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Performance prediction and design optimization of turbine blade profile with deep learning method

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

Abstract

Aerodynamic design optimization of the blade profile is a critical approach to improve performance of turbomachinery. This paper aims to achieve the performance prediction with deep learning method and realize fast design optimization of a turbine blade. Two parameterization methods based on geometric relationships (PGR) and neural network (PNN) are proposed, which can generate smooth and complete blade profiles. A dual convolutional neural network (DCNN) is constructed to predict the physical fields and aerodynamic performance. The implementations of DCNN are accomplished based on the datasets generated by the two parameterization methods respectively, which are called PGR-DCNN and PNN-DCNN model. Results show that the prediction accuracy increases and then keeps stable as train size increases. The two models can offer the detailed physical field distribution within 3 ms and accurately predict the aerodynamic performance. The prediction errors of performance parameters for 99% samples in validation set are less than 0.5% with PGR-DCNN model, which are significantly better than conventional machine learning methods. Finally, based on the accurate predictive models, the gradient-based design optimization for rotor blade profile is completed in 38 s. The efficiency of the two optimal blades reaches 89.29% and 88.92% respectively, which verifies the feasibility of our method.

Suggested Citation

  • Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pa:s0360544222012543
    DOI: 10.1016/j.energy.2022.124351
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    1. 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).
    2. 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).

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