用神经网络替代传统模拟，快速预测高温超导磁体电流分布。
A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network
- 用全连接残差网络建模，替代耗时的有限元法。
- 外推几何参数时，磁损耗误差低于10%，中心磁场平均误差1.2%。
- 可用于大规模超导磁体快速优化设计，适合工程研发人员。
高温超导磁体的有限元方法（FEM）在大尺度下计算耗时，限制了米级REBCO螺线管的快速优化。本文基于全连接残差神经网络（FCRN）构建代理模型，用于预测REBCO螺线管中的电流密度分布。模型基于T-A公式生成的FEM数据集训练，评估涵盖快速升流与稳态场景，验证损失低于全连接网络（FCN）。在超出训练集范围的几何参数外推中，案例1的磁化损耗相对误差低于10%，案例2的中心磁场平均误差为1.2%。此外，将稳态代理模型用于快速磁体设计，在约束条件下快速找到最优解，与FEM结果相比中心磁场相对误差仅为0.2%。该模型具备快速预测能力，可作为大型高温超导磁体智能设计的有效工具。
原文摘要 · Abstract (English)
Finite element methods (FEM) for high-temperature superconducting (HTS) magnets become time-consuming at larger scales, restricting the rapid optimization of meter-scale REBCO solenoids. In this work, a surrogate model based on a fully connected residual neural network (FCRN) is developed to predict the current density distribution in REBCO solenoids. Trained on datasets generated from FEM simulations by the T-A formulation, the FCRN model is evaluated under both fast ramping and steady-state scenarios, showing a lower validation loss than the fully connected network (FCN). When extrapolating geometric parameters beyond the training set, the model achieves a relative error of below 10 % for magnetization losses in Case 1 and an average error of 1.2 % for the central magnetic field in Case 2. Furthermore, deploying the steady-state surrogate model for rapid magnet design found the optimal solution within the parameter space under constraints, with a relative central magnetic field error of 0.2 % compared to FEM results. With rapid predictions, this surrogate model offers an efficient tool for the intelligent design of large-scale HTS magnets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。