Experimental and Numerical Investigation of Flow Field and Soot Particle Size Distribution of Methane-Containing Gas Mixtures in a Swirling Burner
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The formation of soot in a swirling flow is investigated experimentally and numerically in the context of biogas combustion using a CO 2 -diluted methane/oxygen flame. Visualization of the swirling flow field and characterization of the burner geometry is obtained through PIV measurements. The soot particle size distributions under different fuel concentrations and swirling conditions are measured, revealing an overall reduction of soot concentration and smaller particle sizes with increasing swirling intensities and leaner flames. An axisymmetric two-dimensional CFD model, including a detailed combustion reaction mechanism and soot formation submodel, was implemented using a commercial computational fluid dynamics (CFD) code (Ansys Fluent). The results are compared with the experiments, with similar trends observed for the soot size distribution under fuel-lean conditions. However, the model is not accurate enough to capture soot formation in fuel-rich combustion cases. In general, soot particle sizes from the model are much smaller than those observed in the experiments, with possible reasons being the inappropriate modeling in Fluent of governing mechanisms for soot agglomeration, growth, and oxidation for CH 4 -CO 2 mixtures.
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".