Collaborative Research: MSPA-CSE: State Estimation and Predictability of High-Dimensional Complex Systems--Theory and Experiment

合作研究：MSPA-CSE：高维复杂系统的状态估计和可预测性——理论与实验

• 批准号: 0434225
• 负责人: Edward Ott
• 金额: $ 49.98万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0434225&HistoricalAwards=false
• 关键词: Collaborative; Research; MSPA; CSE; State

The goals of this project are to develop techniques for the detection and prediction of low-dimensional features in high-dimensional, spatially extended systems, to investigate how coherent structures within the system affect predictability, to test control strategies for high-dimensional, spatially extended systems, and to improve understanding of uncertainty as a function of spatial scale in such systems. One motivation for the work is the desire to develop better state estimation techniques for complex environmental systems. The model system for this work is that of shallow thermal convection. The project combines laboratory experiment with theory and modeling. As well as being a good model system, convection is important in many environmental systems in the atmosphere, ocean and interior of the Earth, and in industrial settings. Thermal convection exhibits multi-scale spatial and temporal complexity and laboratory convection experiments provide plentiful, high quality, observational data under relatively well-controlled conditions. The laboratory experiments use carbon dioxide as the convecting medium in a shallow cell. Specific spiral-defect chaos flow patterns can be initiated using a computer-steered laser system that selectively heats parts of the fluid. The primary theoretical tool is a state estimation technique in which a local ensemble Kalman filter is applied to a numerical Navier-Stokes solver. This will be applied to a range of flow patterns of increasing complexity exhibited by the convection cell as the Rayleigh number is increased. The data to be assimilated will be taken from shadowgraph images of the convection. Later stages of the project will include experiments in control of the convection using selective heating guided by output from the state estimation system. It is anticipated that the results of this work will be applicable to other spatially-extended complex systems.

该项目的目标是开发用于检测和预测高维空间扩展系统中的低维特征的技术，研究系统内的相干结构如何影响可预测性，测试高维空间扩展系统的控制策略，并提高对此类系统中空间尺度函数的不确定性的理解。 这项工作的一个动机是希望为复杂的环境系统开发更好的状态估计技术。 本工作的模型系统是浅层热对流。 该项目将实验室实验与理论和建模相结合。 对流不仅是一个很好的模型系统，而且在大气、海洋和地球内部的许多环境系统以及工业环境中也很重要。 热对流具有多尺度的时空复杂性，实验室对流实验在相对良好的控制条件下提供了丰富，高质量的观测数据。 实验室实验采用二氧化碳作为对流介质，在浅池中进行。 特定的螺旋缺陷混沌流动模式可以使用计算机操纵的激光系统，选择性地加热部分流体。 主要的理论工具是一种状态估计技术，其中一个本地集合卡尔曼滤波器应用于数值Navier-Stokes求解器。 这将被应用到一系列的流动模式的复杂性增加所表现出的对流细胞作为瑞利数的增加。 要同化的数据将取自对流的阴影图像。 该项目的后期阶段将包括使用由状态估计系统的输出引导的选择性加热来控制对流的实验。 预计这项工作的结果将适用于其他空间扩展的复杂系统。