100 ? granular composition w 1 5 w 2 w 2 5 sphere α

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100 ζ Granular composition w = 1 . 5 w = 2 . 0 w = 2 . 5 sphere ( α = 1 . 0) 5.6 3.6 2.1 prolate ellipsoid ( α = 2 . 0) 6.7 5.7 5.2 prolate ellipsoid ( α = 3 . 0) 19.5 9.6 6.5 prolate ellipsoid ( α = 4 . 0) 21.5 22.7 10.6 cube 19.6 17.2 7.5 mixture 16.3 8.1 5.6 042905-11
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ARMAN PAZOUKI et al. PHYSICAL REVIEW E 96 , 042905 (2017) collections of monodisperse spheres in experiments that lead to high internal stress, the DEM-P has the advantage; it is faster than the DEM-C and apt at capturing both the microscale and macroscale responses of the material. The DEM-P runs into difficulties when handling complex geometries owing to its (i) ad hoc approach to producing the friction and contact forces under these circumstances and (ii) sensitivity to contact information, i.e., the geometrical or collision detection component, when small variations in contact information lead to sizable changes in forces. To a point, we found the DEM-P insensitive to shearing rates, which could be increased, and to contact stiffness, which could be decreased, both by orders of magnitude. This lack of sensitivity can be traded for larger simulation step sizes that led in some experiments, e.g., the STT, to significant speed-ups over the DEM-C. The latter was very apt at handling granular material with complex element geometries when the experiment did not lead to high internal stresses. Hopper flows are very suitable, less so triaxial or other high-load shear tests. The DEM-C had two shortcomings: (a) its emphatic embrace of the rigid-body abstraction and (b) its coupled system-level solution process, which is computationally taxing. Because of (a), the DEM-C is incapable of capturing wave propagation in granular material and struggles with the STT as it cannot employ the local particle deformation mechanism that facilitates and modulates shearing in granular material. This limitation can be addressed by reverting to a finite-element method to account for grain deformation. Computationally, this is prohibitively expensive. In relation to (b), large granular dynamics problems are going to stymie the DEM-C. Moreover, the DEM-C forfeits one of its strong points since the ability to use large steps h becomes a nonfactor given the spatial and time scales on which granular dynamics takes place. The DEM-C is anticipated to be competitive in fluidized bed, particulate flow, and robotics problems in which the size of the optimization problem is small and/or the simulation can advance with large h . We found the DEM-C to be robust, which makes it permissive and forgiving. Indeed, stopping the DEM-C solution after few iterations, long before convergence, produces macroscale results that are acceptable, yet not highly accurate. This is handy in engineering applications when the microscale behavior is of secondary interest. For instance, when designing a piece of equipment that pushes a pile of granular material, accurately resolving the microscale response of the granular material is perhaps of little concern. Instead, the priority is in producing a good overall load history that the implement acting on the granular material experiences during a work cycle. A similar
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