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Record W3015309842 · doi:10.31593/ijeat.669064

A comparative analysis of four-pole brushless DC motors with different slot and winding arrangement based on THD values

2020· article· en· W3015309842 on OpenAlexfundno aff
Cemil Ocak, Adem Dalcalı

Bibliographic record

VenueInternational Journal of Energy Applications and Technologies · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
FundersCanadian Institute for Theoretical Astrophysics
KeywordsHarmonicsTotal harmonic distortionDC motorControl theory (sociology)Distortion (music)VoltageFinite element methodBrushed DC electric motorElectromagnetic coilComputer scienceHarmonicHarmonic analysisAC motorMathematicsElectrical engineeringAcousticsPhysicsEngineeringMathematical analysisStructural engineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Brushless DC motors (BLDC) are widely used in variable speed drive applications. Nowadays the relationship between the number of poles, slots and winding arrangements in BLDC motors continues to be a very challenging topic. The main objective of this work was to develop and compare four-pole BLDC motors with different slot numbers (6, 12 and 15) and winding arrangements. Three-dimensional finite element analysis (FEA) was conducted to characterize the proposed designs. The Total Harmonic Distortion (THDv) which tells the amount of harmonics present in the voltage have been obtained and compared based on the different slot numbers and winding arrangement. In the study, an extensive investigation of the THDv values in four-pole BLDC motors having different slot and winding configurations has been carried out. From the simulated results it is evident that the lowest THDv and corresponding sinusoidal back emf can be obtained by implementing 15 slots within four-pole designs. The performance values have been examined comparatively by analysing the motors at the rated condition.

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 score0.445
Threshold uncertainty score0.284

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 head0.018
GPT teacher head0.236
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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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