Abstract: A number of recent missions by national space agencies (NASA, JAXA, and ESA) to irregularly shaped asteroids has initiated an interest in accurately modeling the irregular gravitational potential field of these bodies. In this study, we examine using non-uniform mascon distributions derived from unstructured volume meshes to model the gravitational potential fields of irregular bodies. The type and topology of the unstructured mesh and its effect on the accuracy of the mascon model is examined. Meshes consisting of either tetrahedral cells or higher-order polyhedral cells with varying degrees of cell-size grading are considered. A unit sphere is used as a test case to compare numerical calculated mascon-based potentials with analytical results. Mascon models are then applied to asteroid 25143 Itokawa. The grid-dependence of the potential field and a spacecraft trajectory are examined as well as the effects of a variable density distribution. Results suggests that with the right mesh type and topology a greater than 90% reduction in the require number of mascons can be achieved in comparison to uniform distributions without sacrificing accuracy.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).