Investigation of differential diffusion in lean, premixed, hydrogen-enriched swirl flames
Bibliographic record
Abstract
Hydrogen combustion is a promising alternative to fossil fuel combustion in an effort to reduce our carbon footprint. However, hydrogen combustion is prone to thermodiffusive instabilities largely dependent on differential diffusion, a phenomenon that can lead to higher probabilities of flashback in industrial burners, given hydrogen’s high reactivity and diffusivity. This paper evaluates low-swirl flames of methane and air enriched with hydrogen to highlight the onset of differential diffusion. Testing was conducted in a fully controllable swirl burner, where bulk velocity Uav = 13 m/s and swirl number S = 0.6 were kept constant for each hydrogen-methane blend to isolate increases in flame surface area from increases in turbulence intensity. Furthermore, each fuel blend of hydrogen and methane is evaluated at the same laminar flame speed of SLo = 0.267 m/s to isolate flame stretch effects on the turbulent burning rate. Combined hydroxyl (OH) PLIF and stereoscopic PIV at the National Research Council of Canada were used to analyze the OH fluorescence in a 2D-3C velocity field for each flame condition. High-speed PIV at McGill University was used to resolve local flame phenomena, such as local flame displacement velocity and flame stretch rate. Using these techniques, it can be observed that the flame displaces axially in response to turbulent flame speed while exhibiting increases in flamefront wrinkling. This increased corrugation due to flame stretch is highlighted in the PDFs of local curvature and κSf and is further evidenced by a shift towards positive curvatures (κ > 0) for increasing H2 volume fraction. This trend suggests that there is a strong correlation with increases in turbulent burning rate and positive curvature as a result of differential diffusion, but it is not necessarily a control mechanism of the most forward propagating points proposed by the theory of leading points.
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".