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Record W2809714535 · doi:10.1139/tcsme-2017-0125

Numerical study of the effect of gas-to-liquid ratio on the internal and external flows of effervescent atomizers

2018· article· en· W2809714535 on OpenAlexaffvenue
M. Mousavi, Ali Dolatabadi

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsConcordia University
Fundersnot available
KeywordsBody orificeMechanicsBreakupVolume of fluid methodInternal flowVolumetric flow rateFlow (mathematics)Mass flow rateMaterials scienceMass flowMass flow meterLarge eddy simulationVolume (thermodynamics)ThermodynamicsPhysicsTurbulenceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

In an effort to capture the complex evolving interface of internal and external flow in an effervescent atomizer, a compressible Eulerian method, along with the volume-of-fluid method coupled with the large eddy simulation model, are employed in a two-phase flow system. Water is injected into the atomizer with a constant mass flow rate of 0.0133 kg/s (i.e., 800 mL/min). The mass flow rate of air is adjusted to vary the gas-to-liquid ratio (GLR) from 0.55% to 2.6%. It is observed that the increase in the GLR is accompanied by an evolution of the internal flow from a complex bubbly flow to an annular flow, which consequently reduces the liquid film thickness at the discharge orifice. Further studies on the internal pressure illustrate the critical condition, which leads to choked flow and pressure oscillations at the discharge orifice. Increasing the GLR was found to affect the internal flow, resulting in changes to primary atomization parameters such as a shortening of the breakup length and a widening of the spray cone angle. The numerical predictions are in good agreement with the experimental results under the same operating conditions.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.424

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicFluid Dynamics and Heat TransferFrench-language works237,207