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Record W4391603081 · doi:10.5539/apr.v16n1p20

Dual-Sided Rotor Design for Performance Boost of Synchronous Reluctance Motors in Electric Vehicles

2024· article· en· W4391603081 on OpenAlexvenueno aff
Luis Teia

Bibliographic record

VenueApplied Physics Research · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRotor (electric)Magnetic reluctanceDual (grammatical number)Computer scienceReluctance motorAutomotive engineeringSwitched reluctance motorControl theory (sociology)Electrical engineeringMagnetArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Increasing environmental awareness is pushing the design of electic motors to favor none rare-earth solutions (i.e., without permanent magnets), and one such example is the SyncRM 2 (or concentrated-coiled SRM2) being proposed for the electric hybrid automobile Toyota Yaris. Following on an already established line of research on this topic, this article proposes a new design that re-assigns most of the magnetic material in the stator to the rotor—resulting in the Dual-sided SyncRM (a variant of the SRM2). The detrimental effect (caused by the extra gap) of slightly reducing the aligned inductance is overwhelmingly outweighed by the beneficial effect of drastically reducing the unaligned inductance. Extensive back-to-back FEMM analysis was conducted, where the recomputed SRM2 matches previous research, providing confidence to the favorable predictions of the Dual-sided SyncRM. Both performances are compared, with the venue being available for download on an open-source database. A realistic photo-rendered three-dimensional model is displayed and also available. An important outcome is the Dual-sided SyncRM torque (and power) increased by 29% (with respect to the SRM2), achieving a saliency ratio of 10 and an efficiency boost to 91% (at the rated operational speed of 1200rpm).

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.038
GPT teacher head0.290
Teacher spread0.253 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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