Large Eddy Simulation

Large Eddy Simulation (LES) is a computational fluid dynamics technique aiming to efficiently model turbulent flows by explicitly resolving large-scale structures while modeling smaller scales. Current research heavily focuses on improving subgrid-scale (SGS) models, employing machine learning (ML) approaches such as neural networks (including Bayesian and graph neural networks), reinforcement learning, and generative adversarial networks (GANs) to learn complex relationships between resolved and unresolved scales. These advancements enhance LES accuracy and efficiency, particularly for complex geometries and high Reynolds numbers, impacting diverse fields like aerospace engineering, weather prediction, and energy systems.