title: Automated variance reduction for MCNP using deterministic methods.
abstract: In order to reduce the user's time and the computer time needed to solve deep penetration problems, an automated variance reduction capability has been developed for the MCNP Monte Carlo transport code. This new variance reduction capability developed for MCNP5 employs the PARTISN multigroup discrete ordinates code to generate mesh-based weight windows. The technique of using deterministic methods to generate importance maps has been widely used to increase the efficiency of deep penetration Monte Carlo calculations. The application of this method in MCNP uses the existing mesh-based weight window feature to translate the MCNP geometry into geometry suitable for PARTISN. The adjoint flux, which is calculated with PARTISN, is used to generate mesh-based weight windows for MCNP. Additionally, the MCNP source energy spectrum can be biased based on the adjoint energy spectrum at the source location. This method can also use angle-dependent weight windows.
authors.affiliation: X-5, Applied Physics Division, Los Alamos National Laboratory, MS F663, Los Alamos, NM 87545, USA. jsweezy@lanl.gov
authors.forename: J
authors.initials: J
authors.lastname: Sweezy
authors.forename: F
authors.initials: F
authors.lastname: Brown
authors.forename: T
authors.initials: T
authors.lastname: Booth
authors.forename: J
authors.initials: J
authors.lastname: Chiaramonte
authors.forename: B
authors.initials: B
authors.lastname: Preeg
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D015233:Models, Statistical; D009010:Monte Carlo Method; D011829:Radiation Dosage; D011835:Radiation Protection / Q000379:methods*; D011874:Radiometry / Q000379:methods*; D012984:Software