The current state-of-the-art of fluid dynamic models of the flow over wing sections are either computationally too expensive to be used in preliminary design phases or overly simplified, neglecting some essential physics. The objective of this proposal is to fill this gap by developing efficient aerodynamic models that capture the main physics of the flow dynamics. Relying on a unique combination of the fundamental knowledge of basic unsteady aerodynamics and the recent advances in data science (for example, machine learning techniques), this project will yield more realistic computations of the aerodynamic loads. The developed theory will be indispensable for preliminary design of next generation flying vehicles (flying taxis, micro-air-vehicles, and drones) and wind turbines, which will improve national security and economic prosperity. The project will include the development of a graduate course on unsteady aerodynamics based on this research, plans are underway to develop a textbook on the subject, and undergraduate students will be involved byway of aerodynamic-related senior projects.

Realizing that the lift generation and vorticity production are essentially viscous processes, a viscous extension of the classical theory of unsteady aerodynamics is proposed. This extension will be achieved by coupling the potential theory with a special boundary layer theory that resolves the flow details in the vicinity of the trailing edge. This approach will provide the circulation dynamics without any need for an auxiliary condition (i.e., Kutta-like condition). Moreover, to generalize this approach, machine learning tools will be employed to extract information about the circulation dynamics from high-fidelity simulations. This modeling approach can be conceptually described by a bow tie architecture: a compressive mapping taking a high-dimensional input space (e.g., flow field from high-fidelity simulation) to a smaller, more compact lower-dimensional core model (e.g., one-dimensional circulation dynamics). Then, the potential flow theory, with the accurate circulation dynamics, will be used to recover a high-dimensional output space of the flow field.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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University of California Irvine
United States
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