NSF-BSF: Investigation of multi-scale turbulence coupling by goal-oriented adaptive surface modulation

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Turbulent flows have a significant influence on the efficient operation of ships, automobiles, and aircraft, as well as on the safe design of buildings, bridges, and wind turbines. The main characteristic that defines turbulent flows is a strong coupling between large-scale structural flow features and fluctuations at extremely small length and time scales. A better understanding of this link across disparate scales is essential for modulating flow characteristics to improve the optimize efficiency of engineering designs and to reduce noise generation. Therefore, the primary aim of this project is to use experiments and computations to leverage novel control and analysis techniques to modulate turbulent flows with the aim of reducing unsteadiness in bluff-body wakes. The project will also provide an invaluable opportunity to promote early enthusiasm for science and engineering among high school students from underrepresented backgrounds, who will be hosted at a week-long engineering summer camp where they will be introduced to various aspects of fluid mechanics that permeate our day-to-day lives.The proposed research will investigate unsteady interactions between large- and small-scale turbulent coherent structures around a cylindrical bluff body by combining distributed surface actuation with deep-learning techniques. The study will help address fundamental gaps in current knowledge regarding non-linear interactions that regulate the spatiotemporal evolution of coherent structures in separated turbulent flows. Prior attempts at building a general understanding of coherent structure interactions have primarily on linear analysis techniques suitable mostly for canonical, equilibrium flows. The proposed research will also develop a better understanding of the suppression of wake unsteadiness. Deep-learning algorithms will be leveraged both to facilitate the coordinated perturbation of the flow via a distributed array of moving surfaces on the cylindrical body and to extract the localized behavior of dynamically important coherent structures from the non-equilibrium system. The unique combination of unsteady flow forcing, distributed adaptive wall response, and deep-learning based control and analysis will offer a broad perspective on the dynamics of turbulent coherent motions.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.