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

湍流对船舶、汽车和飞机的高效运行以及建筑物、桥梁和风力涡轮机的安全设计都有重要影响。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的波动之间的强耦合。更好地了解不同尺度上的这种联系对于调节流动特性、提高工程设计的优化效率和减少噪声产生是至关重要的。因此，本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，目的是减少钝体尾迹的不稳定性。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，他们将在为期一周的工程夏令营中被介绍给他们，在那里他们将了解渗透到我们日常生活中的流体力学的各个方面。拟议的研究将通过结合分布式表面激励和深度学习技术来调查圆柱形钝体周围大小尺度湍流相干结构之间的非稳定相互作用。这项研究将有助于解决目前关于控制分离湍流中相干结构的时空演变的非线性相互作用方面的基本知识空白。先前试图建立对相干结构相互作用的一般理解的主要是线性分析技术，该技术主要适用于规范的平衡流。这项拟议的研究还将对抑制尾迹不稳定有更好的理解。深度学习算法将被用来促进通过圆柱体上分布的运动表面阵列的流动的协调扰动，以及从非平衡系统中提取动态重要的相干结构的局部化行为。非恒定流强迫、分布式自适应墙体响应和基于深度学习的控制和分析的独特组合将为湍流连贯运动的动力学提供一个广阔的视角。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.