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Record W4381619394 · doi:10.11159/ffhmt23.001

Electrical Effects in Liquid Droplets

2023· article· en· W4381619394 on OpenAlexaffvenue
J.M. Floryan

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

It is desired in the production of sprays to find the ways to control the size and distribution of liquid droplets.It is also of interest to provide means for directing the spray towards the target of interest.Both these goals can be addressed using electric fields, e.g.electrostatic spraying/painting.The proper design and efficiency of these techniques rely on the understanding of the fundamental aspects of droplet dynamics when exposed to external electric fields, and especially on the understanding of the processes associated with droplet break up.This study is focused on the dynamics of liquid droplets driven by induced polarization effect and involves both experiments and theory.During experiments droplets were isolated from all other effects using microgravity environment.The evolution of droplets was captured using a high-speed movie camera.Theoretical analysis, which involved numerical simulation of the deforming droplet, was able to reproduce various stages of the deformation process in time up to the formation of Taylor cones.The droplet evolution process can be divided into rapid distortion followed by a combination of capillary instability and formation of Taylor cones and mass removal from the zone of the cones.Rapid distortion theory was proposed to described droplet evolution in the Taylor-cone regime.Good agreement between the experiment and theoretical modeling has been observed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.580

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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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