MétaCan
Menu
Back to cohort
Record W2137937658 · doi:10.3390/machines3010027

A Novel Approach to the Design of Axial-Flux Switched-Reluctance Motors

2015· article· en· W2137937658 on OpenAlexaff
Tim Lambert, Mohammad Biglarbegian, Shohel Mahmud

Bibliographic record

VenueMachines · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSwitched reluctance motorTopology (electrical circuits)Topology optimizationTorque densityControl theory (sociology)StatorReluctance motorTorqueMagnetic reluctanceRotor (electric)InductanceComputer scienceEngineeringMechanical engineeringPhysicsStructural engineeringElectrical engineeringMagnetFinite element methodVoltage

Abstract

fetched live from OpenAlex

This paper presents the design of a new axial-flux switched-reluctance motor (AFSRM) topology for in-wheel drive vehicle applications. The features of the topology include a short flux path and an outer-rotor configuration. The proposed topology also uses a sintered-lamellar soft magnetic composite core material, and permits displacement of the rotor along the suspension axis, which reduces damage to the stator caused by impacts and vibrations. The combination of these features makes this new topology competitive with other in-wheel motors in regard to torque density, durability, and cost. To describe the behaviour of the topology, a model of the topology is developed using a new integral inductance function. That model is used to select the design parameters of an 8/6 AFSRM, for which a fuzzy controller is also developed to control the phase current. Several simulations of the 8/6 AFSRM are performed to calculate its energy conversion efficiency, thermal performance, and torque density, and results indicate that the new AFSRM has a higher energy conversion efficiency, and can produce more torque/kg than other switched-reluctance motors used for in-wheel drive vehicle applications.

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: Simulation or modeling · Consensus signal: none
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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.223
Teacher spread0.194 · 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 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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMachinesSame topicElectric Motor Design and AnalysisFrench-language works237,207