Fluid effects in model granular flows
Bibliographic record
Abstract
Abstract Pore fluid plays a crucial role in many granular flows, especially those in geophysical settings. However, the transition in behaviour between dry flows and fully saturated flows and the underlying physics that relate to this are poorly understood. In this paper, we report the results of small-scale flume experiments using monodisperse granular particles with varying water content and volume in which the basal pore pressure, total pressure, flow height and velocity profile were measured at a section. We compare the results with theoretical profiles for granular flow and with flow regimes based on dimensional analysis. The runout and the centre of mass were also calculated from the deposit surface profiles. As the initial water content by mass was increased from zero to around 10%, we first observed a drop in mobility by approximately 50%, as surface tension caused cohesive behaviour due to matric suction. As the water content was further increased up to 45%, the mobility also increased dramatically, with increased flow velocity up to 50%, increased runout distance up to 240% and reduced travel angle by up to 10° compared to the dry case. These effects can be directly related to the basal pore pressure, with both negative pressures and positive pore pressures being measured relative to atmospheric during the unsteady flow. We find that the initial flow volume plays a role in the development of relative pore pressure, such that, at a fixed relative water content, larger flows exhibit greater positive pore pressures, greater velocities and greater relative runout distances. This aligns with many other granular experiments and field observations. Our findings suggest that the fundamental role of the pore fluid is to reduce frictional contact forces between grains thus increasing flow velocity and bulk mobility. While this can occur by the development of excess pore pressure, it can also occur where the positive pore pressure is not in excess of hydrostatic, as shown here, since buoyancy and lubrication alone will reduce frictional forces. Graphical abstract
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".