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Record W4390660364 · doi:10.1109/access.2024.3351220

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

2024· article· en· W4390660364 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.549
Threshold uncertainty score0.368

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 science0.0000.000
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 head0.080
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