- PhD, Mechanical Engineering, Carnegie Mellon University, 2013
- MS, Mechanical Engineering, Carnegie Mellon University, 2010
- BS, Mechanical Engineering, Case Western Reserve University, 2008
Dr. DuPont’s research focuses on the development and application of computational design tools for renewable energy systems and sustainable product design.
Dr. DuPont's research focuses on the development and application of computational design tools for solving real-world sustainability issues. Tackling these challenges computationally - like the need for renewable energy systems optimization, collaborative energy systems design, and sustainable product development - can help in breaking down barriers to implementation and boost the acceptance of sustainable design by providing an accurate picture of the behavior of the system prior to its development.
While completing her Ph.D. in Mechanical Engineering at Carnegie Mellon University (2013), Dr. DuPont studied the optimization of wind farm micrositing and turbine geometry selection by developing an advanced Extended Pattern Search (EPS) algorithm within a multi-agent system that utilized realistic wind site parameters, such as wind data, topographical variation, and atmospheric stability conditions. She is continuing to explore the computational optimization of wind farms, as well as wave energy conversion systems and collaborative energy systems. Dr. DuPont is currently exploring the means through which renewable energy systems have been integrated into the traditional U.S. electricity supply, in order to better design large-scale collaborative energy systems to meet increased electricity demand. In addition, Dr. DuPont is interested in building computational tools to help product developers better understand the life-cycle impact of a potential design during the early design phase, enabling product developers to make substantive sustainable design decisions as part of the traditional design process.
Dr. DuPont's work has been published by ASME and AIAA, and her work is funded by Oregon State University, NSF, NETL, and OregonBEST/BPA.