PREDICTION OF OIL DROPLET MOVEMENT AND SIZE DISTRIBUTION: LAGRANGIAN METHOD AND VDROP-J MODEL
Bibliographic record
Abstract
ABSTRACT (2017-306): During subsurface oil releases, oil disperses into droplets whose trajectories depend on the droplet size. We report the measurements of the droplet size distribution (DSD) obtained from the release of diesel at 135 GPM from a horizontal pipe in the Ohmsett tank. The DSD was predicted using the model VDROP-J and matched the observation. Subsequently, the movement of the droplets was tracked using a Lagrangian Particle Tracking (LPT) approach. Various forces affecting the migration of the droplets were considered, these include drag, buoyancy, lift, and added mass force. It was found that the lift force is negligible. The added mass force was negligible for droplets smaller than 500 μm. Visual observation and modeling indicated that large droplets (larger than 300 μm) tend to separate from the plume and migrate upward independently, which affects, not only the DSD of large droplets but also the resulting daughter droplets. This is an issue that has not been addressed in the literature. Our findings indicate that the DSD is needed to better predict the trajectory of oil blowouts.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".