Analysis of the LIFT Variance-Reduction Method Applied to Monte Carlo Radiation Transport Simulations of a Realistic Nonproliferation Test Problem

Abstract The Local Importance Function Transform (LIFT) method is a sophisticated automated variance-reduction technique for Monte Carlo simulation of radiation transport problems. In previous publications, the LIFT method was tested on geometrically simple problems with a coarse representation of radiation energy dependence, and the performance of the method was found to be promising when compared to traditional weight windows–based variance-reduction techniques. In this work, the LIFT method is tested on a spatially complex benchmark test problem with a more realistic representation of energy dependence (50 energy groups) and heterogeneous materials. The performance of the method in comparison with a CADIS (Consistent Adjoint Driven Importance Sampling)–based weight windows method and an analog Monte Carlo simulation is studied. A multigroup Monte Carlo code that utilizes portions of the framework of the deterministic tool Attila has been developed such that the overhead time in implementing the variance-reduction techniques is minimal. The Monte Carlo simulations are performed on an arbitrary tetrahedral mesh created by the mesh generator in Attila. A method to transfer the deterministic solution generated on a finer mesh to a coarser mesh for implementing the hybrid simulations has been developed, and the results are quantified.

[1]  Erin A. Miller,et al.  Transport Test Problems for Hybrid Methods Development , 2011 .

[2]  Edward W. Larsen,et al.  Automatic variance reduction for three-dimensional Monte Carlo simulations by the local importance function transform-II: Numerical results , 1997 .

[3]  J. F. Briesmeister.  MCNP-A General Monte Carlo N-Particle Transport Code , 1993 .

[4]  Jim E. Morel,et al.  Physics guide to CEPXS: A multigroup coupled electron-photon cross-section generating code , 1989 .

[5]  Douglas E. Peplow,et al.  Review of Hybrid (Deterministic/Monte Carlo) Radiation Transport Methods, Codes, and Applications at Oak Ridge National Laboratory (Selected Papers of the Joint International Conference of Supercomputing in Nuclear Applications and Monte Carlo : SNA + MC 2010) , 2011 .

[6]  Edward W. Larsen,et al.  Automatic Variance Reduction for Three-Dimensional Monte Carlo Simulations by the Local Importance Function Transform—I: Analysis , 1997 .

[7]  Edward W. Larsen,et al.  A modified Monte Carlo “Local Importance Function Transform” method , 2015 .

[8]  Douglas E. Peplow,et al.  Monte Carlo Shielding Analysis Capabilities with MAVRIC , 2011 .

[9]  E. Somasundaram,et al.  Implementation of hybrid variance reduction methods in a multi group Monte Carlo code for deep shielding problems , 2013 .

[10]  Alireza Haghighat,et al.  MONTE CARLO VARIANCE REDUCTION WITH DETERMINISTIC IMPORTANCE FUNCTIONS , 2003 .

[11]  J. Mika,et al.  Neutron Transport with Anisotropic Scattering , 1961 .

[12]  Scott A. Turner,et al.  Automatic variance reduction for Monte Carlo simulations via the local importance function transform , 1996 .

[13]  Sunil Arya,et al.  ANN: library for approximate nearest neighbor searching , 1998 .