Desenvolvimento de um modelo lagrangeano para dispersão de poluentes em condições de vento fraco

• Autor(a) principal: Sallet, Marieli
• Data de Publicação: 2007
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFPel - Guaiaca
• Texto Completo: http://repositorio.ufpel.edu.br/handle/ri/2183
• topic: Modelo de dispersão lagrangeano; Condição de vento fraco; Meandro do vento; Método iterativo de Picard; Lagrangian dispersion model; Low wind condition; Plume meandering; Picard Iterative Method; CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA

Currently, the search for analytical solutions for the dispersion problems is one of the main research subjects in the pollutant dispersion modeling. These solutions become important due to the intention to obtain dispersion models that generate reliable results in a small computational time, which are of great interest for regulatory air quality applications. Lagrangian particle models are an important and effective tool to simulate the atmospheric dispersion of airborne pollutants. These models are based on the Langevin equation, which is derived from the hypothesis that the velocity is given by the combination between a deterministic term and a stochastic term. In this work is presented a new Lagrangian particle model to simulate the pollutant dispersion in low wind speed conditions. During low wind speed, the diffusion of a pollutant in the planetary boundary layer (PBL) is indefinite and it has been observed that the plume is subject to a great deal of horizontal undulations, which are called plume meandering. The method proposed leads to a stochastic integral equation whose solution has been obtained through the Method of Successive Approximations or Picard s Iteration Method. The integral equation is written in terms of the real and imaginary parts of the complex function before performing the multiplication of the integrating factor, expressed by the Euler formula, inside and outside of the integral solution. To take account the meandering effect, the Frenkiel s Eulerian autocorrelation functions for low wind conditions is included naturally in the model. The new approach has been evaluated through the comparison with experimental data and other different dispersion models. Particularly, the results obtained by the model agree very well with the experimental data, indicating the model represents the dispersion process correctly in low wind speed conditions. It is also possible to verify that the new model results are better than ones obtained by the other models. The analytical feature of the technique and the natural inclusion of the Frenkiel s Eulerian autocorrelation function become the model more accurate than other models.