Ensemble Modeling Method for Aero-Engines Based on Automatic Neural Network Architecture Search Under Sparse Data

Xiong, Guanghuan; Tan, Xiangmin; Cao, Guanzhen; Hong, Xingkui; Lu, Xingen; Zhu, Junqiang

doi:10.3390/aerospace12090804

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Open AccessArticle

Ensemble Modeling Method for Aero-Engines Based on Automatic Neural Network Architecture Search Under Sparse Data

by

Guanghuan Xiong

^1,2,3

,

Xiangmin Tan

^1,2,3,*

,

Guanzhen Cao

^1,2,3

,

Xingkui Hong

^1,2,3

,

Xingen Lu

^1,2,3

and

Junqiang Zhu

^1,2,3

¹

Institute of Engineering Thermophysics Chinese Academy of Sciences, No. 11 North Fourth Ring West Road, Beijing 100190, China

²

National Key Laboratory of Science and Technology on Advanced Light-Duty Gas-Turbine, Beijing 100190, China

³

University of Chinese Academy of Sciences, Beijing 100049, China

^*

Author to whom correspondence should be addressed.

Aerospace 2025, 12(9), 804; https://doi.org/10.3390/aerospace12090804

Submission received: 3 July 2025 / Revised: 27 August 2025 / Accepted: 2 September 2025 / Published: 5 September 2025

(This article belongs to the Section Aeronautics)

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Abstract

In this paper, the problem of aero-engines ensemble modeling under sparse data is addressed. Firstly, the Makima method is used to interpolate and complement the sparse data by analyzing the experimental data of a specific real aero-engine. In this way, the data sparsity problem due to sampling or transmission is solved equally well. Secondly, the Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network is brought in as the computational structure of the model. Based on the Automatic Neural Network Architecture Search (ANAS) method, the hyperparameters of the model can be searched efficiently, and the performance is improved. Third, a novel ensemble modeling method based on the Makima method, the NARX model, and the ANAS method is proposed to realize high-precision modeling throughout the entire operation process of the aero-engine from the idle state to the full throttle state. Finally, the proposed method is validated by simulations and experiments, and the results illustrate the innovation and correctness.

Keywords: aero-engine; Makima interpolation; Nonlinear Auto-Regressive with Exogenous Inputs; Automatic Neural Network Architecture Search; ensemble modeling

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MDPI and ACS Style

Xiong, G.; Tan, X.; Cao, G.; Hong, X.; Lu, X.; Zhu, J. Ensemble Modeling Method for Aero-Engines Based on Automatic Neural Network Architecture Search Under Sparse Data. Aerospace 2025, 12, 804. https://doi.org/10.3390/aerospace12090804

AMA Style

Xiong G, Tan X, Cao G, Hong X, Lu X, Zhu J. Ensemble Modeling Method for Aero-Engines Based on Automatic Neural Network Architecture Search Under Sparse Data. Aerospace. 2025; 12(9):804. https://doi.org/10.3390/aerospace12090804

Chicago/Turabian Style

Xiong, Guanghuan, Xiangmin Tan, Guanzhen Cao, Xingkui Hong, Xingen Lu, and Junqiang Zhu. 2025. "Ensemble Modeling Method for Aero-Engines Based on Automatic Neural Network Architecture Search Under Sparse Data" Aerospace 12, no. 9: 804. https://doi.org/10.3390/aerospace12090804

APA Style

Xiong, G., Tan, X., Cao, G., Hong, X., Lu, X., & Zhu, J. (2025). Ensemble Modeling Method for Aero-Engines Based on Automatic Neural Network Architecture Search Under Sparse Data. Aerospace, 12(9), 804. https://doi.org/10.3390/aerospace12090804

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Ensemble Modeling Method for Aero-Engines Based on Automatic Neural Network Architecture Search Under Sparse Data

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