CFD-Based Erosion and Corrosion Modeling of a Pipeline with CO2-Containing Gas–Water Two-Phase Flow
Bibliographic record
Abstract
A natural gas transportation pipeline with a gas–water two-phase flow containing CO2 is prone to severe flow-assisted corrosion (FAC). The accumulation location of the water phase in the pipeline and the wall shear stress distribution are important factors affecting the severity of this phenomenon. In this work, computational fluid dynamics (CFD) simulations were performed using the realizable k-ε model and volume of fluid (VOF) model to determine the gas–water volume fraction distribution and wall shear stress in a gas–water two-phase pipeline and established a pipeline corrosion prediction model. By determining where the water phase accumulates in the pipeline, the potential corrosion area could be predicted. By alleviating the phenomena of excessive local wall shear stress and bubble cavitation, the FAC due to the formation of stress and acid gas can be controlled. The simulation results lay a certain foundation for the corrosion research of gas–liquid two-phase flow pipelines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".