Sand Pile Modeling of Multiseeded HTS Bulk Superconductors: Current Densities Identification by Genetic Algorithms

Pina, João Murta; Pereira, Pedro; Valadas, D.; Ceballos, J M; Álvarez, Alfredo

doi:10.1109/tasc.2012.2234187

Cited by 9 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been also some investigations on the superconducting tapes/wires with respect to the limitations which are imposed by superconducting machines. The goal of these studies is to prepare and analyse tapes and wires for the sake of being used in superconducting machines [185,186].…”

Section: Ai For Superconducting Machines and Transformersmentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Section: Ai For Superconducting Machines and Transformersmentioning

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.

View full text Add to dashboard Cite

show abstract

“…The growth processes of the two seed domains are assumed to be independent without the mutual influence of the material fabrication. In fact, there is a weakly coupled boundary between them [55,56]. The size effect of the rectangular two-seeded bulk is focused on in this section.…”

Section: Influence Of the Longitudinal Gap Between Square Bulks On Le...mentioning

confidence: 99%

A reconstructed three-dimensional HTS bulk electromagnetic model considering J _c spatial inhomogeneity and its implementation in a bulks’ combination system

Cheng

Zheng

Huang

et al. 2021

Supercond. Sci. Technol.

View full text Add to dashboard Cite

High-temperature superconducting (HTS) bulks in HTS Maglev systems are always arrayed in a combination to make full use of the applied magnetic field of the permanent magnet guideway (PMG). An excellent combination scheme improves the overall levitation and guidance performance significantly. In this paper, a three-dimensional (3D) electromagnetic model of the real HTS-PMG maglev system with an HTS bulk array was established. This model comprehensively expresses the influence of various factors on the E – J relationship and the 3D spatial distribution of J c, including internal factors such as the inhomogeneity and anisotropy of electromagnetic characteristics, as well as external factors such as applied magnetic field and working temperature. A ternary function was proposed to describe the uneven distribution of J c caused by the bulk’s growth process, which is an interesting phenomenological modeling attempt. In the simulations of the bulks’ combinations, perfect magnetic conductor boundary conditions were applied on the contact surface to simulate two bulks touching each other. Besides, the research target includes reproducing the shapes, the orientations, and the combination scheme of HTS bulks in the real PMG magnetic field. The calculation results of levitation force of the cylindrical bulk under different spatial orientations above the PMG were compared with the experimental results, through which the accuracy of the model was verified. On this basis, the influence of the magnetic field generated by the superconducting current on the nearby bulk was further explored. It was found that this magnetic field has a small contribution to the total levitation force and a relatively obvious influence on the guidance force. When the lateral displacement is large, such as 5 mm, the magnetic field generated by the superconducting current slightly increases the total guidance force stiffness. According to more simulated conditions, some optimization strategies on bulk combinations were proposed. This work provides not only a 3D descriptive model for fitting the real multi-bulk-combination maglev scenarios but also some optimization strategies for the HTS maglev transportation applications.

show abstract

“…The seeds, which act as nucleation centers, are placed on the top surface of YBCO compacted pellets and subjected to heat treatment processes, followed by melt texture growth heating cycles in order to meet the bulks. Unfortunately, the TSMG process is confined to small-scale and simple material geometries [265][266][267]. Besides scale limitations, bulks are susceptible to cracking due to irregular thermal expansion of their body, and therefore, long-term operation can be compromised [268].…”

Section: Superconducting Materials Based Electrical Machinesmentioning

confidence: 99%

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

et al. 2020

View full text Add to dashboard Cite

The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

show abstract

Sand Pile Modeling of Multiseeded HTS Bulk Superconductors: Current Densities Identification by Genetic Algorithms

Cited by 9 publications

References 12 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

A reconstructed three-dimensional HTS bulk electromagnetic model considering J _c spatial inhomogeneity and its implementation in a bulks’ combination system

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

Contact Info

Product

Resources

About

Sand Pile Modeling of Multiseeded HTS Bulk Superconductors: Current Densities Identification by Genetic Algorithms

Cited by 9 publications

References 12 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

A reconstructed three-dimensional HTS bulk electromagnetic model considering J c spatial inhomogeneity and its implementation in a bulks’ combination system

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

Contact Info

Product

Resources

About

A reconstructed three-dimensional HTS bulk electromagnetic model considering J _c spatial inhomogeneity and its implementation in a bulks’ combination system