Turbulence Model

Turbulence modeling aims to accurately predict the complex, chaotic behavior of fluids, crucial for diverse applications from aircraft design to weather forecasting. Current research heavily utilizes machine learning, particularly deep neural networks (including convolutional neural networks, Fourier neural operators, and generative adversarial networks), often integrated with diffusion models or coupled with traditional methods like Large Eddy Simulations (LES) and Reynolds-Averaged Navier-Stokes (RANS) equations, to improve accuracy and efficiency. These advancements focus on enhancing spectral representation, quantifying uncertainty, and achieving better generalization across different flow regimes and Reynolds numbers. The resulting improvements in predictive accuracy and computational efficiency have significant implications for various scientific fields and engineering applications.