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Record W2094742411 · doi:10.1115/es2007-36264

Exergetic Assessment of a Turbocharged Stationary Diesel Engine

2007· article· en· W2094742411 on OpenAlexafffund
Mehmet Kanoğlu, İbrahim Dinçer, Marc A. Rosen

Bibliographic record

VenueASME 2007 Energy Sustainability Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
FundersUniversity of Ontario Institute of Technology
KeywordsTurbochargerIntercoolerExergyAutomotive engineeringDiesel fuelGas compressorEnvironmental scienceDiesel engineTurbineParametric statisticsExergy efficiencyEngineeringProcess engineeringMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

An exergetic analysis is presented of a turbocharged stationary diesel engine with a power output of about 19 MW. The system studied consists of a diesel engine, a turbine, a compressor, an intercooler and a radiator. The sites of exergy destructions are identified and quantified and the exergy efficiencies of various components determined. The exergy efficiency of the engine is found to be 40.5% at the specified reference state. The greatest exergy destruction occurs in the engine itself, which account for 84% of total exergy destruction in the system. A parametric investigation shows that the exergy losses of all system components increase with increasing reference-environment temperature. The results provide valuable information regarding the exergetic characteristics of turbocharged stationary diesel engines and appear to be useful for designers. The use of turbocharged stationary diesel engines has increased considerably in recent years as potential small-scale power generating solutions and in vehicle applications, due to their good power output, which helps overcome problems associated with some extreme operating conditions.

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)0.001
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: none
Teacher disagreement score0.793
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.246
Teacher spread0.241 · 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 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
Published2007
Admission routes2
Has abstractyes

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