A multilevel flow agnostic LES approach using deep learning

Turbulent flows in nature and engineering are characterized by an enormous range of scales, rendering their direct numerical simulation (DNS) prohibitively expensive. A well-established practical alternative is large eddy simulation (LES), which resolves the large scales, while modeling the entire range of small scales. However, conventional LES closure models fall short in applications where the controlling physical processes predominantly occur at small scales, such as scalar mixing, particle transport, chemical reactions. In this talk, we introduce a novel modeling approach for LES, where a multilevel strategy is employed instead of modeling the entire range of small scales. By leveraging tensor representation theory​, a general functional closure is obtained in terms of filtered velocity gradients at each level, which is then represented using tensor-based neural networks. Training and performance of the model is assessed using DNS data from isotropic and wall turbulence, demonstrating significant improvement over conventional LES approaches. Extensions to other scenarios are discussed, highlighting the versatility of the approach for broad range of flows.