MétaCan
Menu
Back to cohort
Record W4255376640 · doi:10.1115/gt2013-95156

Numerical Study on the Effect of Real Syngas Compositions on Ignition Delay Times and Laminar Flame Speeds at Gas Turbine Conditions

2013· article· en· W4255376640 on OpenAlexaff
Olivier Mathieu, Eric L. Petersen, Alexander Heufer, Nicola Donohoe, Wayne K. Metcalfe, Henry J. Curran, Felix Güthe, Gilles Bourque

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsRolls-Royce (Canada)
Fundersnot available
KeywordsSyngasCombustionDiluentLaminar flame speedIgnition systemSyngas to gasoline plusLaminar flowPremixed flameMaterials scienceChemistryThermodynamicsChemical engineeringOrganic chemistryHydrogenCombustor

Abstract

fetched live from OpenAlex

Depending on the feedstock and the production method, the composition of syngas can include (in addition to H2 and CO) small hydrocarbons, diluents (CO2, water, and N2), and impurities (H2S, NH3, NOx, etc.). Despite this fact, most of the studies on syngas combustion do not include hydrocarbons or impurities and in some cases not even diluents in the fuel mixture composition. Hence, studies with realistic syngas composition are necessary to help designing gas turbines. The aim of this work was to investigate numerically the effect of the variation in the syngas composition on some fundamental combustion properties of premixed systems such as laminar flame speed and ignition delay time at realistic engine operating conditions. Several pressures, temperatures, and equivalence ratios were investigated. To perform this parametric study, a state-of-the-art C0-C5 detailed kinetics mechanism was used. Results of this study showed that the addition of hydrocarbons generally reduces the reactivity of the mixture (longer ignition delay time, slower flame speed) due to chemical kinetic effects. The amplitude of this effect is however dependent on the nature and concentration of the hydrocarbon as well as the initial condition (pressure, temperature, and equivalence ratio).

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: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.731

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)0.0010.000

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 head0.006
GPT teacher head0.221
Teacher spread0.214 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicCombustion and flame dynamicsFrench-language works237,207