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Record W2013916500 · doi:10.1109/tmag.2011.2175443

Alternative Rotor Designs for High Performance Brushless Permanent Magnet Machines for Hybrid Electric Vehicles

2012· article· en· W2013916500 on OpenAlexaff
David G. Dorrell, Min‐Fu Hsieh, Andrew M. Knight

Bibliographic record

VenueIEEE Transactions on Magnetics · 2012
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMagnetTorque densityTorqueOffset (computer science)Automotive engineeringRotor (electric)Ferrite (magnet)Computer scienceMechanical engineeringMaterials scienceElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

This paper investigates different rotor topologies based on the spoke design as an alternative to the internal permanent magnet design currently used in the Toyota Prius 2004 design. Reference is made to the 2010 model where the magnet material and maximum torque are reduced, but the speed is doubled. A rare-earth spoke design is shown to be a viable alternative. The paper then goes further and investigates a ferrite alternative. While the torque density is reduced, it illustrates that the spoke magnet arrangement may a viable solution and may open up ferrite magnet use. To offset the reduction in torque density then the speed may be increased; this is the solution Toyota used in the 2010 machine for rare-earth magnet material reduction.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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

Citations116
Published2012
Admission routes1
Has abstractyes

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