On the Liftoff of Diffusion Flame: An Experimental and Analytical Study
Bibliographic record
Abstract
Abstract The effect of lip thickness of fuel nozzle on the liftoff of an attached diffusion methane flame with and without a coflow was experimentally and analytically investigated. Three fuel nozzles made of a long pipe with the same internal diameter but different nozzle lip thickness were tested. The co-airflow was also varied to assess its impact on the liftoff of jet diffusion flame. The results showed that the effect of fuel nozzle lip thickness on the liftoff of a free jet flame (i.e., no coflow) is marginal. However, the presence of a weak to moderate coflow (0.05 m/s < Uco < 0.3 m/s) reduced the liftoff velocity of an attached flame. The reduction rate of the liftoff velocity with co-airflow was found to be more significant for the fuel nozzle with the smaller lip thickness. At a relatively higher coflow (Uco > ∼0.3 m/s), a more pronounced drop in the liftoff velocity with coflow was observed. Flow field characteristics obtained using PIV measurements showed that the coflow stream experienced a transition to the turbulent regime for Uco > ∼0.3 m/s range which is believed to cause the inception of the flame liftoff to occur. A model for predicting the liftoff velocity of an attached flame in the presence of a coflow was developed based on the stoichiometric mixture velocity equation. This model showed good agreement with the present experimental data. However, although it can predict successfully the observed trends in the literature, further experiments are required to generalize it.
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 | 0.001 | 0.001 |
| 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".