Numerical modeling of geophysical granular flows: 2. Computer simulations of plinian clouds and pyroclastic flows and surges
Bibliographic record
Abstract
Geophysical granular flows display complex nonlinear, nonuniform, and unsteady rheologies, depending on the volumetric grain concentration within the flow: kinetic, kinetic‐collisional, and frictional. To account for the whole spectrum of granular rheologies (and hence concentrations), we have used and further developed for geophysical‐atmospheric applications a multiphase computer model initially developed by U.S. Department of Energy laboratories: (Geophysical) Multiphase Flow with Interphase Exchange. As demonstrated in this manuscript, (G)MFIX can successfully simulate a large span of pyroclastic phenomena and related processes: plinian clouds, pyroclastic flows and surges, flow transformations, and depositional processes. Plinian cloud simulations agree well with the classical plume theory and historical eruptions in the upper altitude of the cloud (HT) versus mass flux diagram. At high mass flux (>107 kg/s), plinian clouds pulsate periodically with time because of the vertical propagations of acoustic‐gravity waves within the clouds. The lowest undercooled temperature anomalies measured within the upper part of the column can be as low as −18 K, which agrees well with El Chichón and Mt. St. Helens eruptions. Vertical and horizontal speed profiles within the plinian cloud compare well with those inferred from simple plume models and from umbrella experiments. Pyroclastic flow and surge simulations show that both end‐members are closely tight together; e.g., an initially diluted flow may generate a denser basal underflow, which will eventually outrun the expanded head of the flow. We further illustrate evidence of vertical and lateral flow transformation processes between diluted and concentrated flows, particularly laterally from a turbulent “maintained over time fluidized zone” near source. Our comprehensive granular rheological model and our simulations demonstrate that the main depositional process is mainly a progressive vertical aggradation.
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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 | 0.001 |
| 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".