Extending turbulence model uncertainty quantification using machine learning

Autor:	Matha, Marcel, Morsbach, Christian
Rok vydání:	2022
Předmět:	Computer Science - Computational Engineering Finance and Science Physics - Fluid Dynamics
Druh dokumentu:	Working Paper
Popis:	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. Comment: NeurIPS2021 - Thirty-fifth Conference on Neural Information Processing Systems, Fourth Workshop on Machine Learning and the Physical Sciences, 5 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.01560 Zobrazit plný text záznamu View this record from Arxiv