A new schedule method for compact propulsion system model

Yu Bai; Zhengchen Zhu; Zhigui Xu; Haoran Guo

doi:10.1515/tjj-2023-0099

Article

A new schedule method for compact propulsion system model

Yu Bai , Zhengchen Zhu , Zhigui Xu and Haoran Guo

Published/Copyright: February 9, 2024

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From the journal International Journal of Turbo & Jet-Engines Volume 41 Issue 4

Abstract

The PSC (Performance Seeking Control) based on CPSM (Compact Propulsion System Model) has been verified by NASA. However, the CPSM has poor accuracy at off-design points. Therefore, a new basepoint schedule method is proposed to improve the CPSM accuracy at off-design points. At the off-design point, the thermodynamic parameters which is a function of temperature is an importance factor that influence the accuracy of model based on parameter corrections. Therefore, the temperature of fan inlet is taken into account during scheduling the basepoint vector. The simulations have shown that the accuracy of CPSM is at its best when the engine operates at a point where the temperature of the fan inlet is equal to the one of the basepoint. With the increase or decrease of the temperature of the fan inlet, the modeling errors of CPSM will increase. The simulations also demonstrate that the relative errors of the improved CPSM decrease significantly compared to those of the conventional CPSM at the off-design point.

Keywords: aero-engine; steady model; compact propulsion system model; performance seeking control

Corresponding author: Haoran Guo, Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China, E-mail: 635554926@qq.com

Research ethics: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors state no conflict of interest.
Research funding: This was supported in part by the Fundamental Research Funds for the Central Universities under Grant NT2019004, in part by the National Natural Science Foundation of China under Grant 51576096, in part by Q. Lan and the 333 Project, in part by the Research Funds for Central Universities under Grant NF2018003, in part by Six Talents Peak Project of Jiangsu Province.
Data availability: Not applicable.

References

1. Montazeri-Gh, M, Rasti, A, Jafari, A, Ehteshami, M. Design and implementation of mpc for turbofan engine control system. Aerosp Sci Technol 2019;92:99–113.10.1016/j.ast.2019.05.061Search in Google Scholar

2. Zheng, QG, Wang, Y, Sun, FY, Zhang, H. Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor. Proc Inst Mech Eng Pt I J Syst Contr Eng 2019;234:330–7.10.1177/0959651819853395Search in Google Scholar

3. Zheng, Q, Zhang, H. A global optimization control for turbo-fan engine acceleration schedule design [J]. Proc Inst Mech Eng G J Aerosp Eng 2018;232:308–16, https://doi.org/10.1177/0954410016683412.Search in Google Scholar

4. Liu, TJ, Du, X, Su, XM, Richter, H, Zhu, F. Robust tracking control of aero-engine rotor speed based on switched lpv model. Aerosp Sci Technol 2019;91:382–90.10.1016/j.ast.2019.05.031Search in Google Scholar

5. Orme, J, Conners, T. Supersonic flight test results of a performance seeking control algorithm on a NASA-15 spacecraft. In: 30th Joint propulsion conference and exhibit; 1994.Search in Google Scholar

6. Haibo, Z, Jianguo, S. Application of feasible descent sequential linear programming to aeroengine online optimization [J]. Acta Aeronaut Astronaut Sin 2010;31:663–70.Search in Google Scholar

7. Nie, Y, Li, Q, Wang, Y, Variable cycle engine performance seeking control Research based on SQCQP algorithm [J]. J Beijing Univ Aeronaut Astronaut 2017;43:2564–72.Search in Google Scholar

8. Sun, F, Miao, L, Zhang, H. A study on the installed performance seeking control for aero-propulsion under supersonic state [J]. Int J Turbo Jet Engines 2016;33:341–51, https://doi.org/10.1515/tjj-2015-0036.Search in Google Scholar

9. Zheng, Q, Miao, L, Zhang, H, Ye, Z. On-board real-time optimization control for turbofan engine thrust under flight emergency condition [J]. Proc IME J Syst Control Eng 2017;231:554–66, https://doi.org/10.1177/0959651817710127.Search in Google Scholar

10. Zhao, YP, Huang, G, Hu, QK, Tan, JF, Wang, JJ, Yang, Z. Soft extreme learning machine for fault detection of aircraft engine. Aerosp Sci Technol 2019;91:70–81. https://doi.org/10.1016/j.ast.2019.05.021.Search in Google Scholar

11. Zheng, Q, Xu, Z, Zhang, H, Zhu, Z. A turboshaft engine NMPC scheme for helicopter autorotation recovery maneuver [J]. Aero Sci Technol 2018;76:421–32, https://doi.org/10.1016/j.ast.2018.01.034.Search in Google Scholar

12. Zheng, Q, Fang, J, Hu, Z, Zhang, H. Aero-engine on-board model based on batch normalize deep neural network. IEEE Access 2019;7:1.10.1109/ACCESS.2018.2885199Search in Google Scholar

13. Li, ZQ, Zhao, YP, Cai, ZY, Xi, PP, Pan, YT, Huang, G, et al.. A proposed self-organizing radial basis function network for aero-engine thrust estimation [J]. Aero Sci Technol 2019;87:167–77, https://doi.org/10.1016/j.ast.2019.01.033.Search in Google Scholar

14. Zheng, Q, Fu, D, Wang, Y, Chen, H, Zhang, H. A study on global optimization and deep neural network modeling method in performance-seeking control. Proc Inst Mech Eng C J Mech Eng Sci 2020;234:46–59.10.1177/0959651819852477Search in Google Scholar

15. Zheng, Q. , Xiang, D, Chen, C, Zhang, H. Research on aero-engine performance seeking control based on the NN-PSM on-board model [J]. Proc Inst Mech Eng G J Aerosp Eng 2022;236:3505–17, https://doi.org/10.1177/09544100221088360.Search in Google Scholar

16. Ren, LH, Ye, ZF, Zhu, Y, Xu, ZY, Zhao, YP. Aero-engine on-board model based on big Quick Access Recorder data [J]. Proc Inst Mech Eng G J Aerosp Eng 2023;237:824–34, https://doi.org/10.1177/09544100221110290.Search in Google Scholar

17. MAINE, T, GILYARD, G, LAMBERT, H. A preliminary evaluation of an F100 engine parameter estimation process using flight data [C]. 26th joint propulsion conference. 1990:1921 p.10.2514/6.1990-1921Search in Google Scholar

18. NOBBS, S, JACOBS, S, DONAHUE, D. Development of the full-envelope performance seeking control algorithm [C]. 28th joint propulsion conference and exhibit. 1992:3748 p.10.2514/6.1992-3748Search in Google Scholar

19. Orme, J, Conners, T. Supersonic flight test results of a performance seeking control algorithm on a NASA-15 spacecraft [C]. 30th joint propulsion conference and exhibit. 1994:3210 p.10.2514/6.1994-3210Search in Google Scholar

20. Volponi, AJ. Gas turbine parameter corrections [J]. J Eng Gas Turbines Power 1999;121:613–21, https://doi.org/10.1115/1.2818516.Search in Google Scholar

21. Fang, J, Zheng, Q, Zhang, H. Research on compact propulsion system dynamic model based on deep neural network [J]. Proc Inst Mech Eng G J Aerosp Eng 2022;236:2496–507, https://doi.org/10.1177/09544100211064387.Search in Google Scholar

22. Malloy, DJ, Webb, AT, Kidman, DS . F-22/f119 Propulsion system ground and flight test analysis using modeling and simulation techniques [C]. ASME turbo expo 2002: power for land, sea, and air. American Society of Mechanical Engineers. 2002:1–8 pp.10.1115/GT2002-30001Search in Google Scholar

23. Volponi, A. Enhanced self tuning on-board real-time model (eSTORM) for aircraft engine performance health tracking; 2008.Search in Google Scholar

Received: 2023-11-09

Accepted: 2023-12-20

Published Online: 2024-02-09

Published in Print: 2024-12-17

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Articles in the same Issue

https://doi.org/10.1515/tjj-2023-0099

Keywords for this article

aero-engine; steady model; compact propulsion system model; performance seeking control