Numerical Simulation of Lean Premixed Stagnation Flames
Bibliographic record
Abstract
Lean premixed combustion counterflow model coupled with detailed chemical kinetics mechanisms are used to study the effects of methane dilution on syngas mixture numerically. The work critically evaluates and examines the effects of methane additions at various methane/syngas ratios and various equivalence ratios (φ = 0.6, 0.65, and 0.7) using the CANTERA package. The results of this study increase the feasibility of incorporating syngas mixture diluted with methane based fuel for lean premixed gas turbine engines. This study indicates that the flame velocity of a syngas mixture (H2:CO-75:25) can be reduced to the flame speed of a methane mixture at an equivalence ratio of 0.6 and standard conditions (1 atm and 298 K) by mixing it with 50% of methane. This could reduce the flashback propensity that syngas mixtures will incur due to the significantly high flame speed. Moreover, this study substantiates that the current detailed mechanism is capable of predicting the one-dimensional reacting flows and it matches remarkably well with experimental data. The approach employed herein can be used to optimize and validate detailed chemical kinetics mechanism without the need to correct the flame speed to zero strain or stretch free from the measured datasets.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".