Dynamics of Lean-Premixed Turbulent Combustion at High Turbulence Intensities
Bibliographic record
Abstract
Premixed turbulent flames of methane-air stabilized on a Bunsen-type burner were studied to investigate the structure of the flame front at a wide range of turbulence intensities. The nondimensional turbulence rms velocity, rms velocity divided by the laminar flame speed, covered the range from about 3 to 24. The equivalence ratio was varied from 0.6 to stoichiometric. The flame front data were obtained using planar Rayleigh imaging, and particle image velocimetry was used to measure instantaneous velocity field for the experimental conditions studied. Flame front thickness increased slightly with increasing nondimensional turbulence rms velocity. There was no significant difference in flame thickening whether the flame thickness was evaluated at progress variable 0.5, corresponding to the reaction zone, or 0.3, corresponding to the preheat zone. Flame front curvature decreased with increasing turbulence rms velocity. Flame front curvature statistics displayed a Gaussian-like distribution, which centered about zero for all the flame conditions studied during the investigation. Flame surface densities evaluated from flame front images showed almost no dependence on the nondimensional turbulence intensity. Flame surface densities integrated over the flame brush volume also did not show any sensitivity to the nondimensional turbulence rms velocity. This was discussed in the framework of a flame surface density-based turbulent premixed flame propagation closure model. The implication is that the conceptual increase in flame surface density with turbulence may not be the dominant mechanism for flame velocity enhancement in turbulent combustion in the region specified as the flamelet combustion regime by the current turbulent premixed combustion diagrams. Further, the applicability of the flamelet approach may be limited to a much smaller range of conditions than presently believed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".