A STOKES NUMBER-BASED STOCHASTIC IMPROVEMENT FOR DISPERSION MODEL FOR LARGE EDDY SIMULATION
Bibliographic record
Abstract
To improve the fidelity of large eddy simulation (LES) of spray jet dispersion, a dynamic subgrid dispersion model is proposed based on the Langevin-type stochastic framework to quantify the effective contribution of the stochastic component of the force as a function of the Stokes number related to the subgrid time scale, which is easily accessed by the LES closure model. The proposed model has two coefficients that require calibration, which were obtained following a rigorous calibration procedure based on forward uncertainty quantification algorithms. The performance of the model is assessed by comparison against a reference direct numerical simulation (DNS) test case. The comparisons for the spray analysis include averages of the number of droplets, mass source term, and droplet diameters conditioned on the vapor mass fraction, together with their Eulerian average at different axial locations. The results showed improved prediction of the particle clustering behavior near the nozzle exit observed in the DNS simulations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".