A boundary-assimilation Fourier neural operator for predicting initial fields of flow around structures

Repeatedly solving flow around structures with varying parameters using computational fluid dynamics (CFD) is often essential for structural design. This study proposes a boundary-assimilation Fourier neural operator (BAFNO) method to address the challenges of manually setting initial conditions for CFD. The focus of the BAFNO is on the generalization ability to predict initial flow fields without relying on observational data. BAFNO addresses the boundary constraint requirements of the existing physics-informed neural operator models in parametric geometries. Inspired by the ghost node method, the domain boundary conditions are assimilated into the loss function instead of adding penalty terms. Meanwhile, the structure boundaries are assimilated into a damping source term using a level set function. BAFNO can flexibly handle parametric geometries with different shapes and quantities. Subsequently, a series of numerical experiments for flow-around structures are conducted to confirm the performance of the BAFNO. The results indicate that the BAFNO has strong generalization capability, and the BAFNO + CFD can obtain dynamic stable fields faster than the direct CFD.