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Record W2533024472 · doi:10.1115/icone24-60510

Study on Thermal Efficiency of SuperCritical Water NPPs

2016· article· en· W2533024472 on OpenAlexaff
Cristina Mazza, Paul Ponomaryov, Yifeng Zhou, Igor Pioro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSupercritical fluidNuclear engineeringNuclear powerEnvironmental scienceElectricity generationProcess engineeringRankine cycleTurbineThermal power stationThermal efficiencyThermalDesalinationThermal hydraulicsWaste managementPower (physics)EngineeringMechanical engineeringHeat transferChemistryThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

As the demand for emission-free energy increases, the continued improvement of Nuclear Power Plants (NPPs) and their thermal efficiencies is crucial to fulfilling that demand. Current NPPs, especially, with water-cooled reactors, have significantly lower thermal efficiencies (32–36%) compared to those of modern advanced thermal power plants (55–62%). Even Generation-III+ water-cooled NPPs will have thermal efficiencies not higher than 37–38%. Therefore, to be competitive on the energy market, new nuclear reactors and NPPs, so-called, Generation-IV concepts, should be designed and commissioned. The paper discusses the vital role that thermal efficiency plays with respect to how far nuclear reactors can be more cost efficient and competitive. An evaluation of thermal efficiencies has been carried out for SuperCritical Water (SCW) NPPs with Rankine “steam”-turbine power cycle using the IAEA DEsalination Thermodynamic Optimization Program (DE-TOP). Various options for improving thermal efficiencies of SCW NPPs have been studied. This study was performed in support of possible designs of the first experimental SCW reactors.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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