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Titel:

Surrogate model benchmark for kω-SST RANS turbulence closure coefficients

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Schlichter, Philipp; Reck, Michaela; Pieringer, Jutta; Indinger, Thomas
Abstract:
AI-based methods show immense potential to assist engineers in further improving vehicle aerodynamics. It is vital to assess the performance of different surrogate models based on the provided training data size and shape to aid future model selections. This study uses data from the simulated flow around the 2D NACA 8810 airfoil. The closure coefficients of the kω-SST RANS turbulence model are varied by Design of Experiment to achieve the desired amount of varying data for the training and valid...     »
Stichworte:
Machine learning Regression methods Benchmark
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Journal of Wind Engineering and Industrial Aerodynamics
Jahr:
2024
Band / Volume:
246
Seitenangaben Beitrag:
105678
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.jweia.2024.105678
Verlag / Institution:
Elsevier BV
E-ISSN:
0167-6105
Hinweise:
Acknowledgments We want to express our gratitude to our industry partner AUDI AG for their support and thank Dr. M. Islam for the opportunity to conduct this research.
Eingereicht (bei Zeitschrift):
21.06.2023
Angenommen (von Zeitschrift):
11.02.2024
Publikationsdatum:
01.03.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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