Survey of machine-learning wall models for large-eddy simulation

Aurelien Francois Vadrot*, Xiang I. A. Yang*, Mahdi Abkar*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

This survey investigates wall modeling in large-eddy simulations (LES) using data-driven machine-learning (ML) techniques. To this end, we implement three ML wall models in an open-source code and compare their performances with the equilibrium wall model in LES of half-channel flow at 11 friction Reynolds numbers between 180 and 1010. The three models have "seen"flows at only a few Reynolds numbers. We test if these ML wall models can be extrapolated to unseen Reynolds numbers. Among the three models, two are supervised ML models and one is a reinforcement learning ML model. The two supervised ML models are trained against direct numerical simulation (DNS) data, whereas the reinforcement learning ML model is trained in the context of a wall-modeled LES with no access to high-fidelity data. The two supervised ML models capture the law of the wall at both seen and unseen Reynolds numbers - although one model requires retraining and predicts a smaller von Kármán constant. The reinforcement learning model captures the law of the wall reasonably well but has errors at both low (Reτ<103) and high Reynolds numbers (Reτ>106). In addition to documenting the results, we try to "understand"why the ML models behave the way they behave. Analysis shows that the error of the supervised ML models is a result of the network design and the error in the reinforcement learning model arises due to the present choice of the "states"and the mismatch between the neutral line and the line separating the action map. In all, we see promises in data-driven ML wall models.

Original languageEnglish
Article number064603
JournalPhysical Review Fluids
Volume8
Issue6
Number of pages26
ISSN2469-9918
DOIs
Publication statusPublished - Jun 2023

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