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Abstract
We present Bento Box, a virtual reality data visualization technique and bimanual 3D user interface for exploratory analysis of 4D data ensembles. Bento Box helps scientists and engineers make detailed comparative judgments about multiple time-varying data instances that make up a data ensemble (e.g., a group of 10 parameterized simulation runs). The approach is to present an organized set of complementary volume visualizations juxtaposed in a grid arrangement, where each column visualizes a single data instance and each row provides a new view of the volume from a different perspective and/or scale. A novel bimanual interface enables users to select a sub-volume of interest to create a new row on-the-fly, scrub through time, and quickly navigate through the resulting virtual “bento box.” The technique is evaluated through a real-world case study, supporting a team of medical device engineers and computational scientists using in-silico testing (supercomputer simulations) to redesign cardiac leads. The engineers confirmed hypotheses and developed new insights using a Bento Box visualization. An evaluation of the technical performance demonstrates that the proposed combination of data sampling strategies and clipped volume rendering is successful in displaying a juxtaposed visualization of fluid-structure-interaction simulation data (39 GB of raw data) at interactive VR frame rates.
Highlights
Science and engineering workflows increasingly rely upon ensembles—“concrete distributions of data, in which each outcome can be uniquely associated with a specific run or set of simulation parameters” (Obermaier and Joy, 2014)
This paper addresses the specific unsolved challenge of visualizing moderate-sized ensembles of state-of-the-art, time-varying fluid-structure interaction simulations run on high-performance computing platforms
The core Bento Box concept and technique described far, which we believe will generalize to other ensemble visualization problems, was inspired by the needs of a specific real-world data analysis problem, using an ensemble of fluid-structureinteraction (FSI) simulations to design improved medical devices
Summary
Science and engineering workflows increasingly rely upon ensembles—“concrete distributions of data, in which each outcome can be uniquely associated with a specific run or set of simulation parameters” (Obermaier and Joy, 2014). Cardiac leads are the electrical cables that connect the heart to an artificial pacemaker device. The data visualized here come from a specific set of simulations designed to understand the impact of lead stiffness and lead length on the blood flow and stresses in the right atrium. A 3 × 3 design was used for the initial study with three lead lengths (108, 110, and 112 mm) and three lead stiffnesses (8, 9, and 10N/mm corresponding to Young’s Modules 1145.92, 1289.16, and 1432.39 MPa), resulting in 9 data instances. One additional run (116 mm, 8 N/mm, and 1145 MPa) was added to the ensemble to understand the extreme case of extending the lead length as far as possible without touching the walls of the atrium
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