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Title: A perspective on machine learning methods in turbulence modeling
Author(s): Beck, Andrea
Kurz, Marius
Issue Date: 2021
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-984691
Subjects: Closure models,
Machine learning,
Turbulence simulation
Abstract: Thiswork presents a review of the current state of research in data-driven turbulence closure modeling. It offers a perspective on the challenges and open issues but also on the advantages and promises of machine learning (ML) methods applied to parameter estimation, model identification, closure term reconstruction, and beyond, mostly from the perspective of large Eddy simulation and related techniques. We stress that consistency of the training data, the model, the underlying physics, and the discretization is a key issue that needs to be considered for a successful ML-augmented modeling strategy. In order to make the discussion useful for non-experts in either field, we introduce both the modeling problem in turbulence as well as the prominentML paradigms andmethods in a concise and self-consistent manner. In this study, we present a survey of the current data-driven model concepts and methods, highlight important developments, and put them into the context of the discussed challenges.
Open Access: Open access publication
License: (CC BY-NC-ND 4.0) Creative Commons Attribution NonCommercial NoDerivatives 4.0(CC BY-NC-ND 4.0) Creative Commons Attribution NonCommercial NoDerivatives 4.0
Sponsor/Funder: Projekt DEAL 2021
Journal Title: GAMM-Mitteilungen
Publisher: Wiley-VCH
Publisher Place: Weinheim
Volume: 44
Issue: 1
Original Publication: 10.1002/gamm.202100002
Page Start: 1
Page End: 27
Appears in Collections:Fakultät für Verfahrens- und Systemtechnik (OA)

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