Reverse Simulation for Specifying the Source of Pollutants in Waters

2011 ◽  
Author(s):  
Daisuke Kitazawa ◽  
Satoshi Abe ◽  
Fujihiro Hamba ◽  
Shinsuke Kato
Keyword(s):  
Numerical Simulation ◽  
Point Source ◽  
Trajectory Analysis ◽  
Water Current ◽  
Frequency Variation ◽  
Gaussian Filter ◽  
Forward Simulation ◽  
Filter Width ◽  
Advection Diffusion Equation ◽  
Reverse Simulation

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.

Numerical Simulation of Flowfield around High Speed Trains Passing by each other at the same Speed

2011 ◽  
Vol 97-98 ◽  
pp. 698-701
Author(s):  
Ming Lu Zhang ◽  
Yi Ren Yang ◽  
Li Lu ◽  
Chen Guang Fan
Keyword(s):  
Numerical Simulation ◽  
Wind Tunnel ◽  
Flow Field ◽  
Point Source ◽  
High Speed ◽  
Stokes Equations ◽  
Field Structure ◽  
Vehicle Speed ◽  
Mesh Method ◽  
High Speed Trains

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.

Turbulent dispersion in a non-homogeneous field

1999 ◽  
Vol 392 ◽  
pp. 45-71 ◽  
Author(s):  
ILIAS ILIOPOULOS ◽  
THOMAS J. HANRATTY
Keyword(s):  
Numerical Simulation ◽  
Direct Numerical Simulation ◽  
Stochastic Model ◽  
Point Source ◽  
Time Scales ◽  
Langevin Equation ◽  
Turbulent Dispersion ◽  
Homogeneous Field ◽  
Fully Developed Flow ◽  
Good Agreement

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.

scholarly journals Study on the Dependence of Reverse Simulation for Identifying a Pollutant Source on Grid Resolution and Filter Width in Cavity Flow

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Satoshi Abe ◽  
Shinsuke Kato ◽  
Fujihiro Hamba ◽  
Daisuke Kitazawa
Keyword(s):  
Cavity Flow ◽  
Grid Resolution ◽  
Hazardous Substance ◽  
Simulation Accuracy ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Filter Width ◽  
Pollutant Source ◽  
Reverse Simulation ◽  
Negative Diffusion

When a hazardous substance is diffused, it is necessary to identify the pollutant source and respond immediately. However, there are many cases in which damage is caused without a clear understanding of where the pollutant source is located. There are three groups of identifying pollutant source information (Liu and Zhai, 2007): the probability method, forward method, and backward method. In our previous study, we proposed reverse simulation, which is categorized as a backward method (Abe and Kato, 2011). Numerical instability by negative diffusion is a principal problem in the backward method. In order to improve the problem, we applied a low-pass filter operation to the concentration flux in the RANS analysis. The simulation secured the numerical stability. However, reverse simulation accuracy is expected to depend on the grid resolution and filter width. In this paper, we introduce reverse simulation results in cavity flow. In particular, we survey the dependence of reverse simulation accuracy on the grid resolution and filter width. Moreover, we discuss the dependence of reverse simulation on the grid resolution and filter width with a one-dimensional diffusion equation. As a result, we found that the simulated negative diffusion varies greatly among the grid resolution and filter width.

Determination of Dynamic Dispersion Coefficient for Solid Particles Flowing in a Fracture With Consideration of Gravity Effect

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Yanan Ding ◽  
Xiaoyan Meng ◽  
Daoyong Yang
Keyword(s):  
Particle Size ◽  
Point Source ◽  
Line Source ◽  
Dispersion Coefficient ◽  
Solid Particles ◽  
Gravity Effect ◽  
Dispersion Coefficients ◽  
Source Condition ◽  
Advection Diffusion Equation ◽  
Gravity Settling

Abstract A robust and pragmatic method has been developed and validated to analytically determine dynamic dispersion coefficients for particles flowing in a parallel-plate fracture, in which gravity settling has been considered due to its significant impact on particle flowing behavior. More specifically, a two-dimensional (2D) advection–diffusion equation together with the initial and boundary conditions has been formulated to describe the flow behavior of finite-sized particles on the basis of coupling the Poiseuille flow with vertical settling. Meanwhile, three types of instantaneous source conditions (i.e., point source, uniform line source, and volumetric line source) have been considered. Explicit expressions, which can directly and time-efficiently calculate dynamic dispersion coefficient, have been derived through the moment analysis and the Green’s function method. By performing the simulation based on the random walk particle tracking (RWPT) algorithm, the newly developed model has been verified to determine particle dispersion coefficients agreeing well with those obtained from the RWPT simulations. It is found that the point source is the most sensitive to gravity effect among different source conditions, while the volumetric line source is affected more than the uniform line source. For particle size larger than its critical value, an increased particle size leads to a decreased asymptotical dispersion coefficient for all the source conditions due to the significant gravity effect, while gravity positively affects the dispersion coefficient at early times for the point source condition. In addition, average flow velocity positively affects the dispersion coefficient for all the source conditions, while the associated gravity effect is influenced only at early times for the point source condition.

Numerical Study on Circulation and Thermohaline Structures With Effects of Icing Event in the Caspian Sea

2010 ◽  
Author(s):  
Daisuke Kitazawa ◽  
Jing Yang
Keyword(s):  
Numerical Simulation ◽  
Water Temperature ◽  
Coriolis Force ◽  
Caspian Sea ◽  
Numerical Study ◽  
Measurement Data ◽  
Ocean Model ◽  
Water Current ◽  
The Caspian Sea ◽  
Volga River

A hydrostatic and ice coupled model was developed to analyze circulation and thermohaline structures in the Caspian Sea. The northern part of the Caspian Sea freezes in the winter. Waters start icing in November and ices spread during December and January. The northern part of the Caspian Sea is covered by ices in severe winters. Ice-covered area is at its maximum during January and February, and then ices begin melting in March and disappear in April. The occurrence of ices must have significant effects on circulation and thermohaline structures as well as ecosystem in the northern Caspian Sea. In the present study, formation of ices is modeled assuming that ices do not move but spread and shrink on water surface. Under the ices, it is assumed that the exchange of momentum flux is impeded and the fluxes of heat and brine salt are given at sea-ice boundary. The ice model was coupled with a hydrostatic model based on MEC (Marine Environmental Committee) Ocean Model developed by the Japan Society of Naval Architect and Ocean Engineers. Numerical simulation was carried out for 20 years to achieve stable seasonal changes in current velocity, water temperature, and salinity. The fluxes of momentum, heat, and salt were estimated by using measurement data at 11 meteorological stations around the Caspian Sea. Inflow of Volga River was taken into account as representative of all the rivers which inflow into the Caspian Sea. Effects of icing event on circulation and thermohaline structures were discussed using the results of numerical simulation in the last year. As a result, the accuracy of predicting water temperature in the northern Caspian Sea was improved by taking the effects of icing event into account. Differences in density in the horizontal direction create several gyres with the effects of Coriolis force. The differences were caused by differences in heat capacity between coastal and open waters, differences in water temperature due to climate, and inflow of rivers in the northern Caspian Sea. The water current field in the Caspian Sea is formed by adding wind-driven current to the dominant density-driven current, which is based on horizontal differences in water temperature and salinity, and Coriolis force.

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