期刊论文

Jiang, CB

英文

Physics of Fluids

1070-6631

2025

37

2

027148

10.1063/5.0250780

National Natural Science Foundation of China10.13039/501100001809 [52479063, 51979015]; National Natural Science Foundation of China [HESS-2114]; Open Fund of the State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University

本校为第一且通讯机构

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...