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Record W4414189808 · doi:10.1016/j.epsr.2025.112230

Detailed electromagnetic transient model of switched reluctance motor drive system

2025· article· en· W4414189808 on OpenAlexaff
Seyedarmin Mirnikjoo, Mohammed Naïdjate, Jean Mahseredjian, Nicolas Bracikowski, Paul Akiki

Bibliographic record

VenueElectric Power Systems Research · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSwitched reluctance motorPermeanceTransient (computer programming)Control theory (sociology)StatorReluctance motorRotor (electric)Magnetic reluctanceMagnetic circuit

Abstract

fetched live from OpenAlex

This paper focuses on the electromagnetic transient modeling of the switched reluctance motor drive system through the detailed circuit-based representation of the machine, power converter and control system. The dynamic model of the switched reluctance machine consists of interconnected mechanical, electrical and magnetic equivalent circuits in simultaneous solution. The mesh-based permeance network is used to model the nonlinear magnetic behavior of the rotor and stator while the airgap is modeled with variable permeances, enabling transient studies without re-meshing or reconnecting nodes during runtime. The accuracy of the proposed model is validated through comparison with a finite element-based model, demonstrating its reliability while offering significantly faster computational performance. The proposed model simulates the complete switched reluctance drive system under transient conditions.

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)0.000
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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