Experimental Measurement of Enhanced and Hindered Particle Settling in Turbulent Gas‐Particle Suspensions, and Geophysical Implications
Bibliographic record
Abstract
Abstract The dynamics of geophysical dilute turbulent gas‐particles mixtures depends to a large extent on particle concentration, which in turn depends predominantly on the particle settling velocity. We experimentally investigate air‐particle mixtures contained in a vertical pipe in which the velocity of an ascending air flux matches the settling velocity of glass particles. To obtain local particle concentrations in these mixtures, we use acoustic probing and air pressure measurements and show that these independent techniques yield similar results for a range of particle sizes and particle concentrations. Moreover, we find that in suspensions of small particles (78 μm) the settling velocity increases with the local particle concentration due to the formation of particle clusters. These clusters settle with a velocity that is four times faster than the terminal settling velocity of single particles, and they double settling speeds of the suspensions. In contrast, in suspensions of larger particles (467 μm) the settling velocity decreases with increasing particle concentration. Although particle clusters are still present in this case, the settling velocity is decreased by 30%, which is captured by a hindered settling model. These results suggest an interplay between hindered settling and cluster‐induced enhanced settling, which in our experiments occur respectively at Stokes number O(100) and O(1). We discuss implications for volcanic plumes and pyroclastic currents. Our study suggests that clustering and related enhanced or hindered particle settling velocities should be considered in models of volcanic phenomena and that drag law corrections are needed for reliable predictions and hazard assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".