Presumed PDF modeling for RANS simulation of turbulent premixed flames
Bibliographic record
Abstract
In this work, a turbulent premixed Bunsen flame is simulated using a RANS approach for turbulence and a flamelet model for turbulence–chemistry interactions. In this flamelet model, the mean reaction rates are approximated using a progress variable approach and a Flame Prolongation of ILDM (FPI) for chemistry reduction. This method requires a presumption for the shape of the probability density function of the reaction progress variable. Two shapes have been examined: a widely used β-function and a modified laminar flamelet PDF. Radial distributions of the calculated temperature field, axial velocity and chemical species mass fraction have been compared with experimental data. This comparison shows that using the modified laminar flamelet PDF leads to predictions that are similar, and often superior to those obtained using the β-PDF. Given that the new PDF is based on the actual chemistry – as opposed to the ad hoc nature of the β-PDF – these results suggest that it is a better choice for the statistical description of the reaction progress variable in a highly strained turbulent field.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".