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Record W4307984236 · doi:10.1063/5.0099324

Investigating the effect of the mean path length on reluctance actuator output force characterization

2022· article· en· W4307984236 on OpenAlexaff
Michael Pumphrey, Mohammad Al Saaideh, Kaitlyn Kuchinkaa, Natheer Alatawneh, Mohammad Al Janaideh

Bibliographic record

VenueReview of Scientific Instruments · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMagnetic reluctanceActuatorMultiphysicsAir gap (plumbing)Finite element methodDisplacement (psychology)Core (optical fiber)Control theory (sociology)Magnetic fluxMechanicsPath (computing)Matrix (chemical analysis)PhysicsComputer scienceMechanical engineeringMagnetic fieldMagnetMaterials scienceEngineering

Abstract

fetched live from OpenAlex

This paper investigates the effect of the flux's mean path length (MPL) on the reluctance actuator's analytical model. It determines the circumstances where the model neglecting the MPL is valid. The analysis is carried out for both C-Core and E-Core reluctance actuators; the analytical results are calculated by using MATrix LABoratory and then validated against a finite element model simulation by using COMputer SOLution Multiphysics. In addition, the experimental results of the magnetic force of C-Core and E-Core reluctance actuators are presented and compared with the analytical model. The comparison is obtained under different input currents and air gaps for two different ferromagnetic materials. It can be concluded that the analytical model is valid only for air gaps with a relatively high air gap displacement and for small air gaps, considering the MPL is necessary for accurate results. This means that whenever the reluctance actuator is proposed for high-precision motion system applications, it is essential that the analysis takes into account the effect of the MPL.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.212
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2022
Admission routes1
Has abstractyes

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