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Comparison between a Series-Hybrid Variable-Flux Memory Motor and a Rare-earth Interior Permanent Magnet Synchronous Motor

2023· article· en· W4390416659 on OpenAlexaff
Akrem Mohamed Aljehaimi, Bassam S. Abdel-Mageed, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsSynchronous motorAC motorMagnetSeries (stratigraphy)Rare earthPermanent magnet motorVariable (mathematics)Flux (metallurgy)Permanent magnet synchronous motorComputer scienceAutomotive engineeringElectrical engineeringElectric motorMaterials scienceEngineeringGeologyMathematicsMetallurgyMathematical analysis

Abstract

fetched live from OpenAlex

This paper compares a series hybrid variable-flux memory-motor, which uses AlNiCo9 magnets in series with rare-earth permanent magnets, with an equivalent conventional rare-earth IPMSM. While pure memory motors have been proven to have higher efficiency than conventional IPMSMs in the high-speed region, the technology suffers from low torque and power during high-speed operations. Thus, hybrid memory motor technology has been introduced. It has been reported that the series-hybrid technology can improve high-speed torque and power. Although the technology has caught the electric vehicle industry’s attention, the literature lacks a detailed comparison between conventional rare-earth IPMSMs and series-hybrid variable-flux motor technologies. Thus, this paper compares a series-hybrid variable-flux IPMSM with an equivalent conventional rare-earth IPMSM regarding torque, efficiency, power, power factor, and drive cycle. This comparison is made on a 6-horsepower motor scale using finite element analysis (FEA) software and experimental results.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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.

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

Citations9
Published2023
Admission routes1
Has abstractyes

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