CFD Application to Flow-Accelerated Corrosion in Feeder Bends
Bibliographic record
Abstract
Feeder piping in CANDU® plants experiences a thinning degradation mechanism called Flow-Accelerated Corrosion (FAC). The piping is made of carbon steel and has high water flow speeds. Although the water chemistry is highly alkaline with room-temperature pH in a range of 10.0–10.5, the piping has FAC rates exceeding 0.1 mm/year in some locations, e.g., in bends. One of the most important parameters affecting the FAC rate is the mass transfer coefficient for convective mass transport of ferrous ions. The ions are created at the pipe wall as a result of corrosion, diffuse through the oxide layer, and are transported from the oxide-layer/water interface to the bulk water by mass transport. Consequently, the local flow characteristics contribute to the highly turbulent convective mass transfer. Plant data and laboratory experiments indicate that the mass transfer step dominates FAC under feeder conditions. In this study, the flow and mass transfer in a feeder bend under operating conditions were simulated using the Fluent™ computer code. Because the flow speed is very high, with the Reynolds numbers in a range of several millions, and because the geometry is complex, experiments in a 1:1 scale were conducted with the main objective to validate flow simulations. The experiments measured pressure at several key locations and visualized the flow. The flow and mass transfer models were validated using available friction-factor and mass transfer correlations and literature experiments on mass transfer in a bend. The validation showed that the turbulence model that best predicts the experiments is the realizable k-ε model. Other two-equation turbulence models, as well as one-equation models and Reynolds stress models were tried. The near-wall treatment used the non-equilibrium wall functions. The wall functions were modified for surface roughness when necessary. A comparison of the local mass transfer coefficient with measured FAC rate in plant specimens shows very good agreement. Visualization experiments indicate secondary flows in the bends. No boundary layer separation was observed in experiments or in simulations.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".