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Progress on the Field Inversion and Machine Learning Approach with the DLR TAU Code

Jäckel, Florian (2021) Progress on the Field Inversion and Machine Learning Approach with the DLR TAU Code. In: 20. STAB-Workshop - Jahresbericht 2021, pp. 81-82. 20. STAB-Workshop 2021, 16.-17.11.2021, Göttingen, Deutschland.

[img] PDF - Only accessible within DLR
[img] PDF - Only accessible within DLR

Official URL: https://www.dlr.de/as/Portaldata/5/Resources/dokumente/veranstaltungen/stab_workshop/STAB-Jahresbericht-2021.pdf


Accurate prediction of turbulent flows is important due to their typical key roles in engineering and in science. However, scale-resolving methods like LES and DNS are often still too expensive for daily use and Reynolds-averaged Navier-Stokes (RANS) simulations are used instead. Here, the influence of turbulence on the mean flow is modeled via a turbulence model. RANS turbulence models have always been largely empirical and their development supported by data. Given the increasing availability of highly accurate data from DNS and LES simulations as well as from experiments, and the increasing maturity of machine learning techniques it is argued that RANS turbulence models could be improved further using machine learning.

Item URL in elib:https://elib.dlr.de/145194/
Document Type:Conference or Workshop Item (Speech)
Title:Progress on the Field Inversion and Machine Learning Approach with the DLR TAU Code
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Jäckel, FlorianFlorian.Jaeckel (at) dlr.deUNSPECIFIED
Date:16 November 2021
Journal or Publication Title:20. STAB-Workshop - Jahresbericht 2021
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 81-82
EditorsEmailEditor's ORCID iD
Series Name:Jahresbericht
Keywords:Data-driven, Turbulence Modelling, Field Inversion, Machine Learning, Spalart-Allmaras
Event Title:20. STAB-Workshop 2021
Event Location:Göttingen, Deutschland
Event Type:Workshop
Event Dates:16.-17.11.2021
Organizer:DLR, STAB
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Efficient Vehicle
DLR - Research area:Aeronautics
DLR - Program:L EV - Efficient Vehicle
DLR - Research theme (Project):L - Digital Technologies
Location: Göttingen
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > CASE, GO
Deposited By: Jäckel, Florian
Deposited On:05 Jan 2022 12:14
Last Modified:22 Feb 2022 18:21

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