How “mixing” affects propagation and structure of intensely turbulent, lean, hydrogen-air premixed flames
Bibliographic record
Abstract
Understanding how intrinsically fast hydrogen-air premixed flames can be rendered much faster in turbulence is essential for the systematic development of hydrogen-based gas turbines and spark ignition engines . Here, we present fundamental insights into the variation of flame displacement speeds by investigating how the disrupted flame structure affects speed and vice-versa. Three DNS cases of lean hydrogen-air mixtures with effective Lewis numbers ( L e ) ranging from about 0.5 to 1, over Karlovitz number ( K a ) range of 100 to 1000 are analyzed. Suitable comparisons are made with the closest canonical laminar flame configurations at identical mixture conditions and their appropriateness and limitations in expounding turbulent flame properties are elucidated. Since near zero-curvature surface locations are most probable and representative of the average flame geometry in such large K a flames, statistical variation of the flame displacement speed and concomitant change in flame structure at those locations constitute the focus of this study. To that end, relevant flame properties are averaged in the direction normal to the zero-curvature isotherm locations to obtain the corresponding conditionally averaged flame structures. In the leanest case with smallest L e , the temperature increases beyond that of the standard laminar flame downstream of the zero-curvature regions, leading to enhanced local thermal gradient and flame speeds in the conditionally averaged structure. These result from increased heat-release rate contribution by differential diffusion ( L e ≪ 1 ) in positive curvatures downstream of the zero-curvature locations. Furthermore, locally, the flame structure is broadened for all cases due to a reversal in the direction of the flame speed gradient. This reversal is caused by cylindrical flame–flame interactions upstream of the zero-curvature regions, resulting in localized scalar mixing within the flame structure. The combined effect of these two non-local phenomena defines the conditionally averaged flame structure and the associated variation of the local flame speed of a premixed flame in turbulence. Novelty and Significance Statement The paper presents fundamental discoveries pertaining to the structure and propagation of intensely turbulent, lean premixed hydrogen-air flames, emerging from the analysis of averaged flame structures conditioned to zero-curvature surface locations. These locations are most probable alongside corresponding to the mean of curvature distribution. Analysis of such structures reveals how non-local effects determine the average flame displacement speed, for the first time. The paper shows that non-local effects within the flame structure address long-standing questions on how premixed flames are broadened in turbulence and why local flame displacement speeds of intensly turbulent, ultra-lean hydrogen-air flames are ubiquitously higher than their standard laminar counterpart.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".