Abstract:
Multidisciplinary Design Optimization (MDO) is a powerful engineering tool that allows designers to incorporate information from all relevant design disciplines simultaneously. In aerospace applications, for example, MDO has been used to produce designs that incorporate both the structural and aerodynamic disciplines. It is not generally possible to optimize the objectives of all disciplines simultaneously, so producing an optimal design requires a human designer to balance the tradeoffs between the various objectives. We propose and implement a novel system that helps the designer explore the various possible tradeoffs and systematically find their most preferred design. We show that the system converges to the most preferred design in a simulated task and discuss how it could be of practical use in a MDO problem.

BIO:  Paul Reverdy received the B.S. degree in engineering physics and the B.A. degree in applied mathematics from the University of California, Berkeley in 2007 and the M.A. degree in mechanical and aerospace engineering from Princeton University in 2011. He is currently a Ph.D. candidate in mechanical and aerospace engineering at Princeton University.  From 2007 to 2009, he worked as a research assistant at the Federal Reserve Board of Governors. His research interests include human decision-making in spatial search tasks, the development of automated vehicles for exploring remote environments, and the formal integration of humans and automation for exploration and search applications.