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Record W2043232661 · doi:10.1109/tia.2013.2252871

Designing and Prototyping a Novel Five-Phase Pancake-Shaped Axial-Flux SRM for Electric Vehicle Application Through Dynamic FEA Incorporating Flux-Tube Modeling

2013· article· en· W2043232661 on OpenAlexaff
Anas Labak, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2013
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFinite element methodInductanceEngineeringSwitched reluctance motorElectromagnetic coilMagnetic fluxPower (physics)Flux (metallurgy)Control theory (sociology)Mechanical engineeringMechanicsVoltageComputer scienceStructural engineeringMaterials sciencePhysicsElectrical engineeringMagnetic field

Abstract

fetched live from OpenAlex

Switched reluctance motors (SRMs) show crucial attributes to applications where light weight, high-temperature adaptability, fault-tolerance capability, ruggedness, and simplicity are strongly required. The axial-flux configuration of SRM has additional features over the radial-flux configuration. This paper presents the design and analysis of a novel axial-flux SRM. Detailed procedures of deriving the output power equation as a function of the motor dimensions and parameters are provided. A modified phase winding design approach is thoroughly explained, a flowchart describing the design algorithm is presented, and the inductance determination by different methods is verified experimentally. The 3-D finite-element analysis (FEA) unveils the excessive end core and radial-flux fringing effects in the axial-flux configuration. An exclusive pole-shape design is also proposed. The operation of the motion model using 3-D dynamic FEA is analyzed, and its prototype development process and static testing are demonstrated.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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

Citations115
Published2013
Admission routes1
Has abstractyes

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