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Record W2173122211 · doi:10.5539/mer.v5n2p89

Thermodynamic Analysis of Different Configurations of Combined Cycle Power Plants

2015· article· en· W2173122211 on OpenAlexvenueno aff
Munzer S. Y. Ebaid, Qusai Z. Al-Hamdan

Bibliographic record

VenueMechanical Engineering Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCombined cycleThermal efficiencyGas turbinesTurbineWork outputThermodynamic cycleWork (physics)Thermal power stationEnvironmental scienceNuclear engineeringMaterials scienceMechanical engineeringThermodynamicsEngineeringChemistryPhysicsCombustion

Abstract

fetched live from OpenAlex

Several modifications have been made to the simple gas turbine cycle in order to increase its thermal efficiency but within the thermal and mechanical stress constrain, the efficiency still ranges between 38 and 42%. The concept of using combined cycle power or CPP plant would be more attractive in hot countries than the combined heat and power or CHP plant. The current work deals with the performance of different configurations of the gas turbine engine operating as a part of the combined cycle power plant. The results showed that the maximum CPP cycle efficiency would be at a point for which the gas turbine cycle would have neither its maximum efficiency nor its maximum specific work output. It has been shown that supplementary heating or gas turbine reheating would decrease the CPP cycle efficiency; hence, it could only be justified at low gas turbine inlet temperatures. Also it has been shown that although gas turbine intercooling would enhance the performance of the gas turbine cycle, it would have only a slight effect on the CPP cycle performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.035
GPT teacher head0.296
Teacher spread0.262 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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