Blowoff and Reattachment Dynamics of a Linear Multinozzle Combustor
Bibliographic record
Abstract
This paper describes the coupled flow and flame dynamics during blowoff and reattachment events in a combustor consisting of a linear array of five interacting nozzles using 10 kHz repetition-rate OH planar laser-induced fluorescence and stereoscopic particle image velocimetry (S-PIV). Steady operating conditions were studied at which the three central flames randomly blew-off and subsequently reattached to the bluff-bodies. Transition of the flame from one nozzle was rapidly followed by transition of the other nozzles, indicating cross-nozzle coupling. Blow-off transitions were preferentially initiated in one of the off-center nozzles, with the transition of subsequent nozzles occurring in a random order. Similarly, the center nozzle tended to be the last nozzle to reattach. The blow-off process of any individual nozzle was similar to that for a single bluff-body stabilized flame, though with cross-flame interactions providing additional means of restabilizing a partially extinguished flame. Subsequent to blowoff of the first nozzle, the other nozzles underwent similar blow-off processes. Flame reattachment was initiated by entrainment of a burning pocket into a recirculation zone, followed by transport to the bluff-body; the other nozzles subsequently underwent similar reattachment processes. Several forms of cross-nozzle interaction that can promote or prevent transition are identified. Furthermore, the velocity measurements indicated that blowoff or reattachment of the first nozzle during a multinozzle transition causes significant changes to the flow fields of the other nozzles. It is proposed that a single-nozzle transition redistributes the flow to the other nozzles in a manner that promotes their transition.
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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".