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Evaluation framework for molten salt reactors and other new nuclear power reactor systems

2024· article· en· W4402695877 on OpenAlexafffund
Elliott J.T. Berg, A. Buijs

Bibliographic record

VenueNuclear Engineering and Design · 2024
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsMcMaster University
FundersUniversity Network of Excellence in Nuclear EngineeringMcMaster University
KeywordsNuclear engineeringNuclear powerMolten saltNuclear reactorMaterials scienceLiquid fluoride thorium reactorMolten salt reactorWaste managementEngineeringMetallurgyNuclear physicsPhysics

Abstract

fetched live from OpenAlex

In recent years there has been a serious effort throughout many nations to advance new nuclear power reactor designs for commercial deployment. There are many competing technologies classes and specific designs among the technologies. The primary objective of this study is to provide a framework to evaluate and ultimately optimize reactor designs, on a cost basis. Although the framework is generally technology independent, it is presented as it pertains to one particular reactor type, Molten Salt Reactors (MSRs). For MSRs the framework provides the basis from which to optimize both the salt composition and key geometric parameters . It is broad in scope and is therefore divided into several metrics of performance, direct cost, waste, safety, proliferation, modularity and feasibility (technical difficulty). This novel framework relates reactor design/construction conditions as well as specific configuration parameters to cost, thereby enriching understanding of the costs and trade-offs associated with numerous design characteristics.

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)0.017
metaresearch head score (Gemma)0.022
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: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.229
Teacher spread0.205 · 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
Published2024
Admission routes2
Has abstractyes

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