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Record W4413021891 · doi:10.1016/j.fuel.2025.136433

NOx formation in ethylene-ethanol dual fuel counter-flow flames

2025· article· en· W4413021891 on OpenAlexafffund
John Z. Wen, Hongsheng Guo

Bibliographic record

VenueFuel · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsNational Research Council CanadaUniversity of Waterloo
FundersOffice of Energy Research and DevelopmentNational Research Council CanadaNatural Resources CanadaNational Research Council
KeywordsNOxEthyleneEthanolChemistryDual (grammatical number)Flow (mathematics)Materials scienceEnvironmental scienceCombustionOrganic chemistryMechanicsCatalysisPhysics

Abstract

fetched live from OpenAlex

Renewable based dual-fuel engine technologies are among these promising and feasible solutions for achieving low-carbon transportation in a near term. This study examines nitrogen oxides (NO x ) emissions on a counter-flow flame burner fueled with ethanol and ethylene at different supply ratios. Instead of air or oxygen, lean premixed ethanol/oxygen/nitrogen was supplied at one side of the burner, while mixture of ethylene/nitrogen was fed from the other side. In comparison to the ethylene–air counter-flow flame, introduction of ethanol from other side of the burner not only increased the maximum flame temperature by about 100⁰C but also produced a broader flame region, both contributing to elevated NO x emissions. Modeling these flames with detailed reaction mechanisms revealed the dominating role of the prompt NO pathway, in spite of the contribution of the thermal NO pathway to certain extent. Detailed reaction pathways of NO x formation were investigated and the dominating pathways suggest the increased oxygen availability in the extended reaction region and the higher flame temperature played significant roles. These findings indicate that introduction of ethanol to the oxidant side may affect the combustion efficiency and fuel chemistry, necessitating careful control of mitigating the NO x emission, which should provide insights for optimizing heavy-duty dual fuel combustion engines.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.618

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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