Recognizing the aeroacoustic information of noise radiated by an unflanged duct based on convolutional neural networks

Accurately recognizing the aeroacoustic information of noise propagating into and radiating out of an aero-engine duct is of both fundamental and practical interest. The aeroacoustic information includes (1) the acoustic properties of the noise source, such as the frequency (f) and the circumferential and radial mode numbers (m, n), and (2) the flight conditions, including the ambient flow speed (M0) and the jet flow speed (M1). In this study, a data-driven model is developed to predict the aeroacoustic information of a simplified aero-engine duct noise from the far-field sound pressure level directivity. The model is constructed by the integration of one-dimensional convolutional layers and fully connected layers. The training and validation datasets are calculated from the analytical model for noise radiation from a semi-infinite unflanged duct based on the Wiener-Hopf method. For a single-spinning mode source, a regression model is established for f, M0, and M1 prediction, and a classification model is built up for m and n prediction. Additionally, for a multi-spinning mode source, the regression model is used to predict the coefficient of each mode. Results show that the proposed data-driven model can effectively and robustly predict the acoustic characteristics of noise propagation in and radiation out of an aero-engine bypass duct.