Dynamics of Sediment-Laden Gravity Currents; Influences of Sedimentation and Coriolis Force
Bibliographic record
Abstract
When a sediment-laden river enters a stratified lake or the coastal ocean, the density difference between the river and the ambient determines whether a surface, sub-surface or bottom-hugging turbidity current can form. In this thesis, I will explore the sedimentation mechanisms occurring beneath surface and sub-surface currents, and how Coriolis force can potentially modify sedimentation patterns from the bottom hugging turbidity currents. It is shown that when a sediment-laden plume overlies a salty ambient, both processes of double-diffusive convection and settling-driven convection can increase the settling rate of particles. Direct velocity measurements found that the velocity of sinking sediment fingers are always larger than the Stokes settling velocity. The parameter that controls the dynamics of settling is density ratio, defined as the ratio of density differences due to the added salt and sediment. For 7.8 µm Silicon Carbide particles, sediment fingering only dominates when the density ratio is near one; otherwise settling-driven convection is the dominant sedimentary process. Thus, sediment fingers control sedimentation beneath lake interflows, whereas settling-driven convection dominates in most oceanic overflows. Furthermore, scaling is developed for the propagation length-scale over which sediment particles fall out of suspension beneath a buoyant sediment-laden river plume due to vigorous settling-driven convection of sediment. When density ratio is close to one, settling-driven convection is vigorous and the propagation length-scale is very small. Finally, in order to understand how the Earth’s rotation might influence erosion and depositional patterns in large oceanic channels, the world’s largest rotating platform was used to show the influence of Coriolis force on the near-bed velocity structure in oceanic density currents flowing in straight and sinuous channels. The experiments were quantified in terms of a dimensionless Rossby number defined in terms of either the channel width (for straight channels) or the radius of channel curvature (for bent channels). Coriolis force can change the direction of the near-bed flow when the bend Rossby number is less than +0.8. Using these results, coupled with changes in density fields, a process model of the evolution of sinuous channels is developed under the effect of Coriolis force in different latitudes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".