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Record W4379878420 · doi:10.2514/6.2023-4153

Energy Harvesting Frictionless Brakes for Short-Haul Aircraft: Thermal and Electromagnetic Feasibility of an Axial-Flux Machine for a Landing Gear Drive System

2023· article· en· W4379878420 on OpenAlexaff
Jakub Deja, Amir Akbari, Iman Dayyani, Martin Skote, David A. Lowther

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsAutomotive engineeringTorqueAerospace engineeringElectric machineTorque densityEngineeringMechanical engineeringMarine engineeringPhysicsMagnetStator

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.236
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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