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Record W3117513233 · doi:10.18280/jesa.530601

Optimal Coil Design of an Electromagnetic Actuator Using Particle Swarm Optimization

2020· article· en· W3117513233 on OpenAlexvenueno aff
Eduardo Chiarello, Juliana Almansa Malagoli

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2020
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsnot available
FundersUniversidade Federal do ParanáFundação Araucária
KeywordsElectromagnetic coilActuatorParticle swarm optimizationFinite element methodVoice coilMechanical engineeringWork (physics)Copper lossComputer scienceEngineeringControl theory (sociology)Electrical engineeringStructural engineering

Abstract

fetched live from OpenAlex

This paper aims to reduce the heating of the electromagnetic actuators of a magnetic bearing. The electrical current of the coils was above normal, so the need for a new coil design to reduce heating due to high currents. In this scenario, the proposed methodology allows minimizing copper losses using Particle Swarm Optimization, so that the best result of the design parameters will be used in the construction of the new coil for the actuator. For the development of this work, it was decided to use a computational tool for public use, FEMM (Finite Element Method Magnetics) to simulate the electromagnetic device. In the simulations, the densities of magnetic fluxes in the core and in the air gap are shown, as well as the energies, electromagnetic forces and losses in the copper of the electromagnetic actuator winding. Finally, an optimal model of the actuator is obtained through the use of optimization techniques. Therefore, the results obtained demonstrate that the proposed methodology is configured as an interesting strategy for the purpose of this work.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.025
GPT teacher head0.234
Teacher spread0.209 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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