Numerical and experimental modeling of two leaks behavior for water-air multiphase flow through a pipeline
Bibliographic record
Abstract
Given that most petroleum companies simultaneously produce and transport both oil and gas, multiphase flows play a crucial role in the oil and gas industry. Common causes of pipeline leaks include corrosion, aging, and metal deterioration. When an incident occurs, the energy company not only incurs financial losses but also triggers safety and environmental concerns. Consequently, the development of a practical technique for simultaneously detecting leaks in pipelines becomes imperative. In the present study, a 3D numerical model is developed using Ansys-Fluent to investigate simultaneous leaks (the first leak measuring 3 mm and the second leak measuring 1.8 mm) within a pipeline. The numerical results are validated against experimental data collected from a flow loop system in the laboratory. Furthermore, the flow behavior in the pipeline and the surrounding area of the leaks is evaluated. For example, it is observed that the escape velocity of the gas phase through the leaks initially decreases significantly before gradually reaching a stable value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".