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Record W3007593873 · doi:10.1139/tcsme-2019-0201

Analysis of permanent magnet servo motor performance with different semi-ferromagnetic sleeve materials

2020· article· en· W3007593873 on OpenAlexvenueno aff
Hongbo Qiu, Yong Zhang, Cunxiang Yang, Ran Yi

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
FundersZhengzhou UniversityZhengzhou University of Light IndustryNational Natural Science Foundation of China
KeywordsMagnetEddy currentCounter-electromotive forceMagnetic flux leakageFinite element methodTorqueControl theory (sociology)Materials scienceMechanical engineeringComputer scienceVoltageEngineeringStructural engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Proposed is a study on the application of a semi-ferromagnetic sleeve in a permanent magnet servo motor (PMSM). First, taking the no-load electromotive force as the research object, the optimal sleeve permeability of the PMSM was determined using the finite element method, and the correctness was verified using an analytical algorithm. In the analytical calculation process, to solve the calculation problem of the no-load leakage flux coefficient caused by the complex distribution of the pole-to-pole leakage flux, an iterative calculation method is proposed. Second, based on the optimization sleeve permeability, the variation of the eddy current loss with different sleeve conductivity was analyzed, and the optimal conductivity value was determined. Finally, variations in current and power factor before and after optimization were compared and analyzed, and the effect of the sleeve electromagnetic characteristics on the motor performance was analyzed. Motor performance after optimization could be consistent with that of before optimization by reducing the thickness of the permanent magnet, which provides a theoretical basis for the optimization analysis of energy and material savings of the PMSM.

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 score0.151
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.008
GPT teacher head0.166
Teacher spread0.158 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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