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Record W2017877714 · doi:10.1115/icone18-29780

Thermal-Hydraulic Modeling of CANDU-SCWR and Linear Dynamic Model Development

2010· article· en· W2017877714 on OpenAlexafffundabout
Peiwei Sun, Jin Jiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear Engineering
KeywordsThermal hydraulicsNuclear engineeringBoundary (topology)ThermalPoint (geometry)Computer scienceEngineeringMechanicsHeat transferPhysicsMathematics

Abstract

fetched live from OpenAlex

CANDU-SCWR is one Generation IV reactor being developed in Canada. Significant amount of efforts has been made to develop CANDU-SCWR. Little work has been done on the dynamic analysis and control design. To observe the dynamic behaviours of CANDU-SCWR, the detailed CANDU-SCWR thermal-hydraulic model is developed. The movable boundary method is adopted for CANDU-SCWR thermal-hydraulic modeling. The benefits of adopting movable boundary are derived from the comparisons with the fixed boundary method. The steady-state results agree well with the design data. The responses of CANDU-SCWR reactor to different disturbances are simulated and analyzed and the results are reasonable in theory. Linear dynamic models are derived from simulation data of CANDU-SCWR thermal-hydraulic model around the design operating point using a system identification technique to facilitate the control system design. The linear dynamic models are validated and it is shown that they can describe the dynamic characteristics of CANDU-SCWR around the design point accurately.

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 score0.060
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.184
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

Citations4
Published2010
Admission routes3
Has abstractyes

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