Topology Optimization of Structures and Materials

GT-MAP Seminars
Friday, September 30, 2016 - 3:00pm
1 hour (actually 50 minutes)
Skiles 257

Bio:  Tomas Zegard is a postdoctoral fellow in the School of Civil and Environmental Engineering at Georgia Tech. He received a PhD in Structural Engineering from the University of Illinois at Urbana-Champaign in 2014. Afterwards, he took a position at SOM LLP in Chicago, an Architecture + Engineering firm specializing in skyscrapers. He has made significant contributions to the field of topology optimization through research papers and free open-source tools.   Xiaojia Zhang is a doctoral candidate in the School of Civil and Environmental Engineering at Georgia Tech. She received her bachelor’s and master’s degrees in structural engineering from the University of Illinois at Urbana-Champaign. Her major research interests are structural topology optimization with material and geometric nonlinearity, stochastic programming, and additive manufacturing.    

Topology optimization, an agnostic design method, proposes new and innovative solutions to structural problems. The previously established methodology of sizing a defined geometry and connectivity is not sufficient; in these lie the potential for big improvements. However, topology optimization is not without its problems, some of which can be controlled or mitigated. The seminar will introduce two topology optimization techniques: one targeted at continuum, and one targeted at discrete (lattice-like) solutions. Both will be presented using state-of-the-art formulations and implementations. The stress singularity problem (vanishing constraints), the ill-posedness of the problem, the large number of variables involved, and others, continue to challenge researchers and practitioners. The presented concepts find potential applications in super-tall building designs, aircrafts, and the human body. The issue of multiple load cases in a structure, a deterministic problem, will be addressed using probabilistic methodologies. The proposed solution is built around a suitable damping scheme based on simulated annealing. A randomized approach with stochastic sampling is proposed, which requires a fraction of the computational cost compared to the standard methodologies.