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Record W2590249015 · doi:10.1109/tec.2017.2672741

Hybrid Acoustic Noise Analysis Approach of Conventional and Mutually Coupled Switched Reluctance Motors

2017· article· en· W2590249015 on OpenAlex
Jianning Dong, James Weisheng Jiang, Brock Howey, Haoding Li, Berker Bilgin, Alan Dorneles Callegaro, Ali Emadi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSwitched reluctance motorStatorNoise (video)AccelerationFinite element methodComputer scienceElectromagneticsAcousticsReluctance motorMagnetic reluctanceRange (aeronautics)EngineeringElectronic engineeringPhysicsMagnetMechanical engineeringAerospace engineeringRotor (electric)Structural engineering

Abstract

fetched live from OpenAlex

This paper presents a method to calculate the acoustic noise of conventional switched reluctance motor (CSRM) and mutually coupled switched reluctance motor (MCSRM). This method is based on dynamic electromagnetic models, combined with analytical estimation of the stator eigenmodes and radiation efficiency, considering the switching effects and frame effects. The proposed method is applied to predict and compare the acoustic noise performances of a CSRM and an MCSRM in a wide speed range. The results are validated using commercial finite element analysis software, JMAG for electromagnetics and ACTRAN for acoustics. An acceleration test based on a setup with a 12/8 CSRM is used for experimental validation. Results show that the proposed method can provide reliable prediction of main acoustic noises during acceleration.

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.

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 score0.787
Threshold uncertainty score0.747

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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