Multi-objective CFD-driven development of coupled turbulence closure models

DOI10.1016/j.jcp.2021.110922OpenAlexW3160326792MaRDI QIDQ2133604

Richard D. Sandberg, Fabian Waschkowski, Joseph Klewicki, Yaomin Zhao

Publication date: 4 May 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.06225

zbMATH Keywords

multi-objective optimizationturbulence modelingevolutionary algorithmmachine learning

Mathematics Subject Classification ID

Turbulence (76Fxx) Basic methods in fluid mechanics (76Mxx) Fluid mechanics (76-XX)

Related Items (3)

Ensemble Kalman method for learning turbulence models from indirect observation data ⋮ Combining direct and indirect sparse data for learning generalizable turbulence models ⋮ A non-intrusive approach for physics-constrained learning with application to fuel cell modeling

Uses Software

• ImageNet
• Tensor2Tensor
• AlexNet

Cites Work

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