Aeromat

A sustainable energy future requires that we both do more with less, and that we fully exploit the renewable energy sources we have available. In this project we explore a common thread between these two approaches, developing tools to better explore and understand aerodynamic design. On the one hand our tools can be used to improve the performance of aerodynamic vehicles, and on the other improving our ability to harvest energy from wind. We develop automated methods for the design of complete aerodynamic structures, using machine-learning techniques to guide iterative experimentation with novel designs. We focus on: Optimization of entire structures, rather than iterative improvement on existing designs Human-machine collaborative design exploration, to discover innovative design concepts Inclusion of structural mechanics and fluid structure interaction into the optimization, design, and modeling process Modeling techniques to support these goals, using data-driven approaches to approximate computationally intensive techniques and simulations In particular we face challenges when creating tools which address these issues in tandem, such as: modeling the performance of designs produced with non-traditional parameterizations broad exploration of possible designs in computationally demanding contexts optimization and modeling of aerodynamic and structural properties simultaneously