Experimental study of the effects of oxygen enrichment on the stability of a low-swirl biogas non-premixed flame
Bibliographic record
Abstract
This experimental study investigates the impact of oxygen enrichment on the stability of a low-swirl biogas non-premixed flame. A synthetic biogas mixture (CH 4 /CO 2 :60/40 by volume) was injected through a central nozzle, while a coaxial airflow, which passes through a low‐swirl generator with a swirl number (S) of 0.39, was enriched with up to 24 % oxygen. The biogas flame is then ignited and stabilized at the burner exit. High-speed imaging was used to investigate the biogas flame stability limits and a two-dimensional Particle Image Velocimetry (PIV) was used to document the reacting flow fields especially near the upper (UBL) and lower blowout (LBL) limits. The results indicate that while oxygen enrichment has a limited effect on expanding the upper blowout limit, it significantly extends the lower blowout limit, particularly at low fuel flow rates, emphasizing its stronger stabilizing role under flame lean conditions. The UBL appears to be mostly controlled by the momentum of the central fuel jet. It is seen that increase in the centerline jet velocity pushes the lifted flame farther downstream and when severely stretched due to elongated vortical structures, the flame becomes instable and susceptible to blowout. Conversely, at the LBL, the blowout is governed by the strength of the swirling co-flow, as increase in co-flow makes the flame leaner and more strained which eventually causes blowout.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".