Energy Harvesting Frictionless Brakes for Short-Haul Aircraft: Thermal and Electromagnetic Feasibility of an Axial-Flux Machine for a Landing Gear Drive System
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-4153.vid The aviation industry is currently responding to climate change with, among other technologies, electrification of aircraft, and the corresponding onboard electrical architecture provides an opportunity for electromagnetic brakes. The present work introduces a multistage yokeless and segmented armature (YASA) electric machine that replaces friction brakes and harvests kinetic energy throughout a landing. The study establishes the optimal trade-off between weight and electromagnetic torque and translates it into the design requirements for the development of an electric machine. Electromagnetic modeling is conducted using a quasi-3D transient approach and static 3D validation. The results reach 120 Nm/kg active material torque density at approximately 50 A/mm² current density. The proposed solution enables fitting an electric machine that decelerates an aircraft at autobrake level LOW for Airbus and the "1" and "2" settings for Boeing. A thermal analysis follows, where a novel cruise altitude cooling method is proposed.
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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) | 0.002 | 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".