Aeroacoustic Data Analysis with Artificial Neural Networks

X.-H. Yu and J. Fu (USA)


artificial neural networks, rotorcraft, acoustic dataanalysis, modeling and simulation, estimation


The blade vortex interaction (BVI) noise level is an important measure in the studies of rotorcraft acoustics. It represents the acoustic noise generated by rotor interaction with the aerodynamic forces generated by previous rotor blade passages, and is a nonlinear function of flight parameters. In recent years, artificial neural networks (ANN) have been successfully applied to system identification and data analysis. This paper focuses on the application of using ANN to analyze the acoustic data gathered from a wind tunnel test of a XV-15 tilt-rotorcraft at NASA Ames Research Center. Satisfactory simulation results are obtained with two different neural network training algorithms.

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