Subgrid Reaction-Diffusion Closure for Large Eddy Simulations Using the Linear-Eddy Model
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Bibliographic record
Abstract
Turbulent combustion models approximate the interaction between turbulence, molecular transport and chemical reactions. Among the many available turbulent combustion models, the present focus is the linear-eddy model (LEM) used as a subgrid combustion model for large eddy simulations. In particular this paper introduces a new LEM closure with the reaction-rate approach to close the filtered chemical source terms in the governing equations for species mass fractions and enthalpy. The new approach is tested using a nonpremixed syngas flame and a bluff-body stabilized premixed flame problem. Simulation results are compared to data from a direct numerical simulation and experiments. This comparison shows that mean and rms quantities compare well with experiments and are in the range of previous simulation studies. These results are obtained with a pressure-based and unstructured computational-fluid-dynamics solver, an approach that is preferred in industry.
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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 it