Using a genetic algorithm to assimilate transport and dispersion variables
This work reports on a genetic algorithm used to optimize the variational problem (GA-Var). Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The dispersion parameters apply to Lagrangian puff space, where one estimates the transport and dispersion parameters.
This calculation is applied to an instantaneous release in a meandering wind field such as that observed during field experiments of a smoke plume. GA-Var proves to be successful at recovering the unknown transport and dispersion parameters.