Data-Driven Model Reduction and Real-Time Estimation and Control of Coherent Structures in Turbulent Flows
湍流中相干结构的数据驱动模型简化和实时估计与控制
基本信息
- 批准号:2052811
- 负责人:
- 金额:$ 46.07万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-01 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
这笔赠款将支持研究,可以促进我们研究,模拟和控制湍流的方式的范式转变,其潜在的好处可以有显着的经济和环境影响。主动流控制方法被设想通过对减少例如飞机、火车和卡车上的阻力的努力具有积极影响(并且因此提高性能并减少燃料消耗和温室气体排放)而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。所研究的方法也可以应用于通过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,使快速新兴的数据科学与控制理论和流体动力学等传统工程领域之间的协同作用。为了促进研究的传播和可重复性,这些算法将通过相关的在线平台和知识库向公众提供。这项研究也将有助于招收研究生的代表性不足和少数群体,帮助本科生的研究和K-12推广,并创建一个包的材料湍流控制的课程。在这个研究项目中,我们将创建新的主动流动控制方法,其中包括检测和操纵的机制,所谓的大尺度相干结构的特点壁边界湍流。这项工作的长期目标是开发一个整体的框架,用于湍流的建模,估计和控制,这可以引起一个范式转变的方式,可以操纵和利用壁有界湍流的特点是大规模的相干结构。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。这项工作的具体目标是:(i)利用力场输入,通过层流和湍流边界层的高保真直接数值模拟生成数据。这些数据与稀疏性促进优化工具一起将形成控制输入的流的降阶描述的基础。(ii)分别基于多模型估计和随机控制技术创建用于流场估计和控制的鲁棒实时算法,以及(iii)使用直接数值模拟来验证用于检测和选择性操纵(例如,朝向或远离目标区域转向)大规模相干结构的算法。在这项工作中开发的算法将被证明在一个抽象版本的风力涡轮机阵列性能优化的选择性转向的大规模motions.This奖项反映了NSF的法定使命,并已被认为是值得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的支持。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Multiple Model Dynamic Mode Decomposition for Flowfield and Model Parameter Estimation
流场多模型动态模式分解和模型参数估计
- DOI:10.2514/6.2022-2427
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Tsolovikos, Alexandros;Suryanarayanan, Saikishan;Bakolas, Efstathios;Goldstein, David B.
- 通讯作者:Goldstein, David B.
On the effect of manipulating Large Scale Motions in a Boundary Layer
关于在边界层中操纵大尺度运动的效果
- DOI:10.2514/6.2022-3771
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Jariwala, Akshit;Tsolovikos, Alexandros;Suryanarayanan, Saikishan;Goldstein, David B.;Bakolas, Efstathios
- 通讯作者:Bakolas, Efstathios
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Efstathios Bakolas其他文献
Efstathios Bakolas的其他文献
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{{ truncateString('Efstathios Bakolas', 18)}}的其他基金
Collaborative Research: Real-Time Trajectory Generation Algorithms for Uncertain Autonomous Systems Based on Gaussian Processes
合作研究:基于高斯过程的不确定自治系统实时轨迹生成算法
- 批准号:
1937957 - 财政年份:2020
- 资助金额:
$ 46.07万 - 项目类别:
Standard Grant
NRI: FND: Efficient algorithms for safety guiding mobile robots through spaces populated by humans and mobile intelligent machines and robots
NRI:FND:用于安全引导移动机器人穿过人类和移动智能机器和机器人居住的空间的高效算法
- 批准号:
1924790 - 财政年份:2019
- 资助金额:
$ 46.07万 - 项目类别:
Standard Grant
EAGER: Microscopic Deployment Algorithms to Achieve Macroscopic Objectives for Spatially Distributed Stochastic Networks of Mobile Agents
EAGER:实现移动代理空间分布式随机网络宏观目标的微观部署算法
- 批准号:
1753687 - 财政年份:2018
- 资助金额:
$ 46.07万 - 项目类别:
Standard Grant
Optimal Path Planning Among Mobile Sources of Threat in Complex Environments
复杂环境下移动威胁源的最优路径规划
- 批准号:
1562339 - 财政年份:2016
- 资助金额:
$ 46.07万 - 项目类别:
Standard Grant
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