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Record W2062937499 · doi:10.1002/cjce.21821

Spray characteristics of a swirl atomiser in trigger sprayers using water–ethanol mixtures

2013· article· en· W2062937499 on OpenAlexvenueaboutno aff
Shanshan Yao, Tiegang Fang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsBreakupSauter mean diameterSpray characteristicsSurface tensionSpray nozzleMaterials scienceNozzleLigand cone angleWeber numberViscosityConical surfaceMechanicsComposite materialThermodynamicsTurbulencePhysics

Abstract

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Abstract Pressure swirl atomisers are widely used in both industry and daily life. It is critical to understand the spray transient behaviour for better design of these systems. This paper presents an experimental study of conical liquid sheets breakup from a swirl atomiser nozzle in trigger sprayers. Spray and atomisation characteristics were measured and analysed. Water–ethanol mixtures were used to simulate different fluids on the breakup and atomisation quality of the spray development process with a wide range of surface tension while maintaining relatively small changes in fluid viscosity and density. The spray images were taken by a high speed digital camera and post‐processed to analyse the global spray structure, spray cone angle, and breakup length. The droplet size and its distribution were measured using a laser diffraction technique. It was observed that the surface waves grow rapidly on the cone‐shaped liquid sheets and breakup into liquid ligaments and droplets during the initial stage of fluid dispensing. Then the spray transitions into the developed stage. Near the end of the dispensing process, the liquid cone collapses with poor atomisation (large droplets) due to momentum loss. The comparison between different fluids showed that the spray cone angle and liquid breakup length decreased with the increase of ethanol percentage ratio. The percentiles parameters, Sauter mean diameter (SMD) and particle size distribution were measured and compared for different locations. High surface tension fluids produce larger droplets than lower surface tension fluids, which have the same trend as the percentiles parameters and SMD. Results also show that droplet size and its distribution depend on the location of the measurement. Generally speaking, smaller droplet size is found for a location away from the nozzle axis in the vertical direction. In the horizontal direction, larger droplet sizes are found for a location closer to the nozzle exit. © 2013 Canadian Society for Chemical Engineering

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.436

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 head0.007
GPT teacher head0.172
Teacher spread0.165 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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