Numerical Simulation of Mixing in Process Deadlegs in Order to Model Microbiologically Influenced Corrosion and Tuberculation at These Locations
Bibliographic record
Abstract
Abstract Operating experience of carbon steel process water systems has demonstrated that corrosion and tuberculation in process deadlegs can be more extensive than the corrosion and tuberculation in the flow leg of the system. The reason why corrosion is seen at these locations is unknown since deadleg lines are also considered to be stagnant. However, deadlegs can have two zones, a mixing zone and a non-mixing or stagnant zone in the deadleg. The mixing zone is created by flow in the main pipe separating at the deadleg opening and creating a vortex. The vortex created means that some mixing in the deadleg is possible having a mixing depth and flow velocity profile dependent on the flow velocity in the main pipe flow leg. This phenomenon, therefore, is referred to as turbulence penetration. The resulting mixing length defines a region of the system having a variable length where microbiologically influence corrosion (MIC) and tuberculation can occur even if the flow velocity in the main pipe flow leg is high enough to prevent corrosion product accumulation. An equation relating mixing length in a process deadleg to Reynolds number is available. It was hoped this relationship may provide a means for modelling corrosion in the different mixing zones and provide a rationale for inspection locations. Experimental tests on corrosion of carbon steel in process deadlegs, however, revealed that corrosion was still seen at locations in the deadleg beyond the expected mixing length that would be created by turbulence penetration alone. Modelling of deadlegs was therefore reviewed and computational fluid dynamics (CFD) calculations were then performed to better understand the effects of turbulence penetration and the resulting mixing length on corrosion in process deadlegs. Understanding gained from these calculations, therefore, also provide a rationale to explore additional environmental conditions resulting in materials transfer into deadlegs that could explain corrosion seen at locations where it would not be expected.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".