Use of kepsilon and komega Models
I've extensively researched kepsilon and omega based models including SST.
I've read about the disadvantages of kepsilon which are that it is only valid for fully turbulent flow and nonseparated flows and that it can have stability issues due to numerical stiffness. Are there any advantages to the kepsilon model? This models seems to still be a widely used model despite its disadvantages. In my (limited) experience, its validity only for fully turbulent flows and nonseparated flows seems to narrow its applicability quite a bit. Based on the advantages of SST and my experience, there does not appear to ever be any reason to choose kepsilon over SST. Is this correct? What are the disadvantages of using SST? Thanks in advance for any information. I would really like to determine the answer to these questions so I would be very grateful for any responses. 
Better the devil you know?
As your research has no doubt shown, there are many aspects of RANS turbulence modelling that are deeply unsatisfactory. So to put it crudely, when faced with having to choose between a number of options known to be "wrong", there is something to be said for choosing an option (for example the kepsilon model) of known "wrongness". SST model in my experience produces good results, certainly much better when dealing with partially separated flows. For example, it does very well for transonic body lift at angle of attack (think of missile bodies). However, it seems to work best without wall functions, yet it is not always practical to mesh down to y+ ~1 wall spacing. Also, the kepsilon to komega switch can produce some dramatically unrealistic effective viscosity distrubutions. It may not affect the results, but it doesn't do much for one's confidence in the generality of the method. Life becomes a lot easier when you have some experimental data to provide some of that confidence.

Thanks for the quick and helpful reply!
I am supposed to become the thermal/fluid expert in the division of my company, so I am trying to build some documentation to which I can (and others) can later refer. I am quoting below some information that I have gathered. I'd appreciate any comments or suggestions. Standard ke The baseline twotransportequation model solving for kinetic energy k and turbulent dissipation ε. Turbulent dissipation is the rate at which velocity fluctuations dissipate. This is the default k–ε model. Coefficients are empirically derived; valid for fully turbulent flows only. In the standard ke model, the eddy viscosity is determined from a single turbulence length scale, so the calculated turbulent diffusion is that which occurs only at the specified scale, whereas in reality all scales of motion will contribute to the turbulent diffusion. The ke model uses the gradient diffusion hypothesis to relate the Reynolds stresses to the mean velocity gradients and the turbulent viscosity. Performs poorly for complex flows involving severe pressure gradient, separation, strong streamline curvature. The most disturbing weakness is lack of sensitivity to adverse pressure gradients; another shortcoming is numerical stiffness when equations are integrated through the viscous sublayer which are treated with damping functions that have stability issues [F. R. Menter, “Zonal Two Equation kw Turbulence Models for Aerodynamic Flows,” AIAA Paper #932906, 24th Fluid Dynamics Conference, July 1993; F. R. Menter, “TwoEquation EddyViscosity Turbulence Models for Engineering Applications,” AIAA Journal, vol. 32, no. 8, pp. 15981605, 1994]. {Notes: The author’s selfinvestigation for flow through a pipe is consistent with the statements that this model is valid for flows without separation and for fully turbulent flow. Compared to a finned problem which had separation and which predicted erroneous results with the ke model, this pipe flow did not have separation and results of ke and kw models showed good agreement for high Reynolds numbers. In this pipe flow, as Reynolds number was decreased, the difference between the inlet pressures predicted by the ke and kw models increased. Note that, based on the author’s limited experience, results for temperature are less sensitive to model choice and for velocity seem indifferent. Pressure results seem highly sensitive to both the model choice and the mesh. Be careful to check all results before deciding that results are valid. For additional details, see section entitled “Comparison of ke and kw Models.”} Pros: Robust. Widely used despite the known limitations of the model. Easy to implement. Computationally cheap. Valid for fully turbulent flows only. Suitable for initial iterations, initial screening of alternative designs, and parametric studies. Cons: Performs poorly for complex flows involving severe pressure gradient, separation, strong streamline curvature. Most disturbing weakness is lack of sensitivity to adverse pressure gradients; another shortcoming is numerical stiffness when equations are integrated through the viscous sublayer which are treated with damping functions that have stability issues [F. R. Menter, “Zonal Two Equation kw Turbulence Models for Aerodynamic Flows,” AIAA Paper #932906, 24th Fluid Dynamics Conference, July 1993; F. R. Menter, “TwoEquation EddyViscosity Turbulence Models for Engineering Applications,” AIAA Journal, vol. 32, no. 8, pp. 15981605, 1994]. Standard kw A twotransportequation model solving for kinetic energy k and turbulent frequency ω. This is the default k–ω model. This model allows for a more accurate near wall treatment with an automatic switch from a wall function to a lowReynolds number formulation based on grid spacing. Demonstrates superior performance for wallbounded and low Reynolds number flows. Shows potential for predicting transition. Options account for transitional, free shear, and compressible flows. The ke model uses the gradient diffusion hypothesis to relate the Reynolds stresses to the mean velocity gradients and the turbulent viscosity. Solves one equation for turbulent kinetic energy k and a second equation for the specific turbulent dissipation rate (or turbulent frequency) w. This model performs significantly better under adverse pressure gradient conditions. The model does not employ damping functions and has straightforward Dirichlet boundary conditions, which leads to significant advantages in numerical stability. This model underpredicts the amount of separation for severe adverse pressure gradient flows. Pros: 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). Cons: Separation is typically predicted to be excessive and early. Requires mesh resolution near the wall. BSL kw A variant of the standard k–ω model. Combines the original Wilcox kw model for use near walls and the standard k–ε model away from walls using a blending function. This eliminates the standard kw model’s strong sensitivity to free stream conditions without sacrificing nearwall performance. SST kw Shear Stress Transport (SST) is a variant of the standard k–ω model. Combines the original Wilcox kw model for use near walls and the standard k–ε model away from walls using a blending function, and the eddy viscosity formulation is modified to account for the transport effects of the principle turbulent shear stress [F. R. Menter, “Zonal Two Equation kw Turbulence Models for Aerodynamic Flows,” AIAA Paper #932906, 24th Fluid Dynamics Conference, July 1993; F. R. Menter, “TwoEquation EddyViscosity Turbulence Models for Engineering Applications,” AIAA Journal, vol. 32, no. 8, pp. 15981605, 1994]. Also limits turbulent viscosity to guarantee that τT~k. The transition and shearing options are borrowed from standard k–ω. No option to include compressibility. SST kw Shear Stress Transport (SST) is a variant of the standard k–ω model. Combines the original Wilcox kw model for use near walls and the standard k–ε model away from walls using a blending function, and the eddy viscosity formulation is modified to account for the transport effects of the principle turbulent shear stress [F. R. Menter, “Zonal Two Equation kw Turbulence Models for Aerodynamic Flows,” AIAA Paper #932906, 24th Fluid Dynamics Conference, July 1993; F. R. Menter, “TwoEquation EddyViscosity Turbulence Models for Engineering Applications,” AIAA Journal, vol. 32, no. 8, pp. 15981605, 1994]. Also limits turbulent viscosity to guarantee that τT~k. The transition and shearing options are borrowed from standard k–ω. No option to include compressibility. Pros: Offers similar benefits as standard k–ω. The SST model accounts for the transport of turbulent shear stress and gives highly accurate predictions of the onset and the amount of flow separation under adverse pressure gradients. SST is recommended for high accuracy boundary layer simulations. Cons: Dependency on wall distance makes this less suitable for free shear flows compared to standard kw. Requires mesh resolution near the wall. A Reynolds Stress model may be more appropriate for flows with sudden changes in strain rate or rotating flows while the SST model may be more appropriate for separated flows. 
Turbulence Models
I will share my experience with using komega SST with low Reynolds number correction vs. kepsilon model. For a very large multiple liquid/gas systems with conjugate heat transfer and huge amount of radiation we tested both types of turbulence models. The system had both very low velocity flow domains and one flow domain which was very high temperature, high velocity with jets,recirculating flows etc. The kepsilon model (both realizable and RNG) had difficulty converging and seemed to also limit the energy equation from converging as well. The komega SST method resulted in more robust convergence and lower oscillation in the residuals. However, as all CFD users know convergence does not equate to accuracy. We have not correlated this model to test data so we cannot verify the accuracy. In addition the mesh was so large and complex that it was impossible to craete a boundary layer mesh so we could not guarentee that the yplus values at the wall were close to 1.0.
We are currently modling another case involving combined turbulence and laminar flow (maybe 1% of fluid domain turbulent and the rest laminar) with natural convection and species diffusion. We have experienced a lot of convergence issues using the kepsilon model and we are now trying the komega SST model with low Reynolds Number correction. I will let you know how this turns out. Overall our experience with the komega model has been good but more data needs to be gathered. William :) 
Sorry to bump this old thread.
Just wanted to say, isnt DES (the ultimate mutant so far) aparent suitable solution?From my understanding, DES acts as:

hello seniors i too having a problem in selecting the model i having a problem in solving a transient case over the compressible flow in a combustion process in a scram jet . please suggest me the turbulence model for the effective combustion which model is most suitable to do so. among kepsilon , k omega, transistion sst which will give more effective combustion over the circular combustor. ?....

Hello everyone,
I'm using OpenFOAM tosimulate a flow in a plant and a dam and I want to change the turbulence model from KEpsilon to KOmega. The problem is that I don't know what I have to change in the files :  RASPropreties  transportPropreties  turbulencePropreties and I don't what condition and how to set k and Omega for this model of turbulence Can someone help me ? 
Hi Dadou,
I hope I can still help. I also just started learning but as far as I know you need to change:  RASProperties: "kEpsilon" to "kOmega" (see pages U99 & U184 in User Guide)  transportProperties: if the fluid stays the same, keep it the same. "nu" is the kinematic viscosity of the fluid (see page U21 in User Guide)  turbulenceProperties: keep it "RASModel" since you want to stay in Reynoldsaveraged stress modelling (see page U184 in User Guide) For estimating k and Omega take a look at THIS Please correct me if I'm wrong! As I said, I also just started learning :rolleyes: 
SST and kepsilon myth
I have heard so many times saying people SST is a better model than epsilon based models. This may be true if you are interested only in the region where the separation occurs especially for negative pressure gradients. If you are interested in the flow development after the attachment, lowRe keps or realizable kepsilon model is definitely a better choice.
I have even seen performing realizable kepsilon with low y+ treatment model performing better than SST in certain transient flows involving strong flow separation. To me it seems that is not right to say that SST is better than kepsilon. One must be careful in swirling and rotating flows, there kepsilon model has certain difficulties. 
"better wall treatment "..ofc sst is based on the seperation point on the wall..thats why (some) prefers DES, in which thx to the nonconst term in dissipation , it can work as kepsilon at regions far enough from wall (flow development)

Turbulence error
Hello CFD member,
I am using LRT komega for my work: 2 phase flow. But when I ran, the kinetic energy and omega had negative value. I dont know what and why got that error. Anyone can help me? 
Please help me!!!!
Hi every body,
I am newbie to CFD, earlier i have coded inviscid flows through Eulers equation with Roe's scheme, now i want to move for viscous and turbulence please help me like what equation should i solve how to include turbulence. A detailed explanation would be very helpful. Thanks in advance. with regards, Vishnu C 
Hello Foamers!
I am trying simulate a flow through radial diffuser with simpleFoam. In this case, I readed in the literature that RAS models work better without wall functions with mesh more refined near walls. Please, someone help me how can I disable the wall functions in k, epsilon e nut? I tried these configuration for nut, k and epsilon regarding to just wall boundary condition and doesn't work. I tried value close to zero (1e10) for k and epsilon and doesn't work too. nut: walls { type calculated; value uniform 0; } k: walls { type fixedValue; value uniform 0; } epsilon: walls { type fixedValue; value uniform 0; } Thanks. 
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