A new development in magnetic particle tracking technology and its application in a sheared dense granular flow

A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking

Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction.

Measuring vorticity vector from the spinning of micro-sized mirror-encapsulated spherical particles in the flow

We demonstrate a nonintrusive technique that is capable of measuring all three-components of vorticity following small tracer particles in the flow. The vorticity is measured by resolving the instantaneous spin of the microsized spherical hydrogel particles, in which small mirrors are encapsulated. The hydrogel particles have the same density and refractive index as the working fluid-water. The trajectory of the light reflected by the spinning mirror, recorded by a single camera, is sufficient to determine the 3D rotation of the hydrogel particle, and hence the vorticity vector of the flow at the position of the particle. Compared to more conventional methods that measure vorticity by resolving velocity gradients, this technique has much higher spatial resolution. We describe the principle of the measurement, the optical setup to eliminate the effect of particle translation, the calibration procedure, and the analysis of measurement uncertainty. We validate the technique by measurements in a Taylor-Couette flow. Our technique can be used to obtain the multipoint statistics of vorticity in turbulence.