JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method

In recent years, artificial intelligence (AI) technology has been applied in different research fields. In this study, the XGBoost regression model is proposed to estimate JT9D engine thrust. The model performance mean absolute error (MAE) is 0.004845, the mean-squared error (MSE) is 0.000161, and t...

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Bibliographic Details
Published inAerospace Vol. 10; no. 7; p. 639
Main Authors Wen, Hung-Ta, Wu, Hom-Yu, Liao, Kuo-Chien, Chen, Wei-Chuan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Aircraft
Aircraft gas-turbines
Algorithms
Artificial intelligence
artificial intelligence (AI)
coefficient of determination
Efficiency
Gas turbine engines
Heat
mean absolute error
mean-squared error
Mechanical properties
Modelling
Parameter sensitivity
Regression analysis
Regression models
Sensitivity analysis
Thrust
XGBoost regression model
United States
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Summary:In recent years, artificial intelligence (AI) technology has been applied in different research fields. In this study, the XGBoost regression model is proposed to estimate JT9D engine thrust. The model performance mean absolute error (MAE) is 0.004845, the mean-squared error (MSE) is 0.000161, and the coefficient of determination (R2) values of the training, validation, and testing subsets are 0.99, 0.99, and 0.98, respectively. Based on a model sensitivity analysis, the four parameters’ optimal values are as follows: the number of estimators is 900; the learning rate is 0.1; the maximum depth is 4, and the random state is 3. In addition, a comparison between the model performance in this study and that in a previous one was conducted. The MSE value is as low as 0.000021.
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace10070639