CAREER: Automated physics-based distillation of coherent structures and mechanisms in unsteady and turbulent flows

职业：基于物理的自动蒸馏非定常和湍流中的相干结构和机制

• 批准号: 2238770
• 负责人: Scott Dawson
• 金额: $ 51.63万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238770&HistoricalAwards=false
• 关键词: CAREER; Automated; physics; based; distillation

Identifying and understanding the fundamental mechanisms that sustain unsteady and turbulent flows is important for ensuring accurate prediction and effective optimization and control across a broad range of applications, such as for reducing drag on aerodynamic vehicles, improving efficiency in wind energy harvesting devices, and improving patient outcomes via cardiovascular flows. While we have a good understanding of such mechanisms for simple flows, new tools will be required to obtain similar levels of understanding for a broader range of applications of real-world relevance. This project will develop methods that enable such mechanisms to be identified in an automated and unambiguous manner, with minimal data and computational requirements. These technical developments will be coupled with educational and outreach initiatives involving the research community, university students in coursework and research, K-12 students, and members of local community groups on Chicago's South Side.The proposed research will develop two central ideas to address the challenge identified above, with an overall goal of developing a methodology to isolate dominant coherent structures and mechanisms in an unambiguous, automated, and computationally efficient manner. The first idea involves applying sparsity-promoting methods to physics-based modeling tools to uncover minimal-physics models without needing the human insight and/or trial-and-error that would otherwise be required. The second idea considers methods to approximate the behavior of these mechanisms using analytic rather than numerical methods, leveraging ideas from wave-packet pseudo-spectral theory. This formulation in turn enables additional analysis methods conducive to studying a broader class of mechanisms, in particular allowing for highly nonlinear behavior to be modeled. To demonstrate their utility, these methods will be applied on a range of fluid flows, including incompressible and compressible parallel shear flows, flows with secondary mean flow components induced by sidewalls, and cardiovascular flows with more complex geometries. An improved ability to identify and manipulate the coherent structures that exist within turbulent flows can benefit a broad range of applications, with the potential to decrease friction drag on air, sea, and ground transport vehicles, increase the efficiency of wind turbines, and enhance understanding and treatment of cardiovascular diseases.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.

识别和理解维持不稳定和湍流的基本机制对于确保在广泛的应用中进行准确的预测以及有效的优化和控制非常重要，例如减少空气动力学车辆的阻力，提高风能收集设备的效率，以及改善病人的情况通过心血管流量的结果。虽然我们对简单流程的此类机制有了很好的理解，但仍需要新的工具来获得与现实世界相关的更广泛应用的类似水平的理解。该项目将开发一些方法，使这些机制能够以自动化和明确的方式进行识别，并且需要最少的数据和计算要求。这些技术发展将与涉及研究社区、参与课程和研究的大学生、K-12 学生以及芝加哥南区当地社区团体成员的教育和推广计划相结合。拟议的研究将提出两个中心思想来解决上面提到的挑战，总体目标是开发一种方法，以明确、自动化和计算高效的方式隔离主要的相干结构和机制。第一个想法涉及将稀疏性促进方法应用于基于物理的建模工具，以发现最小物理模型，而不需要人类的洞察力和/或反复试验。第二个想法考虑利用波包伪谱理论的思想，使用分析方法而不是数值方法来近似这些机制的行为。这种公式反过来又支持额外的分析方法，有利于研究更广泛的机制，特别是允许对高度非线性行为进行建模。为了证明其实用性，这些方法将应用于一系列流体流动，包括不可压缩和可压缩平行剪切流、具有由侧壁引起的二次平均流分量的流以及具有更复杂几何形状的心血管流。识别和操纵湍流中存在的相干结构的能力的提高可以使广泛的应用受益，有可能减少空中、海上和地面运输车辆的摩擦阻力，提高风力涡轮机的效率，并增强理解该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。