Inertial particle clustering due to turbulence in an air jet
Why this work is in the frame
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Bibliographic record
Abstract
Explosive volcanic eruptions create turbulent plumes of fine ash particles. When these particles collide in the presence of moisture and electrostatic fields they combine into larger aggregates, which can significantly change the atmospheric residence time of the airborne cloud. Previous studies have suggested that turbulence may lead to preferential concentration—also known as clustering—of particles within the flow, increasing the likelihood of collisions and aggregation. But few experimental studies have quantified these processes for volcanic plumes. We investigated this behavior using a particle-laden air jet. By systematically varying the exit speed and the size, density, and concentration of particles, we produced flows with Reynolds numbers of 4940 to 19300, Stokes numbers of 1.0 to 17.4 (based on the convective scale), and particle mass loadings of 0.3 to 3.9%. Specific emphasis is placed on two Stokes numbers of 1.9 and 17.4, which differ by nearly an order of magnitude. Particle image velocimetry was employed to measure the velocity distribution within a two-dimensional rectangular region along the jet centerline in each experiment. Voronoï decomposition was used to quantify the extent of preferential concentration by measuring the distribution of cell sizes around each individual particle. Our results show that particles exhibit clustering behavior when Stokes numbers are close to 1. We also measured the radial distribution functions (RDFs) to quantify the likelihood of particle collisions. At low Stokes number, the RDF magnitude was significantly higher, which corresponds to increased collision frequency in the particle-laden jet. Computational analysis shows that increasing the RDF by a factor of 20 results in a doubling of peak aggregate size.These findings demonstrate that preferential concentration due to turbulent structures could have important effects on collision frequencies, ash aggregation, and electrification in volcanic plumes.
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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 | 0.001 |
| 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