Intelligent Probability Estimation of Quenches Caused by Weak Points in High-Temperature Superconducting Tapes

Fluctuations in the critical current along length of high-temperature superconducting (HTS) tapes manufactured form coated conductors is a common manufacturing phenomenon. These fluctuations originate generation weak points through HTS that may cause quenching later. By means propagation quenches tapes, reliability, stability, and performance device system contain could be seriously degraded. In this study, an artificial intelligence technique based on neural networks (ANN) was proposed to estimate probability caused by points. For purpose, six different were considered with widths, total thicknesses, thicknesses sub-layers. Then, for each one these operating conditions considered, where temperature changed from 40 K 80 K, 1 steps. Under temperature, currents 50% 100% tape current. All resulted more than 5000 data points, analytical modelling performed provide initial inputs outputs ANN model. It should noted simulations conducted method experimentally validated literature. After that, sensitivity analysis select hyperparameters structure ANN-based The last step take advantage trained model, as function MATLAB software package case studies. significant feature model capability estimating under temperatures types tapes.