Simulation of Field Dependence of Critical Current Densities of Bulk High T<sub>c</sub>Superconducting Materials regarding Thermally Activated Flux Motion
Bibliographic record
Abstract
In the upcoming generation, bulk high temperature superconductors (HTS) will play a crucial and a promising role in numerous industrial applications ranging from Maglev trains to magnetic resonance imaging, etc. Especially, the bulk HTS as permanent magnets are suitable due to the fact that they can trap magnetic fields being several orders of magnitude higher than those of the best hard ferromagnets. The bulk HTS LREBa 2 Cu 3 O 7-δ (LREBCO or LRE-123, LRE: Y, Gd, etc.,) materials could obtain very powerful compact superconducting super-magnets, which can be operated at the cheaper liquid nitrogen temperature or below due to higher critical temperatures (i.e., ∼90 K). As a result, the new advanced technology can be utilized in a more attractive manner for a variety of technological and medical applications which have the capacity to revolutionize the field. An understanding of the magnetic field dependence of the critical current density ( J c ( H )) is important to develop better adapted materials. To achieve this goal, a variety of J c ( H ) behaviours of bulk LREBCO samples were modelled regarding thermally activated flux motion. In essence, the J c ( H ) curves follows a certain criterion where an exponential model is applied. However, to fit the complete J c ( H ) curve of the LRE-123 samples an unique model is necessary to explain the behavior at low and high fields. The modelling of the various superconducting materials could be understood in terms of the pinning mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".