Plasma Actuation Control of Boundary Layer Flashback in Lean Premixed Combustor
Bibliographic record
Abstract
Dry low emissions premixed combustion systems have the ability to give larger NOx emissions reduction in comparison to diffusion type of combustors. However, these systems are prone to flashback because the fuel and oxidizers are mixed upstream of the combustion chamber. This is particularly true for premixed systems burning high-reactivity fuels due to their higher flame speed. Flashback is undesirable in gas turbines because it leads to overheating and failure of fuel nozzles and premixing sections. Recently, a novel application of Plasma Actuation through non-thermal Dielectric Barrier Discharge has been shown to significantly delay flashback in the core flow along the axis of the premixer. Building on this successful endeavour, efforts were directed to prove the effectiveness of the control method in situations where flashback was triggered in the boundary layer. Results show that the current application delays the occurrence of flashback in the boundary layer of the premixer to higher equivalence ratios. Improvements in the combustor operability margin of 10 to 14% when burning natural gas-air mixtures, and of about 3.5% when replacing the fuel by an equimolar mixture of natural gas and hydrogen, were achieved. It was found that the proposed application of plasma actuation is even more efficient in preventing flashback in the boundary layer than in the core flow.
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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) | 0.000 | 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".