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Record W1996999685 · doi:10.1115/imece2004-62106

Effect of Cooled and Hot EGR on Performance and Emission Characteristics of S-I Engine

2004· article· en· W1996999685 on OpenAlexaff
Amin Salehi‐Khojin, V Pirouzpanah, M. Mahinfalah, Steve Weinzierd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsBarrick Gold (Canada)
Fundersnot available
KeywordsNOxExhaust gas recirculationDew pointEnvironmental scienceCombustionPollutantExhaust gasWork (physics)Engine powerInternal combustion engineAutomotive engineeringWaste managementPower (physics)ChemistryThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Exhaust gas recirculation (EGR) is the most effective and practical method for reduction of NOx emission in Internal Combustion Engines. However, improper application of EGR in the engine causes higher power loss, excessive specific fuel consumption (SFC) and an increase in the concentration of other pollutants. For this reason, the amount and temperature of EGR must be tailored for each engine in order to get optimum performance and emission results. In this research work, after comparison of hot and cooled EGR for an S-I engine, pollutant behavior has also been studied around Dew point temperature of exhaust gasses. Results indicate that the pollutant’s behavior depend on the EGR mixture (amount and temperature), thermal capacity of EGR and amount of water condensation. For example, when the amount of EGR is 10% there is a higher reduction of NOx with ignorable power loss. Results also showed that the optimum temperature in experimental conditions and in this particular engine is about 340–343 Kelvin, which is just above the Dew point of exhaust gases.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.258

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 head0.003
GPT teacher head0.196
Teacher spread0.192 · 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 designBench or experimental
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

Citations2
Published2004
Admission routes1
Has abstractyes

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