Prediction of AC Loss of REBCO Lap Joint Using Artificial Intelligence-Based Models

The AC loss of the high temperature superconducting (HTS) cable with joints is a key factor to estimate the cooling effect of the HTS cable. In this work, an artificial intelligence-based model is proposed to predict the AC loss of REBCO lap joints when carrying alternating current. The training data was obtained from simulation by a 3-D lap joint model based on H-formulation. The artificial intelligence-based model is demonstrated to evaluate the effect of inhomogeneity of joint resistance on AC loss. It is shown that the AC loss of lap joints in the cable is increased when the nonuniformity of joint resistance is higher. The proposed model can increase the speed of calculating the statistical results depending on the mass of investigating data.