Deep learning solver unites SDGSAT-1 observations and Navier–Stokes theory for oceanic vortex streets

Abstract

The world’s first scientific satellite for sustainable development goals (SDGSAT-1) provides valuable data about offshore small-scale ocean phenomena, including the Kármán vortex street phenomenon. Although the simulation of the oceanic vortex street phenomenon is crucial for understanding not only the mechanisms of vortex formation in fluid dynamics but also their impact on the surrounding environment, the traditional simulation relies on the strong theoretical hypothesis of Navier–Stokes equations. Here, we propose a self-supervised neural network with high generalization ability to implement Navier–Stokes equations, simulating realistic oceanic vortex streets. Specifically, the physical informed convolutional neural network is first employed to determine the corresponding pressure and velocity fields, achieving accurate simulation of oceanic vortex streets with lower computational cost; Then, the observational islands in SDGSAT-1 imagery are embedded as obstacles, meanwhile, the marine background field including wind and terrain is synchronously incorporated to achieve more realistic simulation results compared with traditional methods; Finally, the morphological parameters of oceanic vortex streets are calculated and associated analysis are carried out to deepen our understanding of small scale vortex street phenomena. In addition, the experimental results demonstrated our proposed method can obtain promising time efficiency. With this partial differential equation deep learning solver framework combining observation and theory, there will be potential to expedite the cognitive process of oceanic phenomena.