A point-based deep learning approach for efficient dynamic-boundary flow prediction and its applications in aero-structure interaction

Abstract

Efficient prediction of dynamic-boundary flow fields in fluid–structure interaction (FSI) remains difficult because of strong nonlinearity, moving geometries, and the high cost of conventional computational fluid dynamics (CFD). To address this, we propose dynamic boundary flow net (DBF-Net), a point-based deep learning approach designed to replace the CFD solver in a Python-based FSI loop for efficient unsteady flow prediction around deforming structures. DBF-Net adopts a hierarchical encoder–decoder architecture for unstructured point clouds: Set Abstraction modules extract multi-scale local geometric and flow features, Feature Propagation layers recover high-resolution fields, and a DynamicFusionUnit models temporal evolution across successive time steps. The trained network is coupled with a Python-based FSI solver to replace the CFD solver in the fluid loop. The method is assessed on three transonic aeroelastic cases: forced oscillation of a two-degree-of-freedom Isogai airfoil, aeroelastic response of the same airfoil at different speed indices, and FSI of a flexible three-dimensional AGARD 445.6 wing. The results show that DBF-Net predicts the unsteady flow and coupled aeroelastic response with good accuracy. In the tested cases, the proposed framework provides about a 67× speedup for the two-dimensional simulations and up to 2.3×103 acceleration for the three-dimensional simulations relative to CFD-based FSI. These results indicate that DBF-Net is a promising surrogate for efficient aeroelastic analysis of systems with dynamic boundaries.