Robust Optimization in HTS Cable Based on Design for Six Sigma

Liu, Xinying; Wang, Shuhong; Qiu, Jie; Zhu, Jun; Guo, Youguang; Lin, Zhi Wei

doi:10.1109/tmag.2007.916279

Cited by 26 publications

(2 citation statements)

References 9 publications

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“…The total AC loss of the cable can be considered in the optimisation algorithm and should be minimised [135]. The reliability of the designed HTS cable is another constraint [136]. Another imposed constraint is related to mechanical load considerations, such as stress, strain, torsion, and twisting, among others, which could also reduce the feasible domain to essentially find the optimal solution faster [137,138].…”

Section: Ai For Superconducting Cablesmentioning

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: Ai For Superconducting Cablesmentioning

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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show abstract

“…Recently, there have been advancements in the manufacturing and commercial availability of long-lengths high-temperature superconductors (HTSs). This has triggered the use of HTS material in various large-scale power applications such as electric motors [1,2], transformers [3], fault current limiters [4][5][6], power cables [7,8], maglev trains [9][10][11], wind generators [1], and ship propulsion [12]. HTS material enables an increase in the efficiency of machines, along with a massive reduction in size, and, hence, it is possible to design compact, lightweight, and efficient electrical machines [13].…”

Section: Introductionmentioning

confidence: 99%

Experimental Setup for Measurement of AC Loss in HTS under Rotating Magnetic Field

Soomro

Guo

et al. 2022

Energies

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High-temperature superconducting materials have shown great potential for the design of large-scale industry applications. However, they are complicated under AC conditions, resulting in penalties such as power loss or AC loss. This loss has to be considered in order to design reliable and efficient superconducting devices. Furthermore, when superconductors are used in rotating machines, they may be exposed to rotating magnetic fields, which is critical for the design of such machines. Existing AC loss measuring techniques are limited to measuring under one-dimensional AC magnetic fields or transport currents. Therefore, it is essential to develop and investigate robust experimental techniques to investigate the loss mechanism in HTS machines. In this paper, a new and novel experimental technique has been presented to measure AC loss in rotating magnetic field conditions. The loss under rotating magnetic fields is measured and compared by numerical modeling methods, and the results show a strong correlation with the numerical modeling and show the effectiveness of the experimental setup.

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Comparison study of cable geometries and superconducting tape layouts for high-temperature superconductor cables

Shao

Gao

2018

Cryogenics

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Robust Optimization in HTS Cable Based on Design for Six Sigma

Cited by 26 publications

References 9 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

Experimental Setup for Measurement of AC Loss in HTS under Rotating Magnetic Field

Comparison study of cable geometries and superconducting tape layouts for high-temperature superconductor cables

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