Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Abstract

More than a century after the discovery of superconductors (SCs), numerous studies have been accomplished to take the advantage of SCs in physics, power engineering, quantum computing, electronics, communications, aviation, health care, and defence-related applications. However, there are still challenges that hinder the full-scale commercialization of SCs, such as the high cost of superconducting wires/tapes, technical issues related to AC losses, the structure of superconducting devices, the complexity and high cost of the cooling systems, the critical temperature, and manufacturing related issues. In the current century, massive advancements have been achieved on artificial intelligence (AI) techniques by offering disruptive solutions to handle engineering problems. Consequently, AI techniques can be implemented to tackle those challenges facing superconductivity and act as a shortcut towards full commercialization of SCs and their applications. AI approaches are capable of providing fast, efficient, and accurate solutions for the technical, manufacturing, and economic problems with a high level of complexity and nonlinearity in the field of superconductivity. In this paper, concept of AI and the widely used algorithms are first given. Then a critical topical review is presented for those conducted studies that used AI methods for improvement, design, condition monitoring, fault detection and location of superconducting apparatuses in large scale power applications, as well as the prediction of critical temperature and the structure of new SCs, and any other related applications. The topical review are presented in three main categories, AI for large-scale superconducting applications, AI for superconducting materials, and AI for physics of superconductors. In addition, the challenges to apply AI techniques to the superconductivity and its applications are given. Eventually, future trends on how to integrate AI techniques with superconductivity towards the commercialization are discussed.

Ultra-fast Surrogate Model for Magnetic Field Computation of a Superconducting Magnet Using Multi-layer Artificial Neural Networks

“…Superconducting magnets face multiple challenges during design, manufacturing, and test stages such as manufacturing tolerances, shimming coil design, inhomogeneity of magnetic field, quench-related issues, and the extremely time-consuming magnetic field computation procedure. Artificial intelligence (AI)-based techniques are the shortcuts towards the solutions for these challenges [4,5].…”

DC Electro-Magneto-Mechanical Characterization of 2G HTS Tapes for Superconducting Cable in Magnet System Using Artificial Neural Networks

“…UPERCONDUCTING technology is generally very promising for being used for applications in power systems, aviation industry, and healthcare sector in devices such as magnetic resonance imaging/nuclear magnetic resonance magnets [1]- [5], High Temperature Superconducting (HTS) cables [6]- [8], superconducting machines [9], [10], superconducting fault current limiters [11], [12], superconducting magnetic energy storage units [13], [14], and HTS transformers [15], [16]. In superconducting magnets, as one of the most successfully commercialised applications of superconductors, low temperature superconducting wires and This manuscript was received 04 May 2022, revised 10 July 2022, accepted 22 July 2022.…”

“…These models are capable of considering multiple variables for determining the characteristic of HTS tapes without giving up on the accuracy or estimation speed. Last recently, AI models were used to design [21], monitor [22], and estimate [23] different characteristics and performances of superconducting devices. In [24], a novel AI approach was introduced to estimate the stress and Jc in HTS tapes.…”

Machine-learning approach for discovery of conventional superconductors

“…In the search for high critical temperature (T c ) superconductors, significant progress has been made during the last decade [1][2][3]. Among thousands of hydride-based superconducting materials computationally predicted [4][5][6][7][8][9][10][11][12], mostly at very high pressures, e.g., P 100 GPa, dozens of them, e.g., H 3 S [1], LaH 10 [2], and CSH [3], were synthesized and tested. This active research area is presumably motivated by Ashcroft, who, in 2004, predicted [13] that high-T c superconductivity may be found in hydrogen dominant metallic alloys, probably at high P .…”

“…Machine-learning (ML) methods have recently emerged in the discoveries of superconductors [9,10]. As sketched in Fig.…”