Development of a 3D Sizing Model for All-Superconducting Machines for Turbo-Electric Aircraft Propulsion
Bibliographic record
Abstract
Distributed propulsion in aircraft brings many advantages in terms of efficiency and noise reduction. While the distribution can be done mechanically through the use of gears and transmissions, electrical propulsion allows for lower maintenance needs, higher efficiency, and lower emissions through the complete decoupling of the gas turbines and the propulsion fans. Such systems have been investigated in the past and NASA is executing on a development plan to bring turbo-electric propulsion systems for transportation aircraft by 2035. The very high specific power required for the airborne generators and motors can only be achieved by using superconductors. Analytical 2-D sizing models have been created and showed very promising results. NASA is now funding the development of higher fidelity models for superconducting machines in which an actual 3-D representation of the geometry is considered. The magnetic flux distribution is calculated using Biot-Savart's law coupled with the magnetic moment method for the backiron. The code also includes thermal and mechanical models allowing for a full and accurate design. The paper describes the model architecture and the methods used to perform high-temperature superconducting machine sizing and optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".