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Volume 10
Issue 7
10.3390/aerospace10070639
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Article

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

by
Hung-Ta Wen
1,
Hom-Yu Wu
2,
Kuo-Chien Liao
1,* and
Wei-Chuan Chen
3
1
Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413, Taiwan
2
Department of Mechanical Engineering, Lungwa University of Science and Technology, Taoyuan 333, Taiwan
3
Aerospace Technology Research and Development Center, Taichung 433, Taiwan
*
Author to whom correspondence should be addressed.
Aerospace 2023, 10(7), 639; https://doi.org/10.3390/aerospace10070639
Submission received: 10 June 2023 / Revised: 9 July 2023 / Accepted: 14 July 2023 / Published: 15 July 2023
(This article belongs to the Special Issue Machine Learning for Aeronautics)
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Abstract

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.
Keywords: artificial intelligence (AI); XGBoost regression model; mean absolute error; mean-squared error; coefficient of determination; sensitivity analysis artificial intelligence (AI); XGBoost regression model; mean absolute error; mean-squared error; coefficient of determination; sensitivity analysis

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MDPI and ACS Style

Wen, H.-T.; Wu, H.-Y.; Liao, K.-C.; Chen, W.-C. JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method. Aerospace 2023, 10, 639. https://doi.org/10.3390/aerospace10070639

AMA Style

Wen H-T, Wu H-Y, Liao K-C, Chen W-C. JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method. Aerospace. 2023; 10(7):639. https://doi.org/10.3390/aerospace10070639

Chicago/Turabian Style

Wen, Hung-Ta, Hom-Yu Wu, Kuo-Chien Liao, and Wei-Chuan Chen. 2023. "JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method" Aerospace 10, no. 7: 639. https://doi.org/10.3390/aerospace10070639

APA Style

Wen, H.-T., Wu, H.-Y., Liao, K.-C., & Chen, W.-C. (2023). JT9D Engine Thrust Estimation and Model Sensitivity Analysis Using Gradient Boosting Regression Method. Aerospace, 10(7), 639. https://doi.org/10.3390/aerospace10070639

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