Flow and Mass Transfer in Bends Under Flow-Accelerated Corrosion Wall Thinning Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it