Numerical Study on the Effect of Real Syngas Compositions on Ignition Delay Times and Laminar Flame Speeds at Gas Turbine Conditions
Bibliographic record
Abstract
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 in 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 for the ignition delay times, namely 1, 10, and 35 atm, 900–1400 K, and ϕ = 0.5 and 1.0. For laminar flame speed, temperatures of 300 and 500 K were studied at pressures of 1 atm and 15 atm. Results 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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".