Predicting drag on rough surfaces by transfer learning of empirical correlations

DOI10.1017/jfm.2021.1041OpenAlexW3170360278WikidataQ111521269 ScholiaQ111521269MaRDI QIDQ5019243

Shervin Bagheri, Sangseung Lee, Alexander Stroh, Jia Sheng Yang, Pourya Forooghi

Publication date: 3 January 2022

Published in: Journal of Fluid Mechanics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2106.05995

zbMATH Keywords

machine learning

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Uses Software

• SIMSON
• PyTorch
• Adam

Cites Work

• Unnamed Item
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• Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
• Data-driven prediction of unsteady flow over a circular cylinder using deep learning
• T<scp>URBULENT</scp> F<scp>LOWS OVER</scp> R<scp>OUGH</scp> W<scp>ALLS</scp>
• Predicting the Drag of Rough Surfaces
• Data-driven prediction of the equivalent sand-grain height in rough-wall turbulent flows

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