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Development of explicit algebraic LES wall models using consistent CFD-driven Machine Learning

delete2025-11-25
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OA
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A
Arthur Shiniti Cato *
M
Melissa Kožul
R
Richard D. Sandberg
DOI：10.1016/j.ijheatfluidflow.2025.110157delete
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Abstract

Abstract

En 中文
• A new Machine Learning methodology to develop wall models for Large Eddy Simulation (LES) is presented. • The wall models developed are explicit algebraic functions and therefore human-interpretable. • The method is numerically consistent and driven by in situ CFD outputs. • The proposed framework does not require dense high-fidelity data sets. • The framework can be applied to flows with separation and large pressure gradients.
Keywords:
Fluid mechanics
Wall models
Machine learning
Computational fluid dynamics
Large eddy simulation
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Journal

I
International Journal of Heat and Fluid Flow
IF:
3.1
Papers:
379
Citations:
0

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