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Dynamic reliability analysis for the reusable thrust chamber: A multi-failure modes investigation based on coupled thermal-structural analysis

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  • Yaqun, Qi
  • Ping, Jin
  • Ruizhi, Li
  • Sheng, Zhang
  • Guobiao, Cai

Abstract

To evaluate the dynamic reliability of the thrust chamber in the reusable rocket engine (RRE) under multi-failure modes, a reliability analysis based on the coupled thermal-structural finite element model and the multiple response surface (MRS) is proposed. Through the deterministic thermal-structural simulation, two major failure modes, the static strength failure and cyclic cumulative damage, are examined. While the random variables are introduced concerning working loads, geometrical dimensions, and material properties. The reliability of a typical reusable LOX/H2 rocket engine thrust chamber is examined by the proposed method. The feasibility of the proposed method is validated by comparing our results with that from the traditional Monte Carlo simulation and the influences of sampling methods as well as set sizes are investigated. This study provides an efficient and steadfast approach to evaluate the reliability of thrust chambers in the reusable rocket engine and quantifies the importance ranking of working loads, geometrical dimensions, and material properties for the chamber reliability, which provides a valuable insight into the reliability-based design and optimization process for reusable rocket engines

Suggested Citation

  • Yaqun, Qi & Ping, Jin & Ruizhi, Li & Sheng, Zhang & Guobiao, Cai, 2020. "Dynamic reliability analysis for the reusable thrust chamber: A multi-failure modes investigation based on coupled thermal-structural analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020305810
    DOI: 10.1016/j.ress.2020.107080
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    1. Alban, Andres & Darji, Hardik A. & Imamura, Atsuki & Nakayama, Marvin K., 2017. "Efficient Monte Carlo methods for estimating failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 376-394.
    2. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2018. "Time-variant fatigue reliability assessment of welded joints based on the PHI2 and response surface methods," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 120-130.
    3. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
    4. Xiang, Zhengliang & Bao, Yuequan & Tang, Zhiyi & Li, Hui, 2020. "Deep reinforcement learning-based sampling method for structural reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    5. Tao, Tao & Zio, Enrico & Zhao, Wei, 2018. "A novel support vector regression method for online reliability prediction under multi-state varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 35-49.
    6. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.
    7. Xiao, Ning-Cong & Zuo, Ming J. & Zhou, Chengning, 2018. "A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 330-338.
    8. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.
    9. Rajpal, P.S. & Shishodia, K.S. & Sekhon, G.S., 2006. "An artificial neural network for modeling reliability, availability and maintainability of a repairable system," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 809-819.
    10. Nam, Woochul & Oh, Ki-Yong & Epureanu, Bogdan I., 2019. "Evolution of the dynamic response and its effects on the serviceability of offshore wind turbines with stochastic loads and soil degradation," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 151-163.
    11. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2020. "Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    12. Sadovský, Z. & Guedes Soares, C., 2011. "Artificial neural network model of the strength of thin rectangular plates with weld induced initial imperfections," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 713-717.
    13. Rajabalinejad, M. & Meester, L.E. & van Gelder, P.H.A.J.M. & Vrijling, J.K., 2011. "Dynamic bounds coupled with Monte Carlo simulations," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 278-285.
    14. Gao, Haifeng & Wang, Anjenq & Zio, Enrico & Bai, Guangchen, 2020. "An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    15. Ni, Pinghe & Li, Jun & Hao, Hong & Yan, Weimin & Du, Xiuli & Zhou, Hongyuan, 2020. "Reliability analysis and design optimization of nonlinear structures," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
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    1. Qi, Yaqun & Jin, Ping & Cai, Guobiao & Li, Ruizhi, 2022. "A Bi-stage Multi-objective Reliability-based Design Optimization Using Surrogate Model for Reusable Thrust Chambers," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Teng, Da & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi & Chen, Jun-Yu, 2024. "Vectorial generative adversarial surrogate modeling reliability evaluation framework for engineering structural systems," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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