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Synthesis of a Series-Hybrid Permanent-Magnet Variable Flux Motor

2023· article· en· W4386472652 on OpenAlexafffund
Bassam S. Abdel-Mageed, Akrem Mohamed Aljehaimi, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersConcordia University
KeywordsMagnetDemagnetizing fieldTorqueDirect torque controlTorque rippleControl theory (sociology)Flux (metallurgy)Torque densityMagnetizationRippleMechanicsMaterials sciencePhysicsMechanical engineeringEngineeringElectrical engineeringComputer scienceVoltageInduction motorMagnetic fieldThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

This work focuses on synthesizing a series hybrid variable flux permanent magnet motor (SHVFPMM) from a single permanent magnet variable flux motor (VFM) and a rare-earth permanent magnet motor (PMM). Initially, the permanent magnet (PM) sizing process for the standard VFM is presented in terms of loading demagnetization, reversible demagnetization range, demagnetization/re-magnetization currents, average torque and torque ripple. Then, the PMM is investigated mainly in terms of average torque and torque ripple. The outcome of this analysis is then used as the starting point for designing the SHVFPMM. The PM proportions for the SHVFPMM is then analyzed. It is noted that there exists a limited range of PM proportions below which the variable flux PM is demagnetized under load condition and beyond which the torque density does not warrant the excessive increase in the de/remagnetization currents. For validating the proposed synthesis criteria, a 5 hp SHVFPMM is tested experimentally which lies within the derived PM design map.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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 designBench or experimental
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

Citations5
Published2023
Admission routes2
Has abstractyes

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