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Machine learning applied to X-ray tomography as a new tool to analyse the voids in RRP Nb3Sn wires
19-05-2021
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.
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Interest in niobium-tin (Nb
3Sn) 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 Nb

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Geneva ,Genè ,Switzerland ,M Yusoffm Zeehaida ,N Aimi Salihah , ,University Of Geneva ,Department Of Quantum Matter Physics ,Restacked Rod Process ,Finite Element Models ,Quantum Matter Physics ,Aimi Salihah ,ஜிநீவ ,சுவிட்சர்லாந்து ,பல்கலைக்கழகம் ஆஃப் ஜிநீவ ,துறை ஆஃப் குவாண்டம் விஷயம் இயற்பியல் ,வரையறுக்கப்பட்ட உறுப்பு மாதிரிகள் ,குவாண்டம் விஷயம் இயற்பியல் ,

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