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Record W4413104921 · doi:10.1109/access.2025.3597296

Mechanical Design of a Switched Reluctance Motor With Small Airgap Length

2025· article· en· W4413104921 on OpenAlexafffund
Alexander Forsyth, S. Ravichandran, Thisuri H. Indiketiya, Ashish Kumar Sahu, Sudesh V. Pathirannahalage, Batuhan Sirri Yilmaz, Brock Howey, Nir Vaks, Mohammad Ehsan Abdollahi, Berker Bilgin

Bibliographic record

VenueIEEE Access · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSwitched reluctance motorReluctance motorMagnetic reluctanceControl theory (sociology)Computer scienceElectrical engineeringEngineeringMagnetRotor (electric)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the mechanical design of a 70 kW Switched Reluctance Motor prototype with a nominal airgap length of 0.4 mm with a relatively large stator outer diameter of 280 mm. It presents the mechanical design considerations and manufacturing tolerances that are vital for an electric motor to maintain component integrity and meet performance requirements. Multi-stage design of the mechanical components and fittings for the complete mechanical system are presented and justified with finite element analysis results. The radial and axial stack-up analysis are presented which account for the dimensional variations during the manufacturing of motor components. The assembly of the motor prototype is described, which demonstrates that the axial and radial alignment of the motor components are achieved while maintaining the small airgap length required. The motor assembly is then verified with static end-of-line tests, such as winding insulation and housing leakage tests. Additionally, experimental modal analysis of the housing assembly is performed to examine the influence of the winding, housing, and potting on its modal frequencies and damping characteristics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.243
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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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