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December 2, 2013, 05:54 
Help to make LES simulation faster

#1 
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Luca
Join Date: Mar 2013
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I'l trying to run a wind tunnel simulation with an LES turbulence model with pisoFoam. The domain is made of 17M cells. The simulation in made of a channel with some roughness blocks in the firsts 3/4 of it, and with a building model in the last quarter. Actually I'm solving with a time step equal to 0.0001s (10'000 Hz). This is done to keep the maxCo below one. Actually my maxCo is ~0.88. The problem is that the meanCo is something like 0.007 and the simulation is running very slowly. With 252 CPUs it's taking more or less 2.9s per iterations. This means that it takes 1 day to solve 1.25 seconds. Since i need to simulate at least 23 minutes and i can't use more CPU, what can i do? I was thinking to increase the time step since the meanCo is way smaller than 1, but this would cause the maxCo to be bigger than 1 in some points. If this point is in the roughness region (where i don't really care if the solution is wrong in some small portions) do you think the solution will still converge? How can i know "where" the Courant is bigger than 1? Do you have other ideas other than increasing the timestep? Maybe use pimpleFoam? Thanks a lot. Regards, Luca 

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December 2, 2013, 06:34 

#2  
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Bernhard
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It is a bit difficult to give any advise, could you maybe share fvSchemes and a snippet of the log file?
Quote:


December 2, 2013, 08:22 

#3 
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Håkon Strandenes
Join Date: Dec 2011
Location: Norway
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A low timestep and high resolution is some of the nature of LES. It's unavoidable. You can of course adjust the mesh in problematic areas, and perhaps end up with a slightly larger timestep, however, in the end you cannot overcome the fact that LES is computationally intensive by nature. If you only can afford a RANS simulation, stick to that. Perhaps you can use a RANS model to initiate a physical sane initial condition to save some time?
Last edited by haakon; December 2, 2013 at 08:23. Reason: Typo 

December 2, 2013, 08:55 

#4 
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Lieven
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Location: Leuven, Belgium
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I'm with haakon on this one. LES is by definition expensive so don't expect to be able to do it cheaply. If you find a way to do this, it will probably make you a very rich man ;). I would even recommend to further reduce your time step such that also maxCo is significantly smaller than 1.0 (for time integration accuracy). I would not switch to pimpleFoam since underrelaxation is quite unphysical in an LES context.
As I see it, there are two things you can do: 1. adjust your mesh. If you can't afford a simulation of 17M cells, just don't do it then. A converged solution on a 4M cells mesh is probably better than a halfway converged solution on a 17M cells. 2. Use a cheaper LES turbulence model (if you can) e.g. dynamics models are more expensive to compute than the classical smagorisky model. Using a RANS model to compute an initial field could help you speed up the conversion but this is certainly not guaranteed. Cheers, L 

December 2, 2013, 09:34 

#5 
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Luca
Join Date: Mar 2013
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Hi Bernhard. Thank you for reading.
Here is my fvSchemes: Code:
ddtSchemes { default backward; } gradSchemes { default Gauss linear; grad(p) Gauss linear; grad(U) Gauss linear; } divSchemes { default none; div(phi,U) Gauss linear; div(phi,k) Gauss limitedLinear 1; div(phi,B) Gauss limitedLinear 1; div(phi,nuTilda) Gauss limitedLinear 1; div(B) Gauss linear; div((nuEff*dev(T(grad(U))))) Gauss linear; } laplacianSchemes { default none; laplacian(nuEff,U) Gauss linear corrected; laplacian((1A(U)),p) Gauss linear corrected; laplacian(DkEff,k) Gauss linear corrected; laplacian(DBEff,B) Gauss linear corrected; laplacian(DnuTildaEff,nuTilda) Gauss linear corrected; } interpolationSchemes { default linear; interpolate(U) linear; } snGradSchemes { default corrected; } fluxRequired { default no; p ; } Code:
Time = 1.2372 Courant Number mean: 0.00771093 max: 0.898045 DILUPBiCG: Solving for Ux, Initial residual = 4.38996e05, Final residual = 8.81599e09, No Iterations 1 DILUPBiCG: Solving for Uy, Initial residual = 0.0012287, Final residual = 2.04545e07, No Iterations 1 DILUPBiCG: Solving for Uz, Initial residual = 0.000869898, Final residual = 1.5147e07, No Iterations 1 DICPCG: Solving for p, Initial residual = 0.00549978, Final residual = 0.000262121, No Iterations 3 time step continuity errors : sum local = 2.52331e10, global = 5.10701e14, cumulative = 2.4203e09 DICPCG: Solving for p, Initial residual = 0.000364781, Final residual = 9.84024e07, No Iterations 97 time step continuity errors : sum local = 9.47303e13, global = 4.74805e14, cumulative = 2.42025e09 ExecutionTime = 84873 s ClockTime = 85168 s forceCoeffs output: Cm = 2.37554 Cd = 39.8978 Cl = 64.6208 Cl(f) = 34.6859 Cl(r) = 29.9349 Time = 1.23725 Courant Number mean: 0.00771094 max: 0.89373 DILUPBiCG: Solving for Ux, Initial residual = 4.39007e05, Final residual = 8.81516e09, No Iterations 1 DILUPBiCG: Solving for Uy, Initial residual = 0.00122869, Final residual = 2.04562e07, No Iterations 1 DILUPBiCG: Solving for Uz, Initial residual = 0.0008699, Final residual = 1.51441e07, No Iterations 1 DICPCG: Solving for p, Initial residual = 0.0055296, Final residual = 0.000264223, No Iterations 3 time step continuity errors : sum local = 2.54366e10, global = 5.4272e14, cumulative = 2.4202e09 DICPCG: Solving for p, Initial residual = 0.000368202, Final residual = 9.85297e07, No Iterations 115 time step continuity errors : sum local = 9.48546e13, global = 5.09298e14, cumulative = 2.42015e09 ExecutionTime = 84875.5 s ClockTime = 85170 s forceCoeffs output: Cm = 2.38226 Cd = 39.8857 Cl = 64.572 Cl(f) = 34.6683 Cl(r) = 29.9038 Time = 1.2373 Courant Number mean: 0.00771095 max: 0.889406 DILUPBiCG: Solving for Ux, Initial residual = 4.39019e05, Final residual = 8.81608e09, No Iterations 1 DILUPBiCG: Solving for Uy, Initial residual = 0.00122868, Final residual = 2.04589e07, No Iterations 1 DILUPBiCG: Solving for Uz, Initial residual = 0.000869913, Final residual = 1.51427e07, No Iterations 1 DICPCG: Solving for p, Initial residual = 0.00552333, Final residual = 0.000261467, No Iterations 3 time step continuity errors : sum local = 2.51721e10, global = 5.30145e14, cumulative = 2.4201e09 DICPCG: Solving for p, Initial residual = 0.00036565, Final residual = 9.99106e07, No Iterations 94 time step continuity errors : sum local = 9.61889e13, global = 5.46456e14, cumulative = 2.42004e09 ExecutionTime = 84877.9 s ClockTime = 85172 s forceCoeffs output: Cm = 2.36583 Cd = 39.8604 Cl = 64.5751 Cl(f) = 34.6534 Cl(r) = 29.9217 Thank you very much 

December 2, 2013, 10:01 

#6 
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Bernhard
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Where I said fvSchemes I meant fvSolution, excuse me. Did you ever try to solve the pressure equation using a GAMG method? On 100200 cpus it is said to be more efficient in solving the pressure equation (generally) than PCG. You are using more processors, but it might be worth experimenting a bit with these settings.


December 2, 2013, 10:15 

#7 
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Luca
Join Date: Mar 2013
Posts: 68
Rep Power: 6 
I know LES is computationally expensive. I don't expect to make it quickly and easily. I was just asking if there was a way to make it a little faster. Maybe with some improvements I could achieve to solve the case twice or four time faster. That would mean a lot of gain, even if the computation is still very expensive.
I will try to initialise the case with a RANS or with a coarser mesh. I will also try to switch to GAMG and see if i find any improvements. About the LES model I already use a Smagorinsky one, so I can't use a cheaper one. Lastly, there are many papers that suggest that RANS is not so good in my field of study (wind flow around a low rise building), so i can't switch to that. PS: this is my fvSolution: Code:
solvers { p { solver PCG; preconditioner DIC; tolerance 1e06; relTol 0.05; } pFinal { solver PCG; preconditioner DIC; tolerance 1e06; relTol 0; } U { solver PBiCG; preconditioner DILU; tolerance 1e05; relTol 0; } k { solver PBiCG; preconditioner DILU; tolerance 1e05; relTol 0; } nuTilda { solver PBiCG; preconditioner DILU; tolerance 1e05; relTol 0; } } PISO { nCorrectors 2; nNonOrthogonalCorrectors 0; pRefCell 0; pRefValue 0; } 

December 2, 2013, 11:40 

#8 
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Lieven
Join Date: Dec 2011
Location: Leuven, Belgium
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Hi PJ,
Two remarks about the fvSchemes: 1. you should set relTol of p also to 0. 2. Are you sure nCorrectors = 2 is enough for your mesh? If you have a fully orthogonal mesh, you can set this to 0. If not, 2 is most likely too little... Cheers, L 

December 3, 2013, 03:07 

#9 
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Bernhard
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December 3, 2013, 04:41 

#10 
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Lieven
Join Date: Dec 2011
Location: Leuven, Belgium
Posts: 297
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How sorry, your fully correct Bernhard!
I wasn't paying attention to the pFinal entry :D (the solver I'm using doesn't have it). Cheers, Lieven 

December 3, 2013, 06:03 

#11 
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Luca
Join Date: Mar 2013
Posts: 68
Rep Power: 6 
Thank everybody for your kind help.
Now i'm running some tests on a smaller 2M cells case to benchmark a bit the solutions you proposed. I know that LES is expensive, I'm not asking to run it on my laptop in few hours. But in my lab we already ran 1520M cells cases that were solving about 1015 seconds every day. In a week we had our 12 minutes simulation. I could accept to run the simulation in 2 weeks, but not in 8. The problem with this case is that I have some very small cells near the model and there the Co number is 100 times bigger than everywhere else. To maintain the Co < 1 there i have to solve with a timestep 100 times smaller and though this slow down the simulation a lot. I was looking for the best way to solve this. Make a coarser mesh is of course and option, but it's not good for our purpose. I was therefore looking is a better solution exists. Thank you very much. I will post the results of the benchmark as soon as i have them 

December 4, 2013, 05:27 

#12 
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Philipp
Join Date: Jun 2011
Location: Germany
Posts: 1,297
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Hi, you mentioned that you use LES because RANS doesn't work. Did you try the SAS model?
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