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.
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