Dynamics of Flame Lift-Off in Biogas Swirl Flames
Bibliographic record
Abstract
Lift-off limits and mechanism of biogas swirl flames were investigated in a gas turbine model combustor using high-repetition-rate OH* chemiluminescence and simultaneous particle image velocimetry (PIV) and OH planar laser induced fluorescence (PLIF). The biogas fuel was represented by 60% methane and 40% carbon dioxide, volumetrically. The test matrix consisted of three preheat temperatures and three target adiabatic flame temperatures, with a total of 9 test cases. Total lift-off was defined as a distinct and complete flame detachment from the burner nozzle, which was approached by increasing both air and fuel flow rates at a fixed equivalence ratio. With the increase of bulk velocity, more intermittent lift-off events were observed, with the flame temporarily detaching from the nozzle before reattaching. By analyzing flow-flame interactions during these events from the temporally-resolved PIV and OH PLIF measurements, the lift-off mechanism was observed. As a precursor of lift-off, a local flame extinction event occurred near the flame base due to a pulse of high strain-rate on the flame. Noticeable flow and flame asymmetry subsequently developed, which led to further thinning and wrapping-up of the flame base. Further local extinction subsequently occurred due to the high strain-rate associated with the asymmetric flow. This eventually caused the entire flame base to quench and the flame to detach. Analysis of the vorticity field indicated that the flow asymmetries were due to the formation of a helical precessing vortex core (PVC) after the first local extinction event as a result of density field change near the nozzle exit.
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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) | 0.001 | 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".