An Economical Model for Simulating Turbulence Enhancement of Droplet Collisions and Coalescence
Bibliographic record
Abstract
Abstract ClusColl, an economical simulation method for droplet motions and collisions in turbulent flows, has been developed, implemented, tested, and applied. In the Linear Eddy Model, permutations called triplet maps representing individual turbulent eddies implement turbulent advection of fluid in 1‐D. This captures flow processes down to the smallest turbulent eddy (Kolmogorov microscale), but the inertial response of small Stokes number droplets to turbulence has important features at scales down to the droplet radius, notably sub‐Kolmogorov‐scale clustering of finite‐inertia droplets that can increase collision rates significantly. Additionally, shear due to the smallest scales of turbulence increases collision rates of zero‐inertia droplets. In ClusColl, a 3‐D triplet map for droplets captures both effects. We implemented collision detection, enabling simulation of droplet collisions and coalescence, and a sedimentation treatment in ClusColl. Published direct numerical simulations (DNSs) of monodispersions were used to tune parameters. For sedimenting droplets in turbulence, ClusColl's turbulent enhancement of bidisperse collision kernels agrees reasonably well with published DNS results. We compared ClusColl and DNS coalescence growth results. For weak turbulence (ε≤100 cm2/s3), ClusColl's turbulent enhancement of coalescence growth closely matches that of the DNS. For ε≥200 cm2/s3, lack of accurate collision efficiencies precludes definitive quantitative evaluation of ClusColl's coalescence growth. In a comparison of coalescence growth dependence on the droplet size distribution width and on turbulent enhancement, ClusColl indicates that the latter dramatically accelerates cloud droplet conversion into raindrops, while the former has significantly less impact.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".