A Limited-Memory Framework for Conditional Point Sampling for Radiation Transport in 1D Stochastic Media

Conditional Point Sampling (CoPS) is our newly proposed Monte Carlo method for transport in stochastic media that has been demonstrated to achieve highly accurate mean response results and to compute variance of the mean caused by random spatial mixing. The ability of CoPS to efficiently characterize the effects of random spatial mixing beyond the mean is hindered by the algorithm’s potentially unbounded computer memory footprint. Thus, in previous work, we established two limited-memory techniques for CoPS to improve required computer memory, i.e., recent memory (RM) CoPS and amnesia radius (AR) CoPS, the latter of which enables CoPS to tractably compute probability density functions (PDFs) of response. In this work, we create a limited-memory framework that allows CoPS to combine the advantages of limited-memory techniques and populate the framework with the two inaugural techniques of RM and AR. The proposed framework enables the user to control the computational performance of CoPS by making problem-specific trade-offs between accuracy, computer memory footprint, and characterization of response distributions based on input parameters. We present mean leakage results, material-dependent scalar flux, leakage PDFs, and computer memory footprint computed using this new framework. By selecting different input parameters in our proposed limited-memory framework, CoPS is demonstrated to roughly match the accuracy and computer memory footprint of the established approximate method Chord Length Sampling or to provide response distribution information comparable to the brute-force benchmark approach while improving the computer memory footprint compared to the original CoPS algorithm.