Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics

Abstract

Analysing the aerodynamic loads of an aircraft using computational fluid dynamics is a user's and computer-intensive task. An attractive alternative is to leverage on neural networks bypassing the need of solving the governing fluid equations at all flight conditions of interest. Neural networks have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph neural networks which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The demonstration is for the NASA Common Research Model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal, having used an existing available database.