Control of Lean Blowout in a Swirl-Stabilized Dump Combustor at Different Levels of Premixing Based on Flame Colour
Bibliographic record
Abstract
Presently, the use of lean combustion has been growing in industrial furnaces, aero-engine, and rocket engine applications due to their low NOX emission and minimum maintenance requirements. On the other hand, due to the excessively low combustion reaction rate, the operation at a low fuel–air ratio leads to the sudden shutdown of the engines, which is termed lean blowout (LBO). Therefore, to operate land-based and aircraft engines at an extremely lean fuel–air mixture, control of LBO has become indispensable. In the present work, we develop several feedback in-loop control strategies using a pixel-averaged intensity ratio between the red and blue components of the flame emission. During a lean operation, based on the quantification of the proximity of the combustion dynamics to the LBO limits using this color ratio, the control strategies make the solenoid valves active to inject a secondary pilot fuel and energize the flame base. We notice that a proper choice of the color ratio as a threshold enhances the stability of both premixed and partially premixed combustion using a low pilot fuel. Besides, we test NOX emission for unpiloted and piloted operations and find that the emission is only marginally affected during a pilot injection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".