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Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties

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  • Cheng, Hongzhi
  • Zhou, Chuangxin
  • Lu, Xingen
  • Zhao, Shengfeng
  • Han, Ge
  • Yang, Chengwu

Abstract

Axial compressors are inevitably affected by various uncertain factors in the process of manufacture and operation. These uncertainties obviously lead to reduced efficiency and large performance dispersion. However, researches on uncertainty quantification and robust design of compressors still faces severe difficulties due to the complexity of compressor structure and internal flow. This paper aims to present an automated and effective framework for uncertainty quantification and aerodynamic robustness optimization of axial compressor. The manufacturing error distribution is derived from the measurement data of machined fan blades, and the sparse grid-based polynomial chaos expansion method is used to propagate the uncertain factors and predict the probability density distribution of the fan performance. A novel surrogate model that combines a self-organizing mapping and a back-propagation neural network is constructed to explore and visualize the correlation between uncertainty parameters and performance responses. Robust aerodynamic design optimization is achieved based on the genetic algorithm. The results indicate that the coupled neural network model exhibits good accuracy for uncertain approximate modeling. Compared with the prototype fan, the optimized fan's mean isentropic efficiency and pressure ratio increase by 0.97% and 0.72%, respectively. The standard deviation of isentropic efficiency, pressure ratio, and mass flow rate decrease by 46.3%, 21.4%, and 15.2%, respectively. The present study provides a reference and exploration for uncertainty quantification and robust optimization of advanced refined multi-stage turbomachinery.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223014056
    DOI: 10.1016/j.energy.2023.128011
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    References listed on IDEAS

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    1. Xia, Zhiheng & Luo, Jiaqi & Liu, Feng, 2019. "Statistical evaluation of performance impact of flow variations for a transonic compressor rotor blade," Energy, Elsevier, vol. 189(C).
    2. Cavazzini, G. & Giacomel, F. & Ardizzon, G. & Casari, N. & Fadiga, E. & Pinelli, M. & Suman, A. & Montomoli, F., 2020. "CFD-based optimization of scroll compressor design and uncertainty quantification of the performance under geometrical variations," Energy, Elsevier, vol. 209(C).
    3. 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).
    4. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    5. Thapa, Mishal & Missoum, Samy, 2022. "Uncertainty quantification and global sensitivity analysis of composite wind turbine blades," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. 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.
    7. 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).
    8. 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).
    9. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
    10. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
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    1. 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).

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