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Record W3214373174 · doi:10.4031/mtsj.55.5.13

Experimental Investigation of Oil Droplet Size Distribution in Underwater Oil and Oil-Air Jet

2021· article· en· W3214373174 on OpenAlexaff
Ruixue Liu, Cosan Daskiran, Fangda Cui, Wen Ji, Lin Zhao, Brian Robinson, Thomas King, Kenneth Lee, Michel C. Boufadel

Bibliographic record

VenueMarine Technology Society Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsSubseaDispersantOil dropletPetroleum engineeringJet (fluid)Environmental scienceOil spillUnderwaterMarine engineeringMaterials scienceMechanicsGeologyEngineeringDispersion (optics)PhysicsChemical engineeringOptics

Abstract

fetched live from OpenAlex

Abstract Understanding the droplet size distribution of subsea oil releasing is important to predict the subsequent transport and degradation of the spilled oil. Single- and multi-phase oil jet experiments were conducted in the Ohmsett facility, including pure oil jet and oil-air jet through a 10.7-mm pipe and a 4.7-mm pipe. Measurements of the vertical jet hydrodynamics and the oil size distribution were obtained. The reported results help to extend subsea oil spills experiment scale into a meso-scale, and the measurement range of the oil size is widened up to 2 cm. Moreover, the application of dispersant and involvement of air phase provided valuable scientific evidence for the usage of dispersants to treat oil spills. The data confirmed the effectiveness of the dispersant that reduces oil droplet size and also suggested that the participation of the gas phase facilitated the decreasing of oil droplet size. The results were compared to the numerical simulation tool VDROP-J. While the hydrodynamics showed good consistency, the system-dependent coefficient needed a slight revision to coincide with presented data. This also confirmed the significance of experimental materials for further validation and development of oil spill models, where we also provided guidance and discussion on conducting and post-processing the oil spill experiment.

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 score0.182
Threshold uncertainty score0.659

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)0.0010.000

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 head0.207
Teacher spread0.202 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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