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Stochastic response analysis and robust optimization of nonlinear turbofan engine system

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Abstract

The quantitative analysis of the uncertainty influence on thermodynamic parameters and performance parameters is usually difficult to achieve due to the strong nonlinear working process in engine systems. It is very important to fully consider the uncertainty influences in the performance design for tapping the working potential. Therefore, this paper establishes a stochastic dynamic model based on the nonlinear characteristics of engine systems, analyzes the time-varying influence law of uncertainty on the responses of thermodynamic parameters and ascertains the stochastic response distribution features of thermodynamic parameters and performance parameters. Based on the quantitative analysis results, an optimization method considering stochastic responses is further established to design the performance parameters of engine systems and compared with the traditional mechanism model and Monte Carlo simulation. The results indicate that the thermodynamic parameters and performance parameters all show the normal distributions, and the fuel system actuation uncertainty has a greater impact on the stochastic responses. The robust optimization method significantly reduces the uncertainty influence and improves the thermodynamics performance with an 84.4% probability. Moreover, it reduces the computational cost by more than half. This work provides an effective method for the quantitative analysis of the uncertainty influence in engine systems and provides a useful reference for the robust performance design of new type engine.

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References

  1. Cheung, J., Scanlan, J., Wong, J., Forrester, J., Eres, H.: Application of value-driven design to commercial aeroengine systems. J. Aircr. 49(3), 688–702 (2012)

    Google Scholar 

  2. Cao, D., Bai, G.: A study on aeroengine conceptual design considering multi-mission performance reliability. Appl. Sci. 10(13), 4668 (2020)

    Google Scholar 

  3. Ding, S., Yuan, Y., Xue, N., Liu, X.: An onboard aeroengine model-tuning system. J. Aerosp. Eng. 30(4), 04017018 (2017)

    Google Scholar 

  4. Xiao, L.: Aeroengine multivariable nonlinear tracking control based on uncertainty and disturbance estimator. J. Eng. Gas Turbines Power 136(12), 121601 (2014)

    Google Scholar 

  5. Zhu, S.P., Huang, H.Z., Peng, W., Wang, H., Mahadevan, S.: Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliab. Eng. Syst. Saf. 146, 1–12 (2016)

    Google Scholar 

  6. Jin, P., Lu, F., Huang, J., Kong, X., Fan, M.: Life cycle gas path performance monitoring with control loop parameters uncertainty for aeroengine. Aerosp. Sci. Technol. 115, 106775 (2021)

    Google Scholar 

  7. Dong, P., Tang, H., Chen, M., Zou, Z.: Overall performance design of paralleled heat release and compression system for hypersonic aeroengine. Appl. Energy 220, 36–46 (2018)

    Google Scholar 

  8. Zhang, J., Tang, H., Chen, M.: Robust design methodologies to the adaptive cycle engine system performance: preliminary analysis. Energy Procedia 158, 1521–1529 (2019)

    Google Scholar 

  9. Cai, C., Zheng, Q., Zhang, H.: A new method to improve the real-time performance of aero-engine component level model. Int. J. Turbo Jet-Engines (2020). https://doi.org/10.1515/tjeng-2020-0033

    Article  Google Scholar 

  10. Wang, H., Wang, X., Dang, W., Yao, H., Wang, B.: Generic design methodology for electro-hydraulic servo actuator in aero-engine main fuel control system. Turbo Expo: Power for Land, Sea, and Air. Am. Soc. Mech. Eng. 45752, V006T06A035 (2014)

    Google Scholar 

  11. Shi, Z.Y., Li, X.Z., Li, Y.K., Lin, J.C.: A high-precision form-free metrological method of aeroengine blades. Int. J. Precis. Eng. Manuf. 20(12), 2061–2076 (2019)

    Google Scholar 

  12. Montazeri-Gh, M., Nasiri, M.: Hardware-in-the-loop simulation for testing of electro-hydraulic fuel control unit in a jet engine application. SIMULATION 89(2), 225–233 (2013)

    Google Scholar 

  13. Montazeri-Gh, M., Nasiri, M., Rajabi, M., Jamshidfard, M.: Actuator-based hardware-in-the-loop testing of a jet engine fuel control unit in flight conditions. Simul. Model. Pract. Theory 21(1), 65–77 (2012)

    Google Scholar 

  14. Dwi Atmaji, F.T., Noviyanti, A.A., Juliani, W.: Implementation of maintenance scenario for critical subsystem in aircraft engine: case study NTP CT7 engine. Int. J. Innov. Enterp. Syst. 1(02), 52 (2017). https://doi.org/10.25124/ijies.v1i01.85

    Article  Google Scholar 

  15. Satish, T.N., Murthy, R., Singh, A.K.: Analysis of uncertainties in measurement of rotor blade tip clearance in gas turbine engine under dynamic condition. Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng. 228(5), 652–670 (2014)

    Google Scholar 

  16. Tao, Z., Guo, Z., Song, L., Li, J.: Uncertainty quantification of aero-thermal performance of a blade endwall considering slot geometry deviation and mainstream fluctuation. J. Turbomach. 143(11), 111013 (2021)

    Google Scholar 

  17. Zhang, M., Liu, Y., Sun, C., Wang, X., Tan, J.: Measurements error propagation and its sensitivity analysis in the aero-engine multistage rotor assembling process. Rev. Sci. Instrum. 90(11), 115003 (2019)

    Google Scholar 

  18. Lu, F., Gao, T., Huang, J., Qiu, X.: A novel distributed extended Kalman filter for aircraft engine gas-path health estimation with sensor fusion uncertainty. Aerosp. Sci. Technol. 84, 90–106 (2019)

    Google Scholar 

  19. Chen, M., Quan, H.L., Tang, H.: An approach for optimal measurements selection on gas turbine engine fault diagnosis. J. Eng. Gas Turbines Power 137(7), 071203 (2015)

    Google Scholar 

  20. Chen, J., Ma, C., Song, D., Xu, B.: Failure prognosis of multiple uncertainty system based on Kalman filter and its application to aircraft fuel system. Adv. Mech. Eng. 8(10), 1687814016671445 (2016)

    Google Scholar 

  21. Zhang, J., Tang, H., Chen, M.: Robust design of an adaptive cycle engine performance under component performance uncertainty. Aerosp. Sci. Technol. 113, 106704 (2021)

    Google Scholar 

  22. Cao, D., Zhao, C., Bai, G.: DCRSM-based aeroengine cycle selection approach for multi-operating conditions performance reliability. Energy Procedia 158, 1537–1546 (2019)

    Google Scholar 

  23. Kaneba, C.M., Mu, X., Li, X., Wu, X.: Event triggered control for fault tolerant control system with actuator failure and randomly occurring parameter uncertainty. Appl. Math. Comput. 415, 126714 (2022)

    MathSciNet  MATH  Google Scholar 

  24. Tang, X., Tao, G., Joshi, S.M.: Adaptive actuator failure compensation for nonlinear MIMO systems with an aircraft control application. Automatica 43(11), 1869–1883 (2007)

    MathSciNet  MATH  Google Scholar 

  25. Ariffin, A.E., Munro, N.: Robust control analysis of a gas-turbine aeroengine. IEEE Trans. Control Syst. Technol. 5(2), 178–188 (1997)

    Google Scholar 

  26. Gou, L., Liu, Z., Fan, D., Zheng, H.: Aeroengine robust gain-scheduling control based on performance degradation. IEEE Access 8, 104857–104869 (2020)

    Google Scholar 

  27. Liu, X., Zhang, L., Luo, C.: Model reference adaptive control for aero-engine based on system equilibrium manifold expansion model. Int. J. Control (2021). https://doi.org/10.1080/00207179.2021.2016979

    Article  Google Scholar 

  28. Liu, F., Chen, M.: Robust adaptive fault-tolerant control for the turbofan aero-engine system. In: 2020 5th International Conference on Advanced Robotics and Mechatronics. IEEE, 489–494 (2020)

  29. Yu, L., Sun, X.M., Gao, Y.F.: Active disturbance rejection control for uncertain nonlinear systems subject to magnitude and rate saturation: Application to aeroengine. IEEE Trans. Syst. Man Cybernet.: Syst. 52(4), 2201–12 (2021)

    Google Scholar 

  30. Zhang, M., Gou, L., Jiang, Z., Sun, C.: Optimization of aero-engine H-infinity robust controller based on quantum genetic algorithm. In: 2021 12th International Conference on Mechanical and Aerospace Engineering. IEEE, 225–231 (2021)

  31. Zhang, Z., Ma, X., Hua, H., Liang, X.: Nonlinear stochastic dynamics of a rub-impact rotor system with probabilistic uncertainties. Nonlinear Dyn. 102(4), 2229–2246 (2020)

    Google Scholar 

  32. Profir, B.: Model validation and uncertainty qualification for the preliminary aero-engine design process. University of Southampton (2019)

    Google Scholar 

  33. Tong, M.T., Jones, S.M., Arcara, P.C.: A probabilistic assessment of NASA ultra-efficient engine technologies for a large subsonic transport. In: ASME Turbo Expo 2004, pp. 1–8. Austria, Vienna (2004)

    Google Scholar 

  34. Tong, M.T.: A probabilistic approach to aero-propulsion system assessment. Turbo Expo: Power for Land, Sea, and Air. Am. Soc. Mech. Eng. 78545, V001T01A001 (2000)

    Google Scholar 

  35. Fei, C.W., Choy, Y.S., Hu, D.Y., Bai, G.C., Tang, W.Z.: Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance. Nonlinear Dyn. 86(1), 205–223 (2016)

    Google Scholar 

  36. Ng, L.W.T., Willcox, K.E.: Monte Carlo information-reuse approach to aircraft conceptual design optimization under uncertainty. J. Aircr. 53(2), 427–438 (2016)

    Google Scholar 

  37. Chen, M., Zhang, J., Tang, H.: Interval analysis of the standard of adaptive cycle engine component performance deviation. Aerosp. Sci. Technol. 81, 179–191 (2018)

    Google Scholar 

  38. Fu, Q., Wang, H., Yan, X.: Evaluation of the aeroengine performance reliability based on generative adversarial networks and Weibull distribution. Proc. Inst. Mech. Eng. Part G: J. Aerosp. Eng. 233(15), 5717–5728 (2019)

    Google Scholar 

  39. Zhang, J., Tang, H., Chen, M.: Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine). Appl. Energy 249, 87–108 (2019)

    Google Scholar 

  40. Zhang, Y., Jin, Y.: Stochastic dynamics of a piezoelectric energy harvester with correlated colored noises from rotational environment. Nonlinear Dyn. 98(1), 501–515 (2019)

    Google Scholar 

  41. McKeand, A.M., Gorguluarslan, R.M., Choi, S.K.: A stochastic approach for performance prediction of aircraft engine components under manufacturing uncertainty. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers 51739, V01BT02A045 (2018)

  42. Lestoille, N., Soize, C., Funfschilling, C.: Sensitivity of train stochastic dynamics to long-term evolution of track irregularities. Veh. Syst. Dyn. 54(5), 545–567 (2016)

    Google Scholar 

  43. Qiao, Z., Elhattab, A., Shu, X., He, C.: A second-order stochastic resonance method enhanced by fractional-order derivative for mechanical fault detection. Nonlinear Dyn. 106(1), 707–723 (2021)

    Google Scholar 

  44. Zhang, S., Yang, J., Zhang, J., Liu, H., Hu, E.: On bearing fault diagnosis by nonlinear system resonance. Nonlinear Dyn. 98(3), 2035–2052 (2019)

    MATH  Google Scholar 

  45. Qiao, Z., Liu, J., Ma, X., Liu, J.: Double stochastic resonance induced by varying potential-well depth and width. J. Franklin Inst. 358(3), 2194–2211 (2021)

    MathSciNet  MATH  Google Scholar 

  46. Shen, M., Yang, J., Sanjuán, M.A.F., Zheng, Y., Liu, H.: Adaptive denoising for strong noisy images by using positive effects of noise. Eur Phys J Plus 136(6), 698 (2021)

    Google Scholar 

  47. Yang, L.P., Wang, L.Y., Wang, J.Q., Zare, A., Brown, R.J.: Nonlinear dynamics of cycle-to-cycle variations in a lean-burn natural gas engine with a non-uniform pre-mixture. Nonlinear Dyn. 104, 1–18 (2021)

    Google Scholar 

  48. Yang, L.P., Bodisco, T.A., Zare, A., Marwan, N., Chu-Van, T., Brown, R.J.: Analysis of the nonlinear dynamics of inter-cycle combustion variations in an ethanol fumigation-diesel dual-fuel engine. Nonlinear Dyn. 95(3), 2555–2574 (2019)

    Google Scholar 

  49. Li, S., Bastani, O.: Robust model predictive shielding for safe reinforcement learning with stochastic dynamics. In: 2020 IEEE International Conference on Robotics and Automation. IEEE, pp. 7166–7172 (2020)

  50. Kühnel, L., Sommer, S., Arnaudon, A.: Differential geometry and stochastic dynamics with deep learning numerics. Appl. Math. Comput. 356, 411–437 (2019)

    MathSciNet  MATH  Google Scholar 

  51. Roberts, R.A., Eastbourn, S.M.: Modeling techniques for a computational efficient dynamic turbofan engine model. Int. J. Aerosp. Eng. (2014). https://doi.org/10.1155/2014/283479

    Article  Google Scholar 

  52. Gou, L., Liu, Z., Fan, D.: Aeroengine robust gain-scheduling control based on performance degradation. IEEE Access 8, 104857–104869 (2020)

    Google Scholar 

  53. Jin, P., Lu, F., Huang, J.: Life cycle gas path performance monitoring with control loop parameters uncertainty for aeroengine. Aerosp. Sci. Technol. 115, 106775 (2021)

    Google Scholar 

  54. Seldner, K., Cwynar, D.S.: Procedures for generation and reduction of linear models of a turbofan engine. NASA Technical Paper 1978

  55. El-Bakry, A.S., Tapia, R.A., Tsuchiya, T., Zhang, Y.: On the formulation and theory of the Newton interior-point method for nonlinear programming. J. Optim. Theory Appl. 89(3), 507–541 (1996)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work is supported by the Science and Technology Department of Ningxia (Grant No. 2022ZDYF1483) and Chinese-German Center for Research Promotion (Grant No. GZ1577).

Funding

This work is funded by the Science and Technology Department of Ningxia (Grant No.1015 2022ZDYF1483) and Chinese-German Center for Research Promotion (Grant No. GZ1577).

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  1. The Key Laboratory of Power Machinery and Engineering of Education Ministry, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Dengji Zhou & Dawen Huang

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Zhou, D., Huang, D. Stochastic response analysis and robust optimization of nonlinear turbofan engine system. Nonlinear Dyn 110, 2225–2245 (2022). https://doi.org/10.1007/s11071-022-07752-5

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