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Record W2737244907 · doi:10.1049/iet-est.2017.0039

Investigation and design of an axial flux permanent magnet machine for a commercial midsize aircraft electric taxiing system

2017· article· en· W2737244907 on OpenAlexafffund
Fabian Kelch, Yinye Yang, Berker Bilgin, Ali Emadi

Bibliographic record

VenueIET Electrical Systems in Transportation · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsAutomotive engineeringEngineeringElectric machineTorquePowertrainElectric motorMagnetFinite element methodRange (aeronautics)Mechanical engineeringAerospace engineeringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

In this study, an electric taxiing drive system for a commercial midsize aircraft is proposed. Four electric motors are integrated in the main landing gears to enable electric drive of the aircraft during the taxiing process. To achieve the same performance, a system level analysis is conducted to investigate the drive cycle requirements. Based on the recorded aircraft taxiing data, a variety of taxiing drive cycles are used as inputs to size the powertrain components. An axial flux permanent magnet (AFPM) machine is then proposed in order to meet the compact space and high torque output requirement. Both analytical calculations and three‐dimensional finite element model are applied to design and improve the machine performance. A wide range of simulations has been conducted and the results confirmed that the proposed AFPM machine fulfils the given requirements for an electric taxiing drive system.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.226
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations21
Published2017
Admission routes2
Has abstractyes

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