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Record W2587969058 · doi:10.1109/ecce.2016.7855510

Thermal analysis of a three-phase 24/16 switched reluctance machine used in HEVs

2016· article· en· W2587969058 on OpenAlexafffund
Michael Kasprzak, James Weisheng Jiang, Berker Bilgin, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSwitched reluctance motorStatorCoolantElectromagnetic coilRotor (electric)Finite element methodThermal analysisLaminationMechanical engineeringOperating temperatureTraction motorThermalOperating pointMaterials scienceElectric motorMagnetic reluctanceControl theory (sociology)EngineeringAutomotive engineeringElectrical engineeringComputer scienceMagnetPhysicsStructural engineeringThermodynamics

Abstract

fetched live from OpenAlex

This paper presents the thermal analysis of a 60 kW switched reluctance motor (SRM) under peak operating conditions for traction application in a hybrid electric vehicle (HEV). The SRM has 24 stator poles and 16 rotor poles, and three-phases. Heat generation losses are determined using finite element analysis (FEA) electromagnetic simulations and these losses are input into a lumped parameter thermal network (LPTN) simulation representing the thermal circuit of the electric machine. A range of coolant inlet temperatures are input and the testing of various priority operating speed points leads to the analysis of the rise in temperature of different components within the machine. By applying temperature limiting constraints of the copper windings and the rotor lamination surface, the operating times with varying coolant inlet temperatures and operating speeds can be determined.

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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