A Random-walk, Particle Tracking Model for Well-mixed Estuaries and Coastal Waters

1993 ◽  
Vol 37 (1) ◽  
pp. 99-110 ◽  
Author(s):  
K.Nadia Dimou ◽  
E.Eric Adams
Keyword(s):  
Random Walk ◽  
Particle Tracking ◽  
Coastal Waters ◽  
Particle Tracking Model ◽  
Tracking Model ◽  
Random Walk Particle Tracking

scholarly journals Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions

2021 ◽  
Author(s):  
Rong Mao ◽  
Jiu Jimmy Jiao ◽  
Xin Luo ◽  
Hailong Li
Keyword(s):  
Random Walk ◽  
Travel Time ◽  
Particle Tracking ◽  
Time Distribution ◽  
High Fidelity ◽  
Mixing Process ◽  
Particle Tracking Model ◽  
Travel Time Distribution ◽  
Tracking Model ◽  
Fidelity Model

Abstract. The travel time distribution (TTD) is a lumped representation of groundwater discharge and solute export responding to rainfall. It reflects the mixing process of water parcels and solute particles of different ages and characterizes reactive transport progress in hillslope aquifers. As a result of the mixing process, groundwater leaving the system at a certain time is an integration of multiple water parcels of different ages from different historical rainfall events. Under nonstationary rainfall input condition, the TTD varies with transit groundwater flow, leading to the time-variant TTD. Most methods for estimating time-variant TTD are constrained by requiring either the long-term continuous hydrogeochemical data or the intensive computations. This study introduces a multi-fidelity model to overcome these limitations and evaluate time-variant TTD numerically. In this multi-fidelity model, groundwater age distribution model is taken as the high-fidelity model, and particle tracking model without random walk is taken as the low-fidelity model. Non-parametric regression by non-linear Gaussian process is applied to correlate the two models and then build up the multi-fidelity model. The advantage of the multi-fidelity model is that it combines the accuracy of high-fidelity model and the computational efficiency of low-fidelity model. Moreover, in groundwater and solute transport model with low P\\'eclet number, as the spatial scale of the model increases, the number of particles required for multi-fidelity model is reduced significantly compared to random walk particle tracking model. The correlation between high and low-fidelity models is demonstrated in a one dimensional pulse injection case. In a two dimensional hypothetical model, convergence analysis indicates that the multi-fidelity model converges well when increasing the number of high-fidelity models. Error analysis also confirms the good performance of the multi-fidelity model.

Computer Simulation of the Inclusion Removal Behavior in the Tundish and Model Evaluation

2014 ◽  
Vol 513-517 ◽  
pp. 4314-4318
Author(s):  
Yun Wang
Keyword(s):  
Computer Simulation ◽  
Random Walk ◽  
Particle Tracking ◽  
Random Walk Model ◽  
Motion Simulation ◽  
Lagrangian Particle Tracking ◽  
Lagrangian Particle ◽  
Inclusion Removal ◽  
Particle Tracking Model ◽  
Tracking Model

Computer simulation was applied to study the inclusion behavior in the tundish with Lagrangian particle tracking model including both the deterministic and random walk model. Comparing with the experiment result, the prediction result revealed that random walk model obtained more accuracy than the deterministic walk model by considering the effect of turbulence fluctuation on the inclusion movement. The accuracy of inclusion motion simulation was determined by the turbulence model and boundary condition.

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.

scholarly journals Spawning Grounds Estimation of Red Sea Bream using Two-way Particle Tracking Model in Hiuchi-nada

2012 ◽  
Vol 68 (2) ◽  
pp. I_1111-I_1115
Author(s):  
Koichi SUGIMATSU ◽  
Hiroshi YAGI ◽  
Akiyoshi NAKAYAMA ◽  
Hiromu ZENITANI ◽  
Yasushi ITO
Keyword(s):  
Red Sea ◽  
Particle Tracking ◽  
Sea Bream ◽  
Red Sea Bream ◽  
Spawning Grounds ◽  
Particle Tracking Model ◽  
Tracking Model
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