Sharif University of TechnologySharif Journal of Civil Engineering2676-476838.22.220220823Smart-particle application for describing the probabilistic particle transport dynamics above threshold conditionsSmart-particle application for describing the probabilistic particle transport dynamics above threshold conditions43532273310.24200/j30.2022.59457.3050FAH FarhadiDept. of Water Science and EngineeringFerdowsi University of Mashhad0000-0003-4940-9571K. EsmailiDept. of Water Science and EngineeringFerdowsi University of Mashhad0000000153540949M. ValyrakisSchool of Engineering University of Glasgow, United KingdomA. ZahiriDept. of Water Engineering Gorgan University of agricultural sciences and natural resources, GorganJournal Article20211212Coarse particle motion behavior plays a crucial role in sediment and hydraulic engineering, though its physics is still not fully understood. Disregarding the inherently stochastic nature of the sediment transport leads to various equations for bedload transport which are now being challenged due to their poor results. By applying sensors, like accelerometers and gyroscopes, particle transport physics could be carried out in a more scrutinized approach. In this study, an instrumented synthetic particle ("so-called" the smart particle) was designed and applied (with different densities) in sets of laboratory experiments that covered a hydraulics domain between low transport regime (near- and above-threshold) and higher transport regime (above threshold with a relatively high Reynolds number) conditions. Using the instrumented particle (smart-particle) could bring opportunities to learn more about the physics of the bed particle transport in rivers for different regimes and could bring data in hand for instantaneous particle changes throughout time (hear 0.004 seconds used for data sampling). Therefore, the kinetic energy as a parameter that delivers the behavior of particle energy interaction between the exposed particle and its surroundings (flow and the bed particles) was chosen to be studied. Since the dynamic features of the particle in transport are stochastic, the probability distribution functions, which could describe the particle behavior, were selected (Weibull, Lognormal, Normal, and Gamma distributions). In this case, it was shown that the Weibull distribution best described the particle kinetic energy in lower transport regimes, while for a higher transport regime, the Log-normal distribution worked better. Furthermore, the energy signals of the particle moving throughout the flume for different transport regimes were derived, and it was shown that the average energy gain and loss of the particle decreased exponentially as the particle Reynolds number increased. The presented results here could also be applied in similar hydraulic conditions in eco-hydraulic topics, specifically macro-plastic movement as bedload in river courses and the Aeolian research.Coarse particle motion behavior plays a crucial role in sediment and hydraulic engineering, though its physics is still not fully understood. Disregarding the inherently stochastic nature of the sediment transport leads to various equations for bedload transport which are now being challenged due to their poor results. By applying sensors, like accelerometers and gyroscopes, particle transport physics could be carried out in a more scrutinized approach. In this study, an instrumented synthetic particle ("so-called" the smart particle) was designed and applied (with different densities) in sets of laboratory experiments that covered a hydraulics domain between low transport regime (near- and above-threshold) and higher transport regime (above threshold with a relatively high Reynolds number) conditions. Using the instrumented particle (smart-particle) could bring opportunities to learn more about the physics of the bed particle transport in rivers for different regimes and could bring data in hand for instantaneous particle changes throughout time (hear 0.004 seconds used for data sampling). Therefore, the kinetic energy as a parameter that delivers the behavior of particle energy interaction between the exposed particle and its surroundings (flow and the bed particles) was chosen to be studied. Since the dynamic features of the particle in transport are stochastic, the probability distribution functions, which could describe the particle behavior, were selected (Weibull, Lognormal, Normal, and Gamma distributions). In this case, it was shown that the Weibull distribution best described the particle kinetic energy in lower transport regimes, while for a higher transport regime, the Log-normal distribution worked better. Furthermore, the energy signals of the particle moving throughout the flume for different transport regimes were derived, and it was shown that the average energy gain and loss of the particle decreased exponentially as the particle Reynolds number increased. The presented results here could also be applied in similar hydraulic conditions in eco-hydraulic topics, specifically macro-plastic movement as bedload in river courses and the Aeolian research.https://sjce.journals.sharif.edu/article_22733_0f70152bcda20b1840130d95c6164300.pdf