MétaCan
Menu
Back to cohort
Record W4390660364 · doi:10.1109/access.2024.3351220

Small Modular Reactors: An Overview of Modeling, Control, Simulation, and Applications

2024· article· en· W4390660364 on OpenAlexafffund
Yulin Wang, Weiran Chen, Linxuan Zhang, Xinyu Zhao, Yiming Gao, Venkata Dinavahi

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsModular designFlexibility (engineering)Computer scienceNuclear powerMolten saltProcess engineeringNuclear reactorNuclear engineeringEnvironmental scienceEngineeringMaterials scienceOperating system

Abstract

fetched live from OpenAlex

A small modular reactor (SMR) is a nuclear reactor that is characterized by its smaller size and capacity when compared to traditional large-scale nuclear reactors. An SMR is often categorized as having an electrical output of less than 300MW and is built to be more mobile, safe, and extensible to deploy. It has been established that SMRs can provide economic and flexibility advantages in a variety of industries thanks to the development, study, and use of multiple types of SMRs in recent years. The goal of this paper is to present a comprehensive overview of several SMR types, including light water reactors (LWRs), liquid metal-cooled reactors (LMRs), molten salt reactors (MSRs), and gas-cooled reactors (GCRs). Each type of reactor will be reviewed in terms of its structural design, modeling control implementation, applications, and impacts concerning the power system.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.080
GPT teacher head0.314
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations57
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueIEEE AccessSame topicNuclear reactor physics and engineeringFrench-language works237,207