Numerical Simulation of Submarine Pipeline Self-Buried on Sediment Seabed
Bibliographic record
Abstract
With the process of the exploitation of the petroleum resources inland, people begin to focus on petroleum buried in the subsea. Now about 100 countries around the world are taking steps to prospecting petroleum offshore, in the meantime, the submarine pipeline plays the role of important tool to transport crude oil, it is not only convenient but also be able to transport largely continuously. In order to guarantee the submarine pipeline get rid of waves, tide, ocean current and other oceanic hydrometric elements and keep the balance on location, usually we should put it buried. The common approach is to dig it and backfill then. In the process, the trencher is necessary. To save more extra expense, people take the measure to load a setting above the bottom hole flow bean which is similar to the fins[1] to realize the burial of the pipeline itself. Here we adopt the bottom hole flow bean technology that China Hangzhou Bay submarine pipeline has utilized to reach the aim of the burial of the pipeline as the realistic basis, what is more, we use the numerical simulation software, FLUENT, aims at the submarine pipeline self-buried on sediment seabed. By the ways of analyzing the modality of the scour hole when there exists bottom hole flow bean or not, the pressure coefficient of the surface of the pipeline, the situation of the flow below the pipeline, to tell the influence of the bottom hole flow bean to the submarine pipeline self-buried. Based on it, we have simulated the process of the submarine pipeline self-buried. Through the simulation we can get to know the erosion and deposition situation of the washings around in the process of falling down so as to show the self-buried of the pipeline which is under the influence of bottom hole flow bean.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".