Incorporating an instrumented particle to monitor the dynamic processes of bed particle motion from entrainment to low transport stages
Authors: Al-Husban Zaid , Farhadi Hamed , AlObaidi Khaldoon , Xu Yi , Valyrakis Manousos 
Published: March 2022

&lt;p&gt;Bed particle motion as bedload entrainment in riverine flows is a topic of interest in scientific and engineering fields. It is responsible for erosion and sedimentation, essential for designing hydraulic structures and river and basin management. Stochastic processes govern the physics of coarse particle motion due to particle-particle (here, bed particles) and fluid-particle interrelations, yet not mainly considered for estimating and describing the bedload flux and motions. Therefore, authentic knowledge of bed particle behavior in different phases of entrainment and transport might lead to a better description of the phenomenon. This study contributes to applying a non-intrusive particle monitoring technique, i.e., an embedded micro-electromechanical system (MEMS) as &amp;#8220;smart particle&amp;#8221; [1], to explore and monitor the dynamics of the initial and the bed particle motion near- and above threshold conditions.&lt;/p&gt;&lt;p&gt;&lt;span&gt;Additionally, the imaging technique was deployed to track and monitor the instantaneous particle velocity and displacement during the transport, which was also applied as a complementary technique to calibrate and assess the MEMS sensor results [2]. The dynamics of incipient particle motion and particle transport were evaluated in sets of hydraulic flume experiments (by applying the instrumented particle) for different flow conditions, which deliver distinct particle entrainment and transport regimes [3-5]. The stochastic frameworks, which best described the hydrodynamic aspects of the entrainment and transport conditions, were chosen and discussed in relation to the near riverbed surface flow hydrodynamic conditions for better comprehension of the conditions leading to incipient entrainment and relatively low bedload transport stages.&amp;#160;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;[1] Valyrakis, M., Alexakis, A. (2016). Development of a &amp;#8220;smart-pebble&amp;#8221; for tracking sediment 2transport. River Flow 2016, MO, USA.&lt;/p&gt;&lt;p&gt;[2] Valyrakis, M., Farhadi, H. (2017). Investigating coarse sediment particles transport using PTV and &amp;#8220;smart-pebbles&amp;#8221;instrumented with inertial sensors, EGU General Assembly 2017, Vienna, Austria, 23-28 April 2017, id. 9980.&lt;/p&gt;&lt;p&gt;[3] Farhadi, H. and Valyrakis, M. (2021). Exploring probability distribution functions best-fitting the kinetic energy of coarse particles at above threshold flow conditions. In &lt;em&gt;EGU General Assembly Conference Abstracts&lt;/em&gt; (pp. EGU21-1820).&lt;/p&gt;&lt;p&gt;[4] AlObaidi, K., Xu, Y., and Valyrakis, M. (2020). The Design and Calibration of Instrumented Particles for Assessing Water Infrastructure Hazards, Journal of Sensor and Actuator Networks, 2020, 9(3), pp.36(1-18), DOI: 10.3390/jsan9030036.&lt;/p&gt;&lt;p&gt;[5] AlObaidi, K. and Valyrakis, M. (2021). Linking the explicit probability of entrainment of instrumented particles to flow hydrodynamics. Earth Surface Processes and Landforms, 46(12), pp.2448-2465.&lt;/p&gt;