doi:10.1016/j.envsoft.2004.06.002
Copyright © 2004 Published by Elsevier Ltd.
Short Communication
An iterative Langevin solution for contaminant dispersion simulation using the Gram–Charlier PDF
Jonas C. Carvalhoa,
,
, Ézio R. Nichimuraa, Marco Túllio M.B. de Vilhenab, Davidson M. Moreiraa and Gervásio A. Degraziac
aUniversidade Luterana do Brasil, Engenharia Ambiental, PPGEAM, Canoas, RS, Brazil
bUniversidade Federal do Rio Grande do Sul, Instituto de Matemática, Porto Alegre, RS, Brazil
cUniversidade Federal de Santa Maria, Departamento de Física, Santa Maria, RS, Brazil
Received 23 February 2004;
revised 11 June 2004;
accepted 22 June 2004.
Available online 18 August 2004.
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Abstract
An alternative numerical method to solve the three-dimensional stochastic Langevin equation applied to the air pollution dispersion is proposed and tested. We obtain a first-order differential equation whose solution is known and determined by an integrating factor. A Langevin model for inhomogeneous turbulence is obtained, considering the Gram–Charlier Probability Density Function (PDF) of turbulent velocity. The calculus process is based on an iterative scheme through the Picard Iterative Method. Numerical simulations and comparisons with measured data from two different tracer experiments are realized, showing a good agreement between predicted and observed values. Furthermore, the results obtained with the new approach are compared with the ones obtained by three different models.
Keywords: Langevin equation; Lagrangian particle model; Gram–Charlier PDF; Picard iterative method; Model evaluation
Table 1.
Statistical indices of the ILS performance and comparison with other three models (Langevin equation integrated according to the Ito calculus, analytical approach of the Eulerian dispersion equation and Gaussian model) for the Copenhagen experiment

Table 2.
Statistical indices of the ILS performance and comparison with other three models (Langevin equation integrated according to the Ito calculus, analytical approach of the Eulerian dispersion equation and Gaussian model) for the Prairie Grass experiment

Table 3.
Computational time comparison between the ILS and Langevin equation integrated according to the Ito calculus as a function of the number of released particles in each time step considering run 8 of the Prairie Grass experiment

Concentration values in g m−2.