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).

该项目将推进两个复杂和以前未解决的应用的能力：（一）测量，控制和防止流体流动中湍流的形成，以实现减阻，和（二）测量晶体颗粒群的大小和形状分布，以及基于模型的反馈控制这些颗粒的制造在真实的时间。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的现实机会。在湍流控制中，计算能力现在允许直接模拟湍流，以及基于模型的控制方法。其次，MEMS技术的出现使得可以设想能够在产生相干结构的尺度下工作的传感器和致动器，该相干结构可以在100-1000 μ m的量级上。最后，最近已经实现了对这些相干结构的更好的基本理解。在测量和控制颗粒群方面，该项目将整合计算和测量能力，以真实的时间分析视频显微镜图像，以确定颗粒大小和形状分布。通过操纵环境变量，如pH值、杂质浓度和温度，我们可以影响演变的颗粒形状，这可以用作晶体结构的标记选择的应用程序非常适合开发DDDAS工具，因为它们具有以下特点：具有大量自由度的复杂模型、高复杂性测量、重要的噪声和不确定性来源以及重大的工业和经济影响。在本项目下进行的研究将开发和演示称为滚动时域估计的状态估计方法，作为真实的实时同化数据和动态非线性模型的算法，并将开发和实施自协方差最小二乘法，用于从测量数据和模型中识别扰动结构。通过从数据中识别扰动结构，导出的模型不必是完美的，以便准确地表示和预测数据，并实现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。工业合作和参加工业联合会为状态估计和模型预测控制（埃克森美孚、伊士曼化学、壳牌）、视频成像（MettlerToledo）以及晶体尺寸和形状分布控制（三菱和葛兰素史克）的技术转让提供了大量机会。