Physics-Infused Machine Learning Based Prediction of VTOL Aerodynamics with Sparse Datasets

TL;DR

This paper introduces physics-infused machine learning models combining neural networks with low-fidelity physics models to accurately predict VTOL aircraft aerodynamics using sparse datasets, enhancing generalization and reducing computational costs.

Contribution

It proposes two novel PIML approaches integrating neural networks with a low-fidelity vortex lattice model and develops an open-source auto-differentiable framework for training these hybrid models.

Findings

One approach outperforms pure data-driven models in accuracy.

The hybrid models maintain computational efficiency.

Results suggest potential for cost-effective aircraft design and control.

Abstract

Complex optimal design and control processes often require repeated evaluations of expensive objective functions and consist of large design spaces. Data-driven surrogates such as neural networks and Gaussian processes provide an attractive alternative to simulations and are utilized frequently to represent these objective functions in optimization. However, pure data-driven models, due to a lack of adherence to basic physics laws and constraints, are often poor at generalizing and extrapolating. This is particularly the case, when training occurs over sparse high-fidelity datasets. A class of Physics-infused machine learning (PIML) models integrate ML models with low-fidelity partial physics models to improve generalization performance while retaining computational efficiency. This paper presents two potential approaches for Physics infused modelling of aircraft aerodynamics which incorporate Artificial Neural Networks with a low-fidelity Vortex Lattice Method model with blown wing effects (BLOFI) to improve prediction performance while also keeping the computational cost tractable. This paper also develops an end-to-end auto differentiable open-source framework that enables efficient training of such hybrid models. These two PIML modelling approaches are then used to predict the aerodynamic coefficients of a 6 rotor eVTOL aircraft given its control parameters and flight conditions. The models are trained on a sparse high-fidelity dataset generated using a CHARM model. The trained models are then compared against the vanilla low-fidelity model and a standard pure data-driven ANN. Our results show that one of the proposed architectures outperforms all the other models at a nominal increase in run time. These results are promising and pave way for PIML frameworks which can generalize over different aircraft and configurations thereby significantly reducing costs of design and control.