MétaCan
Menu
Back to cohort
Record W4387311681 · doi:10.1080/00295450.2023.2249710

Construction of the Dynamic Model and Control System for the Canadian Supercritical Water–Cooled Reactor Power Plant

2023· article· en· W4387311681 on OpenAlexaffabout
Huirui Han, Chao Zhang

Bibliographic record

VenueNuclear Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsWestern University
Fundersnot available
KeywordsSupercritical fluidBoiler feedwaterCondenser (optics)TurbineNuclear engineeringPower stationControl systemNuclear power plantControl theory (sociology)Environmental scienceComputer scienceProcess engineeringControl (management)EngineeringMechanical engineeringChemistryPhysicsElectrical engineeringNuclear physics

Abstract

fetched live from OpenAlex

Canada has proposed the supercritical water–cooled reactor (SCWR) concept as one of the Generation IV nuclear reactors. In the SCWR power plant, the supercritical water is heated in the reactor and then flows to the turbine directly. Therefore, knowledge of the dynamic behaviors of the system is necessary for the stable operation of the power plant. There is still a lack of study on the control system for the proposed SCWR power plant. In this study, a dynamic model for the entire SCWR power plant is constructed that includes the reactor, turbine, condenser, and feedwater pump. Based on the model, the open-loop characteristics of the system when subjected to perturbations in the inputs are analyzed. Subsequently, a feedback control strategy is adopted to regulate the outputs of the system when there are disturbances. The evaluation of the performance of the control system shows that the proposed control system can return the plant back to the operating conditions effectively.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.859
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueNuclear TechnologySame topicHeat transfer and supercritical fluidsFrench-language works237,207