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Record W2734683950 · doi:10.1002/aic.15864

Oil jet with dispersant: Macro‐scale hydrodynamics and tip streaming

2017· article· en· W2734683950 on OpenAlex

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

VenueAIChE Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsDispersantUnderwaterPetroleum engineeringPlumeOil dropletOil spillJet (fluid)Environmental scienceJet fuelWork (physics)Scale (ratio)Marine engineeringMechanicsDispersion (optics)GeologyPhysicsAerospace engineeringEngineeringMeteorologyChemical engineeringMechanical engineeringOpticsOceanography

Abstract

fetched live from OpenAlex

Modeling the movement of oil released underwater is a challenging task due to limitations in measuring the hydrodynamics in an oil‐water system. In this work, we conducted an experiment of horizontal release of oil without and with dispersant. The model VDROP‐J was used and compared to the model JETLAG, a miscible plume trajectory model. Both models were found to reproduce the oil jet hydrodynamics for oil without and with dispersant. The predicted DSD from VDROP‐J matched closely observation for untreated oil. For oil with dispersant, experimental results have shown evidence that tip streaming occurred. For this purpose, a new conceptual module was developed in VDROP‐J to capture the tip streaming phenomenon and an excellent match was achieved with observation. This study is the first to report tip streaming occurring in underwater oil jets, which should have consequences on predicting the DSD when dispersant are used on an underwater oil release. © 2017 American Institute of Chemical Engineers AIChE J , 2017

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.442

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 head0.004
GPT teacher head0.192
Teacher spread0.187 · 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