Plasma assisted combustion of lean premixed flames: High-speed imaging of streamer and flame dynamics
Bibliographic record
Abstract
Siummary form only given. Plasma assisted combustion (PAC) by pulsed-plasma discharge has received increasing attention in the last few years. This technology shows much promise in the field of flame stabilization, and flame acceleration. Here, PAC is of particular interest to the gas turbine industry for use during transient engine operation. Such an operation mode may cause instabilities and lead to the extinction of lean-burning flames.In this contribution we report on the stabilization and anchoring of lean premixed v-shaped flames, through the use of repetitive high-voltage, pulsed-plasma discharges. A high-voltage pulse generator capable of producing impulsions of 0-13 kV at continuous operating pulse repetition frequencies of 0-25 kHz and with short-circuit protection was designed and built for the application. Methane/air flames were stabilized using a 6 kV (300 ns FWHM) pulse applied to a needle shaped anode centered in a convergent nozzle. The effect of pulse repetition frequency on flame anchoring and blow off velocity enhancement was investigated. High-speed imaging was used to observe the effect of the plasma discharges on two separate timescales. First, the production of excited species and streamer dynamics were investigated on the nanosecond timescale. Second, flame dynamics following a high voltage pulse were investigated on the microsecond to millisecond timescale. Our preliminary observations reveal that a 30% increase of the blow off speed can be achieved for lean equivalence ratios (0.60<;φ<;0.80), using a high repetition rate pulsed glow discharge.
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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) | 0.004 | 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".