Smart Dynamic Monitoring and Tracking of Individual Sediment Grains: Design Framework and Experimental Evaluation.

Abstract:

Contemporary sensing equipment is miniaturized to scales that enable implementation of dynamic micro-sensors within natural sediment particles (>c.80mm diameter) containing artificial enclosures. The resulting mobile sensing units record the dynamics of sediment transport from the inertial frame of individual particles, giving an insight on how individual grains experience transporting forces. However, it remains difficult to obtain accurate real-time positional information which is critical for understanding the dynamic data. We have developed a sensing system optimized for monitoring the movement of sediment grains in rivers, which comprises a high-frequency 3-D force unit and an external magnetic telemetry system for accurate positional information. Here we present results from the experimental evaluation of two prototype mobile sensors: the first prototype is a spherically enclosed wireless accelerometer platform (± 6g range) tested through a series of incipient motion experiments under varying slope conditions (0.8 m x 5 m flume, slope range: 0.026 to 0.57, flow increase: 0.037 l.s^-2, University of British Columbia). The second prototype is a complete Inertial Measurement Unit (assembly of a 3-axis micro-accelerometer, gyroscope and magnetometer capable of resolving 9 Degrees Of Freedom for the movement of the unit), enhanced with a 3-axis high-resolution impact sensor calibrated for low frequency/high magnitude impacts (±150g range) and equipped with a system of magnetic coil receivers permitting telemetric tracking of the unit with a 4 Hz frequency. This unit was tested through experiments of sequential displacements under varying flow increase (gradual increase: 0.04 l.s^-2, episodic increase: 0.1 l.s^-2, 0.9 m x 7.5 m flume, slope: 0.02, University of Glasgow). The position was recorded from a lab-scale Magneto-Inductive tracking system and the positional accuracy was tested by cross-comparison with a video recording. Along with the presented results we discuss how the nature, high-resolution and accuracy of the derived measurements mean that collected data can be used to existing deterministic and stochastic models for fluvial bedload transport.