The Rapid Evaluation of Mean Concentration Fields in Lagrangian Stochastic Modelling using a Density Kernel Estimator


  1. Shao, Y.
  2. Yee, E.
Corporate Authors
Defence R&D Canada - Suffield, Ralston ALTA (CAN)
Lagrangian Stochastic (LS) particle models have proven to be a useful computational tool for the description and prediction of dispersion of pollutant releases in complex meteorological situations (e.g., space- and time-varying situations pertaining to complex flow and turbulence). However, simulating the emitted pollutant by following the trajectories of many “marked” fluid elements released from the source distribution brings up the difficulty of the correct estimation of the mean concentration of the dispersing pollutant from the particle trajectory information. Recently, the density kernel estimation method has been proposed and applied successfully to estimate mean concentrations from Lagrangian Stochastic particle models. However, the computational effort needed by this method increases as N2 (assuming the number of receptor locations N sub r at which the concentration is required is comparable to the number of fluid particles N sub p used in the trajectory simulation, so N sub r approximately equal to N sub p ~ N) and, in consequence, the method has not been widely used because of the significant computer resources required. Here, we describe a novel algorithm for calculating the kernel estimate of the mean concentration field whose computational complexity scales only as N. The technique uses a tesselation (subdivision) of space in cubic cells of side length h (where h is the bandwidth of the kernel function), and then associates a linked-list data structure with

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Report Number
DRDC-SUFFIELD-TR-2004-186 — Technical Report
Date of publication
01 Oct 2004
Number of Pages

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