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 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 piping components. The knowledge of local flow effects can be useful for developing targeted inspection plans for piping components, predicting the location of the highest FAC rate for a given piping component, and determining what piping components should be replaced. A similar evaluation applies also to FAC in heat transfer equipment such as heat exchangers and steam generators. The objective of this paper is to examine the role of flow and mass transfer in bends under FAC conditions. Bends experience increased FAC rates compared to straight pipes, and are the most common components in piping systems. When the flow effects are dominant, the FAC rate is proportional to the mass flux of ferrous ions, which, in turn, is proportional to the mass transfer coefficient in the flowing water. The mass transfer coefficient describes the intensity of the transport of corrosion products (ferrous ions) from the oxide-water interface into the bulk water. Therefore, this parameter can be used for predicting the local distribution of the FAC rate. The current paper presents plant and laboratory evidence of the relationship between local mass transfer conditions and the FAC rate in bends. It 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 artefact measurements for short-radius and long radius bends are presented. The effect of the close proximity of two bends on FAC rate is also examined based on CANDU™ NPP inspection data and compared with literature data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".