The use of Artificial Intelligence in Computational Fluid Dynamics (CFD) is very 
promising to propose new physical models. Few studies have been done so far on 
the modeling of wall flows, for which the available physical models are facing 
great difficulties to be applicable and predictive. A recent PhD thesis at IFPEN 
has shown the ability of a neural network trained on high-fidelity wall-resolved 
data to reproduce the physics of a turbulent non-equilibrium boundary layer, 
accurately inferring wall friction from flow variables at a distance 
corresponding to the wall resolution of typical RANS coarse meshes. The present 
thesis aims at continuing this work to include the prediction of wall heat flux, 
a key element for many application areas at IFPEN involving thermal and cooling 
aspects. In particular, the objective is to formulate analytical thermal wall 
laws through the development of an adapted Gene Expression Programming (GEP) 
method. This symbolic regression approach allows to form interpretable 
analytical models, more regular and more robust than methods based on neural 
networks. This new approach will also have the advantage of being more easily 
implemented in any type of CFD code. In a first step, the PhD student will focus 
on the implementation of a GEP methodology with a first validation in terms of 
prediction of wall shear stress on single-phase turbulent canonical flows, and 
the results will be compared to those obtained with neural networks. The 
approach will then be extended to the prediction of wall heat flux from high-
fidelity test cases representative of liquid cooling of electric drive train
components.