Predicting the trajectories of particles in micro-centrifuge devices

In microfluidic devices, micrometer-scale channels and cavities are arranged to capture and sort particles (e.g. separating red blood cells from circulating cancer cells). Rational design of devices requires understanding how these parameters of shape and size determine particle trajectories. But, images of particle trajectories through these devices capture only the 2-dimensional dynamics of the system. Our goal is to be able to quickly and accurately reconstruct the full 3-dimensional trajectories. We present a data assimilation (Ensemble Transform Kalman Filter) study which incorporates a model of the background fluid flow in the device with very precise, partial measurements of the particle position. The framework is tested with observing system simulation experiments that use synthetic data from a hybrid asymptotic-numerical model where we account for the particle disturbance. The set up for this problem is simple as it requires the background flow of a device, the particle size, and partial measurements of the trajectory. The motivation for this project is to provide simpler ways to predict the trajectory of particles in future microfluidic devices.