Chris Henze

Feature extraction of separation and attachment lines

Separation and attachment lines are topologically significant curves that exist on 2D surfaces in 3D vector fields. Two algorithms are presented, one point-based and one element-based, that extract separation and attachment lines using eigenvalue analysis of a locally linear function. Unlike prior techniques based on piecewise numerical integration, these algorithms use robust analytical tests that can be applied independently to any point in a vector field. The feature extraction is fully automatic and suited to the analysis of large-scale numerical simulations. The strengths and weaknesses of the two algorithms are evaluated using analytic vector fields and also results from computational fluid dynamics (CFD) simulations. We show that both algorithms detect open separation lines-a type of separation that is not captured by conventional vector field topology algorithms.

Feature detection in linked derived spaces

This paper describes by example a strategy for plotting and interacting with data in multiple metric spaces. The example system was designed for use with time-varying computational fluid dynamics (CFD) data sets, but the methodology is directly applicable to other types of field data. The central objects embodied by the tool are portraits, which show the data in various coordinate systems, while preserving their spatial connectivity and temporal variability. The coordinates are derived in various ways from the field data, and an important feature is that new and derived portraits can be created interactively. The primary operations supported by the tool are brushing and linking: the user can select a subset of a given portrait, and this subset is highlighted in all portraits. The user can combine highlighted subsets from an arbitrary number of portraits with the usual logical operators, thereby indicating where an arbitrarily complex set of conditions holds. The system is useful for exploratory visualization and feature detection in multivariate data.

Large field visualization with demand-driven calculation

Presents a system designed for the interactive definition and visualization of fields derived from large data sets: the Demand-Driven Visualizer (DDV). The system allows the user to write arbitrary expressions to define new fields, and then apply a variety of visualization techniques to the result. Expressions can include differential operators and numerous other built-in functions. Determination of field values, both in space and in time, is directed automatically by the demands of the visualization techniques. The payoff of following a demand-driven design philosophy throughout the visualization system becomes particularly evident when working with large time-series data, where the costs of eager evaluation alternatives can be prohibitive.

LightForce Photon-pressure Collision Avoidance: Efficiency Analysis in the Current Debris Environment and Long-Term Simulation Perspective

This work provides an efficiency analysis of the LightForce space debris collision avoidance scheme in the current debris environment and describes a simulation approach to assess its impact on the long-term evolution of the space debris environment. LightForce aims to provide just-in-time collision avoidance by utilizing photon pressure from ground-based industrial lasers. These ground stations impart minimal accelerations to increase the miss distance for a predicted conjunction between two objects. In the first part of this paper we will present research that investigates the short-term effect of a few systems consisting of 20 kW class lasers directed by 1.5 m diameter telescopes using adaptive optics. The results found such a network of ground stations to mitigate more than 85 percent of conjunctions and could lower the expected number of collisions in Low Earth Orbit (LEO) by an order of magnitude. While these are impressive numbers that indicate LightForces utility in the short-term, the remaining 15 % of possible collisions contain (among others) conjunctions between two massive objects that would add large amount of debris if they collide. Still, conjunctions between massive objects and smaller objects can be mitigated. Hence, we choose to expand the capabilities of the simulation software to investigate the overall effect of a network of LightForce stations on the long-term debris evolution. In the second part of this paper, we will present the planned simulation approach for that effort. For the efficiency analysis of collision avoidance in the current debris environment, we utilize a simulation approach that uses the entire Two Line Element (TLE) catalog in LEO for a given day as initial input. These objects are propagated for one year and an all-on-all conjunction analysis is performed. For conjunctions that fall below a range threshold, we calculate the probability of collision and record those values. To assess efficiency, we compare a baseline (without collision avoidance) conjunction analysis with an analysis where LightForce is active. Using that approach, we take into account that collision avoidance maneuvers could have effects on third objects. Performing all-on-all conjunction analyses for extended period of time requires significant computer resources; hence we implemented this simulation utilizing a highly parallel approach on the NASA Pleiades supercomputer.

20,000 bits under the sea: how robotics, visualization, and scientific computing are changing the way we explore, discover, and understand our oceans: Copyright restrictions prevent ACM from providing the full text for this work.

Oceans cover more than 70% of our planet, yet more is known about outer space. Computer graphics, robotics, user-interface design, visualization, and scientific computing are changing how oceanographers explore the deep.