Optimization of a turbulence model by using data assimilation

This research investigates the effectiveness of optimization of the parameter values in the Spalart-Allmaras (SA) turbulence model applied to transonic flows. In the optimization, the ensemble Kalman filter which is one of data assimilation methods was employed. The optimization result showed the followings. The parameter values were estimated to weaken the production term and to enhance the collapse and diffusion terms compared to those of the original values. The simulation with the optimized values predicted the smaller turbulent eddy viscosity, and thus the friction drag was smaller than that with the original values. The computed pressure coefficients with the optimized values were agreed with experiment better than those with the original values.