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Wang, Will Wei-Juen
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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 Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1201-1208

Abstract

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.
Co-Authors Hidayat, Muhamad Liao, Kuo-Chien Sung, Jui-Tang Wen, Hung-Ta Wu, Hom-Yu