Physics-constrained machine learning for thermal turbulence modelling at low Prandtl numbers

Matilde Fiore,Lilla Koloszar,Miguel Alfonso Mendez,Matthieu Duponcheel,Yann Bartosiewicz

International Journal of Heat and Mass Transfer（2022）

引用 7|浏览3

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摘要

•A data-driven algebraic model for the turbulent heat flux is developed.•The ANN is trained with DNS data of near unity and low Prandtl number flows.•Invariance and realizability properties are embedded into the model structure.•The model adapts satisfactorily to different Prandtl numbers and flow conditions.•The model is highly sensitive to the accuracy of the momentum modelling.

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关键词

Low-Prandtl,Turbulence,Heat flux,Artificial neural networks

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