Generalizable improvement of the Spalart-Allmaras model through assimilation of experimental data

arXiv:2309.06679v2 Announce Type: replace-cross
Abstract: This study focuses on the use of model and data fusion for improving the Spalart-Allmaras (SA) closure model for Reynolds-averaged Navier-Stokes solutions of separated flows. In particular, our goal is to develop of models that not-only assimilate sparse experimental data to improve performance in computational models, but also generalize to unseen cases by recovering classical SA behavior. We achieve our goals using data assimilation, namely the Ensemble Kalman Filtering approach (EnKF), to calibrate the coefficients …

abstract arxiv cs.lg data experimental fusion improvement performance physics.comp-ph physics.data-an physics.flu-dyn solutions study through type