MétaCan
Menu
Back to cohort
Record W2043238803 · doi:10.3390/su7044411

Exergy, Economic and Environmental Analysis for Simple and Combined Heat and Power IC Engines

2015· article· en· W2043238803 on OpenAlexaff
M.A. Ehyaei, Farideh Atabi, Mohammad Khorshidvand, Marc A. Rosen

Bibliographic record

VenueSustainability · 2015
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyExergy efficiencyCombustionFlue gasInternal combustion engineHeat capacity rateStirling engineInternal combustion engine coolingEnvironmental scienceHeat engineHeat exchangerNOxProcess engineeringElectricity generationWaste managementThermodynamicsAutomotive engineeringEngineeringCombustion chamberMechanical engineeringPower (physics)ChemistryPlate heat exchangerPhysics

Abstract

fetched live from OpenAlex

This study reports the results of exergy, economic and environmental analyses of simple and combined heat and power internal combustion engines. Values of entropy production, second law efficiency are calculated, and an objective function, including initial, operation, maintenance and fuel costs, as well as the external costs of environmental pollutants, such as CO2, CO and NOx, are presented for the flue gas of the internal combustion engine. The results show that entropy generation in the combined heat and power mode is 30% lower than that in the simple internal combustion engine. Also, by excessively increasing the air ratio, the system entropy generation decreases in both cases of simple and combined heat and power IC engines. The greatest portion of entropy generation is related to the combined heat and power internal combustion engine. The gas heat exchanger generates more entropy than the jacket heat exchanger. Lower values of electricity cost and external costs of air pollution are provided by higher values of molar air to fuel ratio. The environmental aspects depend on location of the system and time of engine operation.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.534

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 head0.210
Teacher spread0.205 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations25
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicThermodynamic and Exergetic Analyses of Power and Cooling SystemsFrench-language works237,207