the differences between turbulence models
hi guys,
as we know that there are any several turbulence models to simulate physical problems. it couldn't use one turbulence model to simulate all of the problems. i hope you are could explain to me the differences between the turbulence models. such as kepsilon standard and rng, komega standard and sst, spallartallmaras. thank you very much :) 
Uhm. This is basic CFD. get yourself a good handbook or usermanual for these type of things... Such questions are not likely to get answered on a forum

you can find it in CFD book!!!
it can be downloaded free CFD book on this site http://avaxhome.ws/ebooks/science_bo...070016852.html http://avaxhome.ws/ebooks/science_bo...540318003.html :) 
Good question. I have been wondering this myself.
SST is a good default choice SparlartAllmaras: Eeconomical for large meshes. Performs poorly for 3D flows, free shear flows, flows with strong separation. Suitable for mildly complex (quasi2D) external/internal flows and boundary layer flows under pressure gradient (e.g. airfoils, wings, airplane fuselages, missiles, ship hulls). Standard kepsilon : Robust. Widely used despite the known limitations of the model. Performs poorly for complex flows involving severe pressure gradient, separation, strong streamline curvature. Suitable for initial iterations, initial screening of alternative designs, and parametric studies. RNG kepsilon : Suitable for complex shear flows involving rapid strain, moderate swirl, vortices, and locally transitional flows (e.g. boundary layer separation, massive separation, and vortex shedding behind bluff bodies, stall in wideangle diffusers, room ventilation). Realizable kepsilon : Offers largely the same benefits and has similar applications as RNG. Possibly more accurate and easier to converge than RNG. Standard komega : Superior performance for wallbounded boundary layer, free shear, and low Reynolds number flows. Suitable for complex boundary layer flows under adverse pressure gradient and separation (external aerodynamics and turbomachinery). Can be used for transitional flows (though tends to predict early transition). Separation is typically predicted to be excessive and early. SST komega : Offers similar benefits as standard k–ω. Dependency on wall distance makes this less suitable for free shear flows. Reynolds Stress : Physically the most sound RANS model. Avoids isotropic eddy viscosity assumption. More CPU time and memory required. Tougher to converge due to close coupling of equations. Suitable for complex 3D flows with strong streamline curvature, strong swirl/rotation (e.g. curved duct, rotating flow passages, swirl combustors with very large inlet swirl, cyclones). 
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