DDDAS-SMRP: Measuring and Controlling Turbulence and Particle Populations

该项目将提高两个复杂和以前未解决的应用领域的能力：(I)测量、控制和防止流体流动中湍流的形成，以实现减阻；(Ii)测量晶体颗粒群体的大小和形状分布，以及对这些颗粒的制造进行实时基于模型的反馈控制。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。在湍流控制方面，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的前景。其次，MEMS技术的出现使传感器和执行器有可能在产生湍流的相干结构的尺度上工作，这种结构可以在100-1000微米的量级上工作。最后，最近对这些相干结构有了更好的基本理解。在测量和控制颗粒数量方面，该项目将整合计算和测量能力，实时分析视频显微镜图像，以确定颗粒大小和形状分布。通过控制环境变量，如pH、杂质浓度和温度，我们可以影响不断演变的颗粒形状，这可以作为药物应用中晶体结构的标记(实现晶型控制)。所选的应用程序是开发DDDAS工具的理想选择，因为以下特点：具有大量自由度的复杂模型，高度复杂的测量，显著的噪声和不确定性来源，以及重大的工业和经济影响。在该项目下进行的研究将开发和演示被称为滚动水平估计的状态估计方法，作为实时同化数据和动态、非线性模型的算法，并将开发和实现自协方差最小二乘法，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，推导出的模型不必是完美的，就可以准确地表示和预测数据，并实现基于模型的反馈控制。该项目将开发实现状态估计、干扰识别和基于模型的反馈控制所需的新的优化工具。工业合作和参与工业联合体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)提供了大量的技术转让机会。

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).