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

湍流对船舶、汽车和飞机的有效运行以及建筑物、桥梁和风力涡轮机的安全设计具有重要影响。定义湍流的主要特征是大尺度结构流动特征与极小长度和时间尺度上的波动之间的强耦合。更好地理解这种跨不同尺度的联系对于调节流动特性以提高工程设计的优化效率和减少噪声产生至关重要。因此，该项目的主要目的是利用实验和计算来利用新的控制和分析技术来调节湍流，以减少崖体尾迹的不稳定性。该项目还将提供一个宝贵的机会，促进来自代表性不足背景的高中生早期对科学和工程的热情，他们将参加为期一周的工程夏令营，在那里他们将被介绍到渗透到我们日常生活中的流体力学的各个方面。提出的研究将通过结合分布式表面驱动和深度学习技术来研究圆柱钝体周围大、小尺度湍流相干结构之间的非定常相互作用。该研究将有助于解决当前关于调节分离湍流中相干结构时空演化的非线性相互作用的知识的基本空白。在建立对连贯结构相互作用的一般理解方面，先前的尝试主要是基于线性分析技术，这些技术主要适用于典型的平衡流。提出的研究也将有助于更好地理解尾流不稳定的抑制。深度学习算法将被用于促进通过圆柱体上移动表面的分布式阵列的流动的协调扰动，并从非平衡系统中提取动态重要的相干结构的局部行为。非定常流强迫、分布自适应壁面响应和基于深度学习的控制和分析的独特组合将为湍流相干运动的动力学提供广阔的视角。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.