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Design of a Novel Rare-Earth-Free Variable Flux Motor Combining Iron-Nitride and AlNiCo Magnets

2024· article· en· W4407316386 on OpenAlexaff
Bassam S. Abdel-Mageed, Benoit Blanchard St-Jacques, Ruisheng Shi, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsAlnicoMagnetIron nitrideMaterials scienceFlux (metallurgy)Rare earthMagnetic fluxNitrideMechanical engineeringPhysicsEngineeringNanotechnologyMetallurgyMagnetic field

Abstract

fetched live from OpenAlex

This work presents a novel rare-earth-free variable flux motor (VFM) combining AlNiCo and Iron-Nitride (FeN) magnets. The proposed rotor demonstrates the high torque density feature of the hybrid magnet VFMs along with a wide field regulation range of rare-earth-free VFMs. A newly developed Iron Nitride (FeN) magnet is employed. This new magnet is used to construct the V-shaped portion of the rotor which allowed excellent loading demagnetization withstanding ability along with improved utilization of the reluctance torque. To benefit the parallel hybrid magnet characteristics without excessively increasing the remagnetization current, the spoke type portion of the rotor is made of two different grades of AlNiCo magnets. Initially, the flux variation mechanism is presented in the form of different operational modes. Then, the electromagnetic performance is described showing the machine capability of achieving the same torque density of the existing hybrid magnet VFMs. By fully demagnetizing the proposed machine, 50% of the rated torque can be attained due to the high reluctance torque capability.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.198
Teacher spread0.185 · 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
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

Citations1
Published2024
Admission routes1
Has abstractyes

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