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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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