Evaluation Methods for Corrosion Damage of Components in Cooling Systems of Nuclear Power Plants by Coupling Analysis of Corrosion and Flow Dynamics (III)
Bibliographic record
Abstract
Flow accelerated corrosion (FAC) is divided into two processes: a corrosion (chemical) process and a flow dynamics (physical) process. The former is the essential process to cause FAC and the latter is the accelerating process to enhance FAC occurrence. The chemical process in the surface boundary layer is analyzed to evaluate FAC rate. Contributions of flow dynamics on wall thinning rate due to FAC are expressed as a function of mass transfer coefficient but not that of flow velocity. FAC evaluation procedures were divided into 5 steps as follows. (1) Flow pattern and temperature in each elemental volume along the flow path were obtained with 1D computational flow dynamics (CFD) codes, (2) corrosive conditions, e.g., oxygen concentration and electrochemical corrosion potential (ECP) along the flow path were calculated with a hydrazine oxygen reaction code, (3) precise flow patterns and mass transfer coefficients at the structure surface were calculated with 3D CFD codes, (4) danger zones were evaluated by coupling major FAC parameters, and then, (5) wall thinning rates were calculated with the coupled model of static electrochemical analysis and dynamic double oxide layer analysis at the identified danger zone. Anodic and cathodic current densities and ECPs were calculated with the static electrochemistry model and ferrous ion release rate determined by the anodic current density was used as input for the dynamic double oxide layer model. Thickness of the oxide film and its characteristics determined by the dynamic double oxide layer model were used for the electrochemistry model to determine the resistances of cathodic current from the bulk to the surface and anodic current from the surface to the bulk. Two models were coupled to determine local corrosion rate and ECP for various corrosive conditions. The calculated results of the coupled models had good agreement with the measured ones.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".