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Record W3216329159 · doi:10.1017/jfm.2021.1002

Computational and experimental study of an oil jet in crossflow: coupling population balance model with multifluid large eddy simulation

2021· article· en· W3216329159 on OpenAlexaff
Cosan Daskiran, Fangda Cui, Michel C. Boufadel, Ruixue Liu, Lin Zhao, Tamay M. Özgökmen, Scott A. Socolofsky, Kenneth Lee

Bibliographic record

VenueJournal of Fluid Mechanics · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsPlumeMechanicsLarge eddy simulationJet (fluid)Population balance equationOil dropletReynolds numberPopulationMeteorologyGeologyEnvironmental scienceMaterials sciencePhysicsTurbulenceChemistry

Abstract

fetched live from OpenAlex

Understanding the size of oil droplets released from a jet in crossflow is crucial for estimating the trajectory of hydrocarbons and the rates of oil biodegradation/dissolution in the water column. We present experimental results of an oil jet with a jet-to-crossflow velocity ratio of 9.3. The oil was released from a vertical pipe 25 mm in diameter with a Reynolds number of 25 000. We measured the size of oil droplets near the top and bottom boundaries of the plume using shadowgraph cameras and we also filmed the whole plume. In parallel, we developed a multifluid large eddy simulation model to simulate the plume and coupled it with our VDROP population balance model to compute the local droplet size. We accounted for the slip velocity of oil droplets in the momentum equation and in the volume fraction equation of oil through the local, mass-weighted average droplet rise velocity. The top and bottom boundaries of the plume were captured well in the simulation. Larger droplets shaped the upper boundary of the plume, and the mean droplet size increased with elevation across the plume, most likely due to the individual rise velocity of droplets. At the same elevation across the plume, the droplet size was smaller at the centre axis as compared with the side boundaries of the plume due to the formation of the counter-rotating vortex pair, which induced upward velocity at the centre axis and downward velocity near the sides of the plume.

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 score0.043
Threshold uncertainty score0.308

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 head0.012
GPT teacher head0.272
Teacher spread0.261 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

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