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Investigation of Aluminium and Copper Wound PMSM for Direct–drive Electric Vehicle Application

2019· article· en· W2982651810 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsNatural Resources CanadaMcMaster UniversityUniversity of Windsor
Fundersnot available
KeywordsTorque densityElectromagnetic coilTorqueAutomotive engineeringAluminiumDriving cycleCopper lossMagnetElectric vehicleCopperMaterials scienceMechanical engineeringEngineeringControl theory (sociology)Computer scienceElectrical engineeringPhysicsMetallurgyPower (physics)

Abstract

fetched live from OpenAlex

Abstract This paper investigates aluminium and copper windings for a permanent magnet synchronous machine (PMSM) developed for direct–drive electric vehicle (EV) application. Previously, studies have been conducted on comparison of these windings in terms of thermal and electrical conductivity, cost and mass density. However, the impact of these windings on the machine’s performance in terms of efficiency and torque has not been analysed. In this paper, for the same machine volume and geometry, a comparative analysis of PMSMs with copper and aluminium windings has been performed in terms of efficiency, torque, weight, operating speed range, ohmic losses and temperatures. Furthermore, as these machines are developed for direct–drive EV application, drive–cycle-based analysis was conducted for urban and highway cycles for a 2013 Ford Focus vehicle dynamics model. For these drive cycles, analysis in terms of torque speed characteristics and maximum energy density efficiency for both the machines has been performed.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.474

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 head0.009
GPT teacher head0.191
Teacher spread0.181 · 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