  Liquid metals play a central role in new generation liquid metal cooled
nuclear reactors, for which numerical investigations require the use of
appropriate thermal turbulence models 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 applicability, robustness and
stability. The closure coefficients of the model are predicted by an Artificial
Neural Network (ANN) which is trained with DNS data at different Prandtl
numbers. The validity of the approach was verified through a priori and a
posteriori validation for two and three-dimensional liquid metal flows. The
model provides a complete vectorial representation of the turbulent heat flux
and the predictions fit the DNS data in a wide range of Prandtl numbers
(Pr=0.01-0.71). The comparison with other existing thermal models shows that
the methodology is very promising.
