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Assessment of data-driven Reynolds stress tensor perturbations for uncertainty quantification of RANS turbulence models

Matha, Marcel and Kucharczyk, Karsten and Morsbach, Christian (2022) Assessment of data-driven Reynolds stress tensor perturbations for uncertainty quantification of RANS turbulence models. In: AIAA Aviation 2022 Forum. AIAA AVIATION Forum 2022, 27. Juni – 01. Juli 2022, Chicago, Illinois, USA. doi: 10.2514/6.2022-3767. ISBN 978-1-62410-635-4.

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Official URL: https://arc.aiaa.org/doi/10.2514/6.2022-3767

Abstract

In order to achieve a more simulation-based design and certification process of jet engines in the aviation industry, the uncertainty bounds for computational fluid dynamics have to be known. This work shows the application of machine learning to support the quantification of epistemic uncertainties of turbulence models. The underlying method in order to estimate the uncertainty bounds is based on eigenspace perturbations of the Reynolds stress tensor in combination with random forests.

Item URL in elib:https://elib.dlr.de/186371/
Document Type:Conference or Workshop Item (Speech)
Title:Assessment of data-driven Reynolds stress tensor perturbations for uncertainty quantification of RANS turbulence models
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Matha, Marcelmarcel.matha (at) dlr.dehttps://orcid.org/0000-0001-8101-7303
Kucharczyk, Karstenkarsten.kucharczyk (at) dlr.deUNSPECIFIED
Morsbach, ChristianChristian.Morsbach (at) dlr.dehttps://orcid.org/0000-0002-6254-6979
Date:21 June 2022
Journal or Publication Title:AIAA Aviation 2022 Forum
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI :10.2514/6.2022-3767
ISBN:978-1-62410-635-4
Status:Published
Keywords:RANS, turbulence models, uncertainty quantification, machine learning, data-driven
Event Title:AIAA AVIATION Forum 2022
Event Location:Chicago, Illinois, USA
Event Type:international Conference
Event Dates:27. Juni – 01. Juli 2022
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:23 Jun 2022 12:46
Last Modified:16 Aug 2022 14:27

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