MétaCan
Menu
Back to cohort

ICONE23-1727 Thermal-Hydraulic and Neutronic Analysis of a Re-Entrant Pressure-Channel Supercritical Water-cooled Reactor

2015· article· en· W2633454362 on OpenAlexaff
Wargha Peiman, Igor Pioro, Kamiel Gabriel

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2015
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsThermal hydraulicsNuclear engineeringCoolantPressure dropSupercritical fluidMaterials scienceThermal conductivityHeat transferMechanicsThermodynamicsMechanical engineeringEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The objective of this paper is a study on thermal-hydraulic and neutronic aspects of a pressure-channel Supercritical Water-cooled Reactor (SCWR) with a focus on determination of fuel and cladding/sheath temperatures as well as a pressure drop across a fuel channel. With these objectives, a thermal-hydraulic code has been developed in MATLAB, which calculates a fuel centerline temperature, sheath temperature, coolant temperature and heat-transfer-coefficient profiles. The developed thermal-hydraulic code is coupled with a lattice code and a diffusion code. The neutronic codes were used in order to determine a power distribution inside the core. This paper presents a fuel centerline temperature of a 73-element fuel bundle with UO_2 as a reference fuel, while results are presented for high thermal-conductivity fuels such as UC and UO_2+SiC. The results show that the maximum fuel centerline temperature is significantly lower for high thermal-conductivity fuels. The total pressure drop varied between 108 to 133 kPa per fuel channel.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.602

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 science0.0010.000
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 head0.025
GPT teacher head0.207
Teacher spread0.183 · 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.

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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Proceedings of the International Conference on Nuclear Engineering (ICONE)Same topicNuclear reactor physics and engineeringFrench-language works237,207