Paul Mcglynn

Inversion of Andersen Cascade Impactor Data using the Maximum Entropy Method

The problem of the Andersen cascade impactor data inversion is studied using the maximum entropy method to recover the particle size distribution function from a set of measured masses collected on the impactor stages. The classical entropy maximization and its modification to accommodate data errors are reviewed using the techniques of convex optimization. Numerical experiments were performed to test the method on several unimodal and multimodal distributions commonly observed in practice. The Maximum Entropy Method for data inversion produced excellent agreement between the recovered and test Particle Size Distribution Functions without the need for a priori assumptions.

Numerical calibration of the Andersen cascade impactor using a single jet model

A single jet model is described and used to analyze the performance of an 8-stage Andersen cascade impactor (ACI). The two-dimensional axisymmetric jet flow field was calculated numerically by solving the Navier-Stokes equations and particle trajectories were then analyzed to obtain the collection efficiency curves at different flow rates. The resulting efficiency curves and corresponding cutoff diameters were compared and found to be in good agreement with the available experimental data. We also examined the effect of gravity on impactor performance and discuss the limitations of the single jet model.