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Record W1971440168 · doi:10.1115/icone12-49019

Steam Generator Analysis Tools and Modeling of Degradation Mechanisms

2004· article· en· W1971440168 on OpenAlex
M. Yetisir, John M. Pietralik, R.L. Tapping

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue12th International Conference on Nuclear Engineering, Volume 1 · 2004
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsFoulingBoiler (water heating)PipingDegradation (telecommunications)Nuclear engineeringCorrosionMaterials scienceMechanical engineeringEngineeringEnvironmental scienceProcess engineeringComputer scienceWaste managementComposite materialChemistryElectrical engineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.215
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it