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Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade

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

Abstract

Film cooling is one of the effective cooling methods to ensure the longevity of high thermal load turbines. Due to multiple corresponding design parameters, it is difficult to seek rapid evolution of overall film cooling performance of new design. Currently, some achievements were obtained in plane cooling by implementing deep learning models which have strong nonlinear mapping capability in high-dimensional datasets. To further expand deep learning and achieve high-fidelity prediction on 3D complex cooling configurations, our research introduces deep learning network into the linear cascade of air-cooling turbines. Furthermore, in this work, the Multi-scale Pixel to Pixel (MSPix2Pix) network is proposed to realize the reconstruction of high-resolution adiabatic cooling effectiveness on turbine cascade among sparse dataset, in which high-resolution non-parametric concept and introduce multiple generators and discriminators are utilized. The average structural similarity (SSIM) reached 0.9892 between the predicted images and the CFD results in the test set. The results of the verification experiment show the applicability of MSPix2Pix network for rapid and accurate evaluation of cooling effectiveness on complex three-dimensional geometry with a certain generalization, which provides a certain support for high fidelity prediction of cooling configuration and aero-thermal coupling design in gas-cooling turbine.

Suggested Citation

  • Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032674
    DOI: 10.1016/j.energy.2022.126381
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    References listed on IDEAS

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    1. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
    2. Huang, Renfang & Zhang, Zhen & Zhang, Wei & Mou, Jiegang & Zhou, Peijian & Wang, Yiwei, 2020. "Energy performance prediction of the centrifugal pumps by using a hybrid neural network," Energy, Elsevier, vol. 213(C).
    3. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    4. Rossi, Mosè & Renzi, Massimiliano, 2018. "A general methodology for performance prediction of pumps-as-turbines using Artificial Neural Networks," Renewable Energy, Elsevier, vol. 128(PA), pages 265-274.
    5. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    6. 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).
    7. Wen, Hao & Sang, Song & Qiu, Chenhui & Du, Xiangrui & Zhu, Xiao & Shi, Qian, 2019. "A new optimization method of wind turbine airfoil performance based on Bessel equation and GABP artificial neural network," Energy, Elsevier, vol. 187(C).
    8. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Li, Haiwang & Wang, Meng & You, Ruquan & Liu, Song, 2023. "Thermal radiation correction formula of the scaling criteria for film cooling of turbine blades," Energy, Elsevier, vol. 282(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).
    3. Zhang, Fan & Liu, Cunliang & Ye, Lin & Ran, Yuan & Zhou, Tianliang & Yan, Haonan, 2024. "Study on the film superposition method for dense multirow film Hole layouts," Energy, Elsevier, vol. 293(C).

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