Data-Driven Model Reduction and Real-Time Estimation and Control of Coherent Structures in Turbulent Flows

这笔拨款将支持能够促进我们研究、模拟和控制湍流方式的范式转变的研究，这些湍流的潜在效益可能具有重大的经济和环境影响。主动流量控制方法被设想为在许多现实世界的航空航天和运输工程应用中发挥关键作用，通过对减少阻力的努力产生积极影响，例如飞机，火车和卡车（从而提高性能，减少燃料消耗和温室气体排放）。所研究的方法也可以应用于大型风力涡轮机阵列提取可再生能源的技术。此外，该研究项目将促进全国范围内的努力，以实现快速发展的数据科学与传统工程领域（如控制理论和流体动力学）之间的协同作用。为了促进研究的传播和可重复性，算法将通过相关的在线平台和存储库向公众提供。这项研究还将有助于从代表性不足和少数群体中招募研究生，帮助本科生研究和K-12的推广，并为湍流控制课程创建一揽子材料。在这个研究项目中，我们将创造新的主动流动控制方法，其中包括检测和操纵所谓的大规模连贯结构的机制，这些结构表征了壁界湍流。这项工作的长期目标是为湍流的建模、估计和控制开发一个整体框架，这可以在人们可以操纵和利用以大规模相干结构为特征的壁界湍流的方式上引起范式转变。我们的方法的关键思想在于直接检测和控制被视为机会目标的孤立结构。该工作的具体目标是：(i)通过具有力场输入的层流和湍流边界层的高保真直接数值模拟生成数据。这些数据与促进稀疏性的优化工具一起，将构成控制输入流的简化顺序描述的基础。（ii）分别基于多模型估计和随机控制技术，为流场估计和控制创建鲁棒的实时算法；（iii）使用直接数值模拟验证用于大规模相干结构的检测和选择性操作（例如转向或远离目标区域）的算法。在工作中开发的算法将通过选择性转向大规模运动的风力涡轮机阵列性能优化的抽象版本进行演示。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。

This grant will support research that can promote a paradigm shift in the way we study, simulate and control turbulent flows whose potential benefits can have a significant economic and environmental effect. The active flow control methods are envisioned to play a key role in many real-world aerospace and transportation engineering applications by having a positive impact on efforts to reduce drag on, for example, airplanes, trains, and trucks (and thus increase performance and reduce fuel consumption and greenhouse gas emissions). The researched methods can also find application in the technology of extracting renewable energy by large arrays of wind turbines. In addition, this research project will promote nationwide efforts to enable synergies between the rapidly emerging data sciences and traditional engineering fields such as control theory and fluid dynamics. To promote research dissemination and reproducibility, the algorithms will be made available to the public by means of relevant online platforms and repositories. This research will also help recruit graduate students from underrepresented and minority groups, help undergraduate research and K-12 outreach and create a package of material for a course on turbulent flow control.In this research project, we will create novel active flow control methods which incorporate mechanisms for the detection and manipulation of the so-called large-scale coherent structures that characterize wall-bounded turbulent flows. The long-term goal of this effort is to develop a holistic framework for modeling, estimation and control of turbulent flows which can induce a paradigm shift in the way one can manipulate and exploit wall bounded turbulence characterized by large-scale coherent structures. The key idea of our approach lies in the direct detection and control of isolated structures viewed as targets of opportunity. The specific objectives of the work are (i) Generate data through high-fidelity direct numerical simulations of a laminar and a turbulent boundary layer with force-field inputs. These data together with sparsity promoting optimization tools will form the basis for reduced order descriptions of the flow to control inputs. (ii) Create robust real-time algorithms for flow field estimation and control based on respectively, multi-model estimation and stochastic control techniques, and (iii) Validate the algorithms for detection and selective manipulation (e.g. steering toward or away from a target region) of large-scale coherent structures using direct numerical simulations. The algorithms developed in the work will be demonstrated in an abstracted version of a wind turbine array performance optimization by selective steering of large-scale motions.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.