scholarly journals Light Particle Tracking Model for Simulating Bed Sediment Transport Load in River Areas

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Israel E. Herrera-Díaz ◽  
Franklin M. Torres-Bejarano ◽  
Jatziri Y. Moreno-Martínez ◽  
C. Rodriguez-Cuevas ◽  
C. Couder-Castañeda
Keyword(s):  
Sediment Transport ◽  
Probability Function ◽  
Velocity Fields ◽  
Turbulent Dispersion ◽  
Particle In Cell ◽  
Port Area ◽  
Particle Tracking Model ◽  
Study Zone ◽  
Tracking Model ◽  
Hydrodynamic Fields

In this work a fast computational particles tracer model is developed based on Particle-In-Cell method to estimate the sediment transport in the access zone of a river port area. To apply the particles tracer method, first it is necessary to calculate the hydrodynamic fields of the study zone to determine the velocity fields in the three directions. The particle transport is governed mainly by the velocity fields and the turbulent dispersion. The mechanisms of dispersion and resuspension of particles are based in stochastic models, which describes the movement through a probability function. The developed code was validated using two well known cases with a discrete transformation obtaining a max relative error around 4.8% in both cases. The simulations were carried out with 350,000 particles allowing us to determine under certain circumstances different hydrodynamic scenarios where the zones are susceptible to present erosion and siltation at the entrance of the port.

Incorporating Backward-forward Stochastic Particle Tracking Model into the EFDC model for Probable Sedimentation Source identification in Typhoon events

2021 ◽  
Author(s):  
Wen-Jia Liu ◽  
Christina W. Tsai
Keyword(s):  
Fluid Dynamics ◽  
Sediment Transport ◽  
Particle Tracking ◽  
Sediment Concentration ◽  
Density Currents ◽  
Particle Tracking Model ◽  
Shihmen Reservoir ◽  
Tracking Model ◽  
Environmental Fluid ◽  
Stochastic Particle

<p>The reservoir siltation has been of critical environmental concerns in recent years. The vulnerability and the overdevelopment in the reservoir watershed are the causes of the reservoir sedimentation. While typhoon events happen, in addition to the great amount of sediment volume transported from the upstream to the reservoir region, the density currents may evolve, which will steeply increase turbidity levels for the periods of time. In particular, the Shihmen Reservoir, one of essential hydraulic engineering projects in northern Taiwan, has been exposed to crisis that the sedimentation may fill up in the next few decades. Therefore, in order to maintain the reservoir capacity to an operational extent, modeling the sediment transport patterns in Shihmen Reservoir will utilize the three-dimensional Environmental Fluid Dynamics Code (EFDC) for quantifying sediment concentrations during the typhoon event. Calibration and validation of EFDC are performed by comparing two independent sets of event-based hydrodynamic and sediment concentration data with assistance of the parameter optimization algorithm. Next, the Backward-forward Stochastic Particle Tracking Model (BF-SPTM) is further incorporated into the EFDC hydrodynamic module to check the likelihood of the potential source of sediment particles. Results of simulations are expected to provide a more precise release timing for flow regulation to ensure the effective slag removal for density currents. Additionally, with probable sedimentation sources available for a reservoir, effective land use change and restrictions on overdevelopment of the risk prone areas can be enforced to decrease the sediment yields into the reservoir. It is expected that this incorporation of BF-SPTM into EFDC can be applied to simulate sediment transport in typhoon events, and to provide appropriate reservoir management alternatives.</p><p>Keywords: Environmental Fluid Dynamics Code (EFDC), suspended sediment concentration, Backward-forward Stochastic Particle Tracking Model, Probable sedimentation source</p>

scholarly journals Effect of bathymetric changes on residence time in Buenaventura bay (Colombia)

DYNA ◽  
2019 ◽  
Vol 86 (211) ◽  
pp. 241-248
Author(s):  
Francisco Fernando Garcia Renteria ◽  
Mariela Patricia Gonzalez Chirino
Keyword(s):  
Finite Elements ◽  
Residence Time ◽  
Particle Tracking ◽  
Hydrodynamic Model ◽  
Residence Times ◽  
Particle Tracking Model ◽  
Tracking Model ◽  
Bathymetric Changes

In order to study the effects of dredging on the residence time of the water in Buenaventura Bay, a 2D finite elements hydrodynamic model was coupled with a particle tracking model. After calibrating and validating the hydrodynamic model, two scenarios that represented the bathymetric changes generated by the dredging process were simulated. The results of the comparison of the simulated scenarios, showed an important reduction in the velocities fields that allow an increase of the residence time up to 12 days in some areas of the bay. In the scenario without dredging, that is, with original bathymetry, residence times of up to 89 days were found.

scholarly journals Wind Tunnel Validation of a Particle Tracking Model to Evaluate the Wind-Induced Bias of Precipitation Measurements.

2020 ◽  
Author(s):  
Arianna Cauteruccio ◽  
Elia Brambilla ◽  
Mattia Stagnaro ◽  
Luca Giovanni Lanza ◽  
Daniele Rocchi
Keyword(s):  
Wind Tunnel ◽  
Particle Tracking ◽  
Particle Tracking Model ◽  
Tracking Model

Study on the existence of the transportation particle tracking model in the interactive medium

2021 ◽  
pp. 2150256
Author(s):  
Mohamed Abd Allah El-Hadidy ◽  
Alaa A. Alzulaibani
Keyword(s):  
Waiting Time ◽  
Particle Tracking ◽  
Necessary Conditions ◽  
Particle Detection ◽  
Particle Tracking Model ◽  
Search Plan ◽  
Tracking Process ◽  
Unit Speed ◽  
Tracking Model ◽  
Interactive Medium

This paper assumes that the particle jumps randomly (Guassian jumps) from one point to another along one of the imaginary lines inside the interactive medium. Since this study was done in the space, we consider that the position of the particle at any time [Formula: see text] has a multivariate distribution. The random waiting time of the particle for each Gaussian jump depends on its length. An identical set of programed nanosensors (with unit speed) were used to track this particle. Each line has a sensor that starts the tracking process from the origin. The existence of the necessary conditions which give the optimal search plan and the minimum expected value of the particle detection has been proven. This study is supported by a numerical example.

Hudson River Fishes and their Environment

2006 ◽  
Keyword(s):  
Striped Bass ◽  
River Estuary ◽  
Circulation Model ◽  
Hudson River ◽  
Hudson River Estuary ◽  
Freshwater Flow ◽  
East River ◽  
Particle Tracking Model ◽  
Tracking Model ◽  
Neutrally Buoyant

<em>Abstract.</em>—Our objectives were to determine if striped bass <em>Morone saxatilis </em>larvae were present in the East River and if so, could they have come from the Hudson River. To meet the first objective, we examined entrainment data collected at the Charles Poletti Power Plant (Poletti) during the years 1999 through 2002. To meet the second objective, we examined the simulated release of 168,000 neutrally buoyant, passive particles in the lower Hudson River Estuary, using a particle-tracking model that was linked to an estuarine circulation model. We also compared the abundance of striped bass post-yolk-sac larvae (PYSL) collected in the East River at Poletti with the abundance of striped bass PYSL collected in the Battery region of the lower Hudson River Estuary and the abundance of striped bass PYSL in the Battery region with freshwater flow in the estuary. Striped bass PYSL were collected by entrainment sampling in the East River at Poletti every year from 1999 through 2002. The striped bass PYSL in the East River probably came from the Hudson River Estuary because the median probability that neutrally buoyant, passive particles would be transported from the lower Hudson River Estuary to the upper East River and western Long Island Sound was 0.12, with a median transport time of 2 d, and because the mean density of striped bass PYSL was highest at Poletti and in the Battery region during the same year. The abundance of striped bass PYSL in the Battery region was higher when freshwater flow during May and early June was higher.

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