Extracting the Spatial Correlation of Wall Pressure Fluctuations Using Physically Driven Artificial Neural Network

The spatial correlation of wall pressure fluctuations is a crucial parameter that affects the structural vibration caused by a turbulent boundary layer (TBL). Although the phase-array technique is commonly used in industry applications to obtain this correlation, it has proven to be effective only for moderate frequencies. In this study, an artificial neural network (ANN) method was developed to calculate the convective speed, indicating the spatial correlation of wall pressure fluctuations and extending the frequency range of the conventional phase-array technique. The developed ANN system, based on a radial basis function (RBF), has been trained using discrete simulated data that follow the physical essence of wall pressure fluctuations. Moreover, a normalization method and a multi-parameter average (MPA) method have been employed to improve the training of the ANN system. The results of the investigation demonstrate that the MPA method can expand the frequency range of the ANN, enabling the maximum analysis frequency of convective velocity for aircraft wall pressure fluctuations to reach over 10 kHz. Furthermore, the results reveal that the ANN technique is not always effective and can only accurately calculate the wavenumber when the standard wavelength is less than four times the width of the sensor array along the flow direction.