Selecting a cryogenic cooling system for superconducting machines: General considerations for electric machine designers and engineers

Yazdani-Asrami, Mohammad; Sadeghi, Alireza; Atrey, M. D.

doi:10.1016/j.ijrefrig.2022.05.003

Cited by 29 publications

(10 citation statements)

References 66 publications

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“…They are capable of carrying an extremely high current in comparison to conventional busbars. Hot spots on the body of the busbars, insulation, and their corrosion issues are known challenges of this technology [50][51][52].…”

Section: Applications Of Scs and The Challengesmentioning

confidence: 99%

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

Yazdani-Asrami

Sadeghi

Song

et al. 2022

Supercond. Sci. Technol.

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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.

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Section: Applications Of Scs and The Challengesmentioning

confidence: 99%

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

Yazdani-Asrami

Sadeghi

Song

et al. 2022

Supercond. Sci. Technol.

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“…These critical surfaces are reconstructed at 20 and 65 K, as they are considered relevant and strategic operating temperatures in large-scale power applications in applied superconductivity. Many large-scale power applications would operate at subcooled liquid nitrogen, and therefore their operating temperatures would be essentially 65 K. On the other hand, with the emerging trend of integration of liquid hydrogen (LH 2 ) in many transportation applications of superconductors, including cryo-electric aircraft [41][42][43], the characterization of HTS material at boil off temperature of LH 2 , i.e. 20 K become more critical.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes

Russo

Yazdani-Asrami

Scheda

et al. 2022

Supercond. Sci. Technol.

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For modeling superconductors, interpolation and analytical formulas are commonly used to consider the relationship between the critical current density and other electromagnetic and physical quantities. However, look-up tables are not available in all modeling and coding environments, and interpolation methods must be manually implemented. Moreover, analytical formulas only approximate real physics of superconductors and, in many cases, lack a high level of accuracy. In this paper, we propose a new approach for addressing this problem involving artificial intelligence (AI) techniques for reconstructing the critical surface of high temperature superconducting (HTS) tapes and predicting their index value known as n-value. Different AI models were proposed and implemented, relying on a public experimental database for electromagnetic specifications of HTS tapes, including Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), and Kernel Ridge Regressor (KRR). The ANN model was the most accurate in predicting the critical current of HTS materials, performing goodness of fit very close to 1 and extremely low RMSE. The XGBoost model proved to be the fastest method, with training computational times under 1 second; whilst KKR could be used as an alternative solution with intermediate performance.

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“…31 The prevalent technological application of the Nernst effect is cryogenic cooling, widely employed in quantum computing, superconductors, and cryogenic hardening. 32,33 Sustaining a system at cryogenic temperatures necessitates ongoing monitoring of heat fluxes. 34 A Nernst-Ettingshausen cooler provides more precise temperature control than achieving thermal equilibrium conventionally with a reservoir and liquid nitrogen.…”

Section: Introductionmentioning

confidence: 99%

Revealing large room-temperature Nernst coefficients in 2D materials by first-principles modeling

Rezaei,

Schindler

2024

Nanoscale

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Selecting a cryogenic cooling system for superconducting machines: General considerations for electric machine designers and engineers

Cited by 29 publications

References 66 publications

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

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

Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes

Revealing large room-temperature Nernst coefficients in 2D materials by first-principles modeling

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