MétaCan
Menu
Back to cohort
Record W3044583425 · doi:10.1109/ojia.2020.3011395

Source of Acoustic Noise in a 12/16 External-Rotor Switched Reluctance Motor: Stator Tangential Vibration and Rotor Radial Vibration

2020· article· en· W3044583425 on OpenAlexafffund
Jianbin Liang, Brock Howey, Berker Bilgin, Ali Emadi

Bibliographic record

VenueIEEE Open Journal of Industry Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of Canada
KeywordsStatorRotor (electric)VibrationNoise (video)AcousticsSwitched reluctance motorPhysicsControl theory (sociology)EngineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

Compared with internal-rotor switched reluctance motors (SRMs), external-rotor (ER) SRMs can show different vibration and acoustic noise behavior due to the differences in the designs, e.g. thinner rotor back iron to achieve smaller inertia, and longer stator pole height to improve the electromagnetic performance. This paper presents the considerations in the modeling and analysis of the acoustic noise sources of an ER SRM stator and rotor. The harmonic components of the stator tangential force density and rotor radial force density are analyzed and compared. The vibration modes, the vibration behavior, and acoustic noise of the stator and rotor are also compared. A 12/16 external-rotor SRM designed for a direct-drive E-bike application is used for the modeling, analysis, and experimental validation of vibration and acoustic noise. It is concluded that both the stator tangential vibration and the rotor radial vibration can be sources of the acoustic noise in an ER SRM.

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.244
Teacher spread0.226 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Open Journal of Industry ApplicationsSame topicElectric Motor Design and AnalysisFrench-language works237,207