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Multidisciplinary reliability analysis of turbine blade with shape uncertainty by Kriging model and free-form deformation methods

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  • Fan Yang
  • Zhimin Xu

Abstract

This work presents an integrated approach for the multidisciplinary reliability analysis of turbine blades with shape uncertainty, including the metamodel, the free-form deformation, and the Monte Carlo simulation. The multidisciplinary analysis of turbine blade includes fluid, structure, and thermal analyses, which is time-consuming during integration with multidisciplinary reliability analysis. The metamodel is constructed by adaptive sampling to reduce computational cost. The shape uncertainty with small size changes in reliability analysis should be considered. The geometry-based multidisciplinary analysis may fail to capture the small size changes during the geometry and mesh regeneration process. The main contribution of this article is to introduce the free-form deformation in multidisciplinary reliability analysis to overcome the aforementioned problems. The mesh-based method supported by free-form deformation is proposed. Failure probability analysis of the multidisciplinary blade system is performed using the Monte Carlo simulation and the surrogate model. Through the numerical simulation, it is found that the failure probability increases as the blade shape uncertainty becomes larger. The methodology in this article provides a valuable and applicative way to calculate the risk of blade in multidisciplinary system.

Suggested Citation

  • Fan Yang & Zhimin Xu, 2020. "Multidisciplinary reliability analysis of turbine blade with shape uncertainty by Kriging model and free-form deformation methods," Journal of Risk and Reliability, , vol. 234(4), pages 611-621, August.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:4:p:611-621
    DOI: 10.1177/1748006X19901041
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    References listed on IDEAS

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    1. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
    2. 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.
    3. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    4. Yang, Xufeng & Liu, Yongshou & Mi, Caiying & Tang, Chenghu, 2018. "System reliability analysis through active learning Kriging model with truncated candidate region," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 235-241.
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    1. Xiongxiong You & Mengya Zhang & Diyin Tang & Zhanwen Niu, 2022. "An active learning method combining adaptive kriging and weighted penalty for structural reliability analysis," Journal of Risk and Reliability, , vol. 236(1), pages 160-172, February.

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