Statistics and Dynamics of Intermittent Boundary Layer Flashback in Swirl Flames
Bibliographic record
Abstract
Flame behavior at conditions approaching boundary layer flashback was studied in fuel-lean premixed swirl flames with a central bluff-body flame holder. High-speed chemiluminescence images were collected at over 450 different combinations of fuel composition (hydrogen/methane blends), equivalence ratio, flow rate, and reactant temperature. A selected group of conditions was further examined using simultaneous high-speed stereoscopic particle image velocimetry and OH planar laser induced fluorescence. Over a range of conditions between stable burning in the combustor and total flashback, the flame would intermittently propagate through the bluff-body boundary layer into the reactant feed tube for a period of time before retreating back to the combustion chamber. Statistical characteristics of the flame dynamics, such as the depth and duration of the flashback events, showed consistent relationships across all combinations of operating parameters. Hence, tracking these statistics provides a potential means of anticipating an upcoming flashback event. The number of transient flashback events per second showed particular promise as an early warning sign due its rather gradual change with flame propagation depth. Laser diagnostics revealed local reductions in axial velocity ahead of the tip of the flame protrusions. The strength of these reductions increased as the flame moved farther upstream; however, no total flow reversal was observed.
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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 | 0.000 |
| 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".