Compressor performance prediction: gradient boosting regression model and sensitivity analysis - Liao, Kuo-Chien; Wu, Hom-Yu; Wen, Hung-Ta; Sung, Jui-Tang; Hidayat, Muhamad; Wang, Will Wei-Juen (Indonesian Journal of Electrical Engineering and Computer Science, 2025)
This study introduces the use of gradient boosting regression (GBR) models to estimate the compressor performance of aero-engines. The model exhibits a mean absolute error (MAE) of 0.078, showcasing superior performance compared to previous studies. Through sensitivity analysis, optimal values for three key parameters were determined: 280 estimators, a max depth of 9, and a learning rate of 0.085. Furthermore, a comparison with a prior study revealed an impressive MAE value lower than 0.002, highlighting the GBR model’s success in accurately predicting compressor performance. This demonstrates the model’s effectiveness and predictive accuracy, making it a valuable tool for aero-engine compressor performance estimation.