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Record W2902317652 · doi:10.23919/icems.2018.8548975

Design Optimization and Performance Prediction of Synchronous Reluctance Motors

2018· article· en· W2902317652 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 rippleTorqueRotor (electric)Reluctance motorSwitched reluctance motorControl theory (sociology)Torque densityPower (physics)Direct torque controlFinite element methodComputer scienceEngineeringAutomotive engineeringMechanical engineeringMagnetInduction motorStructural engineeringElectrical engineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

Finite Element (FE)-based design optimization and performance assessment are conducted to analyze and improve the operating capabilities of a series of four flux barrier Transversally Laminated Anisotropic (TLA) Synchronous Reluctance Motors (SynRMs) with various slot per pole per phase (SPP) properties and normal and cut off rotor structures. A Combined Objective Function (COF) is defined and solved to minimize the torque ripple and maximize the saliency ratio of the SynRMs while also supporting the desired developed torque. Power and torque capability, air-gap flux density, electromagnetic losses, efficiency, and power factor of the optimally designed SynRMs are evaluated to introduce a final design. Structural analysis is conducted to ensure that the final design is mechanically robust throughout its entire speed range.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.204

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 head0.008
GPT teacher head0.173
Teacher spread0.166 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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