Data-driven turbulence modeling in separated flows considering physical mechanism analysis

Accurate simulation of turbulent flow with separation is an important but challenging problem. In this paper, a data-driven Reynolds-averaged turbulence modeling approach, field inversion and machine learning implemented to modify the Spalart–Allmaras model separately on three cases, namely, S809 airfoil, periodic hill GLC305 airfoil ice shape 944. Field based discrete adjoint method used quantify model-form uncertainty limited experimental data. An artificial neural network trained predict corrections local features extract generalized knowledge. Physical knowledge nonequilibrium in separating shear layer considered when setting prior uncertainty. The results show that from demonstrate strong consistency underlying physical mechanism turbulence. quantity interest observation data can be reproduced relatively high accuracy by augmented model. addition, validation similar conditions shows certain extent generalization ability.