Benchmarking machine learning models for predicting aerofoil performance

Abstract

This paper investigates the capability of Neural Networks (NNs) as alternatives to the traditional methods to analyse the performance of aerofoils used in the wind and tidal energy industry. The current methods used to assess the characteristic lift and drag coefficients include Computational Fluid Dynamics (CFD), thin aerofoil  and panel methods, all face trade-offs between computational speed and the accuracy of the results and as such NNs have been investigated as an alternative with the aim that it would perform both quickly and accurately. As such, this paper provides a benchmark for the windAI_bench dataset published by the National Renewable Energy Laboratory (NREL) in the USA.
In order to validate the methodology of the benchmarking, the AirfRANS dataset is used as both a starting point and a point of comparison.
This study evaluates four neural networks (MLP, PointNet, GraphSAGE, GUNet) trained on a range aerofoils at 25 angles of attack (4^o to 20^o) to predict fluid flow and calculate lift coefficients (C_L) via the panel method.
GraphSAGE and GUNet performed well during the testing phase, but underperformed during validation.
Accordingly, this paper has identified PointNet and MLP as the two strongest models tested, however whilst the results from MLP are more commonly correct for predicting the behaviour of the fluid, the results from PointNet provide the more accurate results for calculating C_L.