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Extending turbulence model uncertainty quantification using machine learning

Matha, Marcel and Morsbach, Christian (2021) Extending turbulence model uncertainty quantification using machine learning. NeurIPS - Thirty-fifth Conference on Neural Information Processing Systems | Fourth Workshop on Machine Learning and the Physical Sciences, 13.-14. Dez. 2021, Onlinekonferenz.

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Abstract

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.

Item URL in elib:https://elib.dlr.de/146992/
Document Type:Conference or Workshop Item (Poster)
Title:Extending turbulence model uncertainty quantification using machine learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Matha, Marcelmarcel.matha (at) dlr.deUNSPECIFIED
Morsbach, ChristianChristian.Morsbach (at) dlr.dehttps://orcid.org/0000-0002-6254-6979
Date:December 2021
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:turbulence model, uncertainty quantification, machine learning, augmented turbulence model
Event Title:NeurIPS - Thirty-fifth Conference on Neural Information Processing Systems | Fourth Workshop on Machine Learning and the Physical Sciences
Event Location:Onlinekonferenz
Event Type:Workshop
Event Dates:13.-14. Dez. 2021
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 > Numerical Methodes
Deposited By: Matha, Marcel
Deposited On:10 Dec 2021 14:32
Last Modified:10 Dec 2021 14:32

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