A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.
Particle emissions from a high-temperature falling particle receiver with an open aperture were modeled using computational and analytical methods and compared to U.S. particle-emissions standards to assess potential pollution and health hazards. The modeling was performed subsequent to previous on-sun testing and air sampling that did not collect significant particle concentrations at discrete locations near the tower, but the impacts of wind on collection efficiency, especial for small particles less than 10 microns, were uncertain. The emissions of both large (~350 microns) and small (<10 microns) particles were modeled for a large-scale (100 MWe) particle receiver system using expected emission rates based on previous testing and meteorological conditions for Albuquerque, New Mexico. Results showed that the expected emission rates yielded particle concentrations that were significantly less than either the pollution or inhalation metrics of 12 Pg/m3 (averaged annually) and 15 mg/m3, respectively. Particle emission rates would have to increase by a factor of ~400 (~0.1 kg/s) to begin approaching the most stringent standards.