Prediction of compressor aerodynamic noise based on transfer learning Random Forest.

Aerodynamic noise is an important evaluation indicator for high-pressure ratio centrifugal compressors. However, using traditional numerical methods to predict aerodynamic noise requires significant computational resources and time, making it challenging to quickly assess the aerodynamic noise of compressors. This study proposes a transfer learning-based method for predicting the aerodynamic noise of centrifugal compressors. A rich set of aerodynamic noise data from the baseline compressor and a small amount of data from the target compressor (TC) were first obtained through experimental measurements. The transferability between datasets was evaluated using the maximum mean discrepancy method. Then, the pre-trained model was trained using data from the baseline compressor, and its generalization performance was validated. Finally, the pre-trained model was fine-tuned using noise data from the TC, and the model's performance was validated through mean squared error analysis. The results show that the proposed method can effectively and rapidly predict the aerodynamic noise of serial centrifugal compressors, with the overall sound pressure level error of the predicted frequency spectrum being less than 3 dB. Compared with traditional methods, this approach achieves high prediction accuracy with a small amount of training data.