Numerical prediction of isothermally reacting mixing layer using vortex‐in‐cell and filtered density function
Bibliographic record
Abstract
Abstract A large eddy simulation based on the filtered vorticity transport equation and the filtered density function (FDF) transport equation developed in an earlier study is extended to predict a chemically reacting flow with no heat release. The filtered vorticity transport equation is solved using the vortex‐in‐cell scheme in conjunction with the dynamic eddy viscosity subgrid‐scale models. The transport equation for FDF is solved using the Lagrangian Monte‐Carlo method. The methodology is tested on a chemically reacting spatially growing mixing layer with no heat release. The effects of Damköhler number ( Da ) on the concentration structure of the reacting mixing layer, the mean reactant and product concentrations and on the reactant FDF are investigated. It is shown that mixing has a greater effect on scalar field within the vortex structure as compared with the braid regions. Also for high Da , the reaction zones are mainly limited to the thin reacting interfacial zones, i.e. the contact zone between the reactants, whereas for low Da , the reacting zones are spread as reacting pockets within the vortex structure. The effects of Da on mean reactant and product concentrations, root‐mean‐square concentration fluctuations and probability density are discussed. Copyright © 2010 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".