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Record W2809624019 · doi:10.1109/tmag.2018.2843800

A New Brushless AC Electromagnetic Generator Design Using an Experimental and Design Optimization Approach

2018· article· en· W2809624019 on OpenAlexafffund
James Ugwuogo, Safieddin Safavi‐Naeini

Bibliographic record

VenueIEEE Transactions on Magnetics · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStatorRotor (electric)Generator (circuit theory)Electromagnetic coilTorqueMagnetVoltageElectric generatorAir gap (plumbing)Power (physics)Computer scienceHalbach arrayPermanent magnet synchronous generatorInduction generatorControl theory (sociology)Electrical engineeringPhysicsEngineeringMaterials science

Abstract

fetched live from OpenAlex

This paper presents a new brushless alternating current electromagnetic energy generator using an experimental/theoretical and design optimization approach. This type of generator can be used in low-voltage, low-power systems such as in energy harvesting applications. The generator consists of a new structure with the detachable rotor arrangement mainly comprising of the powerful Neodymium rare-earth magnets with a back core on an adjustable height rotor shaft and a stator made up of top and bottom flanges and a single continuous coil arrangement. The special stator and rotor arrangements are optimized for variable speed and variable output power operations. It has a variable air-gap setting between the stator and the rotor which controls the initial amount of torque required to move the rotor which in turn controls the amount of generated output voltage. The finite-element modeling magnetics tool was used in the simulation for the optimization of the new brushless generator design concept.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.233
Teacher spread0.202 · 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 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

Citations4
Published2018
Admission routes2
Has abstractyes

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