Improved Monte Carlo Variance Reduction for Space and Energy Self-Shielding
S. C. Wilson, R. N. Slaybaugh

TL;DR
This paper introduces a Resonance Factor correction to FW-CADIS for Monte Carlo simulations, significantly improving variance reduction in problems with combined space and energy self-shielding, leading to faster and more accurate results.
Contribution
The paper proposes a novel Resonance Factor correction to FW-CADIS, enhancing variance reduction in complex self-shielding regions without affecting other areas.
Findings
Dramatic reduction in maximum 95% confidence interval relative error.
Significant decrease in computational time.
Improved accuracy in regions with combined space and energy self-shielding.
Abstract
Continued demand for accurate and computationally efficient transport methods to solve optically thick, fixed-source transport problems has inspired research on variance-reduction (VR) techniques for Monte Carlo (MC). Methods that use deterministic results to create VR maps for MC constitute a dominant branch of this research, with Forward Weighted-Consistent Adjoint Driven Importance Sampling (FW-CADIS) being a particularly successful example. However, locations in which energy and spatial self-shielding are combined, such as thin plates embedded in concrete, challenge FW-CADIS. In these cases the deterministic flux cannot appropriately capture transport behavior, and the associated VR parameters result in high variance in and following the plate. This work presents a new method that improves performance in transport calculations that contain regions of combined space and energy…
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Taxonomy
TopicsNuclear reactor physics and engineering · Nuclear Materials and Properties · Nuclear Physics and Applications
