Robust Modal Decompositions for Fluid Flows

Robust principal component analysis (RPCA) is a powerful technique from robust statistics that can be used to extract dominant coherent structures from flow fields corrupted with outliers and missing measurements. We demonstrate the effectiveness of RPCA on flows acquired experimentally and by simulation. In all cases, RPCA is able to de-noise these fields and vastly improve subsequent modal analysis. We conclude that RPCA can be used to robustly process particle image velocimetry (PIV) flow fields.

Cross-Flow (i.e. Vertical Axis) Turbine Arrays

Cross-flow turbines, also known as vertical-axis turbines, convert the kinetic energy in moving fluid to mechanical energy using blades that rotate about an axis perpendicular to the incoming flow. Arrays of cross-flow turbines with well-considered geometries and control strategies can out perform equivalent turbines in isolation by up to 30%. We use data-driven methods with a hardware-in-the-loop experimental approach to optimize, control, and model arrays.