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A parameterized-loading driven inverse design and multi-objective coupling optimization method for turbine blade based on deep learning

Author

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  • Zhang, Weihao
  • Li, Lele
  • Li, Ya
  • Jiang, Chiju
  • Wang, Yufan

Abstract

Inverse design is an important part of the initial design stage of blade profile, while the traditional inverse design methods are highly dependent on design experience and physical model simulation, resulting in long design cycles. To accelerate the inverse design, this paper proposes an inverse design method based on load distribution by combining the Variational Auto-Encoder model with the Generative Adversarial Network (VAE-GAN) and U-Net (U-Net1 and U-Net2) using a multi-objective genetic algorithm (GA). The inverse design process is divided into three stages. First, the VAE-GAN model is combined with the GA to generate a batch of load distributions that match the target form. Then the U-Net1 network is used to establish the connection between the load distribution and the blade aerodynamic performance as well as the geometric parameters. The optimal load distribution under multi-objective constraints is filtered by designing the fitness function of the GA. Finally, the blade profile corresponding to the optimal load distribution is inversely designed by the U-Net2 network. Numerical simulation results show that the method can rapidly generate the blade profile corresponding to the specified load distribution, and realize the automatic optimization of aerodynamic performance under certain geometric constraints in the inverse design process.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:281:y:2023:i:c:s0360544223016031
    DOI: 10.1016/j.energy.2023.128209
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    References listed on IDEAS

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    1. Khan, Zain Ullah & Ali, Zaib & Uddin, Emad, 2022. "Performance enhancement of vertical axis hydrokinetic turbine using novel blade profile," Renewable Energy, Elsevier, vol. 188(C), pages 801-818.
    2. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    3. Kamal, Ahmed M. & Nawar, Mohamed A.A. & Attai, Youssef A. & Mohamed, Mohamed H., 2023. "Archimedes Spiral Wind Turbine performance study using different aerofoiled blade profiles: Experimental and numerical analyses," Energy, Elsevier, vol. 262(PB).
    4. Wang, Qi & Yang, Li & Rao, Yu, 2021. "Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades," Energy, Elsevier, vol. 214(C).
    5. Witanowski, Łukasz & Klonowicz, Piotr & Lampart, Piotr & Klimaszewski, Piotr & Suchocki, Tomasz & Jędrzejewski, Łukasz & Zaniewski, Dawid & Ziółkowski, Paweł, 2023. "Impact of rotor geometry optimization on the off-design ORC turbine performance," Energy, Elsevier, vol. 265(C).
    6. Al Jubori, Ayad M. & Al-Dadah, Raya & Mahmoud, Saad, 2017. "Performance enhancement of a small-scale organic Rankine cycle radial-inflow turbine through multi-objective optimization algorithm," Energy, Elsevier, vol. 131(C), pages 297-311.
    7. 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).
    8. Mohamed, M.H. & Dessoky, A. & Alqurashi, Faris, 2019. "Blade shape effect on the behavior of the H-rotor Darrieus wind turbine: Performance investigation and force analysis," Energy, Elsevier, vol. 179(C), pages 1217-1234.
    9. Zhang, Fangfang & Fang, Mingkun & Pan, Jiale & Tao, Ran & Zhu, Di & Liu, Weichao & Xiao, Ruofu, 2023. "Guide vane profile optimization of pump-turbine for grid connection performance improvement," Energy, Elsevier, vol. 274(C).
    10. Wang, Zheng & Zeng, Tiansheng & Chu, Xuening & Xue, Deyi, 2023. "Multi-objective deep reinforcement learning for optimal design of wind turbine blade," Renewable Energy, Elsevier, vol. 203(C), pages 854-869.
    11. 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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