MétaCan
Menu
Back to cohort
Record W3023910696 · doi:10.1109/tte.2020.2991369

Mathematical Model of an Interior PMSM With Aligned Magnet and Reluctance Torques

2020· article· en· W3023910696 on OpenAlexafffund
Rajendra Thike, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTorqueMagnetic reluctanceMagnetControl theory (sociology)Computer scienceMathematical modelDirect torque controlPhysicsEngineeringMechanical engineeringElectrical engineeringArtificial intelligenceVoltage

Abstract

fetched live from OpenAlex

Mathematical modeling of a machine allows a better understanding, and it gives insight on the effect of each variable on the machine dynamics. This article develops the mathematical model of a novel shifted interior permanent magnet synchronous machine (IPMSM) with aligned magnet and reluctance torques. It is a new class of IPMSM where the flux barrier is modified in such a way that the relative positions of the magnet and reluctance axes shift from their regular positions. The angle of shift between the axes is designed such that the magnet and reluctance torques in the machine have their maximum values at the same torque angle. In order to validate the developed mathematical model, a prototype motor is built. This article also presents experimental tests to obtain the parameters of the prototype machine in the developed model, and the model is verified by comparing the torque-angle and torque-speed characteristics obtained using the model with the experimental results obtained on the prototype machine.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.203
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations61
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Transportation ElectrificationSame topicElectric Motor Design and AnalysisFrench-language works237,207