Numerical Simulation of Dune Morphological Changes Under Time-Varying Flows
Bibliographic record
Abstract
The numerical simulation of bedform features in rivers is of interest to engineers to estimate the resistance of the bed to flow and sediment transport. Such models are referred to as morphodynamic models and are typically composed of three sub-models: a hydrodynamic model to reproduce the flow field over the bedforms; a sediment transport model to calculate the movement of the sediment in response to the flow field; and a morphological model to determine the evolution of the bed in response to gradients in the sediment transport rate. As all three of these models are coupled together, they each must provide accurate and robust predictions of their respective processes. The intent of this work is to provide guidance for the further development of practical morphodynamic models for the simulation of fluvial dunes. Two aspects of the hydrodynamic model are investigated. The first aspect concerns the application of low-Reynolds turbulent closures for the simulation of flow over asymmetrical dunes. Specifically, the impacts of the near-wall mesh resolution and choice of turbulence closure on the bed shear stress are considered, as the latter is a primary input to the sediment transport model. The second aspect is the evaluation of rough-wall corrections applied to the k-ω SST model. In the context of morphological models, this work examines the challenges associated with solving the sediment transport continuity equation when applied to the simulation of dune migration and evolution by comparing classical shock capturing methods to high-resolution shock capturing schemes. The findings of this work are as follows: 1. The flow field and turbulent properties of flow over asymmetric, smooth walled dunes are highly sensitive to the near-wall spacing of the computational mesh. 2. The bed shear stress produced by different low-Reynolds turbulence closures and LES models were different in terms of their shape and magnitude. These differences resulted in different morphological responses of the dune for the same flow conditions and sediment properties. 3. Accounting for the roughness of the bed surface of a dune within the hydrodynamic simulation is important to accurately estimate the shear stresses. The rough wall treatments introduced for the low-Reynolds k-ω SST model are numerically efficient and robust. 4. Classical schemes for modelling dune morphology while numerically efficient and easy to implement result in excessive numerical diffusion and require specific scale dependent tuning. On the other hand, the WENO models produced very accurate changes in the dune geometry with minimal numerical diffusion and do not require specific tuning. This comes at the cost of additional computational overhead. Additionally, a standalone chapter applies the current understanding of the relationship between dune geometry and flow properties to reconstruct paleoflows along the Ottawa River based on ancient dunes found along the shoreline identified by airborne LiDAR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".