Numerical modeling of geophysical granular flows: 1. A comprehensive approach to granular rheologies and geophysical multiphase flows
Bibliographic record
Abstract
Geophysical granular materials display a wide variety of behaviors and features. Typically, granular flows (1) are multiphase flows, (2) are very dissipative over many different scales, (3) display a wide range of grain concentrations, and (4), as a final result of these previous features, display complex nonlinear, nonuniform, and unsteady rheologies. Therefore the objectives of this manuscript are twofold: (1) setting up a hydrodynamic model which acknowledges the multiphase nature of granular flows and (2) defining a comprehensive rheological model which accounts for all the different forms of viscous dissipations within granular flows at any concentration. Hence three important regimes within granular flows must be acknowledged: kinetic (pure free flights of grain), kinetic‐collisional, and frictional. The momentum and energy transfer will be different according to the granular regimes, i.e., strain rate dependent in the kinetic and kinetic‐collisional cases and strain rate independent in the frictional case. A “universal” granular rheological model requires a comprehensive unified stress tensor able to adequately describe viscous stress within the flow for any of these regimes, and without imposing a priori what regime will dominate over the others. The kinetic‐collisional viscous regime is defined from a modified Boltzmann's kinetic theory of dense gas. The frictional viscous regime is defined from the plastic potential and the critical state theories which account for compressibility of granular matter (e.g., dilatancy, consolidation, and critical state). In the companion paper [ Dartevelle et al., 2004 ] we will introduce a multiphase computer code, (G)MFIX, which accounts for all the granular regimes and rheology and present typical simulations of diluted (e.g., plinian clouds) and concentrated geophysical granular flows (i.e., pyroclastic flows and surges).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".