DIGITAL PARTICLE IMAGE VELOCIMETRY USING SPLINES IN TENSION

Digital particle imaging velocimetry has become a widely used diagnostic technique for the extraction of quantitative information about flow fields. Multiple-view geometries are used to identify and track individual particles (tracers) at discrete times. Often particle velocities and accelerations are subsequently derived by ill-conditioned methods based on finite-differences of the noisy measurements of the particle positions. Here a different Bayesian approach based on a model of the particles in 3-D velocity space using splines in tension is presented, thus automatically including the physical constraints of finite acceleration. The properties of the new algorithm will be compared with the conventional approach and it is argued that the (exponential) spline model should be formulated in the space where the quantity to be modelled is continous, instead of being placed in the data space.