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Record W2898508373 · doi:10.1109/icelmach.2018.8507217

A Comparative Study of Optimally Designed Synchronous Reluctance Machines

2018· article· en· W2898508373 on OpenAlexaff
Indula Prasad Abeyrathne, Mohammad Sedigh Toulabi, Shaahin Filizadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMagnetic reluctanceTorque rippleRotor (electric)TorqueControl theory (sociology)Computer scienceReluctance motorSwitched reluctance motorDirect torque controlAutomotive engineeringEngineeringVoltageMagnetMechanical engineeringElectrical engineeringInduction motorPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

A comparative study is conducted to compare the operating performance of a number of Synchronous Reluctance Machines (SynRMs) optimally designed based on three distinct Objective Functions (OFs) to (i) minimize the torque ripple, (ii) maximize the saliency ratio, and (iii) simultaneously minimize the torque ripple and maximize the saliency ratio while supporting the required torque. For the optimized SynRMs, a normal round rotor and a rotor with a cutoff are used in the Finite Element (FE)-based rotor design optimization, while the stators are identically designed using classic electric and magnetic loading principles. The SynRMs' dimensions and efficiencies are based on IEC-90S frame size and IE3 efficiency standards, respectively. The design optimization and comparative study show the performance capabilities and operating limits of each design proving the superiority of the optimized SynRMs over a non-optimized SynRM.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.375

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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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
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

Citations2
Published2018
Admission routes1
Has abstractyes

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