Oil Droplets Transport Under a Deep‐Water Plunging Breaker: Impact of Droplet Inertia
Bibliographic record
Abstract
Abstract Oil droplets transport in a deep‐water plunging breaker of height 0.18 m was simulated by coupling computational fluid dynamics with Lagrangian particle tracking. The Reynolds‐averaged Navier‐Stokes equations were solved in a two‐dimensional vertical slice within the computational fluid dynamics code Fluent to reproduce the movement of breaking waves in the absence of wind stress and large‐scale turbulence. The generated plunging breaker generated two additional (residual) breakers, consistent with experimental observations from the literature. The hydrodynamics of the breaker was subsequently used as input to the Lagrangian particle tracking code, NEMO3D, where the equation of motion was solved for each droplet by incorporating the major local forces including those due to the mass of the droplet. The droplet sizes were selected to vary from 100 to 600 μm. It was found that the droplet plume split into three clouds, one below and upstream of the first breaker, one below the second breaker, and one downstream of the third breaker. The largest penetration depth was within the second cloud. The largest entrainment (fraction of surface mass in the water column) occurred for the 100‐μm droplets, while the smallest entrainment occurred for the 600 μm. However, the 300 μm exhibited smaller entrainment than smaller droplets, which is due to the vortical nature of the breaker, which advected the 300 μm horizontally and then upward. This has implications on the biodegradation and dissolution of droplets of various sizes, and on the application of countermeasures such as dispersant.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".