This paper describes a web-enabled tool capable of generating high quality videos and images from multibody dynamics simulation results. This tool, called Chrono::Render, uses the Blender modeling software as the front end with Pixars RenderMan used to create high quality images. Blender is a free and open source tool used to create and visualize 3D content and provides a robust plugin framework which Chrono::Render leverages. To produce the final image, the Blender front end passes data to a RenderMan compliant rendering engine. Along with Pixars PhotoRealistic RenderMan (PRMan), several open source options such as Aqsis, JrMan, or Pixie can be used. Preprocessing is performed on the client side, where the front end generates a work order for the RenderMan compliant rendering engine to process. This work order, which contains several scripts that define the visualization parameters, along with the pre-processed simulation data and other user-defined geometry assets is uploaded to a remote server hosted by the Simulation Based Engineering Lab. This server contains more than a thousand CPU cores used for high performance computing applications, which can be used to render many frames of an animation in parallel. Chrono::Render is free and open source software released under a BSD3 license.

1 Accelerating Multi-Body Simulation and Visualization with GPU Computing 3 Hammad Mazhar, Toby Heyn, Daniel Melanz, Arman Pazouki, Aaron Bartholomew, Dan Negrut, Paramsothy Jayakumar and Alessandro Tasora 1.

This paper describes a web-enabled tool capable of generating high quality videos and images from multibody dynamics simulation results. This tool, called Chrono::Render, uses the Blender modeling software as the front end with Pixars RenderMan used to create high quality images. Blender is a free and open source tool used to create and visualize 3D content and provides a robust plugin framework which Chrono::Render leverages. To produce the final image, the Blender front end passes data to a RenderMan compliant rendering engine. Along with Pixars PhotoRealistic RenderMan (PRMan), several open source options such as Aqsis, JrMan, or Pixie can be used. Preprocessing is performed on the client side, where the front end generates a work order for the RenderMan compliant rendering engine to process. This work order, which contains several scripts that define the visualization parameters, along with the pre-processed simulation data and other user-defined geometry assets is uploaded to a remote server hosted by the Simulation Based Engineering Lab. This server contains more than a thousand CPU cores used for high performance computing applications, which can be used to render many frames of an animation in parallel. Chrono::Render is free and open source software released under a BSD3 license.© 2014 ASME

Objective: In this study, we compared how users locate physical and equivalent three-dimensional images of virtual objects in a cave automatic virtual environment (CAVE) using the hand to examine how human performance (accuracy, time, and approach) is affected by object size, location, and distance.Background: Virtual reality (VR) offers the promise to flexibly simulate arbitrary environments for studying human performance. Previously, VR researchers primarily considered differences between virtual and physical distance estimation rather than reaching for close-up objects.Method: Fourteen participants completed manual targeting tasks that involved reaching for corners on equivalent physical and virtual boxes of three different sizes. Predicted errors were calculated from a geometric model based on user interpupillary distance, eye location, distance from the eyes to the projector screen, and object.Results: Users were 1.64 times less accurate (p < .001) and spent 1.49 times more time (p = .01) targeting virtual versus physical box corners using the hands. Predicted virtual targeting errors were on average 1.53 times (p < .05) greater than the observed errors for farther virtual targets but not significantly different for close-up virtual targets.Conclusion: Target size, location, and distance, in addition to binocular disparity, affected virtual object targeting inaccuracy. Observed virtual box inaccuracy was less than predicted for farther locations, suggesting possible influence of cues other than binocular vision.Application: Human physical interaction with objects in VR for simulation, training, and prototyping involving reaching and manually handling virtual objects in a CAVE are more accurate than predicted when locating farther objects.

The SculptUp system enables the rapid creation of 3D models. All of the models in Figure 3 were created in under five minutes. Scenes such as Figure 1 and 4 could be easily created in ways that would be extremely difficult in traditional modeling systems.

This contribution discusses a multi-physics simulation engine, called Chrono, that relies heavily on parallel computing. Chrono aims at simulating the dynamics of systems containing rigid bodies, flexible (compliant) bodies, and fluid-rigid body interaction. To this end, it relies on five modules: equation formulation (modeling), equation solution (simulation), collision detection support, domain decomposition for parallel computing, and post-processing analysis with emphasis on high quality rendering/visualization. For each component we point out how parallel CPU and/or GPU computing have been leveraged to allow for the simulation of applications with millions of degrees of freedom such as rover dynamics on granular terrain, fluid-structure interaction problems, or large-scale flexible body dynamics with friction and contact for applications in polymer analysis.

Virtual Reality environments have the ability to present users with rich visual representations of simulated environments. However, means to interact with these types of illusions are generally unnatural in the sense that they do not match the methods humans use to grasp and move objects in the physical world. We demonstrate a system that enables users to interact with virtual objects with natural body movements by combining visual information, kinesthetics and biofeedback from electromyograms (EMG). Our method allows virtual objects to be grasped, moved and dropped through muscle exertion classification based on physical world masses. We show that users can consistently reproduce these calibrated exertions, allowing them to interface with objects in a novel way.