Review of the AC loss computation for HTS using <i>H</i> formulation

Shen, Boyang; Grilli, Francesco; Coombs, Tim

doi:10.1088/1361-6668/ab66e8

Cited by 207 publications

(103 citation statements)

References 122 publications

(219 reference statements)

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“…In the first stage, the strength and uniformity of magnetic field are the first priorities to judge the feasibility of this HTS magnet with a specific HTS tape. Other important factors such as the transient loss [23][24][25][26][27], anisotropic field dependence [28][29][30], quench protection [31][32][33], and force analysis will be incorporated into the future design.…”

Section: Discussionmentioning

confidence: 99%

Development of an HTS Magnet for Ultra-Compact MRI System: Optimization Using Genetic Algorithm (GA) Method

Shen

Öztürk

et al. 2020

IEEE Trans. Appl. Supercond.

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This paper presents the design of an HTS magnet for an ultra compact MRI system, potentially for the rapid and early diagnosis of brain trauma. Early diagnosis and therapy stratification can reduce the risk for critically brain ill patients with the use of near patient imaging, and can aid with precision medicine. High temperature superconductors (HTS) have the ability to carry large currents in the cryogen free contrition, which can make the MRI system even smaller and lighter. The design and Genetic Algorithm (GA) optimization were based on the FEM package COMSOL Multiphysics with the LiveLink for MATLAB, together with the GA module in the MATLAB optimization toolbox. The relatively thick HTS tape, ST-12-L from the Shanghai Superconductor Technology ® was chosen and that made more difficulty on optimization process. Genetic Algorithm method improved the optimization performance, and the uniformity achieved 2.36 ppm in a 10 × 10 × 10 cm DSV. Two cases of the end double-pancake were compared. Further sensitivity studies were performed on the homogeneity with its relationship to the magnet length, and the thickness of HTS tape.  Index Terms-High temperature superconductor (HTS), Magnetic resonance imaging (MRI), HTS magnet, Genetic Algorithm (GA), Finite element method.

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Section: Discussionmentioning

confidence: 99%

Development of an HTS Magnet for Ultra-Compact MRI System: Optimization Using Genetic Algorithm (GA) Method

Shen

Öztürk

et al. 2020

IEEE Trans. Appl. Supercond.

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“…The AC loss in HTS cables increases the refrigeration load to the cryogenic system and affects the efficiency of practical devices [10], [11]. To predict the loss behavior and optimize cable designs, efficient and accurate modeling tools are crucial.…”

Section: Introductionmentioning

confidence: 99%

“…To predict the loss behavior and optimize cable designs, efficient and accurate modeling tools are crucial. The main challenges when modeling HTS machines and cables are related to the electromagnetic properties [12] and geometrical complexities [10]. The sharp transition from the superconducting state to the normal state and critical currents depending on both the magnitude and direction of the magnetic field are embedded in the constitutive laws [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Numerical Modeling of AC Loss in HTS Coated Conductors and Roebel Cable Using T-A Formulation and Comparison With H Formulation

Yan

Grilli

2021

IEEE Access

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With recent advances in second-generation high temperature superconductors (2G HTS) and cable technologies, various numerical models based on finite-element method (FEM) have been proposed to help interpret measured AC loss and assist cable design. The T-A formulation, implemented in COMSOL, shows great potential for reducing the overall computation costs. In this paper, the performance of the T-A formulation for calculating the AC loss of coated superconductors and cables were assessed and compared against the widely accepted H formulation, with benchmark model of a single REBCO tape in 2D/3D and a 14strand Roebel cable. Evaluation and comparison on key metrics including the computation time, the number of degrees of freedom and the numerical accuracy were presented, which could provide a reference for researchers in applying the T-A formulation for AC loss calculation.

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“…701647. Kai Zhang, Sebastian Hellmann, Marco Calvi and Thomas Schmidt are all with Paul Scherrer Institute, Villigen, 5232, Switzerland (e-mail: kai.zhang@psi.ch; sebastian.hellmann@psi.ch; marco.calvi@psi.ch; thomas.schmidt@psi.ch) Lucas Brouwer is with Lawrence Berkeley National Laboratory, Berkeley, 94720, USA (email: LNBrouwer@lbl.gov) faster or equivalent computation speed in comparison to the A-V formulation [5], [6], the T-Ω formulation [7], the T-A formulation [8], [9], the well-known H-formulation [10]- [14], or the Minimum Electro-Magnetic Entropy Production (MEMEP) method implemented in other FEM or home-made software [15].…”

Section: Introductionmentioning

confidence: 99%

Magnetization Current Simulation of High-Temperature Bulk Superconductors Using the ANSYS Iterative Algorithm Method

Zhang

Hellmann

Calvi

et al. 2021

IEEE Trans. Appl. Supercond.

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The A-V formulation based iterative algorithm method (IAM), developed at Paul Scherrer Institute, shows excellent performance in modelling the critical state magnetization-current in the ReBCO tape stacks after field-cooling (FC) magnetization. This paper upgrades the function of the IAM, making it realistic to take into account the widely used E-J power law, as well as to model the magnetization-current in bulk high-temperature superconductors (HTS) under a zero-field-cooling (ZFC) cycle, having both the magnetization and demagnetization stages. The simulation results obtained from the ANSYS-IAM are validated with the well-known H-formulation method for both the critical state model and the flux creep model. The computation times from using the two different numerical methods are compared and discussed. The influence factors, including the iteration steps, the initial superconductor resistivity, the ramping time and the reservation coefficient, are studied in detail.

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Review of the AC loss computation for HTS using H formulation

Cited by 207 publications

References 122 publications

Development of an HTS Magnet for Ultra-Compact MRI System: Optimization Using Genetic Algorithm (GA) Method

Development of an HTS Magnet for Ultra-Compact MRI System: Optimization Using Genetic Algorithm (GA) Method

Numerical Modeling of AC Loss in HTS Coated Conductors and Roebel Cable Using T-A Formulation and Comparison With H Formulation

Magnetization Current Simulation of High-Temperature Bulk Superconductors Using the ANSYS Iterative Algorithm Method

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