Comparison of Presumed PDF Models of Turbulent Flames
Bibliographic record
Abstract
Over the past years, the use of a presumed probability density function (PDF) for combustion progress variable or/and mixture fraction has been becoming more and more popular approach to average reaction rates in premixed and partially premixed turbulent flames. Commonly invoked for this purpose is a beta-function PDF or a combination of Dirac delta functions, with the parameters of the two PDFs being determined based on the values of their first and second moments computed by integrating proper balance equations. Because the choice of any of the above PDFs appears to be totally arbitrary as far as underlying physics of turbulent combustion is concerned, the use of such PDFs implies weak sensitivity of the key averaged quantities to the PDF shape. The present work is aimed at testing this implicit assumption by comparing mean heat release rates, burning velocities, and so forth, averaged by invoking the aforementioned PDFs, with all other things being equal. Results calculated in the premixed case show substantial sensitivity of the mean heat release rate to the shape of presumed combustion-progress-variable PDF, thus, putting the approach into question. To the contrary, the use of a presumed mixture-fraction PDF appears to be a sufficiently reasonable simplification for modeling the influence of fluctuations in the mixture fraction on the mean burning velocity provided that the mixture composition varies within flammability limits.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".