Semi-analytical validation of a dynamic large-eddy simulation procedure for turbulent premixed flames via the <i>G</i>-equation
Bibliographic record
Abstract
The performance of a dynamic subgrid model for the turbulent burning speed of a premixed flame is investigated for a series of idealized test cases where the flame front is wrinkled by a multiple-scale shear flow; a rigorous asymptotic subgrid model is also implemented for comparison. Explicit formulae for the flame wrinkled shape and turbulent speed are available to generate a reference database. The role of the subgrid wrinkling models is to achieve the same overall flame shape and propagation speed in a simulation where only the largest scales of the flow are explicitly accounted for. Very good results are obtained when the subgrid burning speed enhancement is estimated using the asymptotic subgrid model. On the other hand, the dynamic model attempts to exploit the scaling observable in the simulation to extrapolate the turbulent burning speed enhancement in the original system. The performance of this strategy is adequate for some regimes but poor for others; the source of the problem is traced back to the existence of a scaling transition that occurs as the flame propagating speed is adjusted during the large-eddy simulation. A modification to the scaling of the enhanced burning is implemented to account for the existence of the two distinct scaling ranges; it improves significantly the predictions of the dynamic model away from the transition, but results in the near-critical range remain predictably very poor compared with the rigorous asymptotic model results. These conclusions based on a priori performance for the reference steady data are confirmed by comparing unsteady large-eddy and direct simulations. Results based on rigorous mathematical tools are possible here because of the separation of length scales in the special class of idealized flow fields used in this study: their relevance to more realistic flows is also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".