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Record W2999043799 · doi:10.12943/cnr.2019.00002

CONCEPTUAL DESIGN OF AN ORGANIC-COOLED SMALL NUCLEAR REACTOR TO SUPPORT ENERGY DEMANDS IN REMOTE LOCATIONS IN NORTHERN CANADA

2020· article· en· W2999043799 on OpenAlexaffvenueabout
Colin Shannon, Paul K. Chan, Hugues W. Bonin

Bibliographic record

VenueCNL Nuclear Review · 2020
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsNuclear engineeringBerylliumCoolantConceptual designEnvironmental scienceCriticalityNuclear powerNuclear reactorNeutron transportUraniumEnriched uraniumWaste managementMaterials scienceEngineeringMechanical engineeringNuclear physicsNeutronPhysics

Abstract

fetched live from OpenAlex

Small nuclear reactors can offer safe, reliable, and long-lasting district heating and electrical power generation to remote locations in northern Canada. A conceptual design of an organic-cooled and moderated reactor based upon the SLOWPOKE-2 research reactor is proposed for potential employment in northern Canada. For viability, this design extends the SLOWPOKE-2’s power to 1 MWth. An added pump circulates the organic coolant, a partially hydrogenated terphenyl mixture known as HB-40, to facilitate greater heat transfer. The reactor incorporates the same low-enriched uranium dioxide fuel as the SLOWPOKE-2. Reactor control is accomplished through hafnium absorber rods and a movable beryllium reflector. The reactor neutronics are simulated using the deterministic code, WIMS-AECL, and the probabilistic code, MCNP 6. The service life of fuel in this reactor operating at full power exceeds 11 years. The conceptual design has demonstrated negative reactivity coefficients indicating strong potential for inherent safety.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score0.747

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.194
Teacher spread0.172 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2020
Admission routes3
Has abstractyes

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