Towards manipulating three-dimensional bluff-body wakes.

风模式的变化和建筑物、地面车辆或裸露管道的背风处的阵风都是湍流钝体尾迹的例子。水流撞击在阀体的前部，并沿两侧分离或分离，从而形成反向流动区域。回流区动力学以脉动的速度和压力为特征。脉动幅度取决于在回流区产生的运动涡旋的强度和相互作用。这些动力学对物体产生有害的非定常作用力，增加阻力并增强下游的分散性。相比之下，钝体附件用于化学反应器或燃烧室，以增强混合并有利于更高的产品产量或更清洁的燃烧。因此，操纵钝体尾迹以抑制或放大动力学的能力具有工程意义。拟议的研究计划是一个了解和操纵尾流的框架。要对尾流动力学背后的物理基础有一个基本的了解。为此，将描述来流和体型对旋涡强度及其相互作用的影响。从这一基线开始，将研究由沿障碍物表面脉动喷流引入的局部强迫对尾流动力学的影响。这些操纵如何影响非定常作用力、阻力和下游混合将被描述。这些研究是通过发展新的技术来描述和模拟涡旋相互作用，基于紧凑的数学公式，预测对局部强迫的响应，并优化控制参数以抑制或放大尾迹波动。优化将基于机器学习原则。这些技术来自数学、信号处理和人工智能社区，突显了这项研究的跨学科性质。该程序开发的知识和模型适用于一大类湍流分离流动，因此对研究界来说是基本感兴趣的。所开发的建模技术与工业应用相关。这些技术产生了流量监测和早期检测的新技术，适用于难以进入或偏远环境的过程控制和损害缓解，如化学反应堆和暴露的管道。在尾流操纵中获得的知识预计将对以下方面具有重要意义：地面运输、降低燃料消耗和提高高速稳定性；以及对于化学反应器或燃烧室，在那里混合控制效率、产品质量和产量。紧凑的建模和机器学习有助于自主系统的设计。总体而言，该计划为培养技能和培训新兴技术方面的高素质人员提供了环境。

Shifting wind patterns and gusting in the lee of buildings, ground vehicles or exposed pipelines are examples of turbulent bluff-body wakes. The flow impinges on the front of the body and detaches, or separates, along the sides resulting in reverse flow regions. The recirculating region dynamics are characterized by fluctuating velocity and pressure. The fluctuation amplitude depends on the strength and interaction of moving vortices generated in the recirculating region. These dynamics induce deleterious unsteady forces on the body, increase drag and enhance dispersion downstream. In contrast, bluff-body appendages are used in chemical reactors or combustors to enhance mixing and benefit higher product yield or cleaner combustion. Hence, the ability to manipulate bluff-body wakes to suppress or amplify dynamics is of engineering interest. The proposed research program is a framework for understanding and manipulating wake flows. A fundamental understanding the physics underlying the wake dynamics is to be gained. To this end, the influence of the on-coming flow and body shape on the strength of vortices and their interactions will be characterized. From this baseline, changes to the wake dynamics due to local forcing, introduced through pulsating jets along the obstacle surface, will be investigated. How these manipulations affect the unsteady forces, drag and downstream mixing will be characterized. These investigations are enabled by new techniques developed to represent and model the vortex interactions, based on compact mathematical formulations, predict the response to local forcing and optimize the manipulation parameters for suppressing or amplifying wake fluctuations. Optimization will be based on machine learning principles. These techniques are drawn from mathematics, signal processing and artificial intelligence communities, highlighting the interdisciplinary nature of this research. The knowledge and models developed in the program are applicable to a broad class of turbulent separated flows and thus of fundamental interest to the research community. The developed modelling techniques are relevant to industrial applications. These yield new technologies for flow monitoring and early-detection, which are suited for process control and damage mitigation for inaccessible or remote environments, such as chemical reactors and exposed pipelines. The knowledge gained in wake manipulation is anticipated to be significant for: ground transportation, reducing fuel consumption and increasing high-speed stability; and for chemical reactors or combustors, where mixing controls efficiency, product quality and yield. Compact modelling and machine learning can contribute to the design of autonomous systems. Overall, this program thus offers a context for developing skills and training highly qualified personnel for emerging technologies.