Reduced order model for unsteady aerodynamic performance of compressor cascade based on recursive RBF

Abstract

Based on Recursive Radial Basis Function (RRBF) neural network, the Reduced Order Model (ROM) of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performance of turbomachinery. One novel ROM called ASA-RRBF model based on Adaptive Simulated Annealing (ASA) algorithm was developed to enhance the generalization ability of the unsteady ROM. The ROM was verified by predicting the unsteady aerodynamics performance of a highly-loaded compressor cascade. The results show that the RRBF model has higher accuracy in identification of the dimensionless total pressure and dimensionless static pressure of compressor cascade under nonlinear and unsteady conditions, and the model behaves higher stability and computational efficiency. However, for the strong nonlinear characteristics of aerodynamic parameters, the RRBF model presents lower accuracy. Additionally, the RRBF model predicts with a large error in the identification of aerodynamic parameters under linear and unsteady conditions. For ASA-RRBF, by introducing a small-amplitude and high-frequency sinusoidal signal as validation sample, the width of the basis function of the RRBF model is optimized to improve the generalization ability of the ROM under linear unsteady conditions. Besides, this model improves the predicting accuracy of dimensionless static pressure which has strong nonlinear characteristics. The ASA-RRBF model has higher prediction accuracy than RRBF model without significantly increasing the total time consumption. This novel model can predict the linear hysteresis of dimensionless static pressure happened in the harmonic condition, but it cannot accurately predict the beat frequency of dimensionless total pressure.