Langer, Stefan (2024) Application of the iteratively regularized Gauss–Newton method to Parameter identification problems in Computational Fluid Dynamics. Computers & Fluids, 284 (106438). Elsevier. doi: 10.1016/j.compfluid.2024.106438. ISSN 0045-7930.
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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S004579302400269X
Kurzfassung
Field Inversion and Machine Learning is an active field of research in Computational Fluid Dynamics (CFD). This approach can be leveraged to obtain a closed-form correction for a given turbulence model to improve the predictions. The fundamental approach is to insert a parameter into the system of RANS equations and determine it in a way such that, for example, a given pressure distribution is better approximated compared to the one obtained with the original set of equations. The goal of this article is twofold. Numerical arguments are presented that these kinds of problems can be severely ill-posed. In the second part, an approach is presented to directly reconstruct the turbulent viscosity field along with an example. The Iteratively Regularized Gauss-Newton Method (IRGNM) is used for a realization. The construction of a problem-adapted norm for a finite volume method is presented. Finally, an outlook is presented on how this approach can be used to possibly modify or improve turbulence models such that not only one, but a larger number of test cases are considered.
elib-URL des Eintrags: | https://elib.dlr.de/206815/ | ||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||
Titel: | Application of the iteratively regularized Gauss–Newton method to Parameter identification problems in Computational Fluid Dynamics | ||||||||
Autoren: |
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Datum: | September 2024 | ||||||||
Erschienen in: | Computers & Fluids | ||||||||
Referierte Publikation: | Ja | ||||||||
Open Access: | Ja | ||||||||
Gold Open Access: | Nein | ||||||||
In SCOPUS: | Ja | ||||||||
In ISI Web of Science: | Ja | ||||||||
Band: | 284 | ||||||||
DOI: | 10.1016/j.compfluid.2024.106438 | ||||||||
Verlag: | Elsevier | ||||||||
Name der Reihe: | Elsevier | ||||||||
ISSN: | 0045-7930 | ||||||||
Status: | veröffentlicht | ||||||||
Stichwörter: | RANS equations Iteratively regularized Gauss–Newton method Field Inversion and parameter identification | ||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||
HGF - Programm: | Luftfahrt | ||||||||
HGF - Programmthema: | Effizientes Luftfahrzeug | ||||||||
DLR - Schwerpunkt: | Luftfahrt | ||||||||
DLR - Forschungsgebiet: | L EV - Effizientes Luftfahrzeug | ||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | L - Digitale Technologien | ||||||||
Standort: | Braunschweig | ||||||||
Institute & Einrichtungen: | Institut für Aerodynamik und Strömungstechnik > CASE, BS | ||||||||
Hinterlegt von: | Langer, Dr.rer.nat. Stefan | ||||||||
Hinterlegt am: | 25 Okt 2024 11:32 | ||||||||
Letzte Änderung: | 25 Okt 2024 11:32 |
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