Lagrangian Stochastic Dispersion Modeling in the Atmospheric Surface Layer with an Embedded Strong Flow Perturbation
AuthorMcAlpine, Jerrold Douglas
AdvisorKoracin, Darko R
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This thesis concerns the development of a modeling method to simulate the dispersion of dust in the atmospheric surface layer (ASL) with an embedded strong flow perturbation. In this study, the wake of a rotorcraft operating near the desert surface is the ASL perturbation of interest. The strong rotorcraft wake flow can entrain large quantities of dust leading to air quality and visibility impacts within and downwind of the wake.The rotorcraft wake is simulated using a parameterized rotor in a computational fluid dynamics model. The unstable desert ASL environment is simulated using Monin-Obukhov theory and parameters taken from measurements from a full-scale experiment.An analysis of the results demonstrates that the model is able to adequately predict the wake flow regime structure but is unable to accurately simulate the dimensions and vorticity of the forward ground vortex. Dust dispersion is simulated using a new 2-tier stabilized Lagrangian Stochastic Model configured to account for the highly 3-dimensional strong flow within the perturbation. The model is validated against the Project Prairie Grass Dispersion Experiment data with a demonstration of the model's ability to accurately predict downwind tracer concentrations.A coupled simulation using the perturbation CFD model simulated flow-field and the 2-tier stabilized dispersion model produces results that compare well to a full-scale helicopter wake dust dispersion experiment. The simulated concentrations of dust downwind of the wake match closely in magnitude and time of impact to the measurements taken during the experiment.