Sampling strategies for incorporating uncertainty and turbulent scattering into outdoor sound propagation calculations.

Abstract

The accuracy of mean sound‐level estimates derived from different sampling methods is studied. The methods considered are Monte Carlo sampling (MCS), Latin Hypercube sampling (LHS), and Latinized Centroidal Voronoi Tessellation sampling (LCVTS). The goal is to determine which sampling method converges most rapidly to the actual mean sound level. The approach involves a model acoustic atmosphere, based on surface‐layer similarity theory and a relaxation model for the ground, with sound fields calculated numerically with a parabolic equation solver. The range of consequences of epistemic uncertainty due to imperfect knowledge of the atmospheric and ground variables is examined through a simplified probabilistic model. Random (aleatory) uncertainty due to turbulent scattering also is considered. The samples are drawn randomly within the domain of uncertainty of the environmental variables. When only epistemic uncertainty is present owing to imprecise knowledge of the environmental variables, LCVTS is found to converge to an accurate estimate with the fewest number of samples, followed by LHS and then MCS. When random turbulent scattering is present, however, the sampling method has little effect on convergence.