Evolution of bubble size distribution from gas blowout in shallow water
Bibliographic record
Abstract
Abstract Gas is often emanated from the sea bed during a subsea oil and gas blowout. The size of a gas bubble changes due to gas dissolution in the ambient water and expansion as a result of a decrease in water pressure during the rise. It is important to understand the fate and transport of gas bubbles for the purpose of environmental and safety concerns. In this paper, we used the numerical model, VDROP‐J to simulate gas formation in jet/plume upon release, and dissolution and expansion while bubble rising during a relatively shallow subsea gas blowout. The model predictions were an excellent match to the experimental data. Then a gas dissolution and expansion module was included in the VDROP‐J model to predict the fate and transport of methane bubbles rising due to a blowout through a 0.10 m vertical orifice. The numerical results indicated that gas bubbles would increase the mixing energy in released jets, especially at small distances and large distances from the orifice. This means that models that predict the bubble size distribution (BSD) should account for this additional mixing energy. It was also found that only bubbles of certain sizes would reach the water surfaces; small bubbles dissolve fast in the water column, while the size of the large bubbles decreases. This resulted in a BSD that was bimodal near the orifice, and then became unimodal.
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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".