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Record W4317513968 · doi:10.1080/00295450.2022.2135334

Conceptual Design of a Micro Nuclear Reactor for Canadian Arctic Communities

2023· article· en· W4317513968 on OpenAlexafffundabout
Jordan Crowell, Eleodor Nichita

Bibliographic record

VenueNuclear Technology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNuclear engineeringEnvironmental scienceCoolantNuclear reactor coreElectricityThermal hydraulicsNatural uraniumEnriched uraniumConceptual designArcticWaste managementUraniumHeat transferMechanical engineeringEngineeringPhysicsNuclear physicsThermodynamicsGeology

Abstract

fetched live from OpenAlex

Small Canadian arctic communities rely on diesel generators for their electricity needs. Providing such generators with fuel year round presents logistical challenges because of inclement weather and the long transportation distances involved. This work presents the conceptual design of a 10-MW(thermal) microreactor that can be used to provide 3.5 MW of electricity as well as district heating to arctic communities. The reactor has a lead-cooled and graphite-moderated core with 13 vertical fuel channels containing high-assay low-enriched uranium fuel enriched to 10%. The core is enclosed in a unpressurized reactor vessel and is passively cooled through natural convection. Stirling engines are used to drive the electrical generators. The hot cylinders of the Stirling engines are located in the unpressurized reactor vessel and are heated directly by the primary coolant. Preliminary neutronic and thermal-hydraulic analyses of the core indicate that the design is technically feasible and that the reactor can function for 2 years and 9 months without refueling.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.044
GPT teacher head0.252
Teacher spread0.208 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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