DDDAS-SMRP: Measuring and Controlling Turbulence and Particle Populations

DDDAS-SMRP：测量和控制湍流和粒子群

• 批准号: 0540147
• 负责人: James Rawlings
• 金额: $ 48万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540147&HistoricalAwards=false
• 关键词: DDDAS; SMRP; Measuring; Controlling; Turbulence

This project will advance capabilities in two complex and previously unaddressed applications: (i) measuring, controlling and preventing the formation of turbulence in fluid flow to achieve drag reduction, and (ii) measuring size and shape distributions of populations of crystalline particles, and model-based feedback control of the manufacture of these particles in real time. Given recent developments in the underlying science and required technology, the project provides for the first time a realistic chance at addressing these two complex applications. In turbulence control, computational power now allows direct simulation of turbulent flows, as well as the promise of model-based control approaches. Second, the advent of MEMS technology makes it possible to envisage sensors and actuators that can work at the scale of turbulence-producing coherent structures, which can be on the order of 100-1000 um. Finally, a better fundamental understanding of these coherent structures has recently been achieved. In measuring and controlling particle populations, the project will integrate computational and measurement capability to analyze video microscopy images in real time to determine particle size and shape distributions. By manipulating environmental variables such as pH, impurity concentration, and temperature, we can influence the evolving particle shape, which can be used as a marker for crystal structure (enabling polymorph control) in pharmaceutical applications.The applications chosen are ideal for developing DDDAS tools because of the following features: complex models with large numbers of degrees of freedom, high complexity measurements, significant sources of noise and uncertainty, and significant industrial and economic impact. The research conducted under this project will develop and demonstrate the state estimation method known as moving horizon estimation as the algorithm for assimilating in real time the data and dynamic, nonlinear model, and will develop and implement the autocovariance least squares method for identifying the disturbance structures from the measurement data and models. By identifying the disturbance structures from data, the derived models do not have to be perfect in order to represent and predict the data accurately and enable model-based feedback control. The project will develop the new optimization tools that are required to enable state estimation, disturbance identification, and model-based feedback control. Industrial collaborations and participation in industrial consortia provide ample opportunities for technology transfer of the state estimation and model predictive control (ExxonMobil, Eastman Chemical, Shell), video imaging (MettlerToledo), and crystal size and shape distribution control (Mitsubishi and GlaxoSmithKline).

该项目将在两个复杂且以前未解决的应用中提高功能：（i）测量，控制和防止流体流量中的湍流形成以减少阻力，以及（ii）测量晶体颗粒种群的大小和形状分布，以及基于模型的基于模型的粒子的反馈控制这些粒子的生产。鉴于基础科学和所需技术的最新发展，该项目首次提供了解决这两个复杂应用程序的现实机会。在湍流控制中，计算功率现在可以直接模拟湍流，以及基于模型的控制方法的希望。其次，MEMS技术的出现使得可以设想可以在产生湍流的相干结构的规模上工作的传感器和执行器，该结构可以按100-1000 UM的顺序进行。最后，最近已经实现了对这些连贯结构的更好的基本理解。在测量和控制粒子种群时，该项目将集成计算能力和测量能力，以实时分析视频显微镜图像，以确定粒径和形状分布。通过操纵环境变量，例如pH，杂质浓度和温度，我们可以影响不断发展的粒子形状，在药物应用中，可以用作晶体结构（启用多晶型物控制）的标志物的标记。所选择的应用是开发DDDAS工具的理想选择，因为它具有以下特征：具有以下重要的自由度和高度的自由度和高度的频率，高度的频率和高度的自由度，并构成了巨大的自由度，并构成了巨大的多种程度，并构成了高度的高度，并构成了高度的繁殖，并构成了高度的繁殖，并构成了高度的繁殖度，并构成了高度繁殖的范围，并构成了高度繁殖的范围，并构成了高度繁殖的范围，并构成了高度的繁殖，并构成了高度的范围。 影响。在该项目下进行的研究将开发并证明称为移动范围估计的状态估计方法是实时同化的算法，数据和动态，非线性模型，并将开发和实施自动驾驶最小二乘最小二乘方法，以从测量数据和模型中识别出干扰结构。通过从数据中识别干扰结构，派生的模型不必是完美的，以准确地表示和预测数据并启用基于模型的反馈控制。该项目将开发新的优化工具，以实现状态估计，干扰识别和基于模型的反馈控制。工业合作和参与工业财团为国家估计和模型预测控制（Exxonmobil，Eastman Chemical，Shell），视频成像（Mettletoledo）以及晶体大小和形状分配控制（Mitsubishi和Glaxosmithkline）提供了足够的机会。