Collaborative Research: Targeting Turbulence Using Smart Particles

合作研究：使用智能粒子瞄准湍流

• 批准号: 1905288
• 负责人: David Goldstein
• 金额: $ 25.79万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1905288&HistoricalAwards=false
• 关键词: Collaborative; Research; Targeting; Turbulence; Using

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.

在美国，大约29%的能源消耗用于运输货物和人员。这些能量大部分用于克服气体和液体湍流产生的阻力；只有略微减少阻力才能节省大量燃料。阻力与湍流有关，在壁面附近形成集中涡量区域。以前的减少阻力的方法，如向流动中引入物质(如聚合物)，没有以有针对性的方式利用湍流的已知结构。当聚合物被注入或排放到近壁湍流边界层时，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。这项工作试图回答：能否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效果？本方法在减少阻力方面的成功预计将推动出现专注于开发微颗粒的技术，这些技术可以检测其自身流动环境的性质并通过改变该环境来作出反应。例如，将首先检查根据其密度将自己分隔成湍流结构并随后溶解的颗粒，但未来的智能颗粒可能会检测到局部流动特性，如流动污渍速率，然后将自己引导到其影响可能最大的流动区域。不难想象，减少船舶、汽车、火车和飞机上的阻力将如何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，允许小于最小湍流长度尺度和适当形状或密度的颗粒携带和释放减阻剂，因为它们以自然方式聚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，剩余的粒子将以破坏性的级联方式迁移到下一个结构中。将确定颗粒特性(特别是颗粒大小、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)和最有效地减少阻力的智能注入技术。对于湍流斑点演化的过渡情况以及平板边界层和槽道流动的完全湍流情况，建议采用直接数值模拟描述流体运动的Navier-Stokes方程来研究这一概念。这些情况涵盖了与船舶和管道内流动相关的典型的过渡和湍流内部和外部流动状态。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。