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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.

Advanced experimental-based data-driven model for the electromechanical behavior of twisted YBCO tapes considering thermomagnetic constraints