Oil/gas jets in water crossflow: The impact of the droplet size
Bibliographic record
Abstract
Abstract During undersea oil blowout in crossflow conditions, the oil droplets entrained horizontally which increased the residence time of droplets in the water column. Knowledge of the trajectory of an oil plume is important for predicting the pathways of hydrocarbons and to devise countermeasures. We conducted large-scale experiments in the Ohmsett tank where we released oil from a one-inch vertical orifice that was towed to produce the behavior of a jet in crossflow. The average oil velocity at the orifice was 1.36 m/s and the crossflow velocity was around 0.27 m/s which resulted in a jet-to-crossflow velocity ratio of 5.0. The results were simulated numerically using the Large Eddy Simulation (LES) turbulence model and the mixture multiphase model within the open-source software OpenFOAM. The instruments including ADVs, LISSTs, shadowgraph cameras, holographic camera, and fluorometers were employed. The oil jet released from the nozzle started to meander in the vertical direction most probably due to weak crossflow. The trajectory and meandering behavior of the oil jet, wavy pattern along the leading edge of the jet and column breakup observed in the experiments were captured well with the numerical simulation. The surface breakup just above the orifice created ligaments and droplets downstream of the jet. Larger oil droplets were observed near the upper boundary of the plume due to their higher buoyancy while the smaller droplets were suspended in the water column and they were entrained by water crossflow. This work reveals that different size of droplets determines the overall shape of plumes mostly the upper and lower boundaries of the plume.
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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 | 0.001 | 0.001 |
| 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".