Hydrodynamic effect of nanosecond repetitively pulsed discharges produced throughout a laminar stagnation flame
Bibliographic record
Abstract
Plasma-assisted combustion has the potential to improve flame stability and combustion performance. Among all the different types of plasma source, nanosecond repetitively pulsed (NRP) discharges have received a lot of attention since they can produce nonequilibrium plasma containing excited species, ionized species, and radicals. These species can then accelerate combustion processes by opening new kinetic pathways. NRP discharges can also affect combustion through thermal and hydrodynamic pathways. This study investigates the hydrodynamic effect of NRP discharges produced throughout the surface of a lean premixed methane-air flame at atmospheric pressure. Time-resolved imaging was used to study the effect of the NRP discharges pulse repetition frequency (PRF). The imaging showed that the flame stabilizes further upstream as the PRF increases and that the flame remains steady and does not relax between consecutive discharges. The flame relaxation was also studied using time-resolved imaging after abruptly turning off the NRP discharges. This revealed that the flame takes about 50 ms to completely relax to its unactuated position. Particle image velocimetry showed that the NRP discharges substantially reduce the flow velocity in their vicinity, which causes the flame to move upstream and stretch. The induced stretch was found to increase flame speed though nonequidiffusive effects. The experiment was repeated with a lean premixed propane-air flame. In those conditions, the flame speed was unaffected by the NRP discharges, as expected for a mixture that is less sensitive to nonequidiffusive effects. Hence, the hydrodynamic effect of NRP discharges appears to dominate over the thermal and kinetic pathways for the conditions investigated.
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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".