Flow, mixing, and flame stabilization in bluff-body burner with decreased central jet velocity
Bibliographic record
Abstract
The flame stabilization is a complex problem, especially when reducing the fuel supply, as it involves complicated interactions of turbulence, mixing, and chemistry. In this study, the flamelet progress variable combined with large eddy simulation has been used to simulate a bluff-body non-premixed flame to reveal the mechanisms of flow, mixing, and flame stabilization during the central fuel jet velocity reduction. The two flow patterns of jet dominant and coflow dominant are first analyzed by the concept of persistence of decay similarity of jet. The results show that this concept is informative to interpret whether and where the flow is jet dominant and understand the competition between two flows in detail, not only for flow but also for mixing. The results further show that the jet–coflow interaction, which has a pronounced impact on flame topology, has a minimal impact on flame stabilization for the bluff-body stabilized non-premixed flames over a wide range of fuel jet velocities, because of approximately close ignition delay time and flow convection velocities. In addition, it is observed that a ribbon-like structure of formaldehyde forms upstream of the hydroxyl. This phenomenon is caused by autoignition which is favored by high temperature in the recirculation zone and takes place far upstream of the flame. That would particularly facilitate flame stabilization in bluff-body burners.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".