Parallel Implementation of a Lagrangian Stochastic Model for Pollution Dispersion

Abstract

Pollutant dispersion models in the atmosphere can describe by Eulerian or Lagrangian approaches. Lagrangian models belong to the class of Monte Carlo methods. This type of method is very flexible, solving more complex problems, however this computational cost is greater than Eulerian models, as it is well established in the atmospheric pollutant and nuclear engineering communities. A parallel version of the Lagrangian particle model - LAMBDA - is developed using the MPI message passing communication library. Performance tests were executed in a distributed memory parallel machine, a multicomputer based on IA-32 architecture. Portions of the pollutant in the air are considered particles emitted from a pollutant source, evolving under stochastic forcing. This yields independent evolution equations for each particle of the model that can be executed by a different processor in a parallel implementation. Speed-up results show that the parallel implementation is suitable for the used architecture.