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Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques

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  • Wang, Yuqi
  • Liu, Tianyuan
  • Meng, Yue
  • Zhang, Di
  • Xie, Yonghui

Abstract

The design efficiency and operating conditions of turbomachinery are important for ensuring the reliability of an energy system. A data-driven design and operation optimization network for turbomachinery is proposed, which can also present the physical field distributions. The blade design optimization and the off-design operation optimization are integrated aiming at the turbine in a solar-based Supercritical Carbon Dioxide Brayton cycle. For prediction effect of the network, the mean relative errors of the trained Dual Convolutional Neural Network for field and performance prediction are mostly less than 0.75% and 3%, respectively. The R-squared of all concerned parameters are above 0.95. For multi-objective optimization process, Automatic Differentiation method is used to obtain the Pareto solutions. The blade design is conducted and the operation of the turbine is optimized under the three scenarios of insufficient pressure ratio, insufficient heat source, and insufficient rotation speed. The field distributions are acquired for different operation schemes. The presented network only needs 0.028s to obtain the turbine performance of a certain condition in comparison with 6min using Computational Fluid Dynamics method. Users can select a certain operation condition according to efficiency and power requirements, thus adapting to a wide range of off-design conditions.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222008830
    DOI: 10.1016/j.energy.2022.123980
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    1. Li, Xingxing & Zhang, Lei & Song, Juanjuan & Bian, Fengjiao & Yang, Ke, 2020. "Airfoil design for large horizontal axis wind turbines in low wind speed regions," Renewable Energy, Elsevier, vol. 145(C), pages 2345-2357.
    2. Yao, Lichao & Zou, Zhengping, 2020. "A one-dimensional design methodology for supercritical carbon dioxide Brayton cycles: Integration of cycle conceptual design and components preliminary design," Applied Energy, Elsevier, vol. 276(C).
    3. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2014. "A component map tuning method for performance prediction and diagnostics of gas turbine compressors," Applied Energy, Elsevier, vol. 135(C), pages 572-585.
    4. Baklacioglu, Tolga & Turan, Onder & Aydin, Hakan, 2015. "Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks," Energy, Elsevier, vol. 86(C), pages 709-721.
    5. 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).
    6. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
    7. Kim, Do-Yeop & Kim, You-Taek, 2017. "Preliminary design and performance analysis of a radial inflow turbine for ocean thermal energy conversion," Renewable Energy, Elsevier, vol. 106(C), pages 255-263.
    8. García Kerdan, Iván & Morillón Gálvez, David, 2020. "Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building," Applied Energy, Elsevier, vol. 280(C).
    9. Di Zhang & Yuqi Wang & Yonghui Xie, 2018. "Investigation into Off-Design Performance of a S-CO 2 Turbine Based on Concentrated Solar Power," Energies, MDPI, vol. 11(11), pages 1-13, November.
    10. 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).
    11. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    12. Jun-Seong Kim & Do-Yeop Kim, 2020. "Preliminary Design and Off-Design Analysis of a Radial Outflow Turbine for Organic Rankine Cycles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    13. Gao, Lei & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2022. "Robustness analysis in supercritical CO2 power generation system configuration optimization," Energy, Elsevier, vol. 242(C).
    14. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    15. 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).
    16. Li, Yunzhu & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Deep learning based real-time energy extraction system modeling for flapping foil," Energy, Elsevier, vol. 246(C).
    17. Son, Seongmin & Jeong, Yongju & Cho, Seong Kuk & Lee, Jeong Ik, 2020. "Development of supercritical CO2 turbomachinery off-design model using 1D mean-line method and Deep Neural Network," Applied Energy, Elsevier, vol. 263(C).
    18. 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.
    19. Yang, Yu & Chen, Shuangtao & Sheng, Chunchen & Xie, Hongtao & Luo, Gaoqiao & Hou, Yu, 2021. "Study on coupling performance of turbo-cooler in aircraft environmental control system," Energy, Elsevier, vol. 224(C).
    20. Rocha, P.A. Costa & Rocha, H.H. Barbosa & Carneiro, F.O. Moura & Vieira da Silva, M.E. & Bueno, A. Valente, 2014. "k–ω SST (shear stress transport) turbulence model calibration: A case study on a small scale horizontal axis wind turbine," Energy, Elsevier, vol. 65(C), pages 412-418.
    21. Sessarego, Matias & Feng, Ju & Ramos-García, Néstor & Horcas, Sergio González, 2020. "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow," Renewable Energy, Elsevier, vol. 146(C), pages 1524-1535.
    22. Chatzopoulou, Maria Anna & Simpson, Michael & Sapin, Paul & Markides, Christos N., 2019. "Off-design optimisation of organic Rankine cycle (ORC) engines with piston expanders for medium-scale combined heat and power applications," Applied Energy, Elsevier, vol. 238(C), pages 1211-1236.
    23. 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).
    24. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    25. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
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