IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v245y2022ics036054422200158x.html
   My bibliography  Save this article

A highly robust thrust estimation method with dissimilar redundancy framework for gas turbine engine

Author

Listed:
  • Zhao, Hang
  • Liao, Zengbu
  • Liu, Jinxin
  • Li, Ming
  • Liu, Wei
  • Wang, Lei
  • Song, Zhiping

Abstract

An accurate thrust estimation method is of great importance for implementing direct thrust control scheme on gas turbine engine in that the in-flight thrust cannot be measured. Meanwhile, some interference factors, such as sensor noise, engine-to-engine variations, engine performance deterioration and changes in atmospheric condition, may have adverse effects on the accuracy of thrust estimation. However, these factors are not fully considered in the design of the existing methods. To ensure the accuracy in practical application, a highly robust thrust estimation (HRTE) method is proposed in this paper. This method is a fusion of three dissimilar hybrid estimation modules, each consisting of a physics-based module and an error-compensated module. Compared with the existing pure data-driven methods, its innovations are as follows: 1) a hybrid framework for thrust estimation is proposed, which helps get rid of the dilemma of learning high-dimensional thrust data and improves the accuracy and robustness. 2) a dissimilar redundancy framework is designed for thrust estimation, which helps improve the adaptivity to those interference factors, especially the tolerance to sensor noise. A series of simulation tests eventually show that the HRTE method has high accuracy and robustness in practical application.

Suggested Citation

  • Zhao, Hang & Liao, Zengbu & Liu, Jinxin & Li, Ming & Liu, Wei & Wang, Lei & Song, Zhiping, 2022. "A highly robust thrust estimation method with dissimilar redundancy framework for gas turbine engine," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s036054422200158x
    DOI: 10.1016/j.energy.2022.123255
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422200158X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123255?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Y.G. & Nilkitsaranont, P., 2009. "Gas turbine performance prognostic for condition-based maintenance," Applied Energy, Elsevier, vol. 86(10), pages 2152-2161, October.
    2. Ji, Zhixing & Qin, Jiang & Cheng, Kunlin & Liu, He & Zhang, Silong & Dong, Peng, 2019. "Performance evaluation of a turbojet engine integrated with interstage turbine burner and solid oxide fuel cell," Energy, Elsevier, vol. 168(C), pages 702-711.
    3. Kim, Sangjo & Kim, Kuisoon & Son, Changmin, 2020. "A new transient performance adaptation method for an aero gas turbine engine," Energy, Elsevier, vol. 193(C).
    4. Bouveyron, C. & Girard, S. & Schmid, C., 2007. "High-dimensional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 502-519, September.
    5. 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).
    6. Patel, Vivek & Savsani, Vimal & Mudgal, Anurag, 2018. "Efficiency, thrust, and fuel consumption optimization of a subsonic/sonic turbojet engine," Energy, Elsevier, vol. 144(C), pages 992-1002.
    7. Zhao, Yong-Ping & Hu, Qian-Kun & Xu, Jian-Guo & Li, Bing & Huang, Gong & Pan, Ying-Ting, 2018. "A robust extreme learning machine for modeling a small-scale turbojet engine," Applied Energy, Elsevier, vol. 218(C), pages 22-35.
    8. Sogut, M. Ziya & Yalcin, Enver & Karakoc, T. Hikmet, 2017. "Assessment of degradation effects for an aircraft engine considering exergy analysis," Energy, Elsevier, vol. 140(P2), pages 1417-1426.
    9. Nazoktabar, Mohsen & Jazayeri, Seyed Ali & Parsa, Mohsen & Ganji, Davoud Domiri & Arshtabar, Kamran, 2019. "Controlling the optimal combustion phasing in an HCCI engine based on load demand and minimum emissions," Energy, Elsevier, vol. 182(C), pages 82-92.
    10. Xiaodong Chang & Jinquan Huang & Feng Lu, 2017. "Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine," Energies, MDPI, vol. 10(7), pages 1-19, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feng, Hailong & Liu, Bei & Xu, Maojun & Li, Ming & Song, Zhiping, 2024. "Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient," Energy, Elsevier, vol. 292(C).
    2. Liao, Zengbu & Zhan, Keyi & Zhao, Hang & Deng, Yuntao & Geng, Jia & Chen, Xuefeng & Song, Zhiping, 2024. "Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    3. Zhang, Xinhai & Wang, Kang & Geng, Jia & Li, Ming & Song, Zhiping, 2024. "A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss," Energy, Elsevier, vol. 294(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Feng, Hailong & Liu, Bei & Xu, Maojun & Li, Ming & Song, Zhiping, 2024. "Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient," Energy, Elsevier, vol. 292(C).
    3. 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).
    4. Charles Bouveyron & Julien Jacques, 2011. "Model-based clustering of time series in group-specific functional subspaces," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 281-300, December.
    5. Joseph Ndong & Ted Soubdhan, 2022. "Extracting Statistical Properties of Solar and Photovoltaic Power Production for the Scope of Building a Sophisticated Forecasting Framework," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    6. Regad, L. & Guyon, F. & Maupetit, J. & Tufféry, P. & Camproux, A.C., 2008. "A Hidden Markov Model applied to the protein 3D structure analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3198-3207, February.
    7. Zijian Qiang & Jinquan Huang & Feng Lu & Xiaodong Chang, 2019. "Robust Sensor Fault Reconstruction via a Bank of Second-Order Sliding Mode Observers for Aircraft Engines," Energies, MDPI, vol. 12(14), pages 1-20, July.
    8. Baek, Seungju & Lee, Hyeonjik & Lee, Kihyung, 2021. "Fuel efficiency and exhaust characteristics of turbocharged diesel engine equipped with an electric supercharger," Energy, Elsevier, vol. 214(C).
    9. Cathy Maugis & Gilles Celeux & Marie-Laure Martin-Magniette, 2009. "Variable Selection for Clustering with Gaussian Mixture Models," Biometrics, The International Biometric Society, vol. 65(3), pages 701-709, September.
    10. Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.
    11. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    12. Atilgan, Ramazan & Onder Turan,, 2020. "Economy and exergy of aircraft turboprop engine at dynamic loads," Energy, Elsevier, vol. 213(C).
    13. Cheng, Xianda & Zheng, Haoran & Dong, Wei & Yang, Xuesen, 2023. "Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters," Energy, Elsevier, vol. 265(C).
    14. Zhang, Xinhai & Wang, Kang & Geng, Jia & Li, Ming & Song, Zhiping, 2024. "A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss," Energy, Elsevier, vol. 294(C).
    15. Deng, Li & Chen, Min & Tang, Hailong & Zhang, Jiyuan, 2024. "Performance evaluation of multicombustor engine for Mach3+-Level propulsion system," Energy, Elsevier, vol. 295(C).
    16. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
    17. Zhiqi Yan & Shisheng Zhong & Lin Lin & Zhiquan Cui, 2021. "Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks," Mathematics, MDPI, vol. 9(17), pages 1-17, September.
    18. Nicolas Jouvin & Pierre Latouche & Charles Bouveyron & Guillaume Bataillon & Alain Livartowski, 2021. "Greedy clustering of count data through a mixture of multinomial PCA," Computational Statistics, Springer, vol. 36(1), pages 1-33, March.
    19. Liu, Hui & Duan, Zhu & Li, Yanfei & Lu, Haibo, 2018. "A novel ensemble model of different mother wavelets for wind speed multi-step forecasting," Applied Energy, Elsevier, vol. 228(C), pages 1783-1800.
    20. Balli, Ozgur & Caliskan, Hakan, 2021. "Turbofan engine performances from aviation, thermodynamic and environmental perspectives," Energy, Elsevier, vol. 232(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:245:y:2022:i:c:s036054422200158x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.