Fuente: PubMed "swarm" Sensors (Basel). 2026 Mar 10;26(6):1741. doi: 10.3390/s26061741.ABSTRACTAccurate monitoring of internal particle motion in dense granular flows remains a significant challenge across various fields, ranging from geophysics to industrial processes. To address the limitations of existing observational techniques, this study presents a novel high-precision magnetic array positioning system based on magnetic dipole theory for dynamically tracking individual particles within opaque granular media. The system integrates an array of nine magnetic sensors with a hybrid optimization algorithm that combines Particle Swarm Optimization (PSO) and gradient-based local refinement, achieving a dynamic positioning accuracy within the maximum measurable range, with a maximum dynamic error of 2.5 ± 0.5 mm and a trajectory continuity exceeding 99%. Deployed in a quasi-two-dimensional rotating drum, the system enables detailed investigation of particle segregation mechanisms. Reconstruction and analysis of the trajectories of a high-density intruder (magnetic bead) allow quantification of the competition among segregation mechanisms through the Froude number. The results reveal three distinct motion phases with increasing rotational speed: a gravity-dominated percolation stage, a transitional collision-diffusion competition stage, and a centrifugal diffusion-dominated stage. Each phase exhibits unique kinematic signatures governed by the interplay of inertial, gravitational, and contact forces. This work not only establishes a robust and accurate sensor-based method for internal granular flow monitoring but also provides new mechanistic insights into segregation dynamics, with implications for understanding geological hazards such as debris flows.PMID:41901912 | PMC:PMC13030828 | DOI:10.3390/s26061741