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Record W4402438632 · doi:10.11159/htff24.260

DNS of syngas autoignition in stratified medium

2024· article· en· W4402438632 on OpenAlexvenueno aff
Rahul Patil, Sheshadri Sreedhara

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2024
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
Fundersnot available
KeywordsSyngasAutoignition temperatureIgnition systemChemistryAerospace engineeringEngineeringHydrogen

Abstract

fetched live from OpenAlex

This paper presents an analysis of syngas combustion using 2D Direct Numerical Simulation (DNS) near Homogeneous Charge Compression Ignition (HCCI) conditions.The study examines combustion phasing achieved through varying levels of stratification which were generated within the domain to replicate different mixture distributions resulting from incomplete mixing of fuel and oxidizer streams.Simulations were categorized into two types: thermal stratification, which mimics the effect of wall cooling, and compositional stratification, which simulates the effects of evaporative cooling of cold fuel.The impact of these stratifications on the combustion dynamics was analyzed.The simulations were conducted under HCCI-relevant initial conditions (1070 K and 41 bar) using syngas as the fuel having an equimolar mixture of H2 and CO.A detailed chemical mechanism involving 21 species and 93 reactions was employed to simulate syngas autoignition.The study found that combustion phasing due to stratification resulted in two distinct combustion modes viz.volumetric ignition and deflagration.Results indicated that H2, being more reactive, was consumed earlier than CO.Thermal stratification led to smoother combustion with increased mixing or temperature fluctuations, while compositional stratification resulted in combustion behavior closer to homogeneous autoignition.

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: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.515

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicGas Dynamics and Kinetic TheoryFrench-language works237,207