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Record W1970530619 · doi:10.1299/jpes.2.874

SCW Pressure-Channel Nuclear Reactor Some Design Features

2008· article· en· W1970530619 on OpenAlexaffabout
Igor Pioro, Mosin KHAN, Victory HOPPS, Chris Jacobs, Ruban PATKUNAM, Sandeep GOPAUL, Kurtulus Bakan

Bibliographic record

VenueJournal of Power and Energy Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNuclear engineeringSupercritical fluidNuclear powerEnvironmental scienceNuclear reactorElectricity generationMaterials scienceProcess engineeringPower (physics)ChemistryThermodynamicsNuclear physicsEngineering

Abstract

fetched live from OpenAlex

Concepts of nuclear reactors cooled with water at supercritical pressures were studied as early as the 1950s and 1960s in the USA and Russia. After a 30-year break, the idea of developing nuclear reactors cooled with SuperCritical Water (SCW) became attractive again as the ultimate development path for water cooling. The main objectives of using SCW in nuclear reactors are: 1) to increase the thermal efficiency of modern Nuclear Power Plants (NPPs) from 30-35% to about 45-48%, and 2) to decrease capital and operational costs and hence decrease electrical energy costs (∼$1000 US/kW or even less). SCW NPPs will have much higher operating parameters compared to modern NPPs (pressure about 25 MPa and outlet temperature up to 625°C), and a simplified flow circuit, in which steam generators, steam dryers, steam separators, etc., can be eliminated. Also, higher SCW temperatures allow direct thermo-chemical production of hydrogen at low cost, due to increased reaction rates. Pressure-tube or pressure-channel SCW nuclear reactor concepts are being developed in Canada and Russia for some time. Some design features of the Canadian concept related to fuel channels are discussed in this paper. The main conclusion is that the development of SCW pressure-tube nuclear reactors is feasible and significant benefits can be expected over other thermal-energy systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.013
GPT teacher head0.190
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 designBench or experimental
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

Citations17
Published2008
Admission routes2
Has abstractyes

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