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Turbulent Heat Flux Modelling in Forced Convection Flows Using Artificial Neural Networks by Matilde Fiore; Lilla Koloszar; Miguel Alfonso Mendez; Matthieu Duponcheel; Yann Bartosiewicz is a Engineering article available to read on EtoBox.

What is Turbulent Heat Flux Modelling in Forced Convection Flows Using Artificial Neural Networks about?

Fluid dynamics of liquid metals plays a central role in new generation liquid metal cooled nuclear reactors, for which numerical investigations require the use of an appropriate thermal turbulence model for low Prandtl number fluids. Given the limitations of traditional modelling approaches and the increasing availability of high-fidelity data for this class of fluids, we propose a machine learning strategy for the modelling of the turbulent heat flux. A comprehensive algebraic mathematical structure is derived and physical constraints are imposed to ensure attractive properties promoting stability and realizability. The closure coefficients of the model are predicted by an Artificial Neural Network (ANN) which is trained with the available Direct Numerical Simulation (DNS) data at different Prandtl numbers. The validation shows that the ANN provides an accurate representation of the heat flux in a wide range of Prandtl numbers (Pr = 0.01–0.71) and compares well with other existing thermal closures. Nevertheless, simulations with different auxiliary models to compute the inputs of the ANN revealed a certain sensitivity of the data-driven formulation on the models used in combinatio

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Author

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

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)