Application of Dielectric Barrier Discharge to Improve the Flashback Limit of a Lean Premixed Dump Combustor
Bibliographic record
Abstract
In recent years, lean-premixed (LP) combustors have been widely studied due to their potential to reduce NOx emissions in comparison to diffusion type combustors. However, the fact that the fuels and oxidizers are mixed upstream of the combustion zone makes LP type of combustors a candidate for upstream flame propagation (i.e., flashback) in the premixer that is typically not designed to sustain high temperatures. Moreover, there has been a recent demand for fuel-flexible gas turbines that can operate on hydrogen-enriched fuels like Syngas. Combustors originally designed for slower kinetics fuels like natural gas can potentially encounter flashback if operated with faster burning fuels like those containing hydrogen as a constituent. There exists a clear need in fuel-flexible lean-premixed combustors to control flashback that will not only prevent costly component damage but will also enhance the operability margin of engines. A successful attempt has been made to control flashback in an atmospheric LP combustor, burning natural gas-air mixtures, via the application of dielectric barrier discharge (DBD). A low-power DBD actuator was designed, fabricated and integrated into a premixer made out of quartz. The actuator was tuned to produce a low magnitude ionic wind with an intention to modify the velocity profile in the premixer. Flashback conditions were created by decreasing the air flow rate while keeping the fuel flow rate constant. Within this experimental setup, flashback happened in the core flow along the axis of the cylindrical premixer. Results show that the utilization of the DBD delays the occurrence of flashback to higher equivalence ratios. Improvements as high as about 5% of the flashback limit have been obtained without compromising the blowout limit. It is anticipated that this novel application of DBD will lead to future demonstrations of the concept under realistic gas turbine operating conditions.
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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".