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。大部分能量用于克服由气体和液体的湍流产生的阻力;只有适度的阻力减少才会导致大量的燃料节省。 阻力与湍流有关，在壁面附近产生涡量集中的区域。 先前用于减小阻力的方法，例如将物质（例如聚合物）引入流中，没有以有针对性的方式利用湍流的已知结构。当聚合物被注入或流血的近壁湍流边界层时，它们变得随机分布，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减阻？本方法在减少阻力方面的成功预期将促使专注于开发微颗粒的技术的出现，所述微颗粒可以检测其自身流动环境的性质并通过改变该环境来响应。例如，将首先检查基于其密度将自身分离成湍流结构并随后溶解的颗粒，但未来的智能颗粒可能会感测局部流动特性，例如流动应变率，并随后将其自身引导到其影响可能最具影响力的流动区域。很容易想象减少船舶、汽车、火车和飞机的阻力将对社会产生广泛的影响。拟议的工作旨在专门针对这些结构，允许颗粒，小于最小湍流长度尺度和适当的形状或密度，携带和释放减阻剂，因为它们以自然的方式收集在这些结构内或周围。理想情况下，当一个这样的结构被破坏时，剩余的颗粒将以破坏性级联的方式迁移到下一个。将确定颗粒特性（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）和在减少阻力方面最有效的智能注入技术。建议研究这一概念，使用直接数值模拟的Navier-Stokes方程（描述流体运动）的过渡情况下的湍流斑演变和充分湍流平板边界层和通道流的情况下。这些情况涵盖了与船舶周围以及管道内流动相关的典型过渡和湍流内部和外部流态。该奖项反映了NSF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.