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Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis

Author

Listed:
  • Cheng Lu

    (School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yun-Wen Feng

    (School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China)

  • Cheng-Wei Fei

    (Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China)

Abstract

The parameters considered in structural dynamic reliability analysis have strong uncertainties during machinery operation, and affect analytical precision and efficiency. To improve structural dynamic fuzzy reliability analysis, we propose the weighted regression-based extremum response surface method (WR-ERSM) based on extremum response surface method (ERSM) and weighted regression (WR), by considering the randomness of design parameters and the fuzziness of the safety criterion. Therein, we utilize the ERSM to process the transient to improve computational efficiency, by transforming the random process of structural output response into a random variable. We employ the WR to find the efficient samples with larger weights to improve the calculative accuracy. The fuzziness of the safety criterion is regarded to improve computational precision in the WR-ERSM. The WR-ERSM is applied to perform the dynamic fuzzy reliability analysis of an aeroengine turbine blisk with the fluid-structure coupling technique, and is verified by the comparison of the Monte Carlo (MC) method, equivalent stochastic transformation method (ESTM) and ERSM, with the emphasis on model-fitting property and simulation performance. As revealed from this investigation, (1) the ERSM has the capacity of processing the transient of the structural dynamic reliability evaluation, and (2) the WR approach is able to improve modeling accuracy, and (3) regarding the fuzzy safety criterion is promising to improve the precision of structural dynamic fuzzy reliability evaluation, and (4) the change rule of turbine blisk structural stress from start to cruise for the aircraft is acquired with the maximum value of structural stress at t = 165 s and the reliability degree ( Pr = 0.997) of turbine blisk. The proposed WR-ERSM can improve the efficiency and precision of structural dynamic reliability analysis. Therefore, the efforts of this study provide a promising method for structural dynamic reliability evaluation with respect to working processes.

Suggested Citation

  • Cheng Lu & Yun-Wen Feng & Cheng-Wei Fei, 2019. "Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis," Energies, MDPI, vol. 12(9), pages 1-16, April.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1588-:d:226085
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    References listed on IDEAS

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    1. Zia-ur-Rehman Gondal & Jongsoo Lee, 2012. "Reliability assessment using feed-forward neural network-based approximate meta-models," Journal of Risk and Reliability, , vol. 226(5), pages 448-454, October.
    2. Meng Zhang & Shan Lu, 2014. "A reliability model of blade to avoid resonance considering multiple fuzziness," Journal of Risk and Reliability, , vol. 228(6), pages 641-652, December.
    3. Juan A. Martinez-Velasco & Gerardo Guerra, 2016. "Reliability Analysis of Distribution Systems with Photovoltaic Generation Using a Power Flow Simulator and a Parallel Monte Carlo Approach," Energies, MDPI, vol. 9(7), pages 1-21, July.
    4. Ravi Anant Kishore & Roop L. Mahajan & Shashank Priya, 2018. "Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator," Energies, MDPI, vol. 11(9), pages 1-17, August.
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