Materials processing, pulsed field magnetization and field-pole application to propulsion motors on Gd123 bulk superconductors
Bibliographic record
Abstract
Gd123 bulk superconductor is one of the promising magnet materials. We studied the materials processing to grow high performance magnet with a doping of nano-sized metal oxides such as ZrO 2 as a candidature of pinning centre. The enhancement of the critical current density was obtained. Growth of nano-sized particles of Gd211 in addition to BaZrO 3 were observed by TEM. The formation of nano-sized particles appears a key to improve the integrated flux trapped inside the bulks and the TEM reveals an intriguing effect of the addition to the microstructure of bulk materials. Magnetization process is crucial especially for an extended machinery. Pulsed field magnetization was applied to the field-pole bulk on the rotor disk of the tested synchronous motor. The trapped flux density of 1.3 T for Gd123 bulk sample and of 60 mm diameter was reached in the limited dimension of the tested motor by a step cooling method down to 38 K with a closed-cycle condensed neon. The pulsed magnetic field was applied with a new type of split-armature coil. A large bulk of 140 mm diameter has also shown a potential flux trapping superior to other smaller specimens. The bulk magnet provides a strong magnetic field around the bulk body itself with high current density relative to a coil winding. A comparative drawing of a "torque density" of a variety of motors which is defined as the torque divided by the volume of the motor indicates a potential advantage of bulk motor as a super permanent magnet motor.
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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 | 0.000 |
| 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".