Direct numerical simulation of roll waves on landslide mudflow
Bibliographic record
Abstract
Roll waves on landslide muds are nonlinear instabilities on non-Newtonian laminar free-surface flow. Existing theories of the roll waves on the mudflow have been developed using long-wavelength approximations and different rheological models. The differences in the approximations and models have led to vastly different parameterizations and expressions for describing the waves. In this paper, we analyze rheological data of landslide mud and conduct direct numerical simulations (DNS) of the roll waves to reconcile the limitations and differences between existing theories. The DNS identifies for the first time the existence of a leading wave emerging as a “front runner” in mudflow instability development. Depending on the Froude and Reynolds numbers, the front runner can be a smooth or breaking wave. For comparison, we also conduct shallow-layer simulation for the instability using a two-equation depth-averaged model. We analyze temporal and spatial instabilities using two rheological models: the Herschel–Bulkley (HB) and power-law models. While HB and PL models have different rheological parameters, they produce the same roll waves on the landslide mudflow. The DNS shows the limitation of the widely used long-wavelength approximations for the dependence of the wave and instability on the Froude and Reynolds numbers. We find universality in the celerity–amplitude relationship in the nonlinear development of roll waves. The amplitude dispersion of the roll waves on the non-Newtonian mudflow follows a linear relationship remarkably similar to roll waves on laminar and turbulent flows of Newtonian fluid.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".