid surfaces, different phenomena canoccur, which depend on the physical properties of the droplets suchas surface tension, viscosity, density, temperature and diameter,and on impingement conditions such as impact angle and velocityof droplets relative to the wall [44e47]. In addition, many studiesinvestigated the influence of wall characteristics such as roughness,temperature and wettability of the surface (e.g. Ref. [48]). Adetailed review of studies on droplet-wall impact can be found inMoreira et al. [49]. Depending on the mentioned conditions,droplets may float and be lost in a liquid film, may be reflected ormay disintegrate into smaller droplets. As the temperature of thewall in the experiments by Sureshkumar et al. [16] is less than theboiling temperature and the Leidenfrost temperature of the drop-lets, for the simulations it is assumed that the droplets areentrained in a water film along the walls after impingement [46].The presence of a liquid on the surface changes the boundarycondition and the impact leads to a liquideliquid interaction. In thiscase, the impact characteristics depend on the surface roughnessbut also the film thickness compared with the droplet size [50].However, the effect of the film thickness is not taken into account.

As mentioned in Section 2, in the experiments a drift eliminatorwas used near the outlet plane. In this study the drift eliminator isnot included in the computational domain because a detaileddescription of its characteristics was not reported by Sureshkumaret al. [16]. However, the impact of such plates is taken into account

using the “escape” boundary condition at the outlet, assuming thatthe upstream impact of the drift eliminator on the air flow patternis negligible. By using this boundary condition, droplets leave thedomainwith their current conditions (i.e. velocity, temperature andvapourmass fraction at the outlet plane) and trajectory calculationsare terminated [41].

3.3. Droplet characteristics

In the experiments by Sureshkumar et al. [16], an image-analysing technique was employed to determine the droplet sizedistribution. The RosineRammler [51]model is used to describe thesize distribution of the droplets in the CFD simulations. This modelassumes an exponential relationship between the droplet diameter,D, and the mass fraction of droplets with diameters greater than D,which can be expressed as [52]:

YD ¼ e�ðD=DÞn

(3)

where YD is the mass fraction of droplets with diameters greaterthan D, D themean drop diameter and n the spread parameter as anindicator of the distribution width. For the current experimentaldata, D and n are 369 mm and 3.67, respectively [13]. The experi-mental data and the RosineRammler curve fit are shown in Fig. 3a.The RosineRammler volume density distribution of the dropletscan be calculated as follows [53,54]:

f3ðDÞ ¼n

D

�D

D

�n�1

e�ðD=DÞn

(4)

Fig. 3b shows the results according to Eq. (4). The total numberof droplet (particle) streams in the CFD simulations is assumed tobe 300 meaning that they are released from 300 uniformly-distributed points on the nozzle opening perimeter. This numberis taken based on the results of a sensitivity analysis provided inRef. [13] by the same authors. The results of this analysis show thatapproximately 300 streams are needed to obtain CFD result that arenearly independent of the number of droplet streams. In this case,using a larger number of streamswould increase the computationaltimewithout any considerable effect on the accuracy of CFD results.Using a lower number of streams, however, would lead to a highdiscrepancy between the CFD results and experimental data. In thisstudy, the smallest droplet diameter to be considered in the sizedistribution of the RosineRammler model is 74 mm, correspondingto the minimum resolution of the droplet measurements. Thelargest droplet diameter is considered 518 mm, based on the largestdroplet diameter in the samples [16]. 20 discrete droplet diameters,

Fig. 3. (a) RosineRammler curve fit (solid line) and experimental data of YD (dots). (b) RosineRammler volume density distribution.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622612

ND, are assumed to be injected from each droplet stream into thedomain. The droplet diameters are distributed at equally spacedintervals of (Dmax � Dmin)/ND.

The spherical drag law is used to estimate the drag coefficientsacting on the droplets. Various correlations for the drag coefficientof spherical droplets (particles) can be found in the literature (e.g.Refs. [55e57]). In the present study the correlation by Morsi andAlexander [58] is used. This correlation proposes the following dragcoefficients for a wide range of Reynolds numbers up to 5 � 104:

Cd ¼ K1

Reþ K2

Re2þ K3 (5)

in which K1, K2 and K3 are constants (Table 2).The flux of droplet vapour into the air, N (kg mol/m2 s), is

calculated using the gradient of the vapour concentration betweenthe droplet surface and the air (Eq. (6)):

N ¼ kc�Ci;s � Ci;∞

�(6)

where kc is the mass transfer coefficient (m/s), Ci,s the vapourconcentration at the droplet surface (kgmol/m3) and Ci,∞ thevapour concentration in the bulk gas (kgmol/m3). Themass transfercoefficient is taken from the Sherwood number correlation [58]:

Sh ¼ kcdpDi;m

¼ 2:0þ 0:6Re0:5d Sc0:33 (7)

where dp is the droplet diameter (m), Di,m diffusion coefficient ofvapour in the air (m2/s), Red the Reynolds number based on thedroplet diameter and the relative velocity. Sc is the Schmidt num-ber (m/rDm,i). Note that the mass of the droplet, in each time step Dt(s), is reduced with a rate of NApMwDt in which Ap is surface area ofthe droplet (m2), Mw the molecular weight of water. Eq. (7) ex-presses the relationship between mass transfer coefficient anddroplet diameter, which can be rewritten as:

Table 2Drag coefficient model constants for different Reynolds numbers.

Re range K1 K2 K3

Re < 0.1 24.00 0.00 0.000.1 < Re < 1 22.73 0.09 3.691 < Re < 10 29.17 �3.89 1.2210 < Re < 100 46.50 �116.67 0.62100 < Re < 1000 98.33 �2778.00 0.361000 < Re < 5000 148.62 �47,500.00 0.365000 < Re < 10,000 �490.546 578,700.00 0.4610,000 < Re < 50,000 �1662.50 541,6700.00 0.52

kc ¼Di;m

dp

2:0þ 0:6

�udpw

�0:5

Sc0:33!

(8)

In the simulations, shrinkage of the droplets is taken into ac-count. Note that, as the drop diameter reduces, the transfer coef-ficient kc increases. The spray is assumed to be dilute and collisionof droplets is not taken into account.

3.4. Spray nozzle characteristics

The water droplets are injected into the computational domainfrom a nozzle with 4 mm diameter positioned in the middle of theinlet plane of the computational domain and oriented horizontallyin downstream direction. The total mass flow rate and temperatureof the injected water droplets are imposed according to theexperimental data (Table 1). The hollow cone spraymodel providedby Ansys/Fluent 12.1 [41] is used. In this model, the sheet velocity,U0, is used for the initial velocity of the droplet streams. U0 iscalculated as Cv (2DP/rw)0.5 where Cv is the nozzle coefficient, DPthe pressure difference along the nozzle and supply pipe and rw thewater density. As recommended by Sureshkumar et al. [17], for thegiven nozzle Cv is approximately 0.9.

3.5. Solver settings

The continuous phase and discrete phase flows are solved in afully coupled manner. Concerning the discrete phase, the dropletmomentum, heat and mass transfer equations are solved in a fullycoupled manner.

For the continuous phase flow, the 3D steady RANS equationsfor conservation of mass, momentum and energy are solved incombinationwith the realizable k-ε turbulence model by Shih et al.[59]. The analysis of the sensitivity of the results to the turbulencemodel byMontazeri et al. [13] showed that none of the investigatedturbulence models was superior over the others. The SIMPLE al-gorithm is used for pressureevelocity coupling, pressure interpo-lation is second order and second-order discretisation schemes areused for both the convection terms and the viscous terms of theequations.

Lagrangian trajectory simulations are performed for the discretephase. The discrete phase interacts with the continuous phase, andthe discrete phase model source terms are updated after eachcontinuous phase iteration. To solve the equations of motion for thedroplets, the Automated Tracking Scheme Selection is adopted tobe able to switch between higher order lower order trackingschemes. This mechanism can improve the accuracy and stability of

Fig. 4. Comparison of calculated (CFD) and measured (exp. [16]) (a) DBT, (b) WBT and (c) specific enthalpy for the reference case.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622 613

the simulations [41]. In this study, trapezoidal and implicit schemesare used for higher and lower order schemes, respectively.

3.6. CFD results and comparison with wind-tunnel experiments

Fig. 4 presents a comparison between the CFD results for thereference case, shown in Table 1, and the wind-tunnel experiments[16]. The comparison is performed for the DBT, WBT and specificenthalpy values at the nine measurement points. The specificenthalpy of moist air, h, can be expressed as h ¼ hdry:air þ x$hv,where hdry.air is the specific enthalpy of dry air (kJ/kgdry.air) given byCpT, where Cp is the specific heat capacity of air (kJ/kgdry.air K) and Tthe dry-bulb air temperature (K). x is the humidity ratio (kgvapour/kgdry.air) and hv the specific enthalpy of water vapour. Note that hvcan be expressed as CpvT þ hevaporation in which Cpv is the specificheat capacity of water vapour (kJ/kg K) and hevaporation is theevaporation heat of water (kJ/kg). The results in Fig. 4 show a goodagreement, within 10% for DBT, 5% for WBT and 7% for h. As pointedout by Montazeri et al. [13], the exact reasons for these deviationsare not clear, but they are probably caused by a combination oflimitations of the LE approach and experimental uncertainties.Apart from the LE approach limitations, the impact of collision ofdroplets, droplets impingement on solid surfaces and the drifteliminator on the air flow are not considered in this study. For theexperimental uncertainties, as mentioned by Sureshkumar et al.[16], the dominant uncertainty of the experiments is for the WBTmeasurements. Other experimental uncertainties might be relatedto the technique used to determine the droplet size distribution.More information on the results and sensitivity of the results to thecomputational parameters can be found in Ref. [13].

4. CFD simulations: parametric analysis for physicalparameters

The parametric analysis is performed for various physical pa-rameters by applying systematic changes to the reference case(Table 1). In every subsection below, one of the parameters is var-ied, while all others are kept the same as in the reference case.

Table 3List of some main parameters of the cases for the sensitivity analysis.

Inlet air

DBT (�C) u (kgvapour/kgdry-air) V (m/s)

Inlet DBT 27.2e43.2 0.0052 3Inlet humidity ratio (u) 39.2 0.0026e0.0130 3Inlet air velocity 39.2 0.0052 3e15Inlet water temperature 39.2 0.0052 3Mean of the distribution ðDÞ 39.2 0.0052 3Spread parameter (n) 39.2 0.0052 3

Some main parameters of the cases that are investigated are givenin Table 3. For each case, the downstream variation of the airtemperature and humidity ratio averaged over vertical cross-sections are investigated. The change in sensible heat of themoist air along the domain is also analysed as an indication of thecooling capacity of the spray system. Note that the sensible heat ofmoist air is calculated as CpTþ xCpvT. In the remainder of this paper,we will refer to this change as sensible cooling of the spray system.

The impact of evaporative cooling by a water spray system onhuman comfort depends on the complex interplay betweendifferent climatic variables [60] and spray characteristics. To gaininsight into the capability of the spray system to enhance thermalcomfort, the Universal Thermal Climate Index (UTCI) [61,62] is alsocalculated along the domain. UTCI is a thermal comfort indicator foroutdoor and semi-enclosed environments. It is an equivalenttemperature, derived based on the Fiala multi-node model [63,64],which reflects the human physiological reaction to meteorologicalparameters including air temperature and humidity, wind speedand mean radiant temperature [65]. In this study, the mean radianttemperature is assumed constant Tmrt ¼ 35 �C for all cases, whilethe other parameters are obtained from the CFD simulations. TheUTCI is used here because in urban areas, water spray systems aregenerally applied in outdoor or semi-enclosed environments. Notehowever that the computational domain in the present study iskept equal to the wind-tunnel test section.

4.1. Impact of inlet dry-bulb air temperature

Fig. 5 shows the impact of the inlet dry-bulb air temperature onthe performance of the spray system along the domain. The eval-uation is for five temperature differences between the inlet air andthewater droplets,DTaed,inlet¼ Tair,inlet� Tdroplet,inlet. The inlet watertemperature is identical for all cases, i.e. Tdroplet,inlet ¼ 35.2 �C.DTaed,inlet ranges from 8 �C to �8 �C. In the last case, for example,the temperature of thewater droplets is 8 �C higher than that of theinlet air. It can be seen that for the highest value of DTaed,inlet, thelargest overall air temperature reduction, i.e. the difference be-tween the inlet and outlet, is achieved (9.3 �C) (Fig. 5a). The overall

Inlet water Droplet distribution

Tin (�C) Dmin (mm) Dmax (mm) ðDÞ ðmmÞ n

35.2 74 518 369 3.6735.2 74 518 369 3.6735.2 74 518 369 3.6731.2e47.2 74 518 369 3.6735.2 10 800 310e430 3.02e4.0035.2 10 800 369 3.3e4.9

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622614

air temperature reduction declines monotonically by reducingDTaed,inlet. For example, when DTaed,inlet ¼ �8 �C the reduction isabout 3.3 �C. Note that for this case, the air temperature increasesuntil about half the length of the domain because of the dominanteffect of convective heat transfer from the droplets, which have ahigher temperature relative to the air.

Fig. 5b presents the change in the humidity ratio along thedomain. The change is approximately independent of the inlet airtemperature. At the outlet plane, the change in the humidity ratioreduces by less than 3% as DTaed,inlet is decreased from 8 to �8 �C.

The variation in the air sensible cooling along the domainrelative to the inlet value is presented in Fig. 5c. The spray systemprovides 9.8 kW sensible cooling for the largest DTaed,inlet. Byreducing DTaed,inlet, the cooling is also reduced monotonically andreaches about 4.1 kW for the case DTaed,inlet ¼ �8 �C, which is stillsubstantial. The performance of the spray system in providing areduction of the UTCI is shown in Fig. 5d. The results are consistentwith the amount of sensible cooling presented in Fig. 5c.

For each case, the moist air conditions for the same cross sec-tions along the domain are presented in the psychrometric chart inFig. 6. It can be seen that because of the cooling and humidificationprocess of the spray system, the dry-bulb temperature of the airreduces, while its wet-bulb temperature increases. In addition, thesensible heat of the air reduces, while its latent heat increasesresulting in the overall increase in the enthalpy of the air. Forexample, for the case DTa-d,inlet¼�8 �C the sensible heat reduces byabout 3.2 kJ/kgdry-air, while the latent heat increment is about14.1 kJ/kgdry-air (Fig. 6). In this case, the overall increase in theenthalpy of the air is about 10.9 kJ/kgdry-air.

A closer look at Fig. 6 reveals that by increasing DTaed,inletfrom�8 �C to 8 �C, the overall sensible heat between the outlet and

Fig. 5. Impact of inlet dry-bulb air temperature: profiles of (a) average air dry-bulb temperadomain.

inlet increases by about 184%, while the latent heat increment isabout 3%.

The cooling efficiency (CE) of the system can be evaluated ac-cording to the data provided on the psychrometric chart. Accordingto the ASHRAE handbook [66], the cooling efficiency is defined asthe ratio of the dry-bulb temperature difference between the outletand inlet of the wind-tunnel test-section, over the web-bulbdepression. The web-bulb depression is the difference betweenthe inlet dry-bulb temperature and inlet wet-bulb temperature[66]. As DTaed,inlet increases from�8 to 8 �C, the cooling efficiency isincreased from about 25% to more than 40%.

4.2. Impact of inlet air humidity ratio u

Fig. 7 presents the impact of the inlet air humidity ratio u of theair on the cooling performance of the system. The evaluation iscarried out for five values of u¼ 0.0026, 0.0052, 0.0078, 0.0104 and0.0130 kgvapour/kgdry-air corresponding to relative humidities 5.9,11.8, 17.6, 22.4 and 29.1%, respectively. Note that the inlet air andinlet water temperatures are identical for all cases and equal to 39.2and 35.2 �C. It can be seen that, as expected, the highest overall airtemperature reduction (Fig. 7a), sensible cooling (Fig. 7c) and UTCIreduction (Fig. 7d) along the domain correspond to the case withthe lowest amount of vapour in the inlet air. For low values of theinlet air humidity ratio the gradient of the vapour concentrationbetween the droplet surface and the air increases leading to ahigher rate of evaporation and higher change in the value of the airhumidity ratio along the domain.

The moist air conditions for all cases in the psychrometric chartare shown in Fig. 8. It can be seen that by increasing the inlet hu-midity ratio, the process line tends to track the constant enthalpy

ture, (b) humidity ratio variation, (c) sensible cooling and (d) UTCI variation along the

Fig. 7. Impact of inlet humidity ratio: profiles of (a) average air dry-bulb temperature, (b) humidity ratio variation, (c) sensible cooling and (d) UTCI variation along the domain.

Fig. 6. Impact of dry-bulb air temperature: moist air conditions on the psychrometric chart.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622 615

Fig. 8. Impact of inlet humidity ratio: moist air conditions on the psychrometric chart.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622616

line and the increase in the wet-bulb temperature of the air isnegligible (about 1%). In this case, the cooling efficiency (CE) de-creases from about 37% for u ¼ 0.0026 to less than 21% foru ¼ 0.0130.

4.3. Impact of inlet air velocity

The impact of the inlet air velocity on the performance of thesystem is shown in Fig. 9 for five velocity differences between theinlet water droplets and the air, DUdea,inlet ¼Udroplet,inlet � Uair,inlet ¼ 7, 10, 13, 16 and 19 m/s. The inlet velocity ofthe droplets is Udroplet,inlet ¼ 22 m/s for all cases. The largest overallair temperature reduction (Fig. 9a) and air humidity ratio increase(Fig. 9b) are achieved for the highest value of DUdea,inlet (i.e. 19 m/s).By increasing the velocity difference from 7 to 19 m/s (i.e. reducingthe inlet air velocity), the overall temperature reduction in theentire domain increases by 89%. In this case, the cooling efficiency(CE) of the system increases from about 3%, for DUdea,inlet ¼ 7 m/s,to more than 35% for the highest velocity difference. The mainreason is that a rise in the relative velocity of the two phases in-creases the heat and mass transfer coefficients of the droplets. Inaddition, the larger DUdea,inlet and therefore the lower Uair,inlet leadsto an increase in the residence time of droplets within the domain,resulting in a larger droplet interaction time with the surroundingair, more time for evaporation and a higher outlet DBT and hu-midity ratio.

As pointed out by Sommerfeld and Qiu [67], a reduction in theinlet air velocity allows a larger radial spread of the droplets in thespray. However, for the range of the relative velocities studied inthis paper (DUdea,inlet ¼ 7e19 m/s) such a spread cannot beobserved, especially close to the nozzle. Fig. 10 shows the temper-ature distributions in the vertical centre plane for the two casesDUdea,inlet ¼ 7 and 19 m/s. It can be seen that for DUdea,inlet ¼ 7 m/s

the air can compress the spray further away from the nozzle, wherethe momentum of the droplets is reduced.

4.4. Impact of inlet water temperature

The impact of the inlet water temperature is investigated for fivevalues of DTaed,inlet ¼ Tair,inlet � Tdroplet,inlet. For comparison pur-poses, the same range of DTaed,inlet presented in subsection 4.1 isused, i.e. DTaed,inlet¼�8,�4, 0, 4 and 8 �C. The inlet air temperatureis 39.2 �C for all cases. The results are shown in Fig. 11. It can be seenthat by decreasing the water temperature by more than 33%, thesensible cooling is increased by more than 180%.

Table 4 compares the sensible cooling provided by the systemfor different values of the inlet air temperature, at a constant watertemperature (Fig. 5), and the inlet water air temperature, at aconstant inlet air temperature (Fig. 11). It shows that for a samevalue of DTaed,inlet, lower values of the inlet air dry-bulb tempera-ture and inlet water temperature improve the cooling performanceof the system to a similar extent. However, as can be seen fromcomparing the columns in Table 4, combinations of inlet air DBTand inlet water temperature with lower absolute values of thesetemperatures yield a slightly higher sensible cooling than combi-nation with higher absolute values.

4.5. Impact of droplet size distribution

In this part, the impact of droplet size distribution on the coolingperformance of the system is presented. The evaluation is carriedout in two parts: first for different values of D, the mean of theRosineRammler distribution and second for different values of n,the spread parameter of the distribution. In this section, thesmallest and largest droplet diameters to be considered in the size

Fig. 9. Impact of inlet velocity: profiles of (a) average air dry-bulb temperature, (b) humidity ratio variation, (c) sensible cooling and (d) UTCI variation along the domain.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622 617

distribution of the RosineRammler model are 10 and 800 mm,respectively.

4.5.1. Impact of the mean of the distribution ðDÞIn order to investigate the impact of the size of the droplets on

the performance of the system, five droplet distributions corre-sponding to different values of D are imposed. To perform anappropriate comparison, all droplets in each distribution experi-ence the same size enlargement/reduction as the one for D,meaning that the whole RosineRammler distribution in Fig. 3b is

Fig. 10. Temperature distribution in cross-section (centre plan

shifted along the x-axis. In this case, for each distribution the massfraction of droplets with diameter D þ D is the same as the one forthe reference case with diameter D, i.e.YD ¼ YDþD, where D is thedroplet size enlargement/reduction of D. It gives�D=D

�nref ¼ ��Dþ DÞ=ðDþ DÞ�ni (Eq. (4)) in which nref is the spreadparameter of the reference case, i.e. 3.67. ni, the spread parameter ofeach distribution, can be calculated by averaging over the entirerange of droplet diameters. The results are shown in Fig. 12. In thisstudy, five values of D are used: 310, 340, 369, 400 and 430 mm. The

e) for cases (a) DUdea,i ¼ 19 m/s and (b) DUdea,i ¼ 7 m/s.

Fig. 11. Impact of inlet water temperature: profiles of (a) average air dry-bulb temperature, (b) humidity ratio variation, (c) sensible cooling and (d) UTCI variation along the domain.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622618

corresponding n values are 3.02, 3.35, 3.67, 4.00 and 4.33,respectively.

The profiles of the average air temperature and the change inthe humidity ratio, sensible cooling and UTCI along the domain fordifferent values of D are shown in Fig. 13. As the water flow rate isconstant for all cases, a reduction in D increases the effective sur-face area of the droplets, resulting in a higher rate of heat and masstransfer from the droplets. For example, as D reduces from 430 to310 mm (~28% reduction), the overall air temperature reductionfrom the inlet to outlet and the change in the humidity ratio alongthe domain increase by more than 16% and 88%, respectively. Inaddition, the sensible cooling of the system rises from 5.5 kW to12.1 kW. Consequently, the UTCI reduction along the domain in-creases from 3.7 �C for D ¼ 430 mm to 8.7 �C for D ¼ 310 mm(Fig. 13d).

4.5.2. Impact of the spread parameter of the distribution (n)In this section, five distributions corresponding to five values of

n, ranging from 3.3 to 4.9, are considered. D, is identical (369 mm)for all cases. The volume density distributions of the droplets are

Table 4Sensible cooling provided by the system for different values of DTaed,inlet.

DTaed,inlet Impact of inlet air DBT (Fig. 5) Impact of inlet watertemperature (Fig. 11)

Inlet airDBT (�C)

Inlet watertemperature(�C)

Sensiblecooling(kW)

Inlet airDBT (�C)

Inlet watertemperature(�C)

Sensiblecooling(kW)

8 43.2 35.2 9.8 39.2 31.2 10.24 39.2 35.2 8.5 39.2 35.2 8.50 35.2 35.2 7.1 39.2 39.2 6.8�4 31.2 35.2 5.6 39.2 43.2 5.2�8 27.2 35.2 4.1 39.2 47.2 3.6

shown in Fig. 14. It can be seen that the width of the distributiondecreases as the spread parameter, n, increases. The higher thevalue of n, the more uniform the droplet size distribution is in thespray.

Fig. 15 indicates the profiles of the average air temperature,humidity ratio, sensible cooling and UTCI variation along thedomain. For a constant value of D, the spray system shows a betterperformance for lower values of n, i.e. wider distribution.

Fig. 12. RosineRammler volume density distributions for five values of D, the mean ofdistribution.

Fig. 13. Impact of the mean of the distribution D: profiles of (a) average air dry-bulb temperature, (b) humidity ratio variation, (c) sensible cooling and (d) UTCI variation along thedomain.

Fig. 14. RosineRammler volume density distributions for five values of n, the spreadparameter of distribution.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622 619

Fig. 16a shows the normalized mass flow rate of evaporateddroplets in the domain for different values of n. It can be seen thatthis amount decreases by increasing n. This reduction is consistentwith the amount of moisture absorbed by the air presented inFig. 15b. Note that widening the distribution leads to an increase inthe number of small and large droplets simultaneously. Fig. 16bshows the proportion of the mass flow rate of the evaporateddroplets for two droplet size groups: droplets with the size di-ameters larger than D ¼ 369 mm and smaller than this value. For allvalues of n, the mass flow rate of the small droplets ðD<DÞ isconsiderably higher than the one for the large droplets ðD>DÞ. Forthe case with n ¼ 3.3, for example, this ratio is about 5.3. In addi-tion, by decreasing n from 4.9 to 3.3, the evaporation rate of thesmall droplets increases by more than 8.3% showing the impor-tance of small droplets in a droplet size distribution.

5. Discussion

This study presents a detailed and systematic parametric anal-ysis to investigate the impact of different physical parameters onthe cooling performance of a water spray system with a hollow-cone nozzle configuration. The LagrangianeEulerian (3D steadyRANS) approachwas used. Some important limitations of this studyneed to be mentioned:

1) This study focuses on the immediate cooling that is achievedclose by the nozzle of a water spray system. More simulationsneed to be performed to investigate the cooling performance ofthe system further downstream of the nozzle. It is also impor-tant to investigate whether complete evaporation takes place ina large-scale domain. This is relevant for application in urban

areas, where not only the cooling effect, but also the wetting ofpeople should be considered.

2) In this study, droplets are injected into the domain parallel tothe inlet flow. The cooling performance of similar systems incross-flow conditions needs to be investigated.

3) In this study, the performance of a spray systemwith a uniforminlet air velocity is investigated. Further studies need to be

Fig. 15. Impact of the spread parameter of distribution, n: profiles of (a) average air dry-bulb temperature, (b) humidity ratio variation, (c) sensible cooling and (d) UTCI variationalong the domain.

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622620

carried out to evaluate the performance of the system immersedin an atmospheric boundary layer.

This study is intended to evaluate the potential of water spraysystems in mitigating the heat stress in the outdoor environmentfor different spray characteristics and air flow conditions. The re-sults contain useful information that can be used as design guid-ance for evaporative cooling system engineers and designers.

The results of this study show that using small droplets with awide size distribution can improve the cooling performance ofsuch a system. It can also lead to complete evaporation of the

Fig. 16. (a) Profile of normalized evaporated droplet mass flow rate for different values ofðD>DÞ.

droplets, which is desired in public spaces to avoid the wetting ofpeople. In addition, injecting the droplets at a higher velocity andlower temperature relative to the ambient air will increase theperformance of the system. Note that spray characteristics such aswater temperature, water flow rate, droplet size and droplet dis-tribution can be adjusted during operation in response to localclimate conditions. On the other hand, meteorological parameterssuch as air velocity and air temperature are of course notcontrollable. The relationships presented in this paper can behelpful to devise appropriate control strategies that will help toensure maximum system performance under varying

n. (b) Proportion of the evaporated droplets for two droplet size groups: ðD<DÞ and

H. Montazeri et al. / Applied Thermal Engineering 75 (2015) 608e622 621

meteorological conditions, such as air temperature, air humidityand air velocity.

6. Conclusions

This paper provides a systematic parametric analysis to evaluatethe evaporative cooling process provided by a water spray systemwith a hollow-cone nozzle configuration. The evaluation is basedon grid-sensitivity analysis and validation with wind-tunnel mea-surements. The impact of several physical parameters is investi-gated: inlet air temperature, inlet air humidity ratio, inlet airvelocity, inlet water temperature and droplet size distribution.Within the range of the parameters studied, the following conclu-sions can be drawn:

1) The inlet dry-bulb air temperature has a strong effect on theamount of sensible cooling providing by a water spray system.The results show that:- For a given value of the inlet water temperature (35.2 �C), asthe temperature difference between the inlet air and the inletwater droplets increases from 0 �C to 8 �C, the sensible coolingcapacity of the system improves by more than 40%.

- Injecting warmer water relative to the inlet air can still providecooling, though the amount of cooling reduces considerablycompared to the case with a colder water. In this case, theimmediate cooling (close by the nozzle) cannot be achieved.

- The impact of the inlet air temperature on the air humidityratio is not significant. At the outlet plane, the change in thehumidity ratio reduces by less than 3% as DTaed,inlet isdecreased from 8 to �8 �C.

2) The moisture content of the inlet air (inlet humidity ratio) in-fluences the rate of evaporation. A lower amount of moisture inthe air improves the performance of the spray system. In thiscase, the cooling efficiency (CE) increases from about 21% foru ¼ 0.0130 to 37% for u ¼ 0.0026.

3) A lower value of the inlet air velocity relative to the droplets(higher velocity difference) improves the cooling performanceof the system. For the considered inlet air velocities, 3e15 m/s,the change in the radial spread of the droplets in the spray isnegligible, especially close to the nozzle.

4) For a constant value of the inlet dry-bulb air temperature,reducing the inlet water temperature improves the performanceof the system.

5) Droplet size distribution is an important parameter that in-fluences the evaporation process of a spray system. The resultsshow that:- As D, the mean of the RosineRammler distribution, is reducedfrom 430 to 310 mm, the cooling performance of the system isimproved by more than 110%.

- For a constant value of D, the spray system shows a betterperformance for lower values of n, i.e. wider distribution.

Acknowledgements

This research was supported by the Dutch Knowledge forClimate Research Program within the theme Climate Proof Cities(CPC).

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