Flow and Mass Transfer in Bends Under Flow-Accelerated Corrosion Wall Thinning Conditions
Bibliographic record
Abstract
The three groups of parameters that affect flow-accelerated corrosion (FAC) are the flow conditions, water chemistry, and materials. Nuclear power plant (NPP) data and laboratory tests confirm that, under alkaline water chemistry, there is a close relationship between local flow conditions and FAC rates in the piping components. The knowledge of the local flow effects can be useful for developing targeted inspection plans for piping components and predicting the location of the highest FAC rate for a given piping component. A similar evaluation applies also to the FAC in heat transfer equipments such as heat exchangers and steam generators. The objective of this paper is to examine the role of the flow and mass transfer in bends under alkaline FAC conditions. Bends experience increased FAC rates compared with straight pipes, and are the most common components in piping systems. This study presents numerical simulations of the mass transfer of ferrous ions and experimental results of the FAC rate in bends. It also shows correlations for mass transfer coefficients in bends and reviews the most important flow parameters affecting the mass transfer coefficient. The role of bend geometry and, in particular, the short and long radii, surface roughness, wall shear stress, and local turbulence, is discussed. Computational fluid dynamics calculations and plant artifact measurements for short- and long-radius bends are presented. The effect of the close proximity of the two bends on the FAC rate is also examined based on CANDU (CANDU is a registered trademark of the Atomic Energy of Canada Limited) NPP inspection data and compared with literature data.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".