Coarse-grained modeling of sheared granular beds. (May 2019)
- Record Type:
- Journal Article
- Title:
- Coarse-grained modeling of sheared granular beds. (May 2019)
- Main Title:
- Coarse-grained modeling of sheared granular beds
- Authors:
- Rao, Arun Ashok
Capecelatro, Jesse - Abstract:
- Highlights: Grid and filter size strongly influence erosion and pattern forming characteristics. Stochastic drag systematically improves prediction of bed height. Sinuous dunes were observed in the turbulent, high Galileo number regime. Abstract: In this work, a volume-filtered Eulerian–Lagrangian (VFEL) approach is employed to evaluate the model fidelity required to accurately predict the dynamics of sub-aqueous sedimentary flows. The VFEL approach allows for interphase exchange terms (e.g., volume fraction and drag) to be computed on a length scale independent of the grid resolution, enabling a comprehensive analysis of the errors associated with numerical discretization and physical models on bed height, particle flux, and bed wavelength. Simulations of initially flat granular beds under both laminar and turbulent shear flow are carried out in featureless and pattern forming regimes. Resulting errors are evaluated by comparing VFEL to the direct numerical simulation (DNS) data of [1, 2]. Grid refinement demonstrates convergence of the bed height and particle flux in all of the cases considered. However, traditional drag laws based on local volume fraction and particle Reynolds number lead to systematic errors in the featureless laminar regime. A stochastic drag law that accounts for the variance in local particle configuration provides improvement of the steady state statistics. The use of a stochastic drag law was found to be less important when the rise in bed height isHighlights: Grid and filter size strongly influence erosion and pattern forming characteristics. Stochastic drag systematically improves prediction of bed height. Sinuous dunes were observed in the turbulent, high Galileo number regime. Abstract: In this work, a volume-filtered Eulerian–Lagrangian (VFEL) approach is employed to evaluate the model fidelity required to accurately predict the dynamics of sub-aqueous sedimentary flows. The VFEL approach allows for interphase exchange terms (e.g., volume fraction and drag) to be computed on a length scale independent of the grid resolution, enabling a comprehensive analysis of the errors associated with numerical discretization and physical models on bed height, particle flux, and bed wavelength. Simulations of initially flat granular beds under both laminar and turbulent shear flow are carried out in featureless and pattern forming regimes. Resulting errors are evaluated by comparing VFEL to the direct numerical simulation (DNS) data of [1, 2]. Grid refinement demonstrates convergence of the bed height and particle flux in all of the cases considered. However, traditional drag laws based on local volume fraction and particle Reynolds number lead to systematic errors in the featureless laminar regime. A stochastic drag law that accounts for the variance in local particle configuration provides improvement of the steady state statistics. The use of a stochastic drag law was found to be less important when the rise in bed height is minimal. Finally, the VFEL approach with stochastic drag is used in a large-eddy simulation framework to simulate pattern formation in turbulent shear flow at relatively high Galileo numbers. … (more)
- Is Part Of:
- International journal of multiphase flow. Volume 114(2019)
- Journal:
- International journal of multiphase flow
- Issue:
- Volume 114(2019)
- Issue Display:
- Volume 114, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 114
- Issue:
- 2019
- Issue Sort Value:
- 2019-0114-2019-0000
- Page Start:
- 258
- Page End:
- 267
- Publication Date:
- 2019-05
- Subjects:
- Eulerian-Lagrangian -- Particle -- Sediment -- Direct numerical simulation -- Drag -- Stochastic -- Large-eddy simulation
Multiphase flow -- Periodicals
Écoulement polyphasique -- Périodiques
Multiphase flow
Periodicals
620.1064 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03019322 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ijmultiphaseflow.2019.03.013 ↗
- Languages:
- English
- ISSNs:
- 0301-9322
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.366000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16679.xml