On the modeling of scalar mixing timescale in filtered density function simulation of turbulent premixed flames
Bibliographic record
Abstract
A new closure of the scalar mixing timescale is formulated to enhance the predictability of large eddy simulation (LES)/filtered density function (FDF) simulations for turbulent premixed flames. Specifically, the new model integrates a dynamic closure for turbulence-induced mixing with a closure for reaction-enhanced mixing, such that the model explicitly accounts for the subgrid mixing due to turbulence and reaction. The model adaptively adjusts the relative contribution from these two aspects according to the local state of combustion and requires no tuning for the mixing rate parameter (CM). To evaluate the model performance, LES/FDF simulations are carried out for the Sydney piloted premixed jet burner flames PM1-50 and PM1-150. Compared with the constant CM model with the baseline CM = 2, the proposed model notably improved the prediction of the overall combustion progress of both flames. The relative importance of the reaction-enhanced mixing in comparison with the turbulence-induced mixing is further investigated. For flame PM1-50, the reaction-enhanced mixing has a prominent impact throughout the combustion progress, resulting in a large variation in CM in the progress variable space. This illustrates the advantage of the proposed model for the flame close to the flamelet regime. For flame PM1-150, the variation in CM during the combustion progress is relatively small owing to the relatively weak reaction-enhanced mixing compared to PM1-50. However, this desired CM is much larger than the order of unity. Therefore, the proposed model also has its advantage for the flame close to the broken-reaction zones regime.
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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 | 0.001 |
| 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 | 0.001 |
| 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".