3-D Finite-Element Thin-Shell Model for High-Temperature Superconducting Tapes
Bibliographic record
Abstract
Thin-shell (TS) models are generally used in the analysis of thin regions with linear constitutive properties, although specialized versions of TS models have been developed for high-temperature superconductor (HTS) tapes. However, due to the intrinsic hypothesis of sheet current density, none of these specialized models can account for all possible configurations of HTS tapes. This article presents a new 3-D time-domain finite-element TS model for the electromagnetic modeling of arbitrary HTS tape configurations. The model is validated against benchmark problems, and it is used to solve a realistic application case, namely the calculation of ac losses in a 14-strand HTS Roebel cable. The 3-D TS model is based on a magnetic field formulation and can take into account the diffusion of the tangential magnetic field into the tapes. The nonlinear behavior of the superconducting tapes is described with an E-J power-law model in a virtual discretization across their thickness. This approach allows the calculation of nonuniform current distributions through the thickness of the tapes, when relevant; thus, losses in multiple HTS tape assemblies in any configuration can be determined accurately. The results of the proposed approach are compared with those obtained with a full 3-D representation of the thin regions and show excellent agreement (a relative difference of less than 2%) while reducing substantially the computational burden (75% fewer DoFs). These features make the new 3-D TS model very promising for simulating large-scale superconducting devices, including high-field magnets and coils of complicated shape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".