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Record W4405179301 · doi:10.1109/mele.2024.3473249

Real-Time Digital Twin: An advanced concept for modeling small modular reactors.

2024· article· en· W4405179301 on OpenAlexaff
Weiran Chen, Venkata Dinavahi

Bibliographic record

VenueIEEE Electrification Magazine · 2024
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEmulationModular designContext (archaeology)Computer scienceField (mathematics)Systems engineeringEmbedded systemComputer architectureEngineeringOperating system

Abstract

fetched live from OpenAlex

This paper presents a brief overview of the development history, advantages, disadvantages, and application scenarios of small modular reactors (SMRs). It explores relevant simulation technologies, human–system interfaces (HSIs), and instrumentation and control (I&C) systems. Particular attention is given to the integration of advanced digital technologies, including digital twins (DTs) and hardware-in-the-loop (HIL) real-time emulation, in the context of SMRs. Finally, a digital twin emulation prototype of a small modular light-water reactor (smLWR) is provided, which is hoped to offer some modest assistance to peers in the field.

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 categoriesMeta-epidemiology (narrow)
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 score0.722
Threshold uncertainty score1.000

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 communication0.0000.002
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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