MétaCan
Menu
Back to cohort

Design of Iron-Nitride-Based Permanent Magnet Variable Flux Motors

2024· article· en· W4408282079 on OpenAlexafffund
Bassam S. Abdel-Mageed, Akrem Mohamed Aljehaimi, Benoit Blanchard St-Jacques, Ruisheng Shi, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersConcordia University
KeywordsMagnetMaterials scienceIron nitrideFlux (metallurgy)Magnetic fluxPermanent magnet motorNitrideAutomotive engineeringMechanical engineeringPhysicsEngineeringMetallurgyComposite materialMagnetic field

Abstract

fetched live from OpenAlex

This work investigates the application of an Iron-Nitride (FeN) Permanent Magnet (PM) material to variable flux motors (VFMs). Two existing VFMs are used as references, a rareearth-free inverted saliency VFM and a series hybrid VFM. Initially, the inverted saliency VFM performance is evaluated for equal usage of PM material. Then, the PMs are resized for achieving similar electromagnetic performance to the reference VFM. It is noted that although the new PM has 25% higher residual flux density than AlNiCo9 PM, it requires high current for remagnetization. However, adopting the FeN magnet to the inverted saliency machine can allow reducing the PM usage by 42% and hence reduced rotor weight would be expected. By replacing the rare-earth magnet of the series hybrid VFM with FeN, comparable constant torque performance is attained with the advantage of avoiding rare-earth magnet demagnetization risks. This shows that for the investigated torque density levels, the FeN magnet has a high potential for designing existing/upcoming regular and hybrid magnet VFMs.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.998

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)0.0030.000

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 head0.009
GPT teacher head0.193
Teacher spread0.184 · 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
GenreMethods

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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicElectric Motor Design and AnalysisFrench-language works237,207