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Titel:

A multi-objective Bayesian optimization environment for systematic design of numerical schemes for compressible flow

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Feng, Yiqi; Schranner, Felix S.; Winter, Josef; Adams, Nikolaus A.
Abstract:
Multi-objective Bayesian optimization (MOBO) is an efficient and robust optimization framework for expensive functions. In this work, we use MOBO to optimize the free parameters of a high-order nonlinear weighted essentially non-oscillatory (WENO) reconstruction scheme to devise a model for implicit large eddy simulations. We concurrently optimize for a low dispersion error and sufficient shock-capturing ability for compressible flows as well as for physically consistent transition occurring in...     »
Stichworte:
Expected hypervolume improvement; Implicit large Eddy simulation; Multi-objective Bayesian optimization; Turbulent flows
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Horizon 2020:
667483
Zeitschriftentitel:
Journal of Computational Physics
Jahr:
2022
Band / Volume:
468
Seitenangaben Beitrag:
111477
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.jcp.2022.111477
WWW:
https://www.sciencedirect.com/science/article/pii/S0021999122005393
Verlag / Institution:
Elsevier BV
E-ISSN:
0021-9991
Hinweise:
Funding text 1 The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yiqi Feng reports financial support was provided by China Scholarship Council. Funding text 2 The first author acknowledges financial support from the China Scholarship Council (No. 201906060144 ). The second, third, and fourth authors acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research a...     »
Eingereicht (bei Zeitschrift):
27.01.2022
Angenommen (von Zeitschrift):
11.07.2022
Publikationsdatum:
01.11.2022
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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