Post-processing method for extracting the resistivity of Rare-Earth Barium Copper Oxide (REBCO) coated conductors in over-critical current conditions from ultra-fast <i>V</i>-<i>I</i> pulsed current measurements
Bibliographic record
Abstract
This paper presents a simple but rigorous method to extract correctly the resistivity of the superconducting Rare-Earth Barium Copper Oxide (REBCO) layer of High Temperature Superconductor coated conductors, when the latter are characterized in over-critical current conditions using ultrafast V-I pulsed current measurements. The pulsed current measurement method is used to reduce the amount of heat generated by the strong current flowing in the sample, but it cannot prevent it completely at current levels well above the critical current. In order to estimate accurately the temperature rise, we developed the so-called “Uniform Current” (UC) model, which consists in a static current sharing model coupled with a 2D thermal solver. The model assumes that the electric field is uniform over the sample cross section. It has been shown that this hypothesis works fine at high currents, but for lower current levels, although still higher than the critical current, one must wait until the magnetic relaxation effects disappear before using the outputs of the UC model. We also derived a theoretical bound for the error related to magnetic relaxation, which can be estimated experimentally by using a rectangular pick-up coil located just above the sample surface. After applying the UC model on an experimental set of data, one obtains a whole set of data points defining the resistivity ρ(J,T) of the REBCO superconductor being characterized.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".