Estimation of critical current density of bulk superconductor with artificial neural network
Gangling Wu, Huadong Yong
Abstract
In the applications of superconducting materials, the critical current density JcB is a crucial performance parameter. The conventional method of measuring JcB of bulk superconductor is magnetization method. However, there are errors in the estimation of JcB in the lower field, and the estimation is not applicable in the region where the magnetic field reverses. In this paper, JcB of the bulk superconductor is determined by the hysteresis and magnetostriction loops with artificial neural network (ANN), respectively. Compared with double-output ANN, the critical current density obtained by single-output ANN is more accurate. Finally, the prediction results given by the hysteresis and magnetostriction loops are discussed.