MODELING THE EFFECT OF WATER SPRAY SUPPRESSION 

ON LARGE-SCALE POOL FIRES 

P. E. DesJardin, LA. Gritzo, and S. R. Tieszen 

Sandia National Laboratories 

ABSTRACT 

In practical fire suppression systems for large rooms or compartments, water sprinklers are often located on or near 

the ceilings. In this configuration, the droplets from the water spray must be large enough to penetrate the high 

temperature thermal plume of the tire and reach the pool surface, yet be small enough to evaporate and provide 

effective suppression of the flame zone. The focus of this research is to investigate computationally the influence of 

initial drop size and spray system configuration on water suppression of medium to large scale (Le., -100kW) pool 

fires. The cases addressed are representative of ongoing experiments at the Naval Research Laboratories. The tire 

scenario examined in this study involves a 123 kW heptane pool fire located in a 3.05m (10 ft) cube enclosed 

facility. Results from this study indicate that (1) for large drops (Dd > 150 pm) an initial rise in temperature is 

observed associated with enhanced turbulent mixing before evaporative cooling takes place; (2) an optimum drop 

size is found that allows for maximum decrease in gas-phase temperature for one of the spray configurations 

examined: and (3) a low pressure spray with more nozzle locations appears to provide improved suppression when 

compared to using a single high pressure nozzle. 

INTRODUCTION 

The purpose of this research is to explore the use of numerical modeling for simulation of fire 

suppression using water sprays. This capability is useful to assess the performance of halon 

alternatives as driven by the ban on halon production. One attractive alternative for halon is the 

use of water sprays. The main physical mechanisms for flame suppression using water sprays 

include the effects of thermal cooling due to evaporation and gas phase heat capacity, oxygen 

displacement, and radiation attenuation due to the liquid spray. 

A partial summary of the research in the use of water sprays for fire suppression can be found in 

the reviews by Tatem et al. [l], Jones et ai. [2], Mawhinney et al. [3], and Grant et ai. [4]. More 

recent numerical studies in this area can be found in the works of Chow et al. [5], Novozhilov et 

al. [6], and Prasad et al. 17, 8,9]. Numerical suppression studies of Prasad et al. focused on the 

optimization of water sprays for the suppression of low Reynolds number (i.e., laminar) jet 

diffusion flames [7,8] and pool fires [9]. The results of these studies demonstrate that suppression efficiency is highly dependent on the size of the water spray droplets as well as the location 

of the injection. Optimal suppression was observed when small drops are injected upward 

through the base of the flame. These conclusions are also supported in the work of Novozhilov 

et al. [6] and Chow et al. [5] for enclosure fires. 

Novozhilov et al. [6] show reasonable agreement with experimental data by only considering the 

thermal effects of the spray, thus indicating that the chemical effects of water vapor on 

exothermic reaction kinetics are not significant. Lentati et al. [IO] support this idea in their study 

examining the thermal and chemical suppression effects of water spray in a counter flow 

diffusion flame. Their results also indicate that the chemical suppression effects of water vapor 

contribute little (less then 10%) to the overall temperature drop indicating that thermal cooling 

due to evaporation of the water spray accounts for the majority of the suppression. 

The complete understanding of water spray suppression of a turbulent fire involves resolving the 

intimate coupling of liquid evaporation, turbulence, finite rate chemical kinetics, and radiation 

heat transfer. The objective of this work is to account directly for the first three of these physical 

processes through subgrid modeling of pool fires for a scenario of engineering interest. 

262 Halon Options Technical Working Conference 2-4 May Zoo0 
A detailed description of the subgrid models developed to account for thermal cooling (spray 

submodel) and chemical kinetics effects (PSR submodel) is provided in the Mathematical Model 

Formulation section. Results are then presented for a square (0.305 m [ 12 in]) 123 kW heptane 

pan fire, which is representative of tests at the Naval Research Laboratory (NRL) [I I]." Nutnerical predictions are presented to examine the sensilivity of volume averaged temperature in the 

test cell to initial drop size specification and suppression system configuration. 

MATHEMATICAL MODEL FORMULATION 

The following mathematical description is limited to a summary of the spray and flame extinguishment model formulations used to account for the thermal and chemical effects of a liquid 

suppressant. respectively. The models are implemented into a general purpose fire simulation 

code, VULCAN. which is based on the KAMELEON-Fire code [12]. VULCAN uses a RANS 

based model suite including a k-e turbulence model [ 131, the EDC combustion model [ 141. a soot 

model [I 51, and a radiation model [ 161. The gas phase conservation equations are discretized on 

a staggered, block-structured grid with second-order upwind differencing for the convective 

terms using a version of the SIMPLE algorithm [ 171. Previous studies using VULCAN for pool 

fire simulations can be found in references [ 18, 19, 201. 

SPRAY SUBMODEL 

The spray submodel is based oil a stochastic separated flow approach 1211. The following transport equations for mass, momentum, and energy are integrated in time for groups of droplets 

(parcels). 

"I 

dt Sc, 

-- dm'l -nD,,p, -B,,,Sh, 

In Eq. (I), the Sherwood (Sh, = h#,,,,Dzf /D,,,, )and Nusselt (Nu , = ~I,~D,, /k, )transfer numbers 

are expressed in terms of film conditions using the Ranz-Marshall correlations [22], 

I/Z SI?, = 2 log( 1 + B,, ) / B,r, [Re ,, Sc , I" / 3 1 

Nu, = 2 log(l+B,~,)/B,,,[Re,"'Pr,"'/3] 

" Mardnghides, A,, Anleitner, R.L., Binnette, C., Austin, E.M. and Sheinson, R.S., Resrrlt.s,fi~~- Self' 

Contui~?rd Totul F/ooding Nulou 1301 A1ter.wtiw Technologies E~uluurion, NRL report. In progress, 

2000, draft on file with the author. 

Halon Options Tcchnicai Workins Conlcrcnce 2-4 May 7000 263 
The coefficient of drag (CD) is modeled as that of a sphere using the relations from Shuen et al. 

~231, 

24(1+Red2”/6)/Re, forRe, <IO00 

cD = (0.44 forRe, > 1000 

The thermodynamic properties at the droplet film surface are obtained by using a thin-skin 

approximation [21] where the film temperature is approximated as a weighted average of the 

droplet and surrounding gas temperatures, Ti = aT, + (1 -a)T,. In this study, 

(3) 

a = MIN(C,, Bi,l) where Bi (= h,D, /kf ) is the thermal Biot number. The model constant CBi 

(set equal to 0.5) defines the transition Bi number for which the droplet can be treated using a 

lumped capacitance approach (Le., T, z T,). This model is used to account for rapid changes in 

droplet temperature as the droplet is transported into a flame zone. Attempts to use a simple 

1/3 rule weighting [24] results in non-physical values of temperature and species mass fractions. 

The film is assumed to he at saturation conditions so that a partial pressure can be calculated 

using a Clausius-Clapeyron relation, Pf = P,cq Expl- h,,, lR(1 /T, - 1 /T,<, )I where heat of 

vaporization, h,, , is expressed as a function of temperature using Watson’s relation 125, 261 (i.e., 

4, =h,,,., [(T, - T, )/(Tc - T,, )11).3x). Once the partial pressure is determined, the mass 

fraction of water vapor is calculated from the ideal equation of state, 

Y, = MW,?, / MW, (P, / P, - 1 + MW,,, I MW, ). Lastly, the mass ( Bnr ) and thermal ( B, ) 

transfer numbers in Eq. (1) are obtained from their definitions as derived from steady-state 

droplet analysis [27]: Bn, = 

Droplet dispersion due to turbulence is implemented using both parcel and subparcel models. 

The parcel model accounts for the effects of large-scale turbulent eddies perturbing a parcel 

trajectory and is based on the random walk model of Gosman and Ioannides [28] as modified by 

Shuen et al. [23]. Turbulent dispersion of the droplets within a parcel is accounted for using the 

group modeling concept of Zhou and Yao [29] where the spatial distribution of droplets within 

each parcel is assumed to have a Gaussian distribution. 

-Y,) /CY, - 1) and B, = C, (Tg -T, )/hIg . 

FLAME EXTINGUISHMENT SUBMODEL 

To account for the first order effects of the water spray on the exothermic chemical reactions in a 

flame, a subgrid model was developed based on Perfectly Stirred Reactor (PSR) theory as formulated by Glarborg et al. [30] in the CHEMKIN I1 [31] software package. The model is constructed through a sequence of PSR precalculations that map out chemical extinction time scales as a 

function of temperature and suppressant mixtures. For the current study, the suppressants 

include H,O, C02, N2, and combinations. In general, extinction times are functions of 

suppressant mixtures, temperature, and pressures and could be used in a tabulated lookup form. 

However, in order to reduce the storage requirements of such a table, mixing rules are used that 

allow the mixture mole fraction of suppressant to be determined using the following expression 

from Saito et al. [32]: 

264 Halon Oplions Technical Working Conference 2-4 May Zoo0 

-~ 
where XL,~,~,,,~~~,~ is the suppressant mole fraction that extinguishes the flame for a specific 

suppressant species, Xic~~,l,i.c is the mole fraction of that particular suppressant species divided by 

rhe total suppressant mole fraction, and XL~~~~,,~~,,~~, is the suppressant mole fraction for extinction 

for the suppressant in the specific fuel/air mixtures. These mixing rules only work for suppressants that are mostly thermal in nature [32] and so are well suited for studying the effects of 

water on flame suppression. An example of a chemical extinction time scale calculation is 

illustrated in Figures la and I b. These figures show the amount of N2 and H20 required [or 

extinguishment of an H2-u;~ mixture as a function of residence time and temperature from the 

PSR-CHEMKIN calculations and representative curve fits. Mixtures of H20 and N? are trcated 

using the results of Figure 1 and the mixing rules of Eq. (4). The results show that, consistent 

with physical intuition, less suppressant is required to extinguish a flame with decreasing residence times (tp.~~l,l~,H.~,,,l) and more suppressant is required for extinguishment at higher temperatures. The VULCAN fire simulation package employs the PSR precalculations by assuming that 

the effective chemical time scale at the subgrid can be represented by a PSR so that r~.,,~,,,,~~.,,l = 

fp%lr,H.oa, and compares this time scale to a representative turbulence time scale at the subgrid, 

91(,,, , which is estimated from classical turbulence theory, i.e., tjI<.,, = Jv/E. If Q~,,~. < Da,,,, rpSK, 

then blowout is assumed to occur and the reaction rates in the EDC combustion model are set 

equal to zero. The calibration parameter, Docr,,, for the model is the critical Damkohler number 

for extinguishment and is calibrated from jet blowoff studies" to a value of 1.367. A similar 

criteria for flame extinguishment has been recently used by Koutmos [33] for LES, hut Do,,.,, 

comes from a dynamic length scale ratio rather than an assumed consrant. More details on the 

PSR flame extinction model forinulation and its calibration can he found in the reference of 

Tieszen and Lopez.' 

NUMERICAL IMPLEMENTATION 

The droplet transport equations of Eq. (I) are numerically integrated using the LSODE package 

for solving systems of stiff ODES [34]. Gas phase properties needed for the evaluation of droplet 

transport processes are determined using linear interpolation from the Eulerian grid of the CFD 

calculations. As a first step, droplets that impact any wall of the computational domain before 

evaporating are simply removed from the calculation allowing a rather conservative prediction of 

suppression (it., dilution of the liquid fuel due to water spray is not modeled). The effects of the 

spray are coupled to the gas phase equations through appropriate source terms in the gas phase 

transport. The source terms are integrated into the gas phase transport equations using a dynamic 

time-splitting subcycling procedure similar to Amsden [35] to provide numerically stable solutions for rapidly evaporating sprays. 

* Tieszen, S.R., and Lopez, A., "Development of a Subgrid Fire Extinguishment Model," Technicd 

report. Sandia National Laboratories, Albuquerque, NM. In final preparation, 2000. Draft on file with 

the author. 

Hdon Option\ Technical Workins Conlercnoc 2-4 M:iy 2000 26.5 
0.9 

L 

.! as P 

E 8 0.6 

B 

8 0.4 

$ 0.7 

as 

- j 0.3 m.105m 

.PPR .XI= 

- ~ FLsQoDI .PIR .5001( r .ps* * Pool g az F”.lbM 

D 

0. I 

0.0 rnl 

mwee rm Ui-nds) nellenDF nmturab) 

(a) (b) 

Figure 1. Blowout suppressant mole fraction vs. residence time for stoichiometric Hz/air 

mixtures with (a) Nz (in excess of air) and (b) HzO suppressants at 1 atm pressure. 

Symbols are from PSR-CHEMKIN calculations and lines are curve fits. 

The VULCAN simulations presented here employ a 34 x 30 x 37 grid to discretize a cubical 

enclosure with a length of 3.05 m (10 ft) on a side. The simulations are first performed for 8600 

time steps to simulate 45 sec of physical time to account for a typical prebum time for a fire to 

ignite and develop. The calculations are then advanced another 3500 time steps to allow for 

simulation of IO sec of physical time to elapse with the water suppression activated. During the 

transient period of spray injection, a total of 33,000 computational parcels are injected into the 

domain with approximately 2000 parcels at any given time in the simulation. 

RESULTS 

A sketch of the NRL test facility is shown in Figure 2. The facility is ventilated using one inlet 

port located near the top of the enclosure and two exit ports located on the opposite corner. The 

volumetic flow rate is maintained at 7.1 m3/sec allowing for one air exchange every 4 min. A 

0.305 m (12”) square pool of heptane is simulated through constant mass flux boundary conditions at the bottom of the domain. The mass flux is chosen as m” = 0.0288kg /m2 -sec based 

on empirical correlations from Drysdale [36] (Le., tiz” = riz: (1 - exp(-kpD) )where for heptane 

riz: = 0.101 and k,B = 1 .I) resulting in a 123-kW fire. In attempt to reproduce conditions from 

existing experimental procedures at the facility, a constant mass flow condition is imposed on the 

inlet boundary while a constant pressure condition is used at the outflow boundaries. After 

45 sec, a constant pressure condition is imposed on both the inlet and outlet boundaries to 

simulate deactivation of the ventilation system. Two spray configurations, representative of the 

systems currently employed at NRL, are considered in this study and are illustrated in Figure 3. 

The first consists of a single high-pressure spray nozzle located at the center top of the enclosure 

while the second uses four lower pressure nozzles that cover each quadrant of the cell. Unfortunately, the exact drop size and velocity distributions, as well as the spray distribution pattern, 

are not well known and could not be obtained from the system manufacturers. Based on estimates of the operating pressures and atomizer cross sectional areas, the same mass flow rate, 

0.53 kg/sec, was determined for both systems and droplet velocities of 166 and 83 m/sec for 

Configurations 1 and 2, respectively were calculated. The spray injection is modeled as a solid 

cone spray that randomly injects computational parcels to cover the specified solid angle. The 

266 Halon Oplionr Technical Working Conlcrence 2-4 May 2000 
v = 7. I k/ min 

A=0.0392n>- (i.e,. X.8 in duct) 

x 

Free ilow 

outlet\. 

A = 0 01x24 m' 

(I e.. 6 In duct) 

(----------, 

1 OS m 

(IO tt) 

Figure 2. Schematic of NRL test facility. 

0.305 x 0.305 m (I? x 12 inch) I heptane fire, 113 kW fire. 

rii = 0.0288 k,y in1 ~ - sec 

injected at 

I u,,, /= lhhniisec 

mass flow rate. 

rii = 0.53 k$ / sec 

Spray parcels 

injected at 

inilcs flow Kite 

/nozzle, 

lu,,, I=X3n,/\ec 

m = 0.13 I$ Jsec 

(b) 

Figure 3. Schematic of spray system configurations using (a) one single high pressure nozzle 

(Configuration 1 j and (b) quadrant approach using four lower pressure nozzles 

(Configuration 2). 

spray angles (a) are computed to allow for a spray to reach the comers of the room in Configuration 1 or a cell quadrant in Configuration 2, if the droplet were to follow an outermost trajectory 

and are set equal to 35.3 and 19.5 deg, respectively. Lastly, four different drop size classes of D,, 

= 25.75, IS0 and 300 brn are used for each configuration to explore the sensitivity of drop size 

and cover the range of plausible drop sizes expected in the experiments. 

Figure 4a shows the volume averaged gas temperature for the lower half (Le., 2 < I .5 m), <TI>, 

upper half (is., 2 > I .Srn), <Tu>, and for the entire domain, <T>, during the preburn part of the 

simulation. As shown, <Tu>, rapidly increases by approximately l00K in the first 30 sec of the 

preburn as the hot gases from the pool fire collect near the ceiling of the enclosure. After 30 sec, 

all temperature averages appear to increase approximately linearly at the same rate of 2 K/sec. 

Halon Oplions Technical Warking Crmlcrcnce 2-4 May 2000 267 
360 

340 ,-' .- 300 

0 5 10 I5 20 25 30 35 40 45 

time (sec) 

Figure 4. Temperature variations during preburn showing (a) volumetric average temperature 

versus time during prebum and (b) instantaneoous snapshot of temperature isocontour 

slice through center of cell (Le., y=O) with superimposed velocity vectors after 45-sec 

prebum. 

Figure 4b shows temperature isocontours with velocity vectors superimposed for a cross section 

through the domain after the preburn period. At this time, the flow shows large vortical flow 

structures that entrain air due to the periodic varicose puffing mode of the pool fire. In addition, 

the location of the peak temperature is shifted toward the outflow boundary resulting in the high 

temperature gases flowing on average along the ceiling to the exit boundary on the opposite wall. 

After the prebum time, the water spray is injected forming a jet of entrained hot gas near the 

ceiling as the local air is transported by droplet drag and directed downward. Figure 5 shows 

instantaneous predictions of the gas phase velocity at the spray injection planes along with 

location and velocity of the droplets after 0.4 sec of spray injection using 150 pm droplets for 

(a) Configuration I and (b) Configuration 2. The maximum magnitude of the gas phase velocity 

for Configuration 2 is approximately 15 m/sec and is about half the peak value of 30 m/sec for 

Configuration 2. This difference is due to the lower mass flow and associated lower droplet 

injection velocities for the lower pressure four-nozzle system in Configuration 2. 

Figure 6 presents volume averaged temperatures after the start of spray injection for Configuration 1 using all four drop size classes showing (a) <TI> and (b) <Tu>. In Figure 6a, the largest, 

300 pn, drops show an initial increase in temperature for the first 2.5 sec after illjection. This 

surprising increase in temperature is due to the enhanced turbulent mixing of the jet and advection on the pool surface before enough water has evaporated to start to cool the flow near the 

base of the fire. This prediction is consistent with previous observations of Atreya et al. 1371 on 

the use of water sprays. In contrast, the injection of the 25 pm drops results in a sudden drop of 

40 K in the average temperature near the base of the fire in the first second, leveling off for 

another 3 sec and then suddenly decreasing again. This temperature history can be attributed to 

the transient jet development. A high concentration of small droplets are located at the leading 

edge of the jet offering effective suppression of the pool fire for a short (I sec) period followed 

by enhanced turbulent mixing that slows the suppression until the initial jet transient has passed 

at 48 sec. The intermediate, 75 and 150 pm, cases also show signs of the transient jet develop268 Halon Options Technical Working Conference 2-4 May 2000 
Figure 5. Instantaneous snapshot of water spray suppression 0.4 sec after injection showing 

isocontours of gas and droplet phase vertical velocity at plane of injection for (a) spray 

configuration 1 (contour slice at y=O.0 m) and (b) spray Configuration 2 (contour slice 

at y=-0.8 m). 

~~ ~ 

420 -t Od-ZSam 

4- Od-150jim 

410 - nd=isllm 

400 

390 

380 

370 

%60 + 

"350 

340 

330 

320 

310 

470 

460 --t Dd-25,,1n 

I - Dd-75pm * Dd=lsOpm 450 

440 --C- Dd=JOOpm 

430 

420 

410 

400 

E390 

r380 

370 " 

360 

350 

340 

330 

320 

310 

3004 

time (oec) 

(b) 

Figure 6. Volume averaged temperalure for Configuration 1 showing (a) <TI> and 

(b) <Tu> verses time. 

ment with some short-lived reduction in temperature due to initial injection of drops followed by 

an increase in temperature and then a more gradual decrease. Figure 6b shows <Tu> time 

history and indicates that, in general, the smaller droplets allow for more effective suppression 

than larger drops with the 25 and 75 pn providing almost same initial reduction in <Tu> for the 

first 4.5 sec after injection. 

Figure 7 show (a) <TI> and (b) <Tu> time history for the second spray system configuration. In 

this case, the enhanced turbulent mixing due to the startingjet from the sprays is not as pronounced as in Configuration I due to the lower injection velocities of the droplets and the spatial 

Halon Oplionr Tcchnical Working Conicrcnce 2-4 Mity ?OOO 269 
offset of the spray injection locations relative to the position of the pool fire. The decrease in 

turbulent mixing, along with the longer residence time of droplets to interact with the fire, allows 

for the spray in Configuration 2 to decrease the <Tu> temperature more quickly than in configuration 1 as shown by comparing Figure 7b to 6b. In addition, Figures 7a and 7b indicate an optimum drop size exists between 25 and 75 pm that is sufficiently large to penetrate the high temperature fire plume, yet be small enough to be an effective suppressant at the flame zones. This 

observation is consistent with the previous water spray suppression studies of pool fires [4]. 

- - Od-25um 1 420 

A Od-Elm 

410 --c4 Dd-lSOyrn 

Od-lOOym 400 

390 

380 

.370 

'350 

340 

330 

320 

310 

30045 46 47 48 49 50 51 52 53 54 55 

E380 

ti (SeC) 

(a) 

Figure 7. Volume averaged temperature 

(b) <Tu> verses time. 

1 --c Od-25pm 470 

460 -c od-75ym 

450 

440 

430 

420 

410 

400 A 

p90 

r380 

"370 

360 

350 

340 

. (bj 

for Configuration 2 showing (a) <TI> and 

CONCLUSIONS 

In summary, a spray-suppression model applicable to simulating the effects of water mist on 

large-scale pool fires has been developed and applied to a practical fire scenario representative of 

experiments at NRL. The findings from this study indicate a strong sensitivity of fire suppression to initial drop size where injection of larger drops may actually cause an increase in overall 

temperature due to enhanced turbulent mixing before enough spray can evaporate to provide 

sufficient cooling. In spray Configuration 2, an optimum drop size was observed indicating that 

nozzles that generate very fine mist (i.e., Dd < 50 pm) will not always provide maximum 

suppression for these conditions. Lastly, within the assumptions imposed for the inlet spray 

conditions and the fire scenario studied, the spray configuration using several lower pressure 

nozzles appeared to be slightly more effective at decreasing the gas phase temperature shortly 

after spray injection than a single high pressure nozzle. 

ACKNOWLEDGMENTS 

This work was supported in part by the US Department of Energy Defense Programs under the 

Engineering Sciences Research Foundation, the Department of Defense Safety and Survivability 

of Aircraft Initiative, and the Next-Generation Fire Suppression Program. This work was 

performed at Sandia National Laboratories, a multiprogram laboratory operated by Sandia 

Corporation, a Lockheed-Martin Company, for the US Department of Energy under contract 

DE-AC04-94AL85000. 

270 Halon Options Technical Working Confercnce 2-4 May 20lKI 
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