I have a query in my mind which is related to the k-epsilon RNG model. When I start working on it, I faced an unusual phenomena about its instability as compared to k-epsilon standard and realizable. Here I quoted convergence problems as a unstable. Perhaps I tried to decode the program which used in fluent but I am still not sure about what the parameters which cause this are. I have two questions:
1. Is it sensitive model in all cases or in some specific cases?
2. Is there a unique way to use this model (unique way means there is particular solver and solution parameter combination or different way of initialization) to overcome this kind of problem and get the stable converged solution?
{While I was decoding the program, I went across fluent programing guide in which I found this suggestion: When you are using the RNG k- epsilon model, an approach that might help you achieve better stable convergence is to obtain a solution with the standard k- epsilon model before switching to the RNG model. Due to the additional non-linearities in the RNG model which cause for instability. I tried this but still I face the same problem}
Could anyone who already worked with this model or experienced this problem explain this?