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<p>Critical current, as a key parameter distinguishing superconductors from ordinary conductors, determines the stability of superconducting devices and systems during operation. To accurately assess the critical performance of superconductors, a genetic algorithm (GA)–optimized back-propagation (BP) neural network model is introduced and used in this paper to predict the critical currents of second-generation high-temperature superconductors (2G HTS). Firstly, a staged optimization is carried out for the neural network structure and hyper-parameters, and the GA-BP model is established by constructing different fitness functions. Next, the prediction accuracy and generalization ability of the GA-BP model are validated by comparing the relative errors between the predicted values from two models in the 180° to 240° anti-angle region. Eventually, the critical current at a specific temperature is predicted by the GA-BP model, and the corresponding <i>J</i><sub><i>c</i>0</sub> is calculated for the finite element calculation of superconducting strips. The calculation results show that the relative error between the maximum current density obtained based on the predicted <i>J</i><sub><i>c</i>0</sub> and the experimental <i>J</i><sub><i>c</i>0</sub> is only 0.907%, indicating that the model can be used to accurately determine the operating state of the superconducting equipment.</p>

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Prediction and Application of Critical Current in 2G HTS Conductors Based on GA-BP Algorithm

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • Nipeng Wang,
  • Wenhai Zhou,
  • Rui Liang,
  • Rongli Jia,
  • Bingxu Su,
  • Tingliang Chen,
  • Leiwen Yue,
  • Jiafeng Cao

摘要

Critical current, as a key parameter distinguishing superconductors from ordinary conductors, determines the stability of superconducting devices and systems during operation. To accurately assess the critical performance of superconductors, a genetic algorithm (GA)–optimized back-propagation (BP) neural network model is introduced and used in this paper to predict the critical currents of second-generation high-temperature superconductors (2G HTS). Firstly, a staged optimization is carried out for the neural network structure and hyper-parameters, and the GA-BP model is established by constructing different fitness functions. Next, the prediction accuracy and generalization ability of the GA-BP model are validated by comparing the relative errors between the predicted values from two models in the 180° to 240° anti-angle region. Eventually, the critical current at a specific temperature is predicted by the GA-BP model, and the corresponding Jc0 is calculated for the finite element calculation of superconducting strips. The calculation results show that the relative error between the maximum current density obtained based on the predicted Jc0 and the experimental Jc0 is only 0.907%, indicating that the model can be used to accurately determine the operating state of the superconducting equipment.