Główne pojęcia
The author aims to enhance the Spalart-Allmaras model's performance in separated flows by calibrating its coefficients using Ensemble Kalman Filtering (EnKF) based on experimental data assimilation.
Streszczenie
The study focuses on improving the Spalart-Allmaras closure model for Reynolds-averaged Navier-Stokes solutions of separated flows. By assimilating sparse experimental data, the recalibrated SA model demonstrates generalization to various flow conditions, showing significant improvements in skin friction and pressure coefficients. The EnKF calibration loop adjusts SA coefficients iteratively, resulting in enhanced accuracy without compromising the model's behavior in attached or unbounded flows.
The research explores the impact of varying key coefficients like σ, Cw2, Cw3, Cv1, Cb1, and fw on flow predictions. The calibrated SA model shows improved accuracy in predicting flow quantities for separated flows while maintaining performance in attached and unbounded scenarios. The EnKF-based calibration methodology proves effective in optimizing the SA model's behavior across different flow conditions.
Statystyki
Despite using observational data from a single flow condition around a backward-facing step (BFS), the recalibrated SA model demonstrates generalization to other separated flows.
Significant improvement is observed in skin friction coefficient (Cf) and pressure coefficient (Cp) for each flow tested.
The mean of members of the posterior Xp is obtained as: b = 1.39, σ = 0.97, Cw2 = 0.78, Cw3 = 0.67, and Cv1 = 8.24.