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Record W3160921330 · doi:10.18280/ijdne.160203

Effects of EGR and Alternative Fuels on Homogeneous Charge Compression Ignition (HCCI) Combustion Mode

2021· article· en· W3160921330 on OpenAlexvenueno aff
Saliha Mohammed Belkebir, Benyoucef Khelidj, Miloud Tahar Abbès

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsCHEMKINHomogeneous charge compression ignitionCombustionNOxIgnition systemExhaust gas recirculationNitrogen oxideAutomotive engineeringEnvironmental scienceProcess engineeringNuclear engineeringWaste managementMaterials scienceChemistryCombustion chamberEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

We present in this article an analysis of the impacts of the exhaust gas recirculation (EGR) and alternative fuels on HCCI combustion mode. The objective is to reduce the pollutant emissions below the levels of established pollution standards. The ANSYS CHEMKIN-Pro software and the combined chemical kinetics mechanism were used to perform simulations for a closed homogeneous reactor under conditions relevant to HCCI engines. The calculation process is based on one single-zone in the combustion chamber. Numerical simulation has proven the ability of the models adopted, which use the essential mechanisms of the fuel combustion process, to reproduce, among other things, the evolution of the formation of chemical species. This study showed that adding hydrogen (H2) to methane (CH4) is an interesting alternative fuel because it reduces ignition time. It was concluded that an increase of EGR rate conducts to a slower combustion process, lower temperatures, and the reduction of nitrogen oxide (NOX) emissions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.501
Threshold uncertainty score0.517

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 integrity0.0000.001
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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