Advancement in the areas of high performance computing and computational sciences have facilitated the generation of an enormous amount of research data by computational scientists - the volume, velocity and variability of Big 'Research' Data has increased across all disciplines. An immersive and non-immersive analytics platform capable of handling extreme-scale scientific data will enable scientists to visualize unwieldy simulation data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain usable information. Our immersive and non-immersive visualization work is an attempt to provide computational scientists with the ability to analyze the extreme-scale data generated. The main purpose of this paper is to identify different characteristics of a scientific data analysis process to provide a general outline for the scientists to select the appropriate visualization systems to perform their data analytics. In addition, we will include some of the details on how to how the immersive and non-immersive visualization hardware and software are setup. We are confident that the findings in our paper will provide scientists with a streamlined and optimal visual analytics workflow.
Major advancements in computational and sensor hardware have enormously facilitated the generation and collection of research data by scientists - the volume, velocity and variety of Big ’Research’ Data has increased across all disciplines. A visual analytics platform capable of handling extreme-scale data will enable scientists to visualize unwieldy data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain useable information. Reconfigurable Visual Computing Architecture is an attempt to provide scientists with the ability to analyze the extreme-scale data collected. Reconfigurable Visual Computing Architecture requires the research and development of new interdisciplinary technological tools that integrate data, realtime predictive analytics, visualization, and acceleration on heterogeneous computing platforms. Reconfigurable Visual Computing Architecture will provide scientists with a streamlined visual analytics tool.
Fifteen years of experience in designing and implementing a VR integration library have produced a wealth of lessons upon which we can further build and improve our capability to write worthwhile virtual reality applications. The FreeVR virtual reality library is a mature library, yet continues to progress and benefit from the insights and requests encountered during application development. We compare FreeVR with the standard provisions of virtual reality integration libraries, and provide an in-depth look at FreeVR itself. We examine what design decisions worked, and which fell short. In particular, we look at how the features of FreeVR serve to restore applications of the past into working condition and aid in providing longevity to newly developed applications.
Over the last decades, Louisiana has lost a substantial part of its coastal region to the Gulf of Mexico. The goal of the
project depicted in this paper is to investigate the complex ecological and geophysical system not only to find solutions
to reverse this development but also to protect the southern landscape of Louisiana for disastrous impacts of natural
hazards like hurricanes. This paper sets a focus on the interactive data handling of the Chenier Plain which is only one
scenario of the overall project. The challenge addressed is the interactive exploration of large-scale time-depending 2D
simulation results and of terrain data with a high resolution that is available for this region.
Besides data preparation, efficient visualization approaches optimized for the usage in virtual environments are
presented. These are embedded in a complex framework for scientific visualization of time-dependent large-scale
datasets. To provide a straightforward interface for rapid application development, a software layer called VRFlowVis
has been developed. Several architectural aspects to encapsulate complex virtual reality aspects like multi-pipe vs.
cluster-based rendering are discussed. Moreover, the distributed post-processing architecture is investigated to prove its
efficiency for the geophysical domain. Runtime measurements conclude this paper.
The Virtual Hydrology Observatory will provide students with the ability to observe the integrated hydrology simulation
with an instructional interface by using a desktop based or immersive virtual reality setup. It is the goal of the virtual
hydrology observatory application to facilitate the introduction of field experience and observational skills into
hydrology courses through innovative virtual techniques that mimic activities during actual field visits. The simulation
part of the application is developed from the integrated atmospheric forecast model: Weather Research and Forecasting
(WRF), and the hydrology model: Gridded Surface/Subsurface Hydrologic Analysis (GSSHA). Both the output from
WRF and GSSHA models are then used to generate the final visualization components of the Virtual Hydrology
Observatory. The various visualization data processing techniques provided by VTK are 2D Delaunay triangulation and
data optimization. Once all the visualization components are generated, they are integrated into the simulation data
using VRFlowVis and VR Juggler software toolkit. VR Juggler is used primarily to provide the Virtual Hydrology
Observatory application with fully immersive and real time 3D interaction experience; while VRFlowVis provides the
integration framework for the hydrologic simulation data, graphical objects and user interaction. A six-sided CAVE<sup>TM</sup> like
system is used to run the Virtual Hydrology Observatory to provide the students with a fully immersive experience.
Conference Committee Involvement (1)
Virtual, Augmented, and Mixed Reality (XR) Technology for Multi-Domain Operations
26 April 2020 | Anaheim, California, United States