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Record W2992959814 · doi:10.1109/icems.2019.8922075

Comparative Performance Analysis of Copper and Aluminum Wound Fractional–Slot PMSMs for High–Speed Traction Application

2019· article· en· W2992959814 on OpenAlexaff
Shruthi Mukundan, Himavarsha Dhulipati, Lucas Chauvin, Buddhika De Silva Guruwatta Vidanalage, Afsaneh Edrisy, Jimi Tjong, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTraction (geology)Torque densityTorqueCopper lossElectromagnetic coilTraction motorAutomotive engineeringMagnetAluminiumDriving cycleCopperComputer scienceMaterials scienceMechanical engineeringEngineeringElectrical engineeringElectric vehicleComposite materialMetallurgyPower (physics)Physics

Abstract

fetched live from OpenAlex

This paper performs a comprehensive comparative analysis of copper and aluminum wound permanent magnet synchronous machines (PMSMs) for high– speed application. While general studies have been conducted in literature on copper and aluminum windings in terms of conductivity, losses and mass density, the impact of these winding materials on the overall machine performance has not been analyzed broadly. Thus, this paper considers a fractional– slot wound PMSM developed for high–speed traction application and analyzes the performance with copper and aluminum windings in terms of torque density, peak efficiency, operating speed range and variation of winding losses with temperature. Furthermore, in order to assess these machines traction capability, a drive–cycle based analysis is conducted for common drive cycles including urban, highway and worldwide lightweight test cycle (WLTC) for a commercially available Ford Focus vehicle. Performance characteristics in terms of torque–speed characteristics and maximum energy density efficiency were compared for both machines.

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 score0.657
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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