A hybrid approach for reconstruction of transonic buffet aerodynamic noise: Integrating random forest and compressive sensing algorithm

Experimental measurements and numerical simulation methods for obtaining aerodynamic noise face issues such as high costs and long periods. A single machine learning method for predicting aerodynamic noise also requires a sufficient amount of data. According to this, this paper proposes a hybrid method integrating Random Forest and Compressive Sensing (RF_CS) to accurately reconstruct transonic buffet aerodynamic noise from sparse data. First, the RF algorithm, known for its strong nonlinear feature extraction capabilities, is used to obtain the basis function. Then, the basis coefficients are calculated using the L1 optimization algorithm based on limited sensor data and basis functions. Finally, a linear combination of basis functions and basis coefficients is used to reconstruct aerodynamic noise, including power spectral density, sound pressure level, and flow modes. Compared to the Compressive Sensing based on Proper Orthogonal Decomposition (POD_CS), the proposed algorithm can effectively reduce error by approximately 2–20 times and decrease the absolute error of modes by about 2–3 orders of magnitude. Specifically, the RF_CS method ensures that the reconstruction errors for power spectral density across various flow conditions are all below 3E-3, achieving generalization from one flow condition to the entire sample space. Additionally, this approach can utilize approximately 10 sensors to reconstruct accurate sound pressure level and modes, with errors within 5E-3 and 5E-5, respectively. This allows for generalization across the entire Mach number space based on a single Mach number condition.