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Uncertainty quantification based optimization of centrifugal compressor impeller for aerodynamic robustness under stochastic operational conditions

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  • Tang, Xinzi
  • Wang, Zhe
  • Xiao, Peng
  • Peng, Ruitao
  • Liu, Xiongwei

Abstract

Centrifugal compressor impeller operates at stochastic boundary conditions. The operational uncertainties cause performance deviation from design value and consequently affect the reliability of the compressor. Considering the stochastic operational uncertainties in the early design stage, this paper presents an uncertainty quantification based optimization of centrifugal compressor impeller with splitter blades for aerodynamic robustness. The nonlinear response relation between the design variables, the aerodynamic boundary uncertainties and the impeller performance is modelled by a combination of the Latin Hypercube Sampling, the three dimensional CFD, the Kriging surrogate model and the Non-intrusive Probability Collocation method. A sensitivity analysis with single and multiple random geometry variations is carried out to identify the most sensitive parameters. The effects of rotor speed uncertainty on pressure ratio and efficiency are quantified. A case study is conducted to search for optimal impellers with higher performance and lower sensitivity to boundary uncertainty using NSGA-II. The optimization is verified by the Monte Carlo method. The results demonstrate that the aerodynamic robustness of the compress impeller with splitter blades is enhanced by the proposed approach, which provides references for compressors, turbines and other turbo machinery.

Suggested Citation

  • Tang, Xinzi & Wang, Zhe & Xiao, Peng & Peng, Ruitao & Liu, Xiongwei, 2020. "Uncertainty quantification based optimization of centrifugal compressor impeller for aerodynamic robustness under stochastic operational conditions," Energy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220300372
    DOI: 10.1016/j.energy.2020.116930
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    References listed on IDEAS

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    1. Kleijnen, J.P.C., 2015. "Regression and Kriging Metamodels with Their Experimental Designs in Simulation : Review," Other publications TiSEM c592e895-1656-43c3-8c7e-f, Tilburg University, School of Economics and Management.
    2. Lin, Yan-Hui & Li, Yan-Fu & Zio, Enrico, 2018. "A comparison between Monte Carlo simulation and finite-volume scheme for reliability assessment of multi-state physics systems," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 1-11.
    3. Lu, Hanan & Li, Qiushi & Pan, Tianyu, 2016. "Optimization of cantilevered stators in an industrial multistage compressor to improve efficiency," Energy, Elsevier, vol. 106(C), pages 590-601.
    4. Moussavi, S. Abolfazl & Hajilouy Benisi, Ali & Durali, Mohammad, 2017. "Effect of splitter leading edge location on performance of an automotive turbocharger compressor," Energy, Elsevier, vol. 123(C), pages 511-520.
    5. Tüchler, Stefan & Chen, Zhihang & Copeland, Colin D., 2018. "Multipoint shape optimisation of an automotive radial compressor using a coupled computational fluid dynamics and genetic algorithm approach," Energy, Elsevier, vol. 165(PA), pages 543-561.
    6. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.
    7. Shiva Mehrabi-Kandsar & Abolfazl Mirzazadeh & Aref Gholami-Qadikolaei, 2017. "The quality function deployment method under uncertain environment using evidential reasoning: a case study of compressor manufacturing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1867-1884, November.
    8. Jack P. C. Kleijnen, 2015. "Kriging Metamodels and Their Designs," International Series in Operations Research & Management Science, in: Design and Analysis of Simulation Experiments, edition 2, chapter 5, pages 179-239, Springer.
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    Cited by:

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    2. Cheng, Hongzhi & Li, Ziliang & Duan, Penghao & Lu, Xingen & Zhao, Shengfeng & Zhang, Yanfeng, 2023. "Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles," Applied Energy, Elsevier, vol. 352(C).
    3. Fu, Jianqin & Wang, Huailin & Sun, Xilei & Bao, Huanhuan & Wang, Xun & Liu, Jingping, 2024. "Multi-objective optimization for impeller structure parameters of fuel cell air compressor using linear-based boosting model and reference vector guided evolutionary algorithm," Applied Energy, Elsevier, vol. 363(C).
    4. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
    5. Wang, Kun & Chen, Fu & Yu, Jianyang & Song, Yanping & Ghorbaniasl, Ghader, 2023. "Effect of uncertain operating conditions on the aerodynamic performance of high-pressure axial turbomachinery blades," Energy, Elsevier, vol. 283(C).
    6. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).

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