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Record W4386933691 · doi:10.3390/cleantechnol5030056

Coupling a Gas Turbine Bottoming Cycle Using CO2 as the Working Fluid with a Gas Cycle: Exergy Analysis Considering Combustion Chamber Steam Injection

2023· article· en· W4386933691 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

VenueClean Technologies · 2023
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyCombined cycleSteam injectionCombustion chamberNuclear engineeringExergy efficiencySteam turbineEnvironmental scienceCombustionSteam-electric power stationWorking fluidTurbineProcess engineeringWaste managementEngineeringMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

Gas turbine power plants have important roles in the global power generation market. This paper, for the first time, thermodynamically examines the impact of steam injection for a combined cycle, including a gas turbine cycle with a two-stage turbine and carbon dioxide recompression. The combined cycle is compared with the simple case without steam injection. Steam injection’s impact was observed on important parameters such as energy efficiency, exergy efficiency, and output power. It is revealed that the steam injection reduced exergy destruction in components compared to the simple case. The efficiencies for both cases were obtained. The energy and exergy efficiencies, respectively, were found to be 30.4% and 29.4% for the simple case, and 35.3% and 34.1% for the case with steam injection. Also, incorporating steam injection reduced the emissions of carbon dioxide.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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