3-D Finite-Element Thin-Shell Model for High-Temperature Superconducting Tapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it