Heat Transfer in Liquid Nitrogen Cooled Superconducting Transformers: An Experimental Investigation
Bibliographic record
Abstract
Abstract A single-phase, 15-kilowatt electric transformer was modified to replace the secondary copper winding with one formed of Yttrium Barium Copper Oxide [YBCO] superconducting ribbon. YBCO ceramics offer exceptional electrical efficiency at high current when properly cooled to cryogenic temperature. For the present research, a high ratio transformer design induced secondary currents above 200 amperes. This exploited the properties of the YBCO in reducing thermal losses inherent to high currents in conventional conductors. YBCO must operate below its critical temperature of 92K to avoid damage and is typically cooled by immersion in a pool of cryogenic liquid. Immersion cooling makes inefficient use of the cryogen, and a novel cooling method was proposed by the current research. The YBCO was routed within an insulating tube through which liquid nitrogen flowed and the entire assembly operated within a vacuum to minimize heat transfer. To conserve cryogen while respecting the YBCO critical temperature of 92K, the latent heat of nitrogen was exploited and the system designed to operate near the nitrogen boiling point of 77K. The resulting multi-phase system was operated at various conditions and a combined efficiency formula proposed which includes the energy cost of cryogenic fluid. The results demonstrated a 5% efficiency improvement using YBCO materials using a novel tube-cooling arrangement and provide a basis for future research and industrial use.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".