Amirkabir University of TechnologyAmirkabir Journal of Civil Engineering2588-297X54420220622Probabilistic description of coarse particle motion above threshold by particle tracking velocimetry method in an experimental studyProbabilistic description of coarse particle motion above threshold by particle tracking velocimetry method in an experimental study1818441610.22060/ceej.2021.19759.7253FAHamedFarhadiPhd candidate for hydro structures, Water Science and Engineering Department, Ferdowsi University of Mashhad0000-0003-4940-9571KazemEsmailiAssociate Professor Department of Water Science and Engineering , Ferdowsi University of Mashhad0000000153540949ManousosValyrakisAssistant professor/School of engineering, University of Glasgow, Glasgow, United KingdomAbdolrezaZahiriWater and soil engineering faculty, Water engineering department, Gorgan university of agricultural sciences and natural resources, Gorgan, IranJournal Article20210317Sediment motion behavior plays an important role in the sediment and hydraulic engineering, though its physics is still not fully understood. Ignoring the stochastic nature of the sediment transport leads to various equations for bedload transport which are now being challenged due to their results. In this study, the non-suspended particle motion (bedload transport) in different hydraulic conditions was assessed by a particle tracking technique called the Particle Tracking Velocimetry (PTV). The results of the PTV were applied to describe the particle behavior throughout the probability distribution functions. Knowing the particle motion behavior would be a guidance to learn more about the parameter/s governing the particle transport in different sediment transport regimes. After calibrating and validating the frames (resulted from the PTV), the instantaneous particle velocity was measured. Different probability distribution functions were assessed with Kolmogorov-Smirnov criterion to find the best function which fits the collected data (i.e. the particle velocity). It was shown that the probability distribution function is Log-Normal for lower particle Reynolds number and on the other hand, in the higher particle Reynolds number, the Normal distribution is best describing the particle velocity. The results of this research also could be applied in similar hydraulic conditions in eco-hydraulic field, specifically macro-plastic movement as bedload in river courses.Sediment motion behavior plays an important role in the sediment and hydraulic engineering, though its physics is still not fully understood. Ignoring the stochastic nature of the sediment transport leads to various equations for bedload transport which are now being challenged due to their results. In this study, the non-suspended particle motion (bedload transport) in different hydraulic conditions was assessed by a particle tracking technique called the Particle Tracking Velocimetry (PTV). The results of the PTV were applied to describe the particle behavior throughout the probability distribution functions. Knowing the particle motion behavior would be a guidance to learn more about the parameter/s governing the particle transport in different sediment transport regimes. After calibrating and validating the frames (resulted from the PTV), the instantaneous particle velocity was measured. Different probability distribution functions were assessed with Kolmogorov-Smirnov criterion to find the best function which fits the collected data (i.e. the particle velocity). It was shown that the probability distribution function is Log-Normal for lower particle Reynolds number and on the other hand, in the higher particle Reynolds number, the Normal distribution is best describing the particle velocity. The results of this research also could be applied in similar hydraulic conditions in eco-hydraulic field, specifically macro-plastic movement as bedload in river courses.https://ceej.aut.ac.ir/article_4416_4b43096efa2c908cf1ec10e7dbac677a.pdf