Sensitivity analysis of turbulence models using automatic differentiation.




Turbulence models are an example of computer simulations that parameterize complicated phenomena and depend on artificial model parameters heuristically justified from empirical information and experimental data. To assess the confidence in the results of such a turbulence simulation, a derivative-based sensitivity analysis is carried out. The sensitivities of the flow over a backward-facing step with respect to parameters of the turbulence model are investigated. The standard k-varepsilon model and the renormalization group (RNG) k-varepsilon model are compared. Both turbulence models are implemented in the FLUENT code to which automatic differentiation is applied using the ADIFOR system. In our case studies, none of the two turbulence models turns out to be the least sensitive with respect to all turbulence parameters.