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Comparison between RELAP/SCDAPSIM/MOD3.4 and MOD3.6+ in Simulating a CANDU6 SBO

2021· article· en· W3216529875 on OpenAlexaboutno aff
Roxana-Mihaela Nistor-Vlad, Daniel Dupleac, Andrei-Răzvan Budu-Stănilă, Victoria Dumitrescu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsBlackoutNuclear engineeringAccident managementEnvironmental scienceCore (optical fiber)Nuclear reactor coreNuclear reactorComputer scienceEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

Romania, as a country that currently operates two CANDU 6 (CANada Deuterium Uranium reactor) units, has extensively investigated the severe accident progression, including studies on the severe accident management measures to be accounted in order to limit the core damage. Most analyses of CANDU 6 SA were performed using RELAP / SCDAPSIM / MOD3.4 a thermalhydraulic and nuclear safety analysis code which was initially designed for Light Water Reactors. A new version of the code has been developed, MOD3.6+, which includes mechanistic models specific to CANDU reactors core degradation under severe accident conditions. In this paper we introduce the best-estimate analysis results from the SA accident analysis for a CANDU 6 reactor using MOD3.6+ with the mechanistic models for fuel channel failure and core disassembly in comparison with the previous results from conservative analyses using conservative assumptions for the phenomena during a Station BlackOut accident.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.305
Teacher spread0.247 · 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 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
Published2021
Admission routes1
Has abstractyes

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