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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.864
Threshold uncertainty score0.196

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)0.0000.000

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 head0.005
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