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RADIONUCLIDE INVENTORY CALCULATIONS FOR DEPLETED FUEL IN A SMALL FLUORIDE MOLTEN SALT REACTOR AND A MICRO-SIZED HIGH-TEMPERATURE GAS-COOLED REACTOR

2023· article· en· W4388965868 on OpenAlexaffabout
Xiaolin Wang, Sinh T. Nguyen

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2023
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsRadionuclideNuclear engineeringMolten salt reactorEnvironmental scienceRadioactive wasteResearch reactorWaste managementModular designSpent nuclear fuelNeutron transportMolten saltNeutronMaterials scienceNuclear physicsEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

The small modular reactor (SMR) is considered to be an enabling technology for providing economical and clean energy in remote areas in Canada. To ensure the SMR technology is developed within a robust framework that addresses environmental and waste management concerns, data are required on radionuclide inventory and characteristics of SMR depleted fuel at the end of reactor service life and at various times thereafter. These data provide essential inputs to assessment of fuel recycle analysis, understanding of environmental impact, and strategy development for waste disposal and management. In this paper, radionuclide inventories of depleted fuel in a small fluoride molten salt reactor (sm-FMSR) and a micro-sized high-temperature gas-cooled reactor (m-HTGR) are calculated using the Monte Carlo neutron transport code Serpent and the point neutron activation and decay code SCALE/ORIGEN. The inventory calculation methods for two selected small modular reactors are described, and radionuclide inventory results from Serpent and ORIGEN are compared. Overall, ORIGEN produces more conservative results than Serpent for both sm-FMSR and m-HTGR. The major characteristics of the radionuclide inventories are discussed for both SMRs.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.827

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 communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.206
Teacher spread0.185 · 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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

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