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Record W4318768074 · doi:10.1109/tmag.2023.3241254

Accurate and Simple Modeling of Eddy Current Braking Torque: Analysis and Experimental Validation

2023· article· en· W4318768074 on OpenAlexaff
Abdullah Muhammad Mahfouz, Mohammed Hussien Mohammed, Shoukry I. Shams, Hossam S. Abbas

Bibliographic record

VenueIEEE Transactions on Magnetics · 2023
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsEddy current brakeTorqueEddy currentControl theory (sociology)MechanicsDragRotational speedPhysicsRetarderThreshold brakingComputer scienceClassical mechanicsAutomotive engineeringEngineering

Abstract

fetched live from OpenAlex

A compact, reliable, and straightforward mathematical modeling of the eddy current (EC) braking torque is proposed in this article. First, the braking magnetic force, which curbs the movement of a rotating disk (RD), is evaluated using the basic laws of electromagnetism. Second, the braking torque is related to the braking force through a polynomial function model. The model parameters are evaluated by plugging in measurement samples in the equation of motion taking into account the nonlinear behavior of the aerodynamic drag forces. The proposed model neither has limitations on the braking system geometry nor the disk rotational speed as long as the measurement samples cover the whole speed range. Moreover, it is validated experimentally for constant and alternating magnetic flux profiles with best fit rates (BFRs) more than 85% and 93% for both rotational speed and braking torque, respectively. Furthermore, programmable braking application is demonstrated using the proposed approach and its applicability is verified experimentally.

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 score0.479
Threshold uncertainty score0.474

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 head0.022
GPT teacher head0.272
Teacher spread0.249 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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