Fouling Of Nuclear Steam Generators: Fundamental Studies, Operating Experience and Remedial Measures Using Chemical Additives
Bibliographic record
Abstract
Fouling remains a potentially serious issue that if left unchecked can lead to degradation of the safety and performance of nuclear steam generators (SGs). It has been demonstrated that the majority of the corrosion product transported with the feed water to the SGs accumulates in the SG on the tube-bundle. By increasing the risk of tube failure and acting as a barrier to heat transfer, deposit on the tube bundle has the potential to impair the ability of the SG to perform its two safety-critical roles: provision of a barrier to the release of radioactivity from the reactor coolant and removal of heat from the primary coolant during power operation and under certain post accident scenarios. Thus, it is imperative to develop improved ways to mitigate SG fouling for the long-term safe, reliable and economic performance of nuclear power plants (NPPs). This paper provides an overview of our current understanding of the mechanisms by which deposit accumulates on the secondary side of the SG, how this accumulation affects SG performance and how accumulation of deposit can be mitigated using chemical additives to the secondary heat-transport system. The paper concludes with some key questions that remain to be addressed to further advance our knowledge of deposit accumulation and how it can be controlled to maintain safe, economic performance of nuclear 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".