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The efficiency of solar panels depends on the operating temperature. As the panel temperature rises, efficiency drops. Thus, the solar energy community aims to understand the factors that influence the operating temperature, which include wind speed, wind direction, turbulence, ambient temperature, mounting configuration, and solar cell material. We use high-resolution numerical simulations to model the flow and thermal behavior of idealized solar farms. Because these simulations model such complex behavior, advanced visualization techniques are needed to investigate and understand the results. Here, we present advanced 3D visualizations of numerical simulation results to illustrate the flow and heat transport in an idealized solar farm. The findings can be used to understand how flow behavior influences module temperatures, and vice versa.

Increasingly, biomedical researchers need to build functional computer models from images (MRI, CT, EM, etc.). The "pipeline" for building such computer models includes image analysis (segmentation, registration, filtering), geometric modeling (surface and volume mesh generation), large-scale simulation (parallel computing, GPUs), large-scale visualization and evaluation (uncertainty, error). In my presentation, I will present research challenges and software tools for image-based biomedical modeling, simulation and visualization and discuss their application for solving important research and clinical problems in neuroscience, cardiology, and genetics.

Modern high performance computers have speeds measured in teraflops and petabytes and handle data set sizes measured in terabytes and petabytes. Although these machines offer enormous potential for solving very large-scale realistic computational problems, their effectiveness will hinge upon the ability of human experts to interact with their simulation results and extract useful information. One of the greatest scientific challenges of the 21st century is to effectively understand and make use of the vast amount of information being produced. Visual data analysis will be among our most important tools in helping to understand such large-scale information. Our research at the Scientific Computing and Imaging (SCI) Institute at the University of Utah has focused on innovative techniques for 3D visual data analysis. In this talk, I will present state- of-the-art visualization techniques and software, applied to problems in computational science, engineering, and medicine and discuss current challenges and future opportunities.

We present a simple yet efficient calibration and registration procedure for improving the overall static display accuracy of the Visual Haptic Workbench. The procedure is used for precisely colocating the visual and haptic workspaces of the system and is divided into three stages. First, we calibrate and register the PHANToM to the display surface of the workbench. Second, we calibrate the tracking system by attaching a rigid extension between the tracker sensor and the PHANToM stylus. Third, we interactively find the remaining unknown display parameters including eye and hotspot offsets as well as a local reference frame. Initial evaluation of the approach indicates that it is possible to improve static display accuracy by at least an order of magnitude for this system.