Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows
In the past decades, Deep Learning (DL) frameworks have demonstrated excellent
performance in modeling nonlinear interactions and are a promising technique to move …
performance in modeling nonlinear interactions and are a promising technique to move …
[图书][B] Neural networks and numerical analysis
B Després - 2022 - books.google.com
This book uses numerical analysis as the main tool to investigate methods in machine
learning and neural networks. The efficiency of neural network representations for general …
learning and neural networks. The efficiency of neural network representations for general …
Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation
Q Malé, CJ Lapeyre, N Noiray - arXiv preprint arXiv:2408.16709, 2024 - arxiv.org
This paper establishes a data-driven modeling framework for lean Hydrogen (H2)-air
reaction rates for the Large Eddy Simulation (LES) of turbulent reactive flows. This is …
reaction rates for the Large Eddy Simulation (LES) of turbulent reactive flows. This is …
[PDF][PDF] Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling
V Xing, CJ Lapeyre - Machine Learning and Its Application to …, 2023 - library.oapen.org
Subgrid-scale flame wrinkling is a key unclosed quantity for premixed turbulent combustion
models in large eddy simulations. Due to the geometrical and multi-scale nature of flame …
models in large eddy simulations. Due to the geometrical and multi-scale nature of flame …
De Gruyter Series in Applied and Numerical Mathematics
R Abgrall - (No Title) - degruyter.com
The study of nonlinear phenomena is concerned in the field of natural science and even
social science. Since many phenomena in nature are essentially nonlinear, nonlinear …
social science. Since many phenomena in nature are essentially nonlinear, nonlinear …