Mechanism and large eddy simulation of dust devils
Bibliographic record
Abstract
Large Eddy Simulations (LES) of vertical convective vortices and dust devils in the terrestrial Convective Boundary Layer (CBL) are presented. A CBL‐scale simulation and a dust‐devil‐scale simulation are preformed at different resolutions. The CBL‐scale simulation is performed to study the generation of vertical vortices and the dust‐devil‐scale simulation is performed to study the detailed structures and stages of dust devil development. The simulation results show that dust devils undergo three stages of development as the swirl ratio increases: a weak vortex stage, a single‐celled vortex stage and a two‐celled vortex stage. The structure of a dust devil can be divided into four regions: outflow, core, corner and inflow layer. The different structures and characteristics of the modelled dust devil during various stages of development and the influence of the surface on the dust devil profile are described using some of the simulation results as illustrations. The causes of the different structures and characteristics are analysed through the interplay between the rotation, the radial pressure gradient, the buoyancy and the axial pressure gradient. Dust devils are a type of concentrated vortex similar to tornadoes. The differences in the structure and characteristics of tornadoes and the modelled dust devils are discussed in this paper. The carrying aloft of sand particles within the modelled dust devil is explored using a Lagrangian coordinate system. Sand particles can be transported by the updraft with particles of varying diameters follow different tracks.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".