Computational Fluid Dynamics Study of Solids Deposition in Heavy Oil Transmission Pipeline
Bibliographic record
Abstract
Previous studies have shown that transmission-quality heavy crude oil carries water-wetted solid particles, that these particles can accumulate on the pipe floor and cause under-deposit corrosion, and that the incidence of accumulation is strongly correlated to locations downstream of over-bends. This paper describes a computational fluid dynamics (CFD) analysis of light and heavy oil flow in a representative segment of a real transmission pipeline in which corrosion has been observed. The purpose was to gain insight into the key processes affecting deposition in heavy oil that do not occur for light oil and to offer suggestions for mitigation. The analysis suggests that the key effect in determining whether particles become trapped is the near-wall velocity of the flow, which is found to be significantly lower for heavy oil compared to light oil, especially downstream of over-bends. This causes particles near the pipe floor to move slowly and makes them susceptible to becoming trapped. It is interesting that the key process affecting deposition is not the tendency of particles to fall to the pipe floor, which occurs more readily in light oil than heavy oil, but, rather, the ability of the flow to keep particles moving along the pipe floor.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".