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Interaction of gas bubbles and oil droplets in subsea oil and gas blowouts – a new development of VDROP-J model.

2017· article· en· W2972770792 on OpenAlex
Lin Zhao, Michel C. Boufadel, Feng Gao, Thomas King, Brian Robinson, Kenneth Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsSubseaOil dropletBubblePetroleum engineeringMechanicsPlumeVolume fractionVolume (thermodynamics)Gas bubbleOil fieldEnvironmental scienceMaterials scienceChemistryGeologyThermodynamicsPhysicsGeotechnical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract (2017-194) The presence of methane bubbles in the oil and gas blowout could greatly reduce the oil droplet sizes. Bubbles tend to introduce energy into the system and separate oil droplets from each other. The interaction of oil droplets and gas bubbles in the near field of a blowout was investigated numerically using the VDROP-J model, whose droplet size distribution (DSD) was thoroughly calibrated. For this purpose, a new numerical scheme has been developed in VDROP-J to account for the interaction of gas bubbles and oil droplets in the blowout, giving simultaneous simulation of bubble and droplet size distribution along the discharged plume. Validation shows improvement of the model compared with the one without considering the gas bubble and oil droplet interactions. Effects of gas volume fraction on the droplet formation are also investigated. This new development will enhance the knowledge in subsea oil and gas blowouts.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.598

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 communication0.0000.001
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 head0.026
GPT teacher head0.262
Teacher spread0.237 · 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