Summary

For the past three decade, Reynolds Average Navier-Stokes models have been widely used in the industry to simulate complex flows. However, these models suffer from limitations. Indeed there are still large discrepancies in the Reynolds stresses between the RANS model and high-fidelity data provided by DNS or experiments. This paper presents a strategy to correct the Menter SST model using an explicit algebraic model and two different neural networks: an multilayer perceptron (MLP) and a generative adversarial network (GAN). Moreover, in order to preserve the physical properties of the Reynolds stress tensor, we introduce a penalisation term in the loss of the GAN.

Abstract

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document

Full Paper

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document
Back to Top
GET PDF

Document information

Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22

Volume Computational Fluid Dynamics, 2022
DOI: 10.23967/eccomas.2022.235
Licence: CC BY-NC-SA license

Document Score

0

Views 4
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?