tailieunhanh - Analysis of inconsistent source sampling in monte carlo weight-window variance reduction methods

This paper develops an original framework that mathematically expresses the coupling of the weight window and source biasing techniques, allowing the authors to explore the impact of inconsistent source sampling on the variance of MC results. A numerical experiment supports this new framework and suggests that certain classes of problems may be relatively insensitive to inconsistent source sampling schemes with moderate levels of splitting and rouletting. | Analysis of inconsistent source sampling in monte carlo weight-window variance reduction methods Nuclear Engineering and Technology 49 (2017) 1172e1180 Contents lists available at ScienceDirect Nuclear Engineering and Technology journal homepage: Original Article Analysis of inconsistent source sampling in monte carlo weight-window variance reduction methods David P. Griesheimer*, Virinder S. Sandhu Naval Nuclear Laboratory, . Box 79, West Mifflin, PA 15122-0079, USA a r t i c l e i n f o a b s t r a c t Article history: The application of Monte Carlo (MC) to large-scale fixed-source problems has recently become possible Received 31 May 2017 with new hybrid methods that automate generation of parameters for variance reduction techniques. Accepted 27 July 2017 Two common variance reduction techniques, weight windows and source biasing, have been automated Available online 14 August 2017 and popularized by the consistent adjoint-driven importance sampling (CADIS) method. This method uses the adjoint solution from an inexpensive deterministic calculation to define a consistent set of Keywords: weight windows and source particles for a subsequent MC calculation. One of the motivations for source Monte Carlo consistency is to avoid the splitting or rouletting of particles at birth, which requires computational Variance Reduction Importance Sampling resources. However, it is not always possible or desirable to implement such consistency, which results in CADIS inconsistent source biasing. This paper develops an original framework that mathematically expresses Weight Windows the coupling of the weight window and source biasing techniques, allowing the authors to explore the impact of inconsistent source sampling on the variance of MC results. A numerical experiment supports this new framework and suggests that certain classes of problems may be relatively insensitive to inconsistent

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• Analysis of inconsistent source sampling
• Monte carlo weight window variance reduction methods
• Moderate levels of splitting and roulettin
• The consistent adjoint driven importance sampling
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