Machine-Learning-Based Detection of Aerodynamic Disturbances Using Surface Pressure Measurements

Abstract

Aerodynamic disturbances (due to gusts, maneuvers, or a combination) leave a signature in the pressures exerted on the wing surface. In this work, the following question is explored: To what extent can the characteristics of these disturbances be parsed from the measured pressures alone? A supervised learning algorithm based on several layers of neural networks is applied. The overall machine-learning architecture is trained and tested on aerodynamic disturbance data generated by an inviscid vortex method applied to a two-dimensional flat plate undergoing a smooth pitchup maneuver. As a surrogate for an incident gust, the critical leading-edge suction parameter (LESP) is perturbed, which in turn dynamically changes the flux of the vorticity from the leading edge. The results are used to train the algorithm to estimate the LESP and angle-of-attack histories from the surface pressure. Two different approaches are used. In the first, which is a purely machine-learning strategy, a combination of convolutional and recurrent neural networks that accept surface pressure measurements as input is used. The overall architecture is shown to achieve an accurate estimation of the LESP and angle of attack. In the second approach, machine learning is integrated with a dynamical systems framework to learn a dynamical model for the angle of attack and the LESP. It is shown that this machine-learned system identification approach achieves somewhat higher accuracy as compared to the purely machine-learning approach with fewer parameters. In both approaches, it is shown that overfitting is mitigated by injecting random noise into the input pressures.