MétaCan
Menu
Back to cohort

Estimation of droplet charge forming out of an electrified ligament in the presence of a uniform electric field

2015· article· en· W2396069060 on OpenAlexafffund
Husam Osman, G.S.P. Castle, Kazimierz Adamiak, Hua-Tzu Fan, Joseph Simmer

Bibliographic record

VenueJournal of Physics Conference Series · 2015
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsElectric fieldRotational symmetryCharge (physics)RADIUSElectrodeVoltageMechanicsSpace chargeElectrostaticsMaterials scienceElectric chargeChemistryPhysicsElectron

Abstract

fetched live from OpenAlex

The charge on a liquid droplet is a critical parameter that needs to be determined to accurately predict the behaviour of the droplet in many electrostatic applications, for example, electrostatic painting and ink-jet printing. The charge depends on many factors, such as the liquid conductivity, droplet and ligament radii, ligament length, droplet shape, electric field intensity, space charge, the presence of adjacent ligaments and previously formed droplets. In this paper, a 2D axisymmetric model is presented which can be used to predict the electric charge on a conductive spherical droplet ejected from a single ligament directly supplied with high voltage. It was found that the droplet charging levels for the case of isolated electrified ligaments are as much as 60 times higher than that in the case of ligaments connected to a planar high voltage electrode. It is suggested that practical atomization systems lie somewhere between these two extremes and that a better model was achieved by developing a 3D approximation of a linear array of ligaments connected to an electrode having variable width. The effect on droplet charge and its radius was estimated for several cases of different boundary conditions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.317

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 head0.016
GPT teacher head0.238
Teacher spread0.223 · 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 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

Citations1
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicElectrohydrodynamics and Fluid DynamicsFrench-language works237,207