Sensitivity Analysis of Discrepancy Terms Introduced in Turbulence Models Using Field Inversion

RANS simulations with the Spalart-Allmaras turbulence model are improved for cases flow separation using Field Inversion and Machine Learning approach. A compensatory discrepancy term is introduced into optimized high-fidelity reference data from experiments. Influences on optimization results respect to regularization, grid resolution areas in which active investigated. Finally, a neural network trained used augment test case.