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Record W2072922064 · doi:10.1155/2013/450161

Analysis of Liquid Zone Control Valve Oscillation Problem in CANDU Reactors

2013· article· en· W2072922064 on OpenAlexaffabout
Elnara Nasimi, Hossam A. Gabbar

Bibliographic record

VenueJournal of Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsControl valvesController (irrigation)Control theory (sociology)Oscillation (cell signaling)Steady state (chemistry)Power (physics)Control (management)Computer scienceFlow (mathematics)Process (computing)Control engineeringEngineeringMechanicsPhysicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

This paper looks at the existing challenges with steady-state Liquid Zone control at some CANDU (CANada Deuterium Uranium) stations, where—contrary to expectations for equilibrium flow—Liquid Zone Control Valve oscillations have proven to be a chronic, unanticipated challenge. Currently, the exact causes of this behaviour are not fully understood, although it is confirmed that the Control Valve oscillations are not due to automatic power adjustment requests or zone level changes due to process leaks. This phenomenon was analysed based on a case study of one domestic nuclear power station to determine whether it could be attributed to inherent controller properties. Next, a proposal is made in an attempt to improve current performance with minimal changes to the existing system hardware and logic using conventional technologies. Finally, a proposal was made to consider Model Predictive Control-based technology to minimize the undesirable Control Valve oscillations at steady state based on the obtained simulation results and discussion of other available alternatives.

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)0.002
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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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