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dc.contributor.authorLeamy, Michael
dc.date.accessioned2009-10-14T16:44:28Z
dc.date.available2009-10-14T16:44:28Z
dc.date.issued2009-09-18
dc.identifier.urihttp://hdl.handle.net/1853/30458
dc.descriptionMichael Leamy of the School of Mechanical Engineering presented a lecture on September 18, 2009 at 2:00 pm in room 2443 of the Klaus Advanced Computing Building on the Georgia Tech campus.en
dc.description.abstractThis talk will discuss two M&S research directions being pursued by the speaker in the GWW School of Mechanical Engineering. Both approaches are explicit and time-marching in nature, and should therefore be amenable to parallelization strategies (and thus collaboration). The first part of the talk will detail a multi-scale physics-based modeling approach for simulating protein dynamics. The multi-scale continuum formulation to be described uses an intrinsic continuum formulation, and subsequent finite element discretization, informed by interatomic potentials (commonly found in molecular dynamics simulations). In the current context, intrinsic refers to a description of the protein's configuration using curvatures and strains vice displacements and rotations. The advantage of doing so is that highly-curved and twisted geometries associated with proteins in their native conformation can be accurately modeled with a sparse discretization. This positively impacts the degrees of freedom required, and more importantly, the time step required for stability. The second part of the talk will discuss a Cellular Automata (CA)-based approach for simulating elastic wave propagation in structures. By generalizing the cell shape to triangles, it will be shown that the approach has the same utility as the structural finite element method, with a number of advantages. First, code development is greatly simplified and fits naturally with modern, object-oriented languages. Second, all interactions are local and thus the method avoids the need for a central authority, easing parallelization. Third, the discontinuous nature of the state representation appears to be responsible for more-accurate wavefront modeling than traditional finite element approaches. Similar treatments are expected to perform as well with electromagnetic wave propagation, and could potentially be event-driven.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesComputational Science and Engineering Seminar Seriesen_US
dc.subjectFinite elementen
dc.subjectCellular automataen
dc.subjectProtein dynamicsen
dc.subjectMultiscaleen
dc.titleMulti-Scale Protein Modeling and Cellular Automata : Two Opportunities for Collaborationen
dc.typeLectureen
dc.typeVideoen
dc.contributor.corporatenameGeorgia Institute of Technology. School of Mechanical Engineering


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