Evaluation of Particle Clustering Algorithms in the Prediction of Brownout Dust Clouds

Abstract

A study of three Lagrangian particle clustering methods has been conducted with application to the problem of predicting brownout dust clouds that develop when rotorcraft land over surfaces covered with loose sediment. A significant impediment in performing such particle modeling simulations is the extremely large number of particles needed to obtain dust clouds of acceptable fidelity. Computing the motion of each and every individual sediment particle in a dust cloud (which can reach into tens of billions per cubic meter) is computationally prohibitive. The reported work involved the development of computationally efficient clustering algorithms that can be applied to the simulation of dilute gas-particle suspensions at low Reynolds numbers of the relative particle motion. The Gaussian distribution, k-means and Osiptsov's clustering methods were studied in detail to highlight the nuances of each method for a prototypical flow field that mimics the highly unsteady, two-phase vortical particle flow obtained when rotorcraft encounter brownout conditions. It is shown that although clustering algorithms can be problem dependent and have bounds of applicability, they offer the potential to significantly reduce computational costs while retaining the overall accuracy of a brownout dust cloud solution.