SUMMARYThe smooth and nonsmooth approaches to the discrete element method (DEM) are examined from a computational perspective. The main difference can be understood as using explicit versus implicit time integration. A formula is obtained for estimating the computational effort depending on error tolerance, system geometric shape and size, and on the dynamic state. For the nonsmooth DEM (NDEM), a regularized version mapping to the Hertz contact law is presented. This method has the conventional nonsmooth and smooth DEM as special cases depending on size of time step and value of regularization. The use of the projected Gauss-Seidel solver for NDEM simulation is studied on a range of test systems. The following characteristics are found. First, the smooth DEM is computationally more efficient for soft materials, wide and tall systems, and with increasing flow rate. Secondly, the NDEM is more beneficial for stiff materials, shallow systems, static or slow flow, and with increasing error tolerance. Furthermore, it is found that just as pressure saturates with depth in a granular column, due to force arching, also the required number of iterations saturates and become independent of system size. This effect make the projected Gauss-Seidel solver scale much better than previously thought.

RehAtt™ is a new concept for rehabilitation of neglect. Training with the VR-method improved spatial attention and showed transfer to improved spatial attention in activities of daily living in chronic neglect. Our results are promising and merit further studies.

The VR-DiSTRO quickly and with a high accuracy identified visuospatial neglect in patients with stroke in this construct validation. The usability among elderly patients with stroke was high. This VR-test battery has the potential to become an important screening instrument for neglect and a valuable adjunct to the neuropsychological assessment.

Abstract-We present a fluid simulation method based on Smoothed Particle Hydrodynamics (SPH) in which incompressibility and boundary conditions are enforced using holonomic kinematic constraints on the density. This formulation enables systematic multiphysics integration in which interactions are modeled via similar constraints between the fluid pseudo-particles and impenetrable surfaces of other bodies. These conditions embody Archimede's principle for solids and thus buoyancy results as a direct consequence. We use a variational time stepping scheme suitable for general constrained multibody systems we call SPOOK. Each step requires the solution of only one Mixed Linear Complementarity Problem (MLCP) with very few inequalities, corresponding to solid boundary conditions. We solve this MLCP with a fast iterative method. Overall stability is vastly improved in comparison to the unconstrained version of SPH, and this allows much larger time steps, and an increase in overall performance by two orders of magnitude. Proof of concept is given for computer graphics applications and interactive simulations.

Abstract-We describe a method for the visual interactive simulation of wires contacting with rigid multibodies. The physical model used is a hybrid combining lumped elements and massless quasistatic representations. The latter is based on a kinematic constraint preserving the total length of the wire along a segmented path which can involve multiple bodies simultaneously and dry frictional contact nodes used for roping, lassoing and fastening. These nodes provide stick and slide friction along edges of the contacting geometries. The lumped element resolution is adapted dynamically based on local stability criteria, becoming coarser as the tension increases, and up to the purely kinematic representation. Kinematic segments and contact nodes are added and deleted and propagated based on contact geometries and dry friction configurations. The method gives dramatic increase on both performance and robustness because it quickly decimates superfluous nodes without loosing stability, yet adapts to complex configurations with many contacts and high curvature, keeping a fixed, large integration time step. Numerical results demonstrating the performance and stability of the adaptive multiresolution scheme are presented along with an array of representative simulation examples illustrating the versatility of the frictional contact model.

The reinforcement learning control of an underground loader was investigated in a simulated environment by using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of a pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point while avoiding collisions, getting stuck, or losing ground traction. This relies on motion and force sensors, as well as on a camera and lidar. Using a soft actor–critic algorithm, the agents learn policies for efficient bucket filling over many subsequent loading cycles, with a clear ability to adapt to the changing environment. The best results—on average, 75% of the max capacity—were obtained when including a penalty for energy usage in the reward.

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