Collaborative Research: Targeting Turbulence Using Smart Particles

在美国，大约29%的能源消耗用于运输货物和人员。这种能量的大部分用来克服气体和液体的湍流所产生的阻力；只要稍微减少阻力，就能节省大量燃油。阻力与紊流有关，在壁面附近形成集中涡度区域。以前减少阻力的方法，如在气流中引入物质（如聚合物），并没有以一种有针对性的方式利用已知的湍流结构。当聚合物被注入或注入近壁湍流边界层时，它们会变得随机分布，这使得在大多数情况下，使用这些添加剂来减少阻力是不切实际的。这项工作旨在回答：是否可以将含有合适添加剂并具有特定物理性质的微粒引入湍流中，以实现比传统方法更大的阻力降低？目前方法在减少阻力方面的成功，预计将推动专注于开发能够探测自身流动环境性质并通过改变该环境作出反应的微粒的技术的出现。例如，基于其密度将自身分离成湍流结构并随后溶解的颗粒将首先被检查，但未来的智能颗粒可能会感知局部流动特性，例如流动染色率，并随后将自己引导到流动区域，在那里它们的效果可能最具影响力。很容易想象减少船舶、汽车、火车和飞机的阻力会对社会产生怎样广泛的影响。拟议的工作旨在通过允许小于最小湍流长度尺度且具有适当形状或密度的颗粒，在这些结构内部或周围以自然方式收集时携带和释放减阻剂，从而专门针对这些结构。理想情况下，当一个这样的结构被破坏时，剩余的粒子将以破坏性级联的方式迁移到下一个结构。颗粒特性（特别是颗粒尺寸、密度、聚合物特性、聚合物释放机制、颗粒注入位置和注入速率）和智能注入技术将被确定为最有效的减阻技术。本文建议对紊流斑演化的过渡情况、完全紊流的平板边界层和通道流动情况，利用描述流体运动的Navier Stokes方程的直接数值模拟来研究这一概念。这些情况涵盖了典型的过渡和湍流的内部和外部流动的有关流动的船舶以及管道内。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。

Approximately 29 percent of all energy consumed in the U.S. is used to transport goods and people. Most of this energy is used to overcome drag forces produced by the turbulent flow of gases and liquids; only a modest reduction in drag would result in large fuel savings. Drag is associated with turbulent flow creating regions of concentrated vorticity near walls. Previous methods for reducing the drag forces, such as introducing substances (such as polymers) into the flow, did not exploit the known structure of the turbulence in a targeted way. When polymers are injected or bled into the near-wall turbulent boundary layer, they become distributed randomly, making it impractical in most cases to use these additives to reduce drag. This work seeks to answer: Can micro-particles containing a suitable additive and having specific physical properties be introduced into turbulent flow to achieve much greater drag reduction than traditional methods? The success of the present approach in reducing drag is expected to motivate the emergence of technologies focused on the development of micro-particles that can detect the nature of their own flow environment and respond by modifying that environment. For example, particles which segregate themselves into turbulent structures based on their density and subsequently dissolve will be examined first, but future smart particles might sense local flow properties, such as flow stain rates, and subsequently direct themselves to regions of the flow where their effects may be most impactful. It is easy to imagine how reducing drag on ships, cars, trains, and airplanes would have a broad impact on society.The proposed work aims to specifically target these structures by allowing particles, smaller than the smallest turbulent length scale and of the appropriate shape or density, to carry and release drag reducing agents as they collect in a natural way within or around such structures. Ideally, as one such structure is disrupted, remaining particles will migrate to the next in a disruptive cascade. Particle properties (especially particle sizes, densities, polymer properties, polymer release mechanisms, particle injection locations, and injection rates) and smart injection techniques that are most effective in reducing drag will be determined. It is proposed to study this concept using direct numerical simulation of the Navier Stokes equations (which describe fluid motion) for the transitional case of turbulent spot evolution and for the fully turbulent flat plate boundary layer and channel flow cases. These situations cover the canonical transitional and turbulence internal and external flow regimes relevant to flow about ships as well as within pipelines.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.