Burning Rates and Surface Characteristics of Hydrogen-Enriched Turbulent Lean Premixed Methane-Air Flames
Bibliographic record
Abstract
The burning rates and surface characteristics of hydrogen-enriched turbulent lean premixed methane-air flames were experimentally studied by laser tomography visualization method using a V-shaped flame configuration. Turbulent burning velocities were measured and the variation of flame surface characteristics due to hydrogen addition was analyzed. The results show that hydrogen addition causes an increase in turbulent burning velocity for lean CH4-air mixtures when the turbulent level in the unburned mixture is not changed. The increase rate of turbulent burning velocity is higher than that of the corresponding laminar burning velocity, suggesting that the increase in turbulent velocity due to hydrogen addition is caused by not only chemical kinetics effect, but also the variation in flame structure due to turbulence. The further analysis of flame surface characteristics and brush thickness indicate that hydrogen addition slightly decreases local flame surface density, but increases total flame surface area because of the increased flame brush thickness. As a result, turbulent burning velocity is intensified by the increase in total flame surface area and the increased laminar burning velocity, when hydrogen is added.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".