Simulating Liquid Pipeline Flow Using the Rotating Cage Method
Bibliographic record
Abstract
Abstract The Rotating Cage is a standardized methodology for investigating the corrosion of metals under flowing conditions. As such, it can be used as a comparative method, for screening inhibitors or identifying the differences in the corrosion-inhibitory properties of different crude oils, as well as simulating flowing pipeline hydrodynamics. It is a complimentary technique to the rotating cylinder electrode and jet impingement method. Whilst it does not permit in situ measurements, it has a distinct advantage over other methods: whilst average corrosion rates are determined through mass loss, the relatively large surface area of the specimens permits statistical analysis of localized corrosion phenomena, monitored through techniques such as laser profilometry. In this article, we seek to build upon earlier work, both experimental and theoretical, in order to better understand the fluid dynamics of the rotating cage method. Computational fluid dynamics (CFD) simulations were used to conduct a parametric study that investigated the dependence of the wall shear stress as a function of several variables, including: rotational velocity, temperature, fluid viscosity and density, for the standardized rotating cage test equipment. The wall shear stress is commonly used to relate experimental test conditions to flowing pipelines, thus the current study confirms the value of the rotating cage method in simulating pipeline flow.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".