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Record W1979348594 · doi:10.1115/icone17-75448

Structural and Neutronic Assessment of Calandria Tube Enhancement Design Options

2009· article· en· W1979348594 on OpenAlexafffundabout
Richard C. Scrannage, Farshad Sani, Daniel M. Hobbs, Allan M. Freeburn, Glenn Harvel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech University
FundersBruce Power
KeywordsNuclear engineeringNeutron transportMaterials scienceCreepStructural engineeringNeutronEngineeringComposite materialNuclear physics

Abstract

fetched live from OpenAlex

Over the lifetime of a CANada Deuterium Uranium (CANDU) Type Reactor, the pressure tubes and calandria tubes deform via creep and sag, eventually requiring replacement. The sagging phenomena leads to fuelling issues, potential contact between the Calandria Tube (CT) and the liquid injection shutdown system or the Pressure Tube (PT). As such, this ageing phenomenon limits the lifetime of the components. Conceptual designs were assessed to determine their potential for reducing the effects of aging by minimizing the magnitude of sagging. Three different design options for a CT have been investigated using numerical simulation techniques. The CT design options include fins and ribs of different sizes and combinations. The fins and ribs provide improved structural integrity over the reactors lifetime. Analyses results have shown that the design options yield an increased overall strength with a minimal impact on neutronics. The analysis has determined that the finned design option is superior in terms of CT strength enhancement and minimizing the impact on neutronics. Furthermore moderator displacement analysis for the finned design option showed an ability to minimize the impact on moderation or even cause moderator displacement potentially decreasing the positive void coefficient.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.254

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2009
Admission routes3
Has abstractyes

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