Characterization of industrial NbTi strands at variable field for JT-60SA toroidal field coils

摘要

The mission of the JT-60SA Tokamak, to be built in Japan, is to contribute to the early realization of fusion energy by its exploitation in support of the iTER program. JT-60SA project is an important part of the "broader approach" activity as a satellite program for ITER. The toroidal field (TF) coils are a European "in kind" contribution and they will partly be built by France. JT-60SA TF coil uses the Cable In Conduit Conductor (CICC) with NbTi superconductor strands. TF conductors will have to operate at 5.7T, 5 K and at current density of 450 A/mm~2 with sufficient margins. In the framework of JT-60SA TF coil manufacture, the variable temperature characterization is an important step to select NbTi strand. At an early stage of design, we had to choose the strand with acceptable performances. During the design qualification and validation stage, it is important to qualify strands in conditions close to the operation conditions. The industry has proposed various strands manufactured with different processes. This work and publication examines a strand with an internal CuNi barrier, which is expected to lead to better current distribution between strands, by more precise calibration and control of the inter-strand resistance. The strands were tested at the Grenoble High Magnetic Field Laboratory facility. The domain (B.T.J) explored was in the range of 4.5-11 T for the magnetic field intensity, 4.2-6.5 K for the temperature and between 40 A/mm~2 and 1200 A/mm~2 for the current density. For each strand, "critical current density" and "current sharing temperature" measurements have been carried out, with a temperature precision of few tens of mK. Once the measurements performed, the fitting parameters (of type J_C = f(B,T))of each strand have been found, by performing regression analysis. This work will lead to select the strand with the best characteristics. In this paper, we present the results of this measurement task, the data and regression analysis (fits, T_(CS), etc.) and the conclusion about the strand choice.