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Record W2461929151 · doi:10.1115/1.4032508

Analysis and Assessment of a Gas Turbine-Modular Helium Reactor for Nuclear Desalination

2016· article· en· W2461929151 on OpenAlexaff
Farrukh Khalid, İbrahim Dinçer, Marc A. Rosen

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyNuclear engineeringElectricity generationRankine cycleDesalinationWaste heatEnvironmental scienceHeat recovery steam generatorTurbineWaste heat recovery unitProcess engineeringWaste managementThermal power stationMechanical engineeringEngineeringThermodynamicsHeat exchangerChemistryPhysics

Abstract

fetched live from OpenAlex

A thermodynamic analysis of the coupling of a reverse osmosis (RO) process with the gas turbine-modular helium reactor (GT-MHR) is presented in which the waste heat is utilized for the generation of steam as it is expanded in a steam turbine. A comprehensive parametric study is carried out to reveal the effect of some parameters such as compression ratio, turbine inlet temperature, recovery ratio, and preheated feed seawater inlet temperature on the exergy efficiencies of the RO process, electricity generation process, electricity generation without steam turbine work output, and overall system. The analysis shows that the exergy efficiency of the electric generation process is increased by 10.3%, if the waste heat from the reactor is utilized. The exergy efficiencies of the RO process, electricity generation process, electricity generation without steam turbine work output, and overall system are found to be 89.0%, 40.0%, 29.7%, and 41.0%, respectively.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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