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Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles

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  • Cheng, Hongzhi
  • Li, Ziliang
  • Duan, Penghao
  • Lu, Xingen
  • Zhao, Shengfeng
  • Zhang, Yanfeng

Abstract

Axial compressors are susceptible to uncertainties during their manufacturing and operation, resulting in reduced efficiency and performance dispersion. However, uncertainty quantification and robust design of compressors remains challenging due to the complexity of structure and internal flow. In this study, an automated framework for uncertainty quantification and robustness optimization of micro axial compressors is proposed. Ten geometrical uncertainties are propagated for the nominal design point and two off-design points, i.e., near stall and choke conditions, respectively. The main objective of this paper is to optimize the aerodynamic robustness performance at these operating points. The sparse grid-based probabilistic collocation method is used to propagate these uncertainties, and a multi-objective genetic algorithm is employed to perform robust optimization based on a novel constructed surrogate model.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013363
    DOI: 10.1016/j.apenergy.2023.121972
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    References listed on IDEAS

    as
    1. Ekradi, Khalil & Madadi, Ali, 2020. "Performance improvement of a transonic centrifugal compressor impeller with splitter blade by three-dimensional optimization," Energy, Elsevier, vol. 201(C).
    2. Wu, Xianguo & Feng, Zongbao & Chen, Hongyu & Qin, Yawei & Zheng, Shiyi & Wang, Lei & Liu, Yang & Skibniewski, Miroslaw J., 2022. "Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Thomazoni, André Luis Ribeiro & Ermel, Conrado & Schneider, Paulo Smith & Vieira, Lara Werncke & Hunt, Julian David & Ferreira, Sandro Barros & Rech, Charles & Gouvêa, Vinicius Santorum, 2022. "Influence of operational parameters on the performance of Tesla turbines: Experimental investigation of a small-scale turbine," Energy, Elsevier, vol. 261(PB).
    4. Guan, Jin & Lv, Xiaojing & Spataru, Catalina & Weng, Yiwu, 2021. "Experimental and numerical study on self-sustaining performance of a 30-kW micro gas turbine generator system during startup process," Energy, Elsevier, vol. 236(C).
    5. 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).
    6. Taimoor Akhtar & Christine Shoemaker, 2016. "Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection," Journal of Global Optimization, Springer, vol. 64(1), pages 17-32, January.
    7. Tola, Vittorio & Lonis, Francesco, 2021. "Low CO2 emissions chemically recuperated gas turbines fed by renewable methanol," Applied Energy, Elsevier, vol. 298(C).
    8. Cheng, Hongzhi & Zhou, Chuangxin & Lu, Xingen & Zhao, Shengfeng & Han, Ge & Yang, Chengwu, 2023. "Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties," Energy, Elsevier, vol. 278(PB).
    9. Zhao, Yongning & Xu, Xiandong & Qadrdan, Meysam & Wu, Jianzhong, 2021. "Optimal operation of compressor units in gas networks to provide flexibility to power systems," Applied Energy, Elsevier, vol. 290(C).
    10. 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).
    11. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    12. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
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