Comparison of Prediction Modeling Methodologies for Aeroacoustic Characterization of Hovering sUAS Rotors

Abstract

This work compares artificial neural network and multivariate orthogonal function modeling methodologies for the prediction and characterization of isolated hovering sUAS rotor aerodynamics and aeroacoustics. Design of Experiments was used to create input feature spaces over 9 input features: the number of rotor blades, rotor size, rotor speed, the amount of blade twist, blade taper ratio, tip chord length, collective pitch, airfoil camber, and airfoil thickness. CAMRAD II and AARON were executed at the points defined by the input feature space to predict aerodynamic and aeroacoustic quantities. These predicted aerodynamic and aeroacoustic data were then used to generate artificial neural networks and polynomial response surface models. The two prediction model methodologies were evaluated over test data previously unseen by the models, which showed good prediction capabilities for both model types, with slightly lower prediction error for the artificial neural networks. A characterization study was performed, which showed that input features correspondent to the spanwise sectional blade lift and drag were the most significant factors to the aerodynamic thrust and power, respectively. It was also shown that the aeroacoustic quantities were highly dependent on variations in rotor speed and size, which affect the Doppler factor for tonal noise and the spanwise Reynolds number for broadband noise.