Surface Particle Obscuration And BRDF Predictions

Abstract

This paper describes an efficient computational approach to determine the obscuration ratio (OR), or particle area coverage, due to surface particulate contamination. The analytical approach utilizes a multi-bin particle size distribution model with incorporation of Raab's particle shape data and Barengoltz's areal density integration method. With this approach, surface contamination involving a wide spectrum of sphere/cylinder particle shape, as well as particle size distribution, can be characterized with ease. The present method also permits establishment of a correlation between the MIL-STD-1246B cleanliness level and the more meaningful obscuration ratio. This correlation will yield useful and time-saving benefits for spacecraft contamination control. Overall, our analytical method using best-available particle shape/size distribution data has resulted in much lower OR values by as much as 32% reduction than previous predictions based on spherical particles. Also discussed in this paper is a numerical method to predict the bidirectional reflectance distribution function (BRDF) for spherical and cylindrical particles as well as particle-laden reflecting surfaces. The method encompasses Mie scattering, diffraction, and reflection solutions for spheres, and diffraction and reflection solutions for randomly oriented cylinders. The BRDF computational scheme complements the OR prediction model, in that the particle deposit quantity and particle-induced light obscuration-scattering characteristics can now be determined in an efficient, in-tandem manner.