Time-varying reliability analysis of stochastic process discretization method for train traction power impeller vibration

QU Xiaozhang1, 2

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (17) : 49.

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PDF(3958 KB)
Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (17) : 49.

Time-varying reliability analysis of stochastic process discretization method for train traction power impeller vibration

  • QU Xiaozhang1,2
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Abstract

vibration failure, which often occurs in the design life cycle of blade structures, since their harsh working environments, become a main problem in turbomachinery. In order to analyze the time-varying reliability of blade vibration in a more effective way, a time-varying reliability analysis of stochastic process discretization method for blade vibration (BV-TRPD) was studied, based on response surface model (RSM). Firstly, the vibration analysis model of blade were established by conducting vibration test and simulating through finite element method. Considering the uncertainties of blade structure size, material parameters and load, the RSM was used to establish the blade vibration limit state equation. The nonlinear exponential function, stochastic model parameters and Gaussian random process of parameter correlation were used to establish the time-varying reliability analysis model of blade vibration. Secondly, on the basis of time-varying reliability analysis techniques such as span rate, the time-varying reliability was transformed into multiple time-invariant systems, and the stochastic process was discretized in time. For the vibration finite element problem of implicit limit state equation, the response surface function between the input parameters and the response extremum was established by sampling. The first order reliability method (FORM) was used to solve the time-varying reliability of the blade vibration problem. Finally, Monte Carlo simulation was used to analyze and compare, and to demonstrate improvement in results. Taking the uncertainties of design, process, load and operating environment into consider, the key parameters affecting the time-varying reliability of blade vibration were studied. Considering the cost, the process control parameters to improve the whole life cycle reliability of blade vibration were put forward to guide practical engineering application.

Key words

Time-varying reliability / Blade vibration / Vibration reliability / Stochastic process discretization / Vibration test

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QU Xiaozhang1, 2. Time-varying reliability analysis of stochastic process discretization method for train traction power impeller vibration[J]. Journal of Vibration and Shock, 2024, 43(17): 49

References

[1] Fei, C., & Bai, G. (2012). Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation. Propulsion and Power Research, 1(1), 58-63. [2] Fei, C. W., Tang, W. Z., & Bai, G. C. (2014). Novel method and model for dynamic reliability optimal design of turbine blade deformation. Aerospace Science and Technology, 39, 588-595. [3] Fei, C., Tang, W., Bai, G., & Ma, S. (2015). Dynamic probabilistic design for blade deformation with SVM-ERSM. Aircraft Engineering and Aero-space Technology: An International Journal, 87(4), 312-321. [4] Fei, C. W., Choy, Y. S., Hu, D. Y., Bai, G. C., & Tang, W. Z. (2016). Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance. Nonlinear Dynamics, 86, 205-223. [5] Lu, C., Feng, Y. W., Liem, R. P., & Fei, C. W. (2018). Improved Kriging with extremum response surface method for structural dynamic reliability and sensitivity analyses. Aerospace Science and Technology, 76, 164-175. [6] Lu, C., Feng, Y. W., Fei, C. W., & Bu, S. Q. (2019). Improved decomposed-coordinated kriging modeling strategy for dynamic probabilistic analysis of multicomponent structures. IEEE Transactions on Reliability, 69(2), 440-457. [7] Zhang, C. Y., Wei, J. S., Wang, Z., Yuan, Z. S., Fei, C. W., & Lu, C. (2019). Creep-based reliability evaluation of turbine blade-tip clearance with novel neural network regression. Materials, 12(21), 3552. [8] Gao, H. F., Zio, E., Guo, J. J., Bai, G. C., & Fei, C. W. (2020). Dynamic probabilistic-based LCF damage assessment of turbine blades regarding time-varying multi-physical field loads. Engineering Failure Analysis, 108, 104193 [9] Lu, C., Fei, C. W., Liu, H. T., Li, H., & An, L. Q. (2020). Moving extremum surrogate modeling strategy for dynamic reliability estimation of turbine blisk with multi-physics fields. Aerospace Science and Technology, 106, 106112. [10] Hu, Z., & Du, X. (2012). Reliability analysis for hydrokinetic turbine blades. Renewable Energy, 48, 251-262. [11] Hu, Z., Li, H., Du, X., & Chandrashekhara, K. (2013). Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades. Structural and Multidisciplinary Optimization, 47(5), 765-781. [12] Hu, Zhen, et al. (2013). Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades. Structural and Multidisci-plinary Optimization 47.5: 765-781. [13] Lin, J., Zhang, J., Yang, S., & Bi, F. (2013). Reliability analysis of aero-engine blades considering nonlinear strength degeneration. Chinese Journal of Aeronautics, 26(3), 631-637. [14] Yue, P., Ma, J., Zhou, C., Zu, J. W., & Shi, B. (2021). Dynamic fatigue reliability analysis of turbine blades under combined high and low cycle loadings. International Journal of Damage Mechanics, 30(6), 825-844. [15] Gao, J., & Yuan, Y. (2021). Reliability model for wind turbine blade composites under alternate action of normal and extreme wind loads. Proceed-ings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 235(21), 5567-5582. [16] Li, B., Zhou, H., Liu, J., & Kang, C. (2021). Multiaxial fatigue damage and reliability assessment of aero-engine compressor blades made of TC4 ti-tanium alloy. Aerospace Science and Technology, 119, 107107. [17] Wang, Y., Han, Z., Yang, Z., Lu, C., & Xue, X. (2021). Time-varying reliability analysis of compressor blisk based on particle swash optimization ex-treme Kriging model. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 39(6), 1240-1248. [18] Wang, Z., Wang, Z., Zhuang, L., & Wang, A. N. (2014). Time-dependent vibration frequency reliability analysis of blade vibration of compressor wheel of turbocharger for vehicle application. Chinese Journal of Mechanical Engineering, 27(1), 205-210. [19] Andrieu-Renaud, C., Sudret, B., & Lemaire, M. (2004). The PHI2 method: a way to compute time-variant reliability. Reliability Engineering & Sys-tem Safety, 84(1), 75-86. [20] Sudret, B. (2008). Analytical derivation of the outcrossing rate in time-variant reliability problems. Structure and Infrastructure Engineering, 4(5), 353-362. [21] Hu Z, Du X (2013a) Time-dependent reliability analysis with joint upcrossing rates. Struct Multidiscip Optim 48(5):893–907. [22] Hu Z, Du X (2013b) A sampling approach to extreme value distribution for time-dependent reliability analysis. J Mech Des 135(7):071003. [23] Jiang, C., Huang, X. P., Han, X., & Zhang, D. Q. (2014). A time-variant reliability analysis method based on stochastic process discretization. Journal of Mechanical Design, 136(9), 091009. [24] Jiang, C., Huang, X. P., Wei, X. P., & Liu, N. Y. (2017). A time-variant reliability analysis method for structural systems based on stochastic process discretization. International Journal of Mechanics and Materials in Design, 13(2), 173-193. [25] Zhang, D., Han, X., Jiang, C., Liu, J., & Li, Q. (2017). Time-dependent reliability analysis through response surface method. Journal of Mechanical Design, 139(4), 041404. [26] Lebrun, R., & Dutfoy, A. (2009). A generalization of the Nataf transformation to distributions with elliptical copula. Probabilistic Engineering Me-chanics, 24(2), 172-178. [27]陈学前,沈展鹏,刘信恩.基于响应面与灵敏度分析的区间不确定性参数识别方法[J].振动与冲击,2019,38(16):267-273. Chen Xueqian, Shen Zhanpeng, Liu Xinen. Interval uncertainty parameter identification method based on response surface and sensitivity analysis [J]. Vibration and Shock, 2019,38 (16): 267-273 [28]沈彦佑,贾民平,朱林.基于拉丁超立方抽样的离心式吸叶机噪声分析与降噪优化研究[J].振动与冲击,2016,35(15):93-97. Shen Yanyou, Jia Minping, Zhu Lin. Research on noise analysis and noise reduction optimization of centrifugal vane suction machine based on Latin hypercube sampling [J]. Vibration and Shock, 2016,35 (15): 93-97 [29] 韦新鹏. 基于FORM的结构时变可靠性分析方法[D].湖南大学,2021. Wei Xinpeng. FORM based structural time-varying reliability analysis method [D]. Hunan University, 2021 [30] Mao, Z., & Todd, M. (2013). Statistical modeling of frequency response function estimation for uncertainty quantification. Mechanical Systems and Signal Processing, 38(2), 333-345. [31] Peeters, B., Van der Auweraer, H., Guillaume, P., & Leuridan, J. (2004). The PolyMAX frequency-domain method: a new standard for modal parame-ter estimation. Shock and Vibration, 11(3-4), 395-409. [32]屈小章. 轨道列车风机系统轻量化及气动性能优化设计[D].湖南大学,2016. Qu Xiaozhang Lightweight and aerodynamic performance optimization design of rail train fan system [D]. Hunan University, 2016 [33]屈小章,韩旭,刘桂萍等.基于气动弹性的风机叶轮结构时变可靠性分析研究[J].中国科学:技术科学,2018,48(04):382-394. Qu Xiaozhang, Han Xu, Liu Guiping, et al. Research on time-varying reliability analysis of fan impeller structure based on aeroelasticity [J]. Chinese Science: Technical Science, 2018,48 (04): 382-394 [34] Qu, X., Liu, G., Duan, S., & Yang, J. (2016). Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller. Journal of Computational Design and Engineering, 3(3), 179-190.
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