DDDAS-SMRP: Measuring and Controlling Turbulence and Particle Populations

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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).