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Record W2207815031

Monte Carlo Simulation for Process Heat Cogeneration System

2013· article· en· W2207815031 on OpenAlexvenueno aff
Shweta Agrawal, Sudhansu Maharana

Bibliographic record

VenueMechanical Engineering Research · 2013
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCogenerationCombustorMonte Carlo methodProcess engineeringElectricityProcess (computing)Work (physics)CombustionThermal energyCombustion chamberGas compressorThermal efficiencyPower (physics)Nuclear engineeringMechanical engineeringElectricity generationEngineeringComputer scienceThermodynamicsElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Cogeneration system could be defined as a system that supplies electricity power and heat energy simultaneously from a single source of fuel. This system is an effective one for industrial and domestic applications where both types of energy are demanded. One of the key issues in this thermal system is to maintain an effective generation of net work and its efficiency during its operation. One of the attempts to address this issue is to develop a thermodynamic model of wet compression and steam injection in combustor of the cogeneration system. Therefore it is necessary to analyze the model and observe through multiple numerical experiments how performance is improving by injecting suitable quantity of water into the compressor and steam into the combustion chamber. Thermodynamic model of wet compression and steam injection in a process heat cogeneration system is established in this paper. The objective of this paper is to find out the critical input parameters and to find out the effect of input parameters on output with the help of Monte Carlo Simulation.

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)0.002
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.026
GPT teacher head0.300
Teacher spread0.274 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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