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Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades

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  • Wang, Qi
  • Yang, Li
  • Rao, Yu

Abstract

The main challenge of establishing a model to predict the flow fields of turbomachinery was insufficient data. This study aimed to establish a generalizable and accurate model on a small-scale dataset to cost-effectively predict the surface pressure distribution of a turbine rotor cascade with widely varying geometries and boundary conditions. To meet this purpose, a novel concept of transfer learning was introduced, which was defined as transferring knowledge from a large-scale but low-fidelity dataset to a small-scale but high-fidelity dataset. A Conditional Generative Adversarial Neural Network was designed as the pre-trained network for the transfer learning to regress the surface pressure distributions. Two models transferred from datasets with different fidelity and an independent model were established and compared in detail. The results showed that the proposed method successfully reduced the modeling cost with a low error in predicting the surface pressure distributions. The model transferred from the higher-fidelity dataset had better generalization performance, which reduced the root mean square error and modeling cost by 40.2% and 9 times, respectively. The presented method could serve as a base framework for modeling surface pressure distribution of complex objects using a small-scale dataset.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s036054422031985x
    DOI: 10.1016/j.energy.2020.118878
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    References listed on IDEAS

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    1. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    2. Han, Wanlong & Zhang, Yifan & Li, Hongzhi & Yao, Mingyu & Wang, Yueming & Feng, Zhenping & Zhou, Dong & Dan, Guangju, 2019. "Aerodynamic design of the high pressure and low pressure axial turbines for the improved coal-fired recompression SCO2 reheated Brayton cycle," Energy, Elsevier, vol. 179(C), pages 442-453.
    3. Li, Lei & Jiao, Jiangkun & Sun, Shouyi & Zhao, Zhenan & Kang, Jialei, 2019. "Aerodynamic shape optimization of a single turbine stage based on parameterized Free-Form Deformation with mapping design parameters," Energy, Elsevier, vol. 169(C), pages 444-455.
    4. Wang, Yabo & Yu, Jianyang & Song, Yanping & Chen, Fu, 2020. "Parameter optimization of the composite honeycomb tip in a turbine cascade," Energy, Elsevier, vol. 197(C).
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    Cited by:

    1. 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).
    2. Wang, Qi & Yang, Li & Huang, Kang, 2022. "Fast prediction and sensitivity analysis of gas turbine cooling performance using supervised learning approaches," Energy, Elsevier, vol. 246(C).
    3. 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).
    4. 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).
    5. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.

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