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Self-enhancing model-based control for active transient protection and thrust response improvement of gas turbine aero-engines

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  • Wei, Zhiyuan
  • Zhang, Shuguang
  • Jafari, Soheil
  • Nikolaidis, Theoklis

Abstract

A self-enhancing active transient protection (SeATP) control approach using model-based strategies is proposed for gas turbine aero-engines, which aims at pro-actively handling surge margin limit and turbine entry temperature limit over the life cycle. The feature of SeATP is a bank of self-enhancing loops with periodically updated controller parameters for different flight cycles. This is realized by an off-line gain tuning via global optimization approach. Additionally, a sensor-based baseline controller and a model-based active transient protection (ATP) controller (with fixed gains) are developed as comparison bases. Numerical simulations for the examined controllers are carried on a validated aero-thermal turbofan engine model for idle to full-power acceleration tests in Matlab/Simulink environment. Simulation results demonstrate that ATP controller owns a considerable thrust response improvement for both the new engine and a severely degraded engine, compared with the baseline controller. Moreover, the proposed SeATP controller ensures a 65.77% recovery rate of thrust response deviation caused by the ATP controller for the degraded engine. Particularly, a low transient surge margin trajectory and a high turbine entry temperature route are fulfilled by the SeATP controller. SeATP controller also shows better robustness performance for degradation variation than ATP controller. Hence, the SeATP control performance is confirmed.

Suggested Citation

  • Wei, Zhiyuan & Zhang, Shuguang & Jafari, Soheil & Nikolaidis, Theoklis, 2022. "Self-enhancing model-based control for active transient protection and thrust response improvement of gas turbine aero-engines," Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032795
    DOI: 10.1016/j.energy.2021.123030
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    References listed on IDEAS

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    1. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).
    2. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    3. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    4. Sziroczak, David & Jankovics, Istvan & Gal, Istvan & Rohacs, Daniel, 2020. "Conceptual design of small aircraft with hybrid-electric propulsion systems," Energy, Elsevier, vol. 204(C).
    5. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    6. Ibrahem, Ibrahem M.A. & Akhrif, Ouassima & Moustapha, Hany & Staniszewski, Martin, 2021. "Nonlinear generalized predictive controller based on ensemble of NARX models for industrial gas turbine engine," Energy, Elsevier, vol. 230(C).
    7. Kim, Sangjo, 2021. "A new performance adaptation method for aero gas turbine engines based on large amounts of measured data," Energy, Elsevier, vol. 221(C).
    8. Ranasinghe, Kavindu & Guan, Kai & Gardi, Alessandro & Sabatini, Roberto, 2019. "Review of advanced low-emission technologies for sustainable aviation," Energy, Elsevier, vol. 188(C).
    9. Kim, Sangjo & Kim, Kuisoon & Son, Changmin, 2020. "A new transient performance adaptation method for an aero gas turbine engine," Energy, Elsevier, vol. 193(C).
    10. Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
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    Cited by:

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    6. Sharifi, Alireza & Salarieh, Hassan, 2023. "An adaptive synergetic controller applied to heavy-duty gas turbine unit," Applied Energy, Elsevier, vol. 333(C).
    7. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    8. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    9. Cai, Changpeng & Wang, Yong & Fang, Juan & Chen, Haoying & Zheng, Qiangang & Zhang, Haibo, 2023. "Multiple aspects to flight mission performances improvement of commercial turbofan engine via variable geometry adjustment," Energy, Elsevier, vol. 263(PA).

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