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Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification

Matha, Marcel and Kucharzyk, Karsten and Morsbach, Christian (2023) Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification. Computers & Fluids (255). Elsevier. doi: 10.1016/j.compfluid.2023.105837. ISSN 0045-7930.

[img] PDF - Only accessible within DLR until 16 April 2025 - Postprint version (accepted manuscript)


In order to achieve a virtual certification process and robust designs for turbomachinery, the uncertainty bounds for Computational Fluid Dynamics have to be known. The formulation of turbulence closure models implies a major source of the overall uncertainty of Reynolds-averaged Navier-Stokes simulations. We discuss the common practice of applying a physics constrained eigenspace perturbation of the Reynolds stress tensor in order to account for the model form uncertainty of turbulence models. Since the basic methodology often leads to overly generous uncertainty estimates, we extend a recent approach of adding a machine learning strategy. The application of a data-driven method is motivated by striving for the detection of flow regions, which are prone to suffer from a lack of turbulence model prediction accuracy. In this way any user input related to choosing the degree of uncertainty is supposed to become obsolete. This work especially investigates an approach, which tries to determine an a priori estimation of prediction confidence, when there is no accurate data available to judge the prediction. The flow around the NACA 4412 airfoil at near-stall conditions demonstrates the successful application of the data-driven eigenspace perturbation framework. Furthermore, we especially highlight the objectives and limitations of the underlying methodology.

Item URL in elib:https://elib.dlr.de/194012/
Document Type:Article
Title:Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Matha, MarcelUNSPECIFIEDhttps://orcid.org/0000-0001-8101-7303UNSPECIFIED
Morsbach, ChristianUNSPECIFIEDhttps://orcid.org/0000-0002-6254-6979UNSPECIFIED
Date:24 February 2023
Journal or Publication Title:Computers & Fluids
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:uncertainty quantification, turbulence models, RANS, machine learning, data-driven, random forest regression
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:27 Feb 2023 10:30
Last Modified:19 Oct 2023 15:49

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