K-Epsilon Turbulence Model and Particle Swarm Optimization Technique to Estimate Multiple Atmospheric Emissions

Source identification techniques can provide source parameters estimations when sudden releases occur or the origin of long-term emissions. These methodologies can be based on optimization techniques or statistical inference, in the framework of the inverse problems. In this work, we propose a methodology based on the minimization of the Tikhonov-type functional, which accounts for the discrepancy between the observed and calculated concentrations also considering information about the source parameters for estimation. This functional is minimized to provide the source parameters using the Particle Swarm Optimization technique. A non-standard k−ϵ model was applied to solve the forward problem. The flow simulation emulated the Cedval A1-1 single-building experiment and the concentration distribution for two scenarios was synthetically produced by simulation. The proposed methodology was effective in identifying the source locations.