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Record W2663158678 · doi:10.1109/ccece.2017.7946709

Modeling and analysis of torque ripple in a brushless DC motor considering spatial harmonics

2017· article· en· W2663158678 on OpenAlexaff
Junxi Cai, Chunyan Lai, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHarmonicsTorque rippleDC motorBrushed DC electric motorTorqueHarmonic analysisControl theory (sociology)Direct torque controlRippleComputer scienceTorque motorAC motorPhysicsElectrical engineeringEngineeringInduction motorElectronic engineeringVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

This paper aims to model and analyze the torque ripple induced by the spatial harmonics in a brushless DC (BLDC) motor. At first, a general mathematical model of BLDC motor is introduced, which assumes an ideal trapezoidal back-EMF. Afterwards, the improved back-EMF model including the spatial harmonics is derived using mathematical equations. Back-EMF tests are conducted on a laboratory BLDC motor to obtain the actual harmonic components for the improved back-EMF model. Moreover, the harmonics in the phase currents are also included in the proposed model to analyze the produced torque ripple in a BLDC motor driven by a 120-degree conduction based three-phase inverter. Simulation studies are conducted to analyze the torque ripple of the laboratory BLDC motor based on the proposed model and the ideal trapezoidal back-EMF model. In the simulation, the drive system of a BLDC motor is also modeled, such that the commutation torque ripple is included, which can yield precise torque waveforms for the analysis of torque ripple in a BLDC motor.

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: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.559

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 head0.021
GPT teacher head0.233
Teacher spread0.213 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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