Extending turbulence model uncertainty quantification using machine learning
Computational Engineering, Finance, and Science
2022-02-07 v2 Fluid Dynamics
Abstract
In order to achieve a more virtual design and certification process of jet engines in aviation industry, the uncertainty bounds for computational fluid dynamics have to be known. This work shows the application of a machine learning methodology to quantify the epistemic uncertainties of turbulence models. The underlying method in order to estimate the uncertainty bounds is based on an eigenspace perturbation of the Reynolds stress tensor in combination with random forests.
Cite
@article{arxiv.2202.01560,
title = {Extending turbulence model uncertainty quantification using machine learning},
author = {Marcel Matha and Christian Morsbach},
journal= {arXiv preprint arXiv:2202.01560},
year = {2022}
}
Comments
NeurIPS2021 - Thirty-fifth Conference on Neural Information Processing Systems, Fourth Workshop on Machine Learning and the Physical Sciences, 5 pages, 4 figures