Scientists have developed a new tool to investigate the internal features of Nb3Sn superconducting wires, combining X-ray tomographic data acquired at beamline ID19 with an unsupervised machine-learning algorithm. The method provides new insights for enhancing wire performance.
Interest in niobium-tin (Nb3Sn) as a material for superconducting wires has recently been renewed because this material has been selected to replace niobium-titanium as the next step in accelerator magnet technology [1]. The design of these magnets relies on the availability of advanced Nb3Sn wires capable of withstanding extreme mechanical and thermal loads. The Restacked Rod Process (RRP) is considered the most promising technology to produce Nb3Sn wires at industrial scale for future accelerator magnets.
Nb3Sn is a brittle superconducting compound that cannot be drawn directly in the form of a wire. Instead, ductile precursor components are embedded in a copper matrix, drawn, brought to the final shape and then heat-treated, so that Nb3Sn forms in a reactive diffusion process. The result is a composite wire with several Nb3Sn sub-elements surrounded by copper. However, the diffusion process can lead to voids, which can play a role in the electro-mechanical and thermal behaviour of the wire. A team of scientists have developed a novel, non-destructive and non-invasive method to investigate the voids in high-performance RRP Nb3Sn superconducting wires, combining X-ray microtomography data at beamline ID19 with an unsupervised machine-learning algorithm, with a view to providing new insights into the development of these wires.
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Image:Fig. 1: a) 3D cross-section of a RRP Nb3Sn wire: Nb3Sn sub-elements (red), sub-element voids (light blue), copper voids (white), copper matrix (grey). b) Longitudinal cross-section of a void generated by Sn diffusion due to a leak in the sub-element. The void is highlighted in red inside the sub-element and in blue in the copper matrix, showing the sub-element failure point.