Characterizing and using defects in high-temperature superconductor cables and magnets

Abstract

High-temperature superconductor (HTS) cables and magnets are enabling a range of high-current and high-field applications, including compact fusion devices aiming to achieve net energy. Defects in HTS pose manufacturing, cost, and operational challenges. A rigorous understanding and predictive capability for defect-induced behavior at relevant scale has not been established. To address this shortcoming, a cable-level defect characterization experimental platform coupled to high-fidelity computational modeling is developed and the concept of using defects as a tool for controlling current distribution in HTS cables is explored. The cable (critical current = 438 A at 77.3 K, self-field) comprises a non-twisted 70 cm-long copper former containing a soldered stack of five rare-earth barium copper oxide (REBCO) tapes (each with a critical current of 115.7 A/4 mm-w at 77.3 K, self-field), which can contain a variety of induced defects. Spatially-resolved electric fields are measured with a high-density voltage tap array and absolute current distribution with six custom-wound embedded Rogowski coils. 3D circuit modeling uses nodal analysis and self-consistently accounts for the magnetic field dependence of critical current. Design, fabrication, calibration, and testing of the cable and instrumentation are described. Model validation results are presented, comparing cables with no defect, one defect, and two defects, and with tapes oriented in two different configurations. A cable containing a model-guided configuration of intentional defects is studied, demonstrating numerically and experimentally the use of defects for forcing more uniform current distributions in REBCO cables, one of many possible applications. The predictive capabilities developed in this work and the demonstration of the use of defects as a tool could enable the design of full-scale HTS cables and magnets that optimize defect tolerance and performance to improve cost, manufacturability, and robustness.