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Record W2032169194 · doi:10.1115/fedsm2008-55046

Effect of Gas-Liquid Ratio on Droplets Centricity and Velocity of an Effervescent Atomizer

2008· article· en· W2032169194 on OpenAlexaff
Sina Ghaemi, David S. Nobes, Payam Rahimi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShadowgraphyShadowgraphSauter mean diameterNozzleBreakupMechanicsMaterials scienceSpray nozzleWeber numberVolumetric flow rateOpticsThermodynamicsPhysicsTurbulenceReynolds number

Abstract

fetched live from OpenAlex

Effervescent atomization is a twin fluid atomization mechanism which is based on the injection of gas into a liquid stream at an upstream location of the exit nozzle. This atomization mechanism is capable of producing droplets with Sauter Mean Diameter (SMD) comparable to other types of atomizers but at lower injection pressures. For the conditions investigated in this paper, liquid at the nozzle exit is an annular film surrounding the gas phase. A shadowgraph system is used to visualize particles shape and study droplets evolution in the spray field. Measurement of droplet SMD and centricity has been conducted at several axial and radial locations for different Gas-Liquid Ratios (GLRs). The shadowgraphy images reveal some non-spherical droplets which consist of both elliptical and coalescing droplets. Results also demonstrate that higher numbers of non-spherical droplets are observed at the near nozzle region and at higher liquid flow rate. In this work, spatial structure of the liquid phase velocity field has also been studied using a StereoPIV technique. The velocity field from StereoPIV measurements has been compared with the shadowgraphy velocity results averaged over different droplet size classes. This comparison has been conducted for the atomizer operating at different GLRs. Comparison of the results demonstrates that at the far-field, StereoPIV velocity field measurement is biased toward the velocity of droplets size classes which have relatively higher probability.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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 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

Citations6
Published2008
Admission routes1
Has abstractyes

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