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ISSN: 2772-8307
Copyright © 2026 Shanghai Jiaotong University. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
cited:14
A record-breaking 35.6 T: Innovative technology propels all-superconducting magnets into new frontiers of high-field research
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Article Number:Article 100240
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Abstract
Corresponding email:qiuliang@mail.iee.ac.cn
In January 2026, the world record for a 35.6 T-35 mm all superconducting user magnet was achieved. The 35.6T user magnet consisted of the REBCO high temperature superconducting insert magnet and a low-temperature superconducting magnet. Furthermore, the REBCO high temperature superconducting insert magnet itself also reached 27.5T in liquid He, which also set a world record for the all high temperature superconducting user magnet. This magnet holds great significance for both cutting-edge scientific research and superconducting magnet technology.
cited:0
An acoustic framework for quantifying hydrogen leakage flow rates in next-generation liquid-hydrogen-cooled superconducting cables
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Article Number:Article 100241
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Abstract
Corresponding email:qq.yang@bit.edu.cn
Liquid-hydrogen-cooled superconducting cables, owing to their high power density, near-zero-resistance power transmission capability, and deep integration with hydrogen energy systems, are regarded as a promising direction for future power grids. However, unavoidable liquid hydrogen leakage —which rapidly undergoes flash boiling and transitions into a high-speed gaseous hydrogen jet after release into the ambient environment—still lacks effective quantitative assessment methods. In this study, a systematic acoustic framework is developed to remotely and quantitatively estimate hydrogen leakage flow rates. Controlled leakage experiments are conducted, first revealing an approximately linear frequency shift of the dominant spectral line at low flow rates, associated with shear-layer instabilities of the gaseous hydrogen jet and governed by jet velocity and leak geometry, as well as a linear increase in integrated acoustic energy within the 20–52 kHz band at higher flow rates. Second, a spectral line–background subtraction method is proposed, enabling robust extraction of narrowband dominant leakage components from broadband turbulent backgrounds. Third, a quantitative mapping between leakage flow rate and multidimensional acoustic features is established: in the low-flow regime (3–8 slm), the characteristic spectral-line frequency enables flow-rate estimation with a maximum relative error of ±2.65%, while in the higher-flow regime (8–10 slm), the integrated acoustic energy in the 20–52 kHz band provides a linear mapping with a maximum relative deviation of ±0.47%. This work provides a new technical pathway for early identification and remote, non-contact quantitative estimation of hydrogen leakage in liquid-hydrogen superconducting cables, which is of significant importance for promoting the safe and reliable operation of liquid-hydrogen superconducting power transmission systems.
cited:0
First artificial intelligence-based non-invasive framework for data-driven kink defect detection in HTS coils for power applications
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Article Number:Article 100242
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Abstract
Corresponding email:mohammad.yazdani-asrami@glasgow.ac.uk
High-temperature superconductor (HTS) coils and windings are fundamental structural elements underpinning a wide range of superconducting technologies and are the crucial components for next-generation superconducting devices across electric transportation and energy systems fields, including fusion reactors for sustainable energy generation, aviation propulsion systems aiming for substantial efficiency gains, and renewable electric power generators integrating superconducting technologies. Nevertheless, the manufacturing processes of these HTS windings commonly introduce localized mechanical stresses that lead to subtle initial bending defects, so-called “kinks”, potentially evolving into a risk of catastrophic failure, and significantly compromising operational reliability. Technically, conventional kink detection techniques, such as optical microscopy, magnetic imaging, and metallurgical techniques, performed deployment challenges of invasive inspections, low inspection efficiency, and insufficient sensitivity to subsurface anomalies when handling complex coil geometries. To address these challenges, this study develops the first non-invasive electrical detection method that autonomously identifies incipient kink defects, leveraging an innovative artificial intelligence (AI)-enabled framework that combines frequency domain analysis with adaptive machine learning classifiers. To experimentally validate the proposed framework, controlled kink defects were induced in a superconducting coil under precise experimental conditions, replicating realistic deformation scenarios encountered during coil winding processes. Voltage signals from coils with induced kink defects and pristine coils were collected and transformed into discriminative spectral features via effective frequency-domain analysis. These spectral characteristics are further refined by pinpointing essential components, thus achieving effective data dimensionality reduction. Subsequently, a K-Nearest Neighbors classifier, enhanced by adaptive distance metrics and robust cross-validation protocols, was employed, attaining a remarkable detection accuracy of up to 98.9%. Critically, acknowledging practical engineering constraints such as cost efficiency, compatibility with industrial monitoring units, and lower sampling requirements, the developed method successfully maintained an acceptable detection rate. Ultimately, this study establishes the proposed analysis paradigm as a proof-of-concept of a non-invasive diagnostic tool for identifying early-stage mechanical defects. The present study highlights the potential relevance of data-driven electrical diagnostics for improving the manufacturing and maintenance capabilities of HTS coil winding processes. This AI-based framework contributes to enabling the next generation of superconducting technologies, with prospective applications in electric transportation and energy systems.
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