Computational Study of Effects of Drop Size Distribution

ILASS-Americas 22nd Annual Conference on Liquid Atomization and Spray Systems, Cincinnati, OH, May 2010

Computational Study of Effects of Drop Size Distribution in Fire Suppression Systems

G. Tanner*, W. Kalata, K. Brown, and R. J. Schick Spray Analysis and Research Services

Spraying Systems Co. North Ave. and Schmale Rd.

P.O. Box 7900 Wheaton, IL 60187 USA

Abstract

As the use of water mist continues to gain acceptance as a practical fire suppression agent, the fire protection indus-try continues using computational fluid dynamics (CFD) to model the formation, delivery and flame interaction of water mist drops. With positive results and incredible progress, several efforts have been made over recent years to improve the fire suppression modeling techniques. However, a simulation is only as meaningful as the quality of the initial assumptions and parameters used to drive the suppression model. With this in mind, the authors incorporate a comprehensive water mist droplet characterization with realistic geometrical and parametric configuration into CFD fire suppression model. In this study, spray simulations in fire suppression scenario were conducted using ANSYS FLUENT CFD package. The main focus for the CFD study was to assess the fire suppression sensitivity to realistic drop size distribution in hydraulic atomizer nozzles. Performed analysis consisted of three different types of hydraulic atomizer nozzles with identical flow capacity conditions under the same geometrical and heat release rate environment. With the swirl type atomizer where Volumetric Median Diameter (VMD) was the smallest, the evaporation rate was the high-est and therefore the suppression process was the most efficient.

*Corresponding Author: [email protected]

ILASS Americas, 22nd Annual Conference on Liquid Atomization and Spray Systems, Cincinnati, OH, May 2010

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Introduction As long as fire suppression and extinguishment has

protected our lives and property, we have been devising ways to deliver the suppression agent to the fire. Ever since the fire sprinklers were invented, the technology and complexity of extinguishment methodology has grown at a truly exponential rate. Today, there are lit-erally hundreds, if not thousands of ways to dispense the extinguishing agent. The innovation and diversity of these systems is seen to be driven by application to solve specific fire problems.

With spray nozzles, extensive design work over many decades has produced highly engineered devices in innumerable types and styles. Engineered sprays have been developed for many thousands of applica-tions and industries, and this has also been largely dri-ven by specific needs. Many spray styles developed originally for other purposes have been applied to fire protection. Setting aside pneumatic atomization or dry chemical extinguishment sprays, consider that hydraulic atomizing and impingement nozzles are available in hollow cone, full cone, spiral, flat, or even square and oval spray patterns. The selection that is commercially available is very broad and can create considerable con-fusion for engineers looking to integrate the proper spray nozzle into an extinguishment or suppression system. With single orifice, cluster heads, and spray angles ranging from 0° to 360° in common use, the fire system designer must determine which type of nozzle works in different fire hazard applications.

With each general application and set of installa-tion parameters, approval agencies and the Authority Having Jurisdiction (AHJ) require only specific cover-age or spray angle and flow density, which are believed to be the most relevant parameters of spray perfor-mance in various situations.

As the applications become more specific to water mist fire suppression, the additional factor of drop size is taken into consideration. However, there is no cur-rent water mist fire protection standard that requires listings to include basic drop size information such as the Volume Median Diameter (VMD) or Sauter Mean Diameter (SMD). Rather, water mist systems must be listed for specific fire hazards requiring live fire per-formance validation, a time consuming and financially burdensome endeavor.

The potential alternative to physical testing is to use advanced computational analysis to design, build, performance test, and certify a fire suppression system before a pipe is ever laid. Computational simulation of water mist fire suppression though is an incredibly complex undertaking. Modeling the physics of water droplet and flame interaction, heat absorption and eva-poration, vapor displacement, fuel combustion, and temperature reduction are just part of what needs to be

analyzed to accurately predict the fire suppression ca-pabilities of a water mist protection system.

One of the more important criteria, however, was also the most overlooked and ignored. In the past, in majority of simulation efforts, water drop size distribu-tion statistics were simplified down to the minimal amount of data required to convey the maximum infor-mation possible. In some cases a single parameter, such as VMD, was the only statistic used.

Perhaps this is because drop size information can be considered to be confusing and overly complex. This is generally the case with fire protection when discussing sprinkler and nozzle drop size statistics. After all, the research focuses on system suppression and extinguishment characteristics and not spray noz-zles. But, is it proper and accurate to fully characterize a water mist or other spray nozzle in such an abridged manner?

Fire protection designers have begun to realize that nozzles cannot be simplified down to a single number such as a representative diameter. Whether that speci-fication is the VMD, SMD or DV0.9, it is not an accurate representation of the entire spray field or spray distribu-tion. Despite this recognition, there are such limited standards and certification guidelines to work from that the system designers and modelers are basically picking whatever statistic is readily available to them without appreciating the inherent limitations and problems of doing so. Can parameters be used that are sufficiently meaningful to completely and properly characterize the spray?

Studies have been conducted in recent years incor-porating more comprehensive particle size characteriza-tion within numerical simulations. [1]. Many studies however are not adapting the simulations to account for the variation of particle size distributions between dif-ferent types of spray nozzles, though it is well unders-tood within the industry how nozzle type, flow rate, pressure and other variables can dramatically change the resulting drop size distributions.

Our purpose here is to utilize computational fluid dynamics (CFD) to assess the fire suppression sensitivi-ty to realistic drop size distribution in hydraulic atomiz-er nozzles. Performed analysis consisted of three dif-ferent types of hydraulic atomizer nozzles with identic-al supply pressure and flow capacity conditions under a realistic geometrical and heat release rate environment.


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Hydraulic Atomizers In previous work by the authors [2,3], several hy-

draulic atomizer nozzles were tested at a typical opera-tion pressure(s) for each style nozzle. See Figs. 1-3 and Table 1. For the purposes of this initial study, the liq-uid flow rates were normalized between each nozzle to make the spray characteristics the main variables on which suppressive and evaporative conditions would depend on.

Swirl type atomizer. See Figure 1. This atomizer features an internal core through which the liquid flow is directed with some tangential velocity component. The liquid is then forced through an exit orifice in a hollow cone pattern.

Figure 1. Swirl type atomizer.

Full cone nozzle. See Figure 2. The full cone noz-

zle features an internal swirl element commonly known as a ‘vane’ that imparts radial velocity and counter-swirl to form a full cone pattern.

Figure 2. Full cone nozzle.

Spiral nozzle. See Figure 3. The spiral nozzle is

essentially a deflector type nozzle that creates a crude full cone spray pattern in a tightly controlled spray an-gle, and usually features the largest possible flow rate for a given pipe connection size.

Figure 3. Spiral nozzle.

Computational Methods

CFD simulations were performed with ANSYS FLUENT version 12.1. The CFD model was loosely reproduced according to the experiments reported by LeFort et al. [4]. The computational domain shown in Figure 4 was set up as square surface in cylindrical coordinates with rotation axis going through the spray injection point on top and the middle of the fire pool on the bottom (2D axisymmetric domain). The spray in-jection is 2 m directly above the center of the fire pool surface. The fire pool surface with 0.265 m radius was setup to emit heat flux which equaled to 951.9 kW/m2, as specified by LeForet et al. [4]. Ceiling and floor were setup as Wall (non-slip and adiabatic) boundary conditions (BC's). The outer vertical edge was set as Pressure Outlet BC which acted as an opening with zero relative pressure and 300 °K backflow tempera-ture.

Figure 4. Axisymmetric setup for the CFD model.

Units Swirl Type Atomizer

Full Cone Nozzle

Spiral Nozzle

Flow Rate m3/s 1.367 x 10-4 1.367 x 10-4 1.367 x 10-4 Velocity m/s 36 28 33

Spray Angle deg. 78 68 120 Dmin (DV0.01) μm 29 44 50 Dmean (DV0.50) μm 96 334 151 Dmax (DV0.99) μm 165 823 249

n* -- 3.5 2.1 3.8

* Rosin-Rammler distribution uniformity constant

Table 1. Drop size injection parameters.


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When multiple transient cases for spray cooling were considered, the 2D axisymmetric assumption al-lowed faster solution convergence with relative small number of elements and simplified computational grid generation. Computational grid (mesh) was created within GAMBIT 2.4. The mesh was built with quadrila-teral (box) elements with "Paving" option to employ gradual size function between spray affected regions (inner part of interior), wall boundaries, outer boundary, and an outer part of interior. The mesh in all simulated case was the same and it consisted of 66,555 cells.

Throughout all simulations the following models were included: k-ε Realizable Turbulence Model with Full Buoyancy Effects, coupled Discrete Phase Model (DPM) for LaGrangian tracking of water droplets, and Species Transport Model to include mixing of air and water vapor due to evaporation. Since spray cooling of heated gas was the focus of this study, radiation model was omitted.

Spray injections were all based on three spray cha-racteristics derived from spray nozzles described by Tanner et al. [2,3] (also see Figures 1-3). The CFD injection characteristics are highlighted in Table 1 above. To keep the comparison consistent, the flow rate for all three spray nozzle cases was the same.

Initially, the process to define injections was pro-grammed in MATLAB where FLUENT journal files were written. These files were commands to define series of grouped injections. The series were used to vary the direction of injections. The number of series depended on the spray angle. The higher the spray an-gle, the larger the number of series. Flow rates were distributed evenly among the injection series. Each se-ries incorporated grouped injections that employed Ro-sin-Rammler droplet distributions as shown in Equation 1. Each group was composed of 25 diameters ranging from Dmin to Dmax ditributed via Rosin-Rammler fuc-tion.

1 (1) Q is the fraction of total volume of drops with di-

ameter less than D. and n are constants inherent to the Rosin-Rammler function associated with the distri-bution center and width, respectively. CFD injections used minimum, mean and maximum diameters (Dmin, Dmean and Dmax respectively) which were based on Ro-sin-Rammler derived DV0.01, DV0.5, and DV0.99 respec-tively.

Ceiling, floor and Pressure Outlet boundaries had Escape DPM-BC's. The droplets that made a contact with such boundary vanished in the computational do-main without any further effect to the momentum and energy. The fire pool had a Wall-Jet DPM-BC. In this BC, droplets impinge onto the wall and depending on their momentum, form a small liquid film or reflect.

This DPM-BC is appropriate for high-temperature walls [5,6].

Initially, steady state simulations were performe (Figure 5 below) to assess the 2D axisymmetric simula-tion with natural convection. Heating (no spray) and cooling (spray active) periods were computed.

In transient simulations, non-spray case (heating period) was computed for 30 seconds with iterative time advancement while the time step (dt) was 0.01 seconds. The three cases had spray periods each 15 seconds long and reheat periods each 5 seconds long (see Figures 7-9). They were performed with the same time advancement as non-spray case. The droplets were injected with the same time step as the main run simulations (dt=0.01 s). In the transient simulations, especially in heating up period, formation and rise of air classical thermals [7] were observed.

Conclusions and Recommendations

Our purpose here has been to utilize computational fluid dynamics to assess the fire suppression sensitivity to realistic drop size distributions. Three different hy-draulic atomizer nozzles were used with identical supply pressure and flow capacity conditions to ex-amine the heat absorption capabilities and evaporation rates.

Via comparison, Figures 7-9 shown that the swirl type atomizer with the smallest VMD provided the highest evaporation rate and the most efficient heat absorption. Though the full cone and spiral nozzles have much greater drop sizes and those particles show the ability to penetrate the high heat zone, the smaller particle sizes generated by the swirl type atomizer greatly reduce the overall heat, providing a better over-all level of fire suppression.

Further work will be conducted to broaden the scope of these fire suppression simulations. This study was limited to axisymmetric simulation of a surface heat release rate environment. Additional studies are expected to incorporate combustion simulations in a 2D and further progress into 3D environment with multiple injection points.

The authors urge caution when including particle size distributions in fire suppression modeling. It is important to consider the different types of spray noz-zles that can be used in various fire suppression appli-cations and the critical variations in spray field proper-ties that affect the results for a system in application. In addition, probing deeper with differences within the spray field such as velocity, momentum, radial distance, fluid pressure, and nozzle discharge coefficient may provide some insight when systems or models do not perform as expected.


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References 1. Blanchard, E., Boulet, P., Desanghere, S., Cesmat,

E., “Numerical Simulation of Radiation Propagation Through Water Mist,” Ninth International Water Mist Conference, London, United Kingdom, Sep-tember 2009

2. Tanner, G.A. and Knasiak, K.F., “Spray Characteri-zation of Typical Fire Suppression Nozzles,” Third International Water Mist Conference, Madrid, Spain, September 2003

3. Tanner, G.A. and Knasiak, K.F., “Water Mist Sim-plification Effects on Fire Suppression Modeling: A Challenge to the Industry,” ILASS-Americas, 20th Annual Conference on Liquid Atomization and Spray Systems, Chicago, May 2007

4. LeFort, G., Marshall, A.W. and Pabon, M., “Evalua-tion of Surfactant Enhanced Water Mist Perfor-mance,” Fire Technology, Volume 45, Number 3, 2009

5. ANSYS FLUENT 12.0 - User's Guide, ANSYS, Inc., Northbrook, IL, 2009

6. ANSYS FLUENT 12.0 - Theory Guide, ANSYS, Inc., Northbrook, IL, 2009

7. Bejan, A., Convection Heat Transfer, 2nd Ed., New York: John Wiley and Sons, 1995


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Figure 5. Summary of steady state simulations.


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Figure 6. Initial heating period (no spray). Top row shows temperature contours in whole domain. Middle row shows temporal temperature contours at center axis and at the fire pool edge. Bottom row shows temporal temperature traces at various heights for center axis and the fire pool edge.


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Figure 7. Cooling and reheating periods for swirl type nozzle. Top row shows temperature contours in whole domain. Middle row shows temporal temperature contours at center axis and at the fire pool edge. Bottom row shows temporal temperature traces at various heights for center axis and the fire pool edge.


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Figure 8. Cooling and reheating periods for full cone type nozzle. Top row shows temperature contours in whole domain. Middle row shows temporal tempera-ture contours at center axis and at the fire pool edge. Bottom row shows temporal temperature traces at various heights for center axis and the fire pool edge.


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Figure 9. Cooling and reheating periods for spiral type nozzle. Top row shows temperature contours in whole domain. Middle row shows temporal temperature contours at center axis and at the fire pool edge. Bottom row shows temporal temperature traces at various heights for center axis and the fire pool edge.

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Computational Study of Effects of Drop Size Distribution