Positions

Postdoctoral Fellowship

Aerodynamic Design Optimization via Machine Learning

The goal of our research team is to create efficient, accurate, and scalable deep neural network (DNN) representations of optimal airfoil and wing shapes. The focus application will be aerodynamic shape optimization. We have already created an initial framework for achieving this in Webfoil. We now want to develop more efficient methods that take into account multiple sources of information and that result in more accurate optimal design. The project requires the use of ADflow and MACH-Aero.

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Postdoctoral Fellowship

CFD-based Design Optimization of Wind Turbines

The goal of our team is to develop a computationally efficient mixed-fidelity control co-design optimization framework for floating offshore wind turbine design. Our project will yield a modular framework to support the broad mission of the DOE ARPA-E Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control (ATLANTIS) program. The program description can be found here. The project requires the use of ADflow, MACH-Aero, and OpenMDAO.

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Postdoctoral Fellowship

Multidisciplinary Optimization of Marine Turbines

The goal of our team is to develop computational tools to optimize the design of hydrokinetic turbines. Our project will support the broad mission of the DOE ARPA-E Riverine Kilo-megawatt Systems (SHARKS) program. The program description can be found here. The project requires the use of OpenMDAO.

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