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Record W4323568536 · doi:10.3390/machines11030362

Switched Reluctance Motor Design for a Light Sport Aircraft Application

2023· article· en· W4323568536 on OpenAlexaff
Mohammad Ehsan Abdollahi, Ahsan Zahid, Nir Vaks, Berker Bilgin

Bibliographic record

VenueMachines · 2023
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTorque densityElectromagnetic coilPropulsionRotor (electric)Switched reluctance motorTorqueTransient (computer programming)Voice coilControl theory (sociology)Automotive engineeringComputer scienceEngineeringMechanical engineeringElectrical engineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents the design of a switched reluctance motor (SRM) for a direct-drive propulsion application for a light sport aircraft. The SRM is designed to replace a 70 kW permanent magnet synchronous motor used in aerospace application with similar dimensional constraints. As a means of achieving high torque density and efficiency, a multi-objective design framework is used to optimize the geometry parameters of the motor. In order to further reduce the weight, rotor cutouts are implemented. The conduction angles for the asymmetric bridge converter are selected by employing a multi-objective genetic algorithm to map the torque speed characteristics of the motor. The core losses are evaluated with the modified Bertotti method to calculate the motor efficiency and determine the steady-state and transient thermal performance at the base speed. The designed coil winding is wound on a spindle winder, and the coil fitting, fill factor, and the coil retention are validated experimentally.

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 score0.002
Threshold uncertainty score0.007

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.011
GPT teacher head0.226
Teacher spread0.216 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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