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Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network

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  • Du, Qiuwan
  • Yang, Like
  • Li, Liangliang
  • Liu, Tianyuan
  • Zhang, Di
  • Xie, Yonghui

Abstract

End wall contraction is an effective approach to reduce the substantial secondary loss in the cascade with low aspect ratio. This paper proposed a series convolutional neural network architecture (SCNN) to improve the design optimization problem of end wall profile of a turbine stator blade. The specific implementation of the architecture is discussed in detail. The effect of the train size and sensitivity analysis of design variables are carried out. Finally, the optimization is completed by the gradient descent method, and the aerodynamic performance of end wall profile before and after optimization is compared. It shows that the SCNN architecture performs outstandingly with 30% training data. It can quickly and accurately provide rich flow field information and performance parameters within 3 ms after training. When the train size is 0.3, the mean prediction errors of mass flow rate and efficiency of each sample are lower than 0.1%, which performs significantly better than the Artificial Neural Network and Gaussian Process Regression model. When the stator blade adopts optimized contracted end wall profile, the power and efficiency are raised by 4.43% and 1.39% respectively with the mass flow rate only changing by 1.48%, which verifies the feasibility of the SCNN architecture.

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  • 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).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028668
    DOI: 10.1016/j.energy.2021.122617
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    6. Zhang, Weihao & Li, Lele & Li, Ya & Jiang, Chiju & Wang, Yufan, 2023. "A parameterized-loading driven inverse design and multi-objective coupling optimization method for turbine blade based on deep learning," Energy, Elsevier, vol. 281(C).
    7. Li, Lele & Zhang, Weihao & Li, Ya & Zhang, Ruifeng & Liu, Zongwang & Wang, Yufan & Mu, Yumo, 2024. "A non-parametric high-resolution prediction method for turbine blade profile loss based on deep learning," Energy, Elsevier, vol. 288(C).
    8. 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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