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Improved hybrid modeling method with input and output self-tuning for gas turbine engine

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Listed:
  • Xu, Maojun
  • Liu, Jinxin
  • Li, Ming
  • Geng, Jia
  • Wu, Yun
  • Song, Zhiping

Abstract

Gas-path model (GPM) plays a key role in the application of sensor fault tolerant control for the gas turbine engine (GTE). However, the modeling accuracy of traditional physics-based GPM is restricted by modeling inaccuracies and measurement uncertainty of sensors, etc., making fault-tolerant control of sensors difficult. In this paper, an improved input and output self-tuning hybrid modeling (IOSTHM) method is proposed for improving the GPM modeling accuracy of a dual shaft turbofan GTE. The proposed IOSTHM consists of an input self-tuning model (ISTM) and an output self-tuning model (OSTM). In the framework of ISTM, a fundamental traditional physics-based model (PBM) is constructed by using the component level modeling method firstly. Based on the PBM and the actual measurement of rotor speed of low-pressure shaft (N1) which has high measurement accuracy, the ISTM is conducted by tuning the measurement of fuel flow in real-time according to the deviation of N1 between engine and PBM, aiming to deal with the measurement uncertainty of fuel flow. Based on the ISTM, the IOSTHM is constructed by combining with an OSTM which is constituted by a bank of residual learning models to reduce the modeling inaccuracies further. The effectiveness of the proposed IOSTHM is evaluated with the verification of simulated flight data and actual ground test data. Both flight and ground verification results reveal that the proposed hybrid model IOSTHM achieves the best modeling performance when compared with other models such as traditional PBM, ISTM, and OSTM, it shows the superiority of this methodology.

Suggested Citation

  • Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019204
    DOI: 10.1016/j.energy.2021.121672
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    References listed on IDEAS

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    2. Wang, Pengfei & Zhu, Ze & Liang, Wenlong & Liao, Longtao & Wan, Jiashuang, 2023. "Hybrid mechanistic and neural network modeling of nuclear reactors," Energy, Elsevier, vol. 282(C).
    3. Huang, Yufeng & Tao, Jun & Sun, Gang & Wu, Tengyun & Yu, Liling & Zhao, Xinbin, 2023. "A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis," Energy, Elsevier, vol. 270(C).
    4. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).

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