Automated Variance Reduction Applied to Nuclear Well-Logging Problems

Abstract

Simulating nuclear well-logging devices with Monte Carlo methods is computationally challenging and requires significant variance reduction to compute detector responses with low statistical uncertainties in reasonable lengths of time. The consistent adjoint-driven importance sampling (CADIS) method, which provides consistent source and transport biasing parameters based on a deterministic adjoint (importance) function, has been demonstrated to be very effective for well-logging simulations and other deep-penetration problems. A recent extension to the CADIS method, FW-CADIS (forward-weighted CADIS), is designed to optimize the calculation of several tallies at once by using an adjoint function based on an adjoint source weighted by the inverse of the forward flux. These advanced variance reduction methods have been incorporated and automated into the MAVRIC sequence of SCALE, making them very easy to use. The CADIS and FW-CADIS methods are demonstrated and compared on simple benchmark models of both neutron- and photon-based well-logging devices. Both advanced variance reduction methods offer a substantial reduction in computing time, compared to analog simulation, for these applications.