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Record W2050989480 · doi:10.1115/fedsm2014-21925

Investigation of Two-Phase Flow in an Effervescent Atomizer

2014· article· en· W2050989480 on OpenAlexafffund
Mona Hassanzadeh Jobehdar, Aly H. Gadallah, Kamran Siddiqui, Wajid A. Chishty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsNational Research Council CanadaWestern University
FundersNational Research Council Canada
KeywordsMixing (physics)Base (topology)AerationMaterials scienceBubbleConical surfaceMechanicsFlow (mathematics)Trailing edgeCombustionTube (container)TurbineCombustion chamberPhase (matter)Analytical Chemistry (journal)ChemistryMechanical engineeringChromatographyComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Aerated-liquid atomization, also called “effervescent atomization”, is a technique that has a wide range of applications such as gas turbine combustors, internal combustion engines, furnaces and burners, and pharmaceutical sprays. We report on an experimental study conducted to investigate the two-phase flow in an Effervescent atomizer. A novel aerator tube base was implemented and tested. It is observed that the novel configuration suppresses the separation bubble at the trailing edge and results in more uniform and smaller bubbles compared to the standard flat base aerator. It has been found that the more uniform and smaller bubbles are generated as the mixing chamber length is reduced. It is concluded that by using a conical base aerator and by reducing the mixing chamber length, the spray steadiness and the atomization process can be significantly improved.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.235
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 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
Published2014
Admission routes2
Has abstractyes

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