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Record W2916342337 · doi:10.1016/j.jare.2019.02.002

The impact of demagnetization on the feasibility of permanent magnet synchronous motors in industry applications

2019· article· en· W2916342337 on OpenAlexaff
A.A. Adly, A S Huzayyin

Bibliographic record

VenueJournal of Advanced Research · 2019
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutomotive engineeringDemagnetizing fieldGas compressorMagnetSynchronous motorElectric motorEnvironmental scienceInduction motorComputer scienceVoltageEngineeringMechanical engineeringElectrical engineeringPhysicsMagnetizationMagnetic field

Abstract

fetched live from OpenAlex

Permanent magnet (PM) motors are rapidly replacing the dominant induction motors in industrial applications including pumps, fans, and compressors. PM motors are also gaining ground in critical sustainable energy applications such as wind systems, photovoltaic pumping systems and electric vehicles. Compared to induction motors, PM have higher efficiency. In this paper, the financial feasibility of replacing induction motors by PM motors at various operating conditions was analyzed on a preliminary basis. The impact of partial demagnetization and full loss of excitation on the feasibility of the replacement was also preliminarily investigated. It is found that the feasibility of replacement was less sensitive to reduction in the life time of PM motors than reduction in efficiency due to partial demagnetization. While detailed and lengthy studies are planned in the future, investigation outcomes suggest that the replacement remains feasible despite risks of demagnetization when utilization rates are above 50%. Details of the investigation are reported in the paper.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.353
Teacher spread0.322 · 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 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

Citations30
Published2019
Admission routes1
Has abstractyes

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