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Investigation of the feasibility of DRAGON/DONJON codes with CARR neutronics calculations

2024· article· en· W6959887307 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsNeutron transportNeutron fluxMonte Carlo methodHomogenization (climate)Nuclear reactor coreNeutronNuclear dataInverseNeutron source

Abstract

fetched live from OpenAlex

BackgroundThe unique core and reflector structure of the inverse flux trap research reactors has raised a challenge to the traditional deterministic neutronics calculation methods applicable to power reactors. The deterministic codes DRAGON/DONJON with powerful geometric modeling capabilities have been maturely applied to power reactor types such as CANDU (Canadian Deuterium Uranium) and pressurized water reactors (PWR), but not been performed neutronics calculations and feasibility analysis on the China Advanced Research Reactor (CARR) with inverse flux trap design.PurposeThis study aims to verify the feasibility of the DRAGON/DONJON codes in CARR neutronics calculations and analysis.MethodsFirstly, when performing homogenization calculation using DRAGON/DONJON codes on various assemblies of CARR, the multi-assembly method was adopted to improve the surrounding impact. The OPTEX reflector optimization method was used to modify the homogenization constants of the reflector. Then, the commonly used multigroup cross-section libraries in DARGON were compared and screened, and SHEM-295 group-structure library was selected. Finally, the calculation results of DRAGON/DONJON codes were compared with the Monte Carlo references and the conventional three-step method, and analysis was conducted on the reasons for the significant deviation in the calculation results.ResultsThe results indicate that the deviation of parameters such as keff eigenvalue near critical operating condition, thermal neutron flux distribution in the active zone of the core and the middle position of the heavy water tank, and power distribution of standard fuel assemblies are relatively small whilst significant deviations in the calculation results appear at the junction of the core and heavy water tank, the vacuum boundary outside the pool, and the follower assemblies.ConclusionsThis study verifies that it is feasible to use the DRAGON/DONJON codes for CARR neutronics calculations and achieve a certain degree of accuracy, meeting the needs of experimental schemes design and rapid calculation and analysis of operating parameters.

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)0.005
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.262
GPT teacher head0.479
Teacher spread0.217 · 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
Published2024
Admission routes1
Has abstractyes

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