Tuesday, 17 July 2012 - NARAC · LLNL-PRES-564114 This work was performed under the auspices of the...

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LLNL-PRES-564114 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Lawrence Livermore National Security, LLC 16th Annual George Mason University Conference on Atmospheric Transport and Dispersion Modeling Tuesday, 17 th July 2012

Transcript of Tuesday, 17 July 2012 - NARAC · LLNL-PRES-564114 This work was performed under the auspices of the...

Page 1: Tuesday, 17 July 2012 - NARAC · LLNL-PRES-564114 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract

LLNL-PRES-564114 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Lawrence Livermore National Security, LLC

16th Annual George Mason University Conference on Atmospheric Transport and Dispersion Modeling

Tuesday, 17th July 2012

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§  Dense gas dispersion!§  Dense gas modeling:!

•  Empirical model!•  Shallow water model!•  RANS model!•  LES model!

§  Validation case: Thorney Island, Trial 26!§  Comparison of different models!§  Conclusions!

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§  Huge amounts of harmful chemicals are stored and transported across the country!

§  Many of these chemicals are heavier than air or show dense gas behavior due to cold temperatures at which they are stored!

§  In the case of an accidental or deliberate release, it is important to predict the dispersion of such chemicals in the atmosphere.!

§  Due to the heavier-than-air behavior of these chemicals, they tend to accumulate near the ground, thus increasing the concentration at breathing height. !

§  The hazard zone coverage area for these chemicals tends to be larger because of slumping effect.!

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§  A number of empirical models have been developed to predict the dispersion of dense gas releases over flat terrain.!

§  However for release in an urban area or over complex terrain, these models are not sufficient. !

§  Computational fluid dynamics have been successfully used to predict such scenarios!

§  As a part of this presentation, results from two different dense gas CFD models will be compared with available experimental data. The models:!•  Reynolds Averaged Navier Stokes Model!•  Large Eddy simulation model!

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•  Solves unsteady incompressible filtered NS equations!

!•  Second order accurate in

space and time!

•  Pressure Poisson equation solved efficiently using multigrid technique!

•  Log-law used at solid surface!

•  Smagorinsky closure!

!

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•  Solves time dependent incompressible RANS equations very quickly!

•  Based on artificial compressibility method (Chorin 1967)!

•  Uses very simplified algebraic turbulence model based on Prandtlʼs mixing-length theory!

•  Grid generation is done in seconds using existing ESRI shape files!

•  Runs in minutes, however, for dense gas simulation, it needs to be run in unsteady mode, which makes it computational cost similar to LES!

!

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Courtesy of Thomas Dunn (SAIC); photos originally from Jim McQuaid

•  Heavier-than-air outdoor releases!

•  Conducted at Thorney Island, UK 1982-1984!

•  Better understand dense gas behavior!

•  Develop and test dense gas models!

!

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§  2000 m3 of gas was released in about 1.5 s. !

§  The smoke marking of the gas was achieved by firing of smoke grenades.!

Gas filled container before the release!

Cloud of heavy gas after the removal of the container"

McQuaid, 1985b

Hall and Waters (1985)

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Deaves (1985) McQuaid and Roebuk (1985)

∼

•  Investigation of the flow and dispersion in the vicinity of an isolated building!

•  9 m high building 50 m downwind !•  Wind speed 1.9 m/s at 10 m AGL!•  Concentration samplers at 6.4 m AGL on the windward face

and 0.4 m AGL on the leeward face!

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Page 11: Tuesday, 17 July 2012 - NARAC · LLNL-PRES-564114 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract

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•  Qualitative, the model produces similar looking dense gas cloud!•  Higher peak concentrations are predicted by the model on both windward

and leeward faces!•  The arrival time of the cloud is well predicted by the model!

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•  Qualitative, the model produces similar looking dense gas cloud!•  Higher peak concentration is predicted by the model on the windward face!•  Lower peak concentration is predicted by the model on the Leeward face !•  The arrival time of the cloud is well predicted by the model!

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§  To understand why models could not accurately predict the peak concentration correctly, we decided to study closely the effect of inflow turbulence !

§  RANS model was chosen as it was easy to vary the inflow turbulence by changing the the friction velocity!

§  The inflow velocity is specified by a log-law!

!

§  Changing the friction velocity u*, changes the inflow turbulence!

u = u*kln z

zo

!

"#

$

%&

vT = (kz)2 dudz!

"#

$

%&= kzu*

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•  The peak concentration predicted by the model on the windward face is found to be very sensitive to the friction velocity!

•  Higher friction velocity produces a lower concentration value because of more mixing at the cloud top.!

!

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Lawrence Livermore National Laboratory LLNL-PRES-564114 16

•  The measurement was taken at 6.4 m on the front face of the cube.!•  The location is very close to the top of the dense gas cloud and hence the

concentration is very sensitive to the inflow turbulence !!

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§  Both the models are able to predict the concentration relatively well.!

§  LES produced dense gas clouds looks more realistic!

§  Friction velocity has a significant effect on the concentration value!

§  Both the models have similar computational cost for dense gas dispersion simulation!


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