Homogeneous Parametric Modeling of Airloads

Abstract

This work proposes two parametric modeling strategies for steady aerodynamic forces. We point out that airloads are homogeneous and introduce a parametrization based on spherical harmonics and a neural network. The parametrization using spherical harmonics enables an analogue of frequency-based truncation and a variation on the Singular Value Decomposition (SVD), constituting an orthogonal decomposition of the modeled airloads. Since neural networks are universal function approximators, the model based on this allows for more flexible parametrizations, including actuations and model inversions. Both parametrization strategies are showcased for model identification and reduction purposes, highlighting their strengths and weaknesses.