LES of a Hydrogen-Enriched Lean Turbulent Premixed Flame
Bibliographic record
Abstract
The application of large-eddy simulation (LES) to the prediction of H2-enriched lean methane-air turbulent premixed combustion is considered. A presumed conditional moment (PCM) subfilter-scale combustion model is coupled with the flame prolongation of intrinsic low-dimensional manifold (FPI) chemistry tabulation technique. The LES and PCM-FPI modelling procedures are then applied to the prediction of laboratory-scale axisymmetric Bunsen-type turbulent premixed flames. Both premixed methane-air and H2-enriched methane-air flames are considered and the predicted solutions are examined and compared to available experimental data. The enriched flame has 20% H2 in terms of mole fraction and lies in the methane-dominated regime. The capability of the LES model to predict the observed behaviour is examined. Hydrogen-hydrocarbon fuel blends appear to be a promising option to synergistically pave the way toward pure hydrogen-based combustion systems while alleviating green-house gas and pollutant emissions related to fossil fuel combustion. The possibility of using hydrogen-enriched hydrocarbon fuels as a means for enabling greater stability of lean premixed flames with significantly reduced emissions of nitrogen oxides is also very appealing. While promising, the wide-spread application of hydrogen-enriched hydrocarbon fuels in practical premixed combustion devices has been limited by an incomplete understanding of hydrogen-enriched combustion. In particular, the current understanding, in terms of theoretical and computational models, is unable to fully explain the experimental observations for such flames.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".