Flow-Accelerated Corrosion Susceptibility Prediction of Recirculating Steam Generator Internals
Bibliographic record
Abstract
Steam generator (SG) components are subjected to corrosive solutions in turbulent flow. Under such conditions, actual component lifetimes may be significantly reduced from their original design lifetimes. Premature replacement of steam generator components before their expected lifetime can be very expensive. Furthermore, degradation of essential components can reduce the steam generator efficiency, thus reducing net profits. Moreover, a SG failure can also be a safety issue. One of the degradation mechanisms affecting secondary-side SG internal structural elements, which are referred to as internals, is Flow-Accelerated Corrosion (FAC). The susceptibility to FAC depends on flow parameters, water chemistry, and materials. All SG internals made of carbon steel are susceptible to FAC to varying degrees. For FAC susceptibility prediction, flow velocity, pH, and oxygen distributions are needed. SG codes, including THIRST (Thermal Hydraulic analysis In STeam generators, a computer code developed by AECL), traditionally solve for thermalhydraulic parameters. A new chemistry module has been added to THIRST, which now makes this code useful for the prediction of local water chemistry parameters in the SG. The THIRST chemistry module is comprised of a multicomponent, multiphase mass transport model coupled with a multiphase chemical equilibrium model. As input, the module requires amine concentrations in the feedwater and reheater drains. The module predicts local distributions of amine concentration in the secondary side. The concentrations predicted by the module are used to compute the pH. The chemistry module was verified against results of other work in the literature and against station blowdown data. Flow and chemistry predictions of THIRST were used to predict FAC susceptibility for internals of a SG with an integral preheater and a SG without it. Ranking of SG locations in order of FAC susceptibility was estimated from an empirical, Kastner-Riedle model. The most susceptible internals are predicted to be those in the upper section of the hot side and those on the cold side that are near the SG centre, while SG lower regions, including the integral preheater, if one exists, are better protected.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".