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

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