Development of a Backward–Forward Stochastic Particle Tracking Model for Identification of Probable Sedimentation Sources in Open Channel Flow

As reservoirs subject to sedimentation, the dam gradually loses its ability to store water. The identification of the sources of deposited sediments is an effective and efficient means of tackling sedimentation problems. A state-of-the-art Lagrangian stochastic particle tracking model with backward–forward tracking methods is applied to identify the probable source regions of deposited sediments. An influence function is introduced into the models to represent the influence of a particular upstream area on the sediment deposition area. One can then verify if a specific area might be a probable source by cross-checking the values of influence functions calculated backward and forward, respectively. In these models, the probable sources of the deposited sediments are considered to be in a grid instead of at a point for derivation of the values of influence functions. The sediment concentrations in upstream regions must be known a priori to determine the influence functions. In addition, the accuracy of the different types of diffusivity at the water surface is discussed in the study. According to the results of the case study of source identification, the regions with higher sediment concentrations computed by only backward simulations do not necessarily imply a higher likelihood of sources. It is also shown that from the ensemble results when the ensemble mean of the concentration is higher, the ensemble standard deviation of the concentration is also increased.

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

10.5194/egusphere-egu21-11346 ◽

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>

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

DYNA ◽

10.15446/dyna.v86n211.79649 ◽

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.

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

10.1002/essoar.10504288.1 ◽

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

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

International Journal of Modern Physics B ◽

10.1142/s0217979221502568 ◽

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.

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