Simulation of an HTS Synchronous Superconducting Generator
Bibliographic record
Abstract
Abstract In this work we present a simulation of a synchronous generator with superconducting rotor windings. As many other electrical rotating machines, superconducting generators are exposed to ripple fields that could be produced from a wide variety of sources: short circuit, load change, mechanical torque fluctuations, etc. Unlike regular conductors, superconductors, experience high losses when exposed to AC fields. Thus, calculation of such losses is relevant for machine design to avoid quenches and increase performance. Superconducting coated conductors are well known to exhibit nonlinear resistivity, thus making the computation of heating losses a cumbersome task. Furthermore, the high aspect ratio of the superconducting materials involved adds a penalty in the time required to perform simulations.The chosen strategy for simulation is as follows: A mechanical torque signal together with an electric load is used to drive the finite element model of a synchronous generator where the current distribution in the rotor windings is assumed uniform. Then, a second finite element model for the superconducting material is linked to calculate the actual current distribution in the windings of the rotor. Finally, heating losses are computed as a response to the electric load. The model is used to evaluate the transient response of the generator.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".