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Applicability of machine learning in uncertainty quantification of turbulence models

Matha, Marcel and Kucharczyk, Karsten (2022) Applicability of machine learning in uncertainty quantification of turbulence models. Other. Institut für Antriebstechnik. 17 S.

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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.

Item URL in elib:https://elib.dlr.de/189642/
Document Type:Monograph (Other)
Title:Applicability of machine learning in uncertainty quantification of turbulence models
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Matha, MarcelUNSPECIFIEDhttps://orcid.org/0000-0001-8101-7303UNSPECIFIED
Date:October 2022
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Number of Pages:17
Keywords:uncertainty quantification, turbulence modeling, Reynolds Averaged Navier Stokes, machine learning, data-driven modeling, random forest regression
Institution:Institut für Antriebstechnik
Department:Numerische Methoden
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Clean Propulsion
DLR - Research area:Aeronautics
DLR - Program:L CP - Clean Propulsion
DLR - Research theme (Project):L - Virtual Engine
Location: Köln-Porz
Institutes and Institutions:Institute of Propulsion Technology
Deposited By: Matha, Marcel
Deposited On:17 Nov 2022 11:20
Last Modified:17 Nov 2022 11:20

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