A wide range <i>E</i> − <i>J</i> constitutive law for simulating REBCO tapes above their critical current
Bibliographic record
Abstract
Abstract When modeling superconducting devices based on rare earth barium copper oxide (REBCO) tapes and operating near or above the critical current value I c , the power-law is not always accurate. In our previous works, we proposed the overcritical current constitutive law, based on a combination of fast pulsed current measurements and finite element analysis. The overcritical current constitutive law was provided in the form of look-up tables and was validated experimentally. We showed that the overcritical current constitutive law could better reproduce experimental measurements than the power-law, and that the power-law predicts a faster quench than the overcritical current constitutive law. In this contribution we use a mathematical expression based on the collective pinning model to analytically describe the overcritical current regime of REBCO tapes based on measurements performed between 77 and 90 K in self-field conditions. The wide-range constitutive law is verified by comparing DC fault measurements with the results of numerical simulations using the overcritical current constitutive law to represent the electrical resistivity of the superconducting layer of REBCO tapes.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".