Numerical simulation of transport from a point source: error analysis

1990 ◽  
Vol 24 (3) ◽  
pp. 693-702 ◽  
Author(s):  
Prasad S. Kasibhatla ◽  
Leonard K. Peters
2011 ◽  
Vol 97-98 ◽  
pp. 698-701
Author(s):  
Ming Lu Zhang ◽  
Yi Ren Yang ◽  
Li Lu ◽  
Chen Guang Fan

Large eddy simulation (LES) was made to solve the flow around two simplified CRH2 high speed trains passing by each other at the same speed base on the finite volume method and dynamic layering mesh method and three dimensional incompressible Navier-Stokes equations. Wind tunnel experimental method of resting train with relative flowing air and dynamic mesh method of moving train were compared. The results of numerical simulation show that the flow field structure around train is completely different between wind tunnel experiment and factual running. Two opposite moving couple of point source and point sink constitute the whole flow field structure during the high speed trains passing by each other. All of streamlines originate from point source (nose) and finish with the closer point sink (tail). The flow field structure around train is similar with different vehicle speed.


1999 ◽  
Vol 392 ◽  
pp. 45-71 ◽  
Author(s):  
ILIAS ILIOPOULOS ◽  
THOMAS J. HANRATTY

Dispersion of fluid particles in non-homogeneous turbulence was studied for fully developed flow in a channel. A point source at a distance of 40 wall units from the wall is considered. Data obtained by carrying out experiments in a direct numerical simulation (DNS) are used to test a stochastic model which utilized a modified Langevin equation. All of the parameters, with the exception of the time scales, are obtained from Eulerian statistics. Good agreement is obtained by making simple assumptions about the spatial variation of the time scales.


2012 ◽  
Vol 49 (3) ◽  
pp. 030101
Author(s):  
王少白 Wang Shaobai ◽  
王春鸿 Wang Chunhong ◽  
饶长辉 Rao Changhui

2002 ◽  
Vol 36 (2) ◽  
pp. 273-291 ◽  
Author(s):  
Jacques Audounet ◽  
Jean-Michel Roquejoffre ◽  
Hélène Rouzaud

Author(s):  
Daisuke Kitazawa ◽  
Satoshi Abe ◽  
Fujihiro Hamba ◽  
Shinsuke Kato

Reverse simulation was carried out to specify the point source of pollutants in closed waters. If pollutants flow out into waters, their source must be specified as soon as possible to take a quick measure against the pollution problem. Actually, a wide variety of pollutants have been released into the aquatic environment, some with catastrophic consequences for aquatic life or man. Numerical simulation is one of the powerful tools to specify the point source of pollutants. An inverse trajectory analysis has been used in the case when an advective process of pollutants is dominant. However, the inverse trajectory analysis does not take the diffusion of pollutants into account. Several analytical techniques are applicable only to simple current fields. The present study proposes reverse simulation to specify the point source of pollutants. The basic equation of the reverse simulation is given by changing the positive time derivative term of the advection-diffusion equation of pollutants into a negative term. First, numerical simulation was executed in a forward direction. The results of water current velocities were preserved for the following reverse simulation. Assuming that the pollutants are subject to the surrounding water current and turbulence like a tracer, the advection and diffusion processes of the tracer could be solved for obtaining the initial condition of reverse simulation, and for the following comparison with the results of reverse simulation. The predicted result of water current velocities in the forward simulation was given to calculate the advection-diffusion equation of the tracer. One of the major problems of reverse simulation is the instability of numerical simulation. A Gaussian filter was used for the spatial distribution of the tracer or of the flux of the tracer to eliminate the numerical instability, and the optimum filter width was discussed. As a result, the instability of reverse simulation was suppressed by applying the Gaussian filter for the spatial distribution of the tracer. However, the concentration of the tracer was not condensed in comparison with the result of the tracer in the forward simulation. When the Gaussian filter was used for the special distribution of the flux of the tracer, the accuracy of the prediction of the point source was improved. This is because the high frequency variation was suppressed, keeping the low frequency variation. The concentration of the tracer was more condensed with smaller filter width in both cases. The future studies are to determine the filter width adequately and to modify the difference equation of the Gaussian filter.


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