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.
Applicability of machine learning in uncertainty quantification of turbulence models
01.10.2022
Sonstige
Elektronische Ressource
Englisch
Applicability of machine learning in uncertainty quantification of turbulence models
Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2022
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