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A New Theoretical Framework to Model Incipient Motion of Sediment Grains and Implications for the Use of Modern Experimental Techniques

  • Andrea Marion
  • Matteo Tregnaghi
Chapter
Part of the GeoPlanet: Earth and Planetary Sciences book series (GEPS)

Abstract

The entrainment of sediments in rivers is recognized to exhibit an intermittent nature, hence incipient motion is inherently a random process that requires an appropriate stochastic description. The effect of near-bed turbulence on grain entrainment and the variation in stability of randomly configured bed particles due to local surface heterogeneity are included into a probabilistic framework based on a concept first proposed by Grass. Bedload transport tests were carried out in a flume where sediment movement was monitored using a three-camera 3D PIV system. Simultaneous grain motion and flow velocity measurements were made on a plane located slightly above and parallel to the sediment bed. Detailed statistical velocity information was acquired to model the velocity distribution at the bed level accounting for the probabilistic distribution of particle exposures. This was combined with the probabilistic distribution of grain resistance to motion, which was obtained from discrete particle modeling (DPM) simulations. The analysis provides detailed insight, in terms of grain dynamics, into the physical aspects that determine the initiation of movement, and the stochastic equations of incipient motion are derived. The key feature of the proposed analysis is the potential of including into the model as much statistical information as one can obtain from experimental observations based on state-of-the-art flow measurement techniques and from the use of numerical simulations performed with discrete particle models.

Keywords

Particle Imaging Velocimetry Sediment Transport Streamwise Velocity Fluid Shear Stress Critical Shear Stress 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Modeling and data analysis was carried out within the Project “PARTS: Probabilistic Assessment of the Retention and Transport of Sediments and Associated Pollutants in Rivers”, funded by the EU Research Executive Agency via an Intra-European Fellowship to Dr Tregnaghi under a Marie Curie action funding scheme.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversity of PaduaPaduaItaly

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