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ICONE23-1375 Pressure-Drop Analysis of a Re-Entrant Fuel Channel In a Pressure-Channel Supercritical Water-Cooled Reactor

2015· article· en· W2654215023 on OpenAlexaff
David Kowalczyk, Fatimah Rafat, Miral Chauhan, Wargha Peiman, Igor Pioro

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPressure dropNuclear engineeringSupercritical fluidThermal hydraulicsElectricity generationInletEnvironmental scienceDrop (telecommunication)ThermalThermal power stationBundlePetroleum engineeringMechanicsMaterials scienceWaste managementEngineeringMechanical engineeringPower (physics)ChemistryHeat transferMeteorologyThermodynamics

Abstract

fetched live from OpenAlex

Modern Generation-III Nuclear Power Plants (NPPs) equipped with water-cooled reactors have gross thermal efficiencies of approximately 30-36%, which are significantly less than those of advanced fossil-fuel and natural-gas thermal power plants (55-62%). Therefore, global effort to progress Generation-IV reactor concepts and NPPs are required to meet the demand for clean, non-fossil-based electrical production. The main objective of this paper is to determine a pressure drop across a fuel channel of a SuperCritical Water-cooled Reactor (SCWR). A generic 1200-MWel SCWR has inlet and outlet temperatures of 350°C and 650°C, respectively, and an inlet pressure of 25 MPa. With such high operating temperatures and pressures, an SCWR NPP can achieve gross thermal efficiencies of approximately 45 - 48%, which is a substantial improvement over the currently operating Generation-III NPPs. For this purpose, a Re-Entrant-Channel design and its associated 78-element fuel bundle are selected as a basis for this analysis. An investigation of a pressure drop resulting from friction, gravity, acceleration and local losses at supercritical conditions has been performed on a basis of one-dimensional steady-state analysis. With this objective, a thermal-hydraulic code has been created with MATLAB, which calculates the pressure drop across a 5-m heated length of a vertical fuel channel. The total pressure drop across the channel was estimated to be about 60 kPa.

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 score0.866
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.231
Teacher spread0.197 · 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

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