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Record W2408323738 · doi:10.1115/1.4036354

MCNP Simulation of In-Core Dose Rates for an Offline CANDU® Reactor

2017· article· en· W2408323738 on OpenAlexafffund
Jordan G. Gilbert, Scott Nokleby, Ed Waller

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2017
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech UniversityArcadis (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear EngineeringAtomic Energy of Canada LimitedUniversity of Ontario Institute of Technology
KeywordsShutdownNuclear engineeringMonte Carlo methodComputer scienceCore (optical fiber)Reliability engineeringShieldChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

Inspections of pressure tubes in CANDU® reactors are a key part of maintaining safe operating conditions. The current inspection system, the channel inspection and gauging apparatus for reactors (CIGAR), performs the job well but is limited by the fact that it can only inspect one channel at a time. A multidisciplinary team is currently developing a novel robotic inspection system. As part of this work, a Monte Carlo N-particle (MCNP) model has been developed in order to predict the dose rates that the improved inspection system will be exposed to and, from this, predict the component lifetime. This MCNP model will be capable of predicting in-core dose rates at any location within the reactor, and as such could be used for other situations where the in-core dose rate needs to be known. Based on estimates from this model, it is expected that at 7 days after shutdown, the improved inspection system could survive in core for approximately 7 h, providing it uses a tungsten shield 2.5 cm in thickness around the integrated circuit components. This is expected to be sufficient to perform a single inspection.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.297

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 communication0.0000.001
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 head0.028
GPT teacher head0.299
Teacher spread0.271 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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