Simulation and experimental study of altitude test facility variable speed compressor system based on RBF hybrid prediction modelling

Accurate prediction of variable-speed compressor performance is critical for Altitude Test Facilities (ATF) and aero-engines, yet it poses a significant challenge. To address this, a hybrid prediction model is proposed, which integrates a Radial Basis Function (RBF) neural network to extract key parameters ( H , F , π c0 ) from compressor performance curves. Within the speed range of 65–105 r/min, the RBF model demonstrates superior accuracy over both Back Propagation (BP) neural networks and conventional interpolation, achieving Mean Absolute Percentage Errors (MAPEs) of 1.72% for relative pressure ratio and 1.53% for actual efficiency. The model is validated through experimental studies and integrated into a variable-speed simulation. Simulation results show overall errors of less than 1% for pressure and mass flow rate compared to experimental data. Furthermore, application of this model to optimize the ATF gas supply system reduced startup time by 16.7% while preventing surge. These verification results confirm that the RBF-based hybrid model provides a reliable framework for performance prediction and simulation of variable-speed compressor systems, offering valuable support for system anti-surge control.