Sensitivity Analysis of Discrepancy Terms introduced in Turbulence Models using Field Inversion
Fluid Dynamics
2021-12-09 v2
Abstract
RANS simulations with the Spalart-Allmaras turbulence model are improved for cases with flow separation using the Field Inversion and Machine Learning approach. A compensatory discrepancy term is introduced into the turbulence model and optimized using high-fidelity reference data from experiments. Influences on the optimization results with respect to regularization, grid resolution and areas in which the optimization is active are investigated. Finally, a neural network is trained and used to augment simulations on a test case.
Keywords
Cite
@article{arxiv.2104.13279,
title = {Sensitivity Analysis of Discrepancy Terms introduced in Turbulence Models using Field Inversion},
author = {Florian Jäckel},
journal= {arXiv preprint arXiv:2104.13279},
year = {2021}
}