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Record W2745275486 · doi:10.1109/iemdc.2017.8002163

PM assisted synchronous reluctance machine design using AlNiCo magnets

2017· article· en· W2745275486 on OpenAlexaff
Seyede Sara Maroufian, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsAlnicoMagnetMagnetic reluctanceTorque densityRotor (electric)Topology (electrical circuits)Permanent magnet synchronous generatorPower densityPower (physics)Mechanical engineeringAutomotive engineeringPower factorComputer scienceElectrical engineeringEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

The absence of field excitation on the rotor of the synchronous reluctance machine, results in poor power factor and low power density when compared to permanent magnet synchronous machines. To mitigate these problems, permanent magnet assisted synchronous reluctance machines were introduced. The inserted permanent magnet boosts the machine's power factor and enhances its power density. In this paper a new topology for permanent magnet assisted synchronous reluctance machines using low cost AlNiCo magnets is proposed. The results of the proposed design with various magnet dimensions using AlNiCo magnets are compared with a previously designed and prototyped synchronous reluctance machine. Simulations for the same magnet dimensions are also carried out using a rare earth magnet material to study the effect of magnet type on the machine's performance. A final design using AlNiCo magnets is chosen based on the gain in the power density, manufacturing cost, and the power factor improvement. The proposed design is also compared with a conventional PM assisted SynRM topology.

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 score0.944
Threshold uncertainty score0.646

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 head0.028
GPT teacher head0.242
Teacher spread0.214 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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