Data-driven aerodynamic kernel functions for boundary element flow models
File(s)
Accepted version
Author(s)
Preston, Ben
Palacios, Rafael
Fasel, Urban
Castrichini, Andrea
Type
Journal Article
Abstract
Amethod for predicting aerodynamic flows using learned kernel functions for the underlying boundary element problem is introduced. A formulation for the baseline potential flow kernels is presented, followed by the methodology of learning the kernel from a CFD dataset. These kernels are given an arbitrary formulation, and a gradient-based approach is used for the learning step, restricting the description to differentiable functions. The problem of relearning a potential flow vortex is presented, followed by learning steady 2D compressible flow around
thick airfoils by using a neural network kernel. The resulting learned kernel solution yielded more accurate velocity and pressure distributions than the potential flow baseline. Lastly, the impact of enforcing rotational and translational invariance properties on the kernel definitions
is investigated, which finds that more generalizable models can be created at the expense of accuracy.
thick airfoils by using a neural network kernel. The resulting learned kernel solution yielded more accurate velocity and pressure distributions than the potential flow baseline. Lastly, the impact of enforcing rotational and translational invariance properties on the kernel definitions
is investigated, which finds that more generalizable models can be created at the expense of accuracy.
Date Acceptance
2026-06-18
Citation
AIAA Journal
ISSN
0001-1452
Publisher
American Institute of Aeronautics and Astronautics
Journal / Book Title
AIAA Journal
Copyright Statement
Copyright This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Publication Status
Accepted