The effect of pressure variation on droplet size distribution of dispersed oil under simulated deep-water conditions
Bibliographic record
Abstract
Droplet size distribution of dispersed oil in deep-water is critical to the transport and biodegradation of spilled oil in deep-sea. Few studies have focused on the effects of pressure on chemically dispersed oil through experiments. This study thus simulated how the crude oil homogenously pre-dispersed by Corexit 9500A using baffled flasks would behave after being exposed to deep-water conditions. Key factors included dispersant-to-oil ratio (DOR), mixing energy (energy dissipation rate and Kolmogorov microscale), and pressure (up to 150 bar). The variations of pressure were demonstrated to have insignificant effects on the size distribution of pre-dispersed oil. Both the average and medium droplet sizes were correlated negatively with DOR and mixing energy in an established model with a p-value ≤ 0.0011. The log-normal and log-logistic distributions provided a reasonable fit to simulate the droplet size distribution. The two parameters of log-logistic distribution were dependent on DOR and mixing energy with a p-value < 0.005. The results would be valuable to advance the understanding of the behaviours and trajectories of chemically dispersed oil under deep-water conditions. The research helped provide more scientific evidence to improve the understanding of dispersed oil behaviours under high pressure and support deep-sea oil spill research and potential extension of the existing results from shallow water to deep water conditions.
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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 | 0.001 |
| 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".