Data-driven models can predict, estimate, and monitor any highly nonlinear and multi-variable behaviour of high-temperature superconducting (HTS) materials, and superconducting devices to analyse their characteristics with a very high accuracy in an almost real-time procedure, which is a significant figure of merit as compared with traditional numerical approaches. The electromechanical behaviour of twisted HTS tapes under different strains, magnetic fields, and temperatures is a complicated problem to be solved using conventional approaches, including finite element-based methods, otherwise, experimental testing is needed to characterise it. This paper aims to offer a data-driven model based on artificial intelligence techniques to predict the electromechanical behaviour of HTS tapes operating under various thermomagnetic conditions. By using the proposed model, normalised critical current value and stress of twisted tapes can be predicted under different temperatures and magnetic flux densities. For this purpose, experimental data were used as inputs to design an adaptive neuro-fuzzy inference system (ANFIS). To achieve the best performance of the prediction system, multiple clustering methods were used, such as the grid partitioning method, fuzzy c-means clustering method, and sub-clustering method. Sensitivity analyses were conducted to find the best architecture of ANFIS to predict and model electromechanical behaviour of twisted tapes with high accuracy.

中文翻译：

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考虑热磁约束的扭曲 YBCO 带机电行为的高级实验数据驱动模型

数据驱动模型可以预测、估计和监测高温超导 (HTS) 材料和超导器件的任何高度非线性和多变量行为，以几乎实时的过程以非常高的精度分析它们的特性，这与传统的数值方法相比，是一个重要的品质因数。在不同的应变、磁场和温度下，扭曲的高温超导带的机电行为是一个复杂的问题，需要使用传统方法（包括基于有限元的方法）来解决，否则，需要通过实验测试来表征它。本文旨在提供一种基于人工智能技术的数据驱动模型，以预测在各种热磁条件下运行的高温超导磁带的机电行为。通过使用所提出的模型，可以预测不同温度和磁通密度下扭带的归一化临界电流值和应力。为此，实验数据被用作设计自适应神经模糊推理系统 (ANFIS) 的输入。为了使预测系统达到最佳性能，采用了多种聚类方法，如网格划分法、模糊c均值聚类法和子聚类法。进行了灵敏度分析以找到 ANFIS 的最佳架构，以高精度预测和模拟绞合带的机电行为。实验数据被用作设计自适应神经模糊推理系统 (ANFIS) 的输入。为了使预测系统达到最佳性能，采用了多种聚类方法，如网格划分法、模糊c均值聚类法和子聚类法。进行了灵敏度分析以找到 ANFIS 的最佳架构，以高精度预测和模拟绞合带的机电行为。实验数据被用作设计自适应神经模糊推理系统 (ANFIS) 的输入。为了使预测系统达到最佳性能，采用了多种聚类方法，如网格划分法、模糊c均值聚类法和子聚类法。进行了灵敏度分析以找到 ANFIS 的最佳架构，以高精度预测和模拟绞合带的机电行为。