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Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques

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  • Wang, Yuqi
  • Du, Qiuwan
  • Li, Yunzhu
  • Zhang, Di
  • Xie, Yonghui

Abstract

Obtaining the off-design characteristics of core components such as turbines and compressors is the basis of off-design analysis for energy systems. However, the characteristics are difficult to accurately acquire in the initial design stage of turbomachinery. Based on deep learning techniques, an accurate and rapid field reconstruction and off-design aerodynamic performance prediction method is proposed. First, a Generative Adversarial Network with the added Bezier layer is employed to establish a database of turbine blade profiles. Then, a Dual Convolutional Neural Network (Dual-CNN) is established to reconstruct the pressure and temperature fields as well as predict the off-design performances of different profiles and working conditions. Based on the above two kinds of neural networks, a turbine in a solar-based supercritical carbon dioxide Brayton cycle is taken as an example. The field reconstruction and off-design performance prediction are conducted on the basis of the established rotor blade profile database. The accuracy of field reconstruction is guaranteed. The off-design performance prediction of the established Dual-CNN indicates that the example blade profile is suitable for operation with larger mass flow rate. Compared with the traditional method, Dual-CNN can reduce the off-design analysis time of one blade geometry from 38.4 h to 7.68s.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020739
    DOI: 10.1016/j.energy.2021.121825
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    References listed on IDEAS

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    1. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
    2. 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.
    3. 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).
    4. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    5. 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.
    6. 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.
    7. Storti, Bruno A. & Dorella, Jonathan J. & Roman, Nadia D. & Peralta, Ignacio & Albanesi, Alejandro E., 2019. "Improving the efficiency of a Savonius wind turbine by designing a set of deflector plates with a metamodel-based optimization approach," Energy, Elsevier, vol. 186(C).
    8. 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).
    9. 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).
    10. 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).
    11. Afrasiabi, Mousa & Mohammadi, Mohammad & Rastegar, Mohammad & Kargarian, Amin, 2019. "Multi-agent microgrid energy management based on deep learning forecaster," Energy, Elsevier, vol. 186(C).
    12. 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.
    13. Liu, Changwei & Gao, Tieyu, 2019. "Off-design performance analysis of basic ORC, ORC using zeotropic mixtures and composition-adjustable ORC under optimal control strategy," Energy, Elsevier, vol. 171(C), pages 95-108.
    14. 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.
    15. Wang, Xiaojing & Zou, Zhengping, 2019. "Uncertainty analysis of impact of geometric variations on turbine blade performance," Energy, Elsevier, vol. 176(C), pages 67-80.
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    Cited by:

    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).
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    3. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).

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