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Record W1978475075 · doi:10.1109/iemdc.2013.6556199

Design and Optimization of a high torque in-wheel surface-mounted PM synchronous motor using concentrated winding

2013· article· en· W1978475075 on OpenAlexaff
Olivier Côté, Ahmed Chebak, Jean-François Méthot

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsStatorMagnetTorqueFinite element methodRotor (electric)Control theory (sociology)Mechanical engineeringProcess (computing)Synchronous motorComputer scienceAutomotive engineeringEngineeringStructural engineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a specific design process of a high-torque direct-drive in-wheel permanent magnet synchronous machine (PMSM) using concentrated winding and grain oriented (GO) silicon steel teeth for a hybrid All-Terrain Vehicle (ATV) application. Mechanical specifications and constraints of an off-road application are first demystified. An analytical design model, considering magnet flux and an induction reaction correction factors, is developed and used to determine geometric dimensions of a radial flux machine topology with surface-mounted permanent magnets (PM) and external rotor. The model also takes into account losses in winding, magnets, stator and rotor yokes and teeth. Electrical parameters of the equivalent machine circuit are determined to adapt the machine to the imposed converter voltage. All critical machine models parameters are validated with finite element (FE) simulations. The analytical design model is used to perform an iterative optimization procedure. This model is corrected during the optimization process by using correction factors derived from 2D FE analysis. The optimization process and some critical design validations are presented.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.401
Threshold uncertainty score0.484

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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