Journal of Modern Physics
Volume 3, Issue 12 (December 2012)
ISSN Print: 2153-1196 ISSN Online: 2153-120X
Google-based Impact Factor: 0.86 Citations h5-index & Ranking
Sensitivity of Lagrangian Particle Tracking Based on a 3D Numerical Model ()
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ABSTRACT
The aim of the paper is to investigate in detail the sensitivity of particles displacement based on method of Lagrangian particle tracking in combination with a 3D Eulerian numerical model that was developed by the first author, namely FSUM. The characteristic parameters used for this research include the possibilities of random movement, settling velocity of solid particle, horizontal and vertical diffusion coefficients and condition of particle fixed with a constant distance under water surface. The first part is on the fluid flow model. It includes 3D Navier-Stokes equations together with the initial and boundary conditions that were numerically solved with the finite difference method and coded with FORTRAN 90/95 using parallel technique with OpenMP. A semi-Lagrangian treatment of the advective terms was used. The second part is related to Lagrangian particle tracking model and was solved with the fourth Runge-Kutta method. Model was applied for Strait of Johor and has been calibrated by using measured data on water level and velocity at one station. Eight cases of simulations with many different options were carried out. Through computed cases it shows that random term and settling velocity are very important factors for the behavior of particle trajectory. Although the random diffusion is minor in comparison with flow velocity, but it can rearrange the initial distribution of particles then the cluster of particles become more dispersive during the process of movement. In addition, introducing settling velocity of particle makes a big change on the trajectory of particle that becomes more suitable to sediment transport. The study gave a comprehensive picture on particle movement. The model also showed its possibilities of multiform applications in simulation and prediction for the different problems in practice.
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