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Record W2074335268 · doi:10.1115/icone17-75603

Thermal Fatigue in CANDU Stations

2009· article· en· W2074335268 on OpenAlexafffund
M. Yetisir

Bibliographic record

VenueVolume 1: Plant Operations, Maintenance, Engineering, Modifications and Life Cycle; Component Reliability and Materials Issues; Next Generation Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicNuclear Engineering Thermal-Hydraulics
Canadian institutionsAtomic Energy (Canada)
FundersCANDU Owners Group
KeywordsPipingNuclear engineeringThermal hydraulicsThermal stratificationThermalEnvironmental scienceBoilingThermal fatigueNuclear reactorTurbulenceHeat transferEngineeringMechanicsMeteorologyPhysicsEnvironmental engineeringThermodynamics

Abstract

fetched live from OpenAlex

The risk of thermal fatigue, and the resulting pressure boundary failures, have resulted in a significant amount of research in the Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) communities in the past two decades. Thermal stresses arising from thermal stratification phenomena are typically not taken into account in design calculations. Because various forms of thermal fatigue events occurred in safety-related piping, non-design basis thermal fatigue has been the subject of a number of U.S. NRC bulletins that led to a significant amount of research by the nuclear industry. This paper discusses the relevance of known thermal fatigue mechanisms to the CANDU® design (a pressurized heavy water reactor), and thermal fatigue operational experience in CANDU plants. These mechanisms are various forms of thermal stratification phenomena, turbulence penetration and large-scale temperature oscillations in mixing fluid streams.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.026
GPT teacher head0.220
Teacher spread0.194 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueVolume 1: Plant Operations, Maintenance, Engineering, Modifications and Life Cycle; Component Reliability and Materials Issues; Next Generation SystemsSame topicNuclear Engineering Thermal-HydraulicsFrench-language works237,207