TY - Type of reference TI - Reconstruction a priori de champs de Simulations des Grandes Echelles Thermiques par Apprentissage Profond AU - Yanis Zatout AU - Adrien Toutant AU - Onofrio Semeraro AU - Lionel Mathelin AU - Françoise Bataille AB - In this paper, we examine a machine learning-based method aimed at improving the accuracy of T-LES fields in the context of highly anisothermal flows. We compare this method with an already existing super-resolution method. We train our convolutional neural network by filtering Direct Numerical Simulation (DNS) snapshots into T-LES ones, and optimize our network to reconstruct DNS small scales from T-LES snapshots. Our results show that the neural network outperforms the classical reconstruction method in terms of the quality of the reconstructed coherent structures, but ends up increasing the Root Mean Square (RMS) values over the DNS ones. DO - 10.21494/ISTE.OP.2023.1015 JF - Entropie : thermodynamique – énergie – environnement – économie KW - Anisothermal flow, Deep Learning, Super-resolution, Heat transfer, Thermal-Large Eddy Simulations, Anisothermal flow, Deep Learning, Super-resolution, Heat transfer, Thermal-Large Eddy Simulations, L1 - https://www.openscience.fr/IMG/pdf/iste_entropie23v4n3_2.pdf LA - fr PB - ISTE OpenScience DA - 2023/10/19 SN - 2634-1476 TT - A priori reconstruction of Thermal-Large Eddy Simulation (T-LES) by Deep Learning Reconstruction a priori de champs de Simulations des Grandes Echelles Thermiques par Apprentissage Profond UR - https://www.openscience.fr/Reconstruction-a-priori-de-champs-de-Simulations-des-Grandes-Echelles IS - Numéro 3 spécial SFT Prix Biot-Fourier
VL - 4 ER -