Steam Generator Analysis Tools and Modeling of Degradation Mechanisms
Bibliographic record
Abstract
The degradation of steam generators (SGs) has a significant effect on nuclear heat transport system effectiveness and the lifetime and overall efficiency of a nuclear power plant. Hence, quantification of the effects of degradation mechanisms is an integral part of a SG degradation management strategy. Numerical analysis tools such as THIRST, a 3-dimensional (3D) thermalhydraulics code for recirculating SGs; SLUDGE, a 3D sludge prediction code; CHECWORKS a flow-accelerated corrosion prediction code for nuclear piping, PIPO-FE, a SG tube vibration code; and VIBIC and H3DMAP, 3D non-linear finite-element codes to predict SG tube fretting wear can be used to assess the impacts of various maintenance activities on SG thermal performance. These tools are also found to be invaluable at the design stage to influence the design by determining margins or by helping the designers minimize or avoid known degradation mechanisms. In this paper, the aforementioned numerical tools and their application to degradation mechanisms in CANDU® recirculating SGs are described. In addition, the following degradation mechanisms are identified and their effect on SG thermal efficiency and lifetime are quantified: primary-side fouling, secondary-side fouling, fretting wear, and flow-accelerated corrosion (FAC). Primary-side tube inner diameter fouling has been a major contributor to SG thermal degradation. Using the results of thermalhydraulic analysis and field data, fouling margins are calculated. Individual effects of primary- and secondary-side fouling are separated through analyses, which allow station operators to decide what type of maintenance activity to perform and when to perform the maintenance activity. Prediction of the fretting-wear rate of tubes allows designers to decide on the number and locations of support plates and U-bend supports. The prediction of FAC rates for SG internals allows designers to select proper materials, and allows operators to adjust the SG maintenance strategy. CANDU nuclear power plants are pressurized heavy-water reactors that differ in design from pressurized water reactors (PWRs). As a result of this difference, degradation mechanisms in PWRs might be somewhat different; for example, unlike CANDU systems, PWRs do not experience significant primary-side fouling. However, the methodologies presented in this paper are applicable to both CANDU and PWR SGs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".