AI super-resolution subfilter modeling for multi-physics flows

Abstract

Many complex simulations are extremely expensive and hardly if at all doable, even on current supercomputers. A typical reason for this are coupled length and time scales in the application which need to be resolved simultaneously. As a result, many simulation approaches rely on scale-splitting, where only the larger scales are simulated, while the small scales are modeled with subfilter models. This work presents a novel subfilter modeling approach based on AI super-resolution. A physics-informed enhanced super-resolution generative adversarial network (PIESRGAN) is used to accurately close subfilter terms in the solved transport equations. It is demonstrated how a simulation design with the PIESRGAN-approach can be used to accelerate complex simulations on current supercomputers, on the example of three fluid dynamics simulation setups with complex features on the supercomputer environment JURECADC/JUWELS (Booster). Further advantages and shortcoming of the PIESRGAN-approach are discussed.