Do Turbulent Premixed Flame Fronts in Spark-Ignition Engines Behave Like Passive Surfaces?
Bibliographic record
Abstract
A widely held belief in the combustion community is that the chemical and hydrodynamic structure of a stretched laminar premixed flame can be preserved in a turbulent flow field over a range of conditions collectively known as the flamelet regime, and the homogeneous charge spark-ignition engine combustion falls within the domain of this regime. The major assumption in the laminar flamelet concept as applied to the turbulent premixed flames is that the flame front behaves as a constant-property passive scalar surface, and an increase in the wrinkled flame surface area with increasing turbulence intensity is the dominant mechanism for the observed flame velocity enhancement. The two approaches that have been recently used for estimating a measure of the wrinkled flame surface area in spark-ignition engines and other premixed flames are the flame surface density concept and fractal geometry. A critical assessment of the experimental fractal parameters reported in literature indicated that these are not capable of correctly predicting the turbulent burning velocity using the available fractal area closure model. A similar conclusion has been reached after examining the surface density data from flames of different geometries including spark-ignition engine flame fronts. The assumption made in fractal and surface density approaches that the turbulent flame front is a passive scalar surface can not be justified, and 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".