Experimental Investigation of Oil Droplet Size Distribution in Underwater Oil and Oil-Air Jet
Bibliographic record
Abstract
AbstractUnderstanding 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".