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Record W3198216358 · doi:10.2218/iclass.2021.5826

Off-centred impacts of droplets on horizontal cylinders with large curvature ratios

2021· article· en· W3198216358 on OpenAlexaff
Jordan Bouchard, Khalil Sidawi, S. Chandra

Bibliographic record

VenueInternational Conference on Liquid Atomization and Spray Systems (ICLASS) · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurvatureEnvironmental scienceMaterials scienceMechanicsGeometryMathematicsPhysics

Abstract

fetched live from OpenAlex

Drop impact on cylindrical objects is a natural phenomenon with important industrial applications, e.g., mesh-style mist eliminators, where the goal is to capture drops on the mesh wires.The impact velocity of the drop on a cylinder is one factor that determines the amount of a drop that is captured, with drops impacting at greater than a threshold velocity only being partially captured by the fibre.The threshold velocity will change depending on how offcentered (eccentric) the drop impacts the cylinder, and the ratio of the fibre and drop diameters, known as the curvature ratio, R * .Currently, little experimental data exists for the threshold velocity of drops impacts in the range of We<14 and curvature ratios R * >1, which this study is designed to provide.This parameter space is equivalent to drops on the order of 10 microns impacting fibers with diameters on the order of 100 microns.We show that the threshold velocity of capture decreases for increasing eccentricity and increases for increasing R * .This is in agreement with results for droplet impacts where the R * <1.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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