In the last decade several efforts were devoted to model sediment-particle transport in rivers as a stochastic process. Experimental observations are therefore needed to validate these models and to provide the correct probability distribution of selected stochastic variables. The kinematics of sand particles is investigated here using nonintrusive imaging to provide a statistical description of bedload transport under incipient motion conditions. In particular, we focus on the alternation between motion (particle steps) and rest regimes to quantify the probabilistic distribution of the particles waiting time, which is suggested by many studies to be responsible for anomalous diffusion. The probability distributions of the particle step time and step length, streamwise and spanwise velocities, acceleration, and waiting time are quantified experimentally. Results suggest that variables describing the particle motion regime are thin-tailed distributed, whereas the waiting times exhibit a power law distribution. A specific class of waiting times during which the grain is observed to oscillate without a net displacement is classified as active and is analyzed separately from the other, so-called deep waiting times. The experimental results, obtained under five different transport conditions, describe grain-scale kinematics and dynamics at different wall shear stress. They provide both a benchmark data set for validating particle-transport numerical simulation and critical input parameters for the stochastic modeling of bedload transport.

A Statistical Description of Particle Motion and Rest Regimes in Open-Channel Flows Under Low Bedload Transport

Pelosi A.;
2019

Abstract

In the last decade several efforts were devoted to model sediment-particle transport in rivers as a stochastic process. Experimental observations are therefore needed to validate these models and to provide the correct probability distribution of selected stochastic variables. The kinematics of sand particles is investigated here using nonintrusive imaging to provide a statistical description of bedload transport under incipient motion conditions. In particular, we focus on the alternation between motion (particle steps) and rest regimes to quantify the probabilistic distribution of the particles waiting time, which is suggested by many studies to be responsible for anomalous diffusion. The probability distributions of the particle step time and step length, streamwise and spanwise velocities, acceleration, and waiting time are quantified experimentally. Results suggest that variables describing the particle motion regime are thin-tailed distributed, whereas the waiting times exhibit a power law distribution. A specific class of waiting times during which the grain is observed to oscillate without a net displacement is classified as active and is analyzed separately from the other, so-called deep waiting times. The experimental results, obtained under five different transport conditions, describe grain-scale kinematics and dynamics at different wall shear stress. They provide both a benchmark data set for validating particle-transport numerical simulation and critical input parameters for the stochastic modeling of bedload transport.
2019
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4744858
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? 21
social impact