Niveau : Formation BAC+5 (Master ou Ingénieur)
Contexte : This internship is part of the SOLAIRE ANR project which includes 2 Ph.D. students. The SOLAIRE
project aims to improve the efficiency of converting concentrated solar energy into electricity using
artificial intelligence. The key component of these power plants is the solar receiver, which converts
concentrated solar energy into thermal energy and transfers it to a heat-carrying fluid, pressured air
in our case. The project focuses on maximizing thermal transfers between the gas and the wall of
the solar receiver, while minimizing pressure losses. This is done through optimization of thermal
transfers and the development of strategies for controlling near-wall turbulence in the solar receiver
using machine learning. To best assess different types of Thermal-Large Eddy Simulation (T-LES)
models in our study case, it is necessary to run simulations with different types of models on different
meshes to obtain the most accurate assessment of these models. This type of simulation offers a good
perspective as it’s comparatively much cheaper than Direct Numerical Simulation (DNS).