Characterization of the Superconducting to Normal Transition of HTS Coated Conductors by Fast Pulsed Current Measurements
Bibliographic record
Abstract
In this paper, we present measurements of the superconducting to normal transition (extendedV-Icurves) of commercial coated conductors with and without stabilizing copper layer. These measurements were realized with a custom developed pulsed current measurement (PCM) system. Currents between 5 to 10 times the critical current of commercial wires (up to 1000 A) can be applied for a period as short as 50-80 mus, limiting the energy released in the sample to a fraction of a Joule. For such short pulses, the temperature rise in the sample is relatively small, which allows characterizing the electrical resistivity of high temperature superconductors (HTS) at high current densities and electric fields. The data obtained will be used to develop more accurate models of HTS in the over-critical current regime, which is major issue for allowing the development of quality simulation tools for optimizing the design of superconducting fault current limiters. The PCM technique is also a very powerful tool for investigating the transient thermal behavior of coated conductors, whose better understanding is required in order to devise reliable fault current limiters based on this technology. So far, the measurements have been restricted to 77 K and self-field, but further works will extend the range of measurements to higher fields and temperatures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".