Applicability of machine learning in uncertainty quantification of turbulence models

Marcel Matha; Karsten Kucharczyk

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arXiv · 2210.16358

Applicability of machine learning in uncertainty quantification of turbulence models

Marcel Matha·Karsten Kucharczyk

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Abstract

White paper: The aim of this work is to apply and analyze machine learning methods for uncertainty quantification of turbulence models. In this work we investigate the classical and data-driven variants of the eigenspace perturbation method. This methodology is designed to estimate the uncertainties related to the shape of the modeled Reynolds stress tensor in the Navier-Stokes equations for Computational Fluid Dynamics (CFD). The underlying methodology is extended by adding a data-driven, physics-constrained machine learning approach in order to predict local perturbations of the Reynolds stress tensor. Using separated two-dimensional flows, we investigate the generalization properties of the machine learning models and shed a light on impacts of applying a data-driven extension.

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Marcel Matha, Karsten Kucharczyk. 2022-10-26. Applicability of machine learning in uncertainty quantification of turbulence models. https://arxiv.org/abs/2210.16358

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