A recursive Monte Carlo method for estimating importance function distributions in deep-penetration problems

Journal Article · Mon Dec 01 00:00:00 EST 1980 · Nucl. Sci. Eng.; (United States)

OSTI ID:6810742

Goldstein, M; Greenspan, E

A recursive Monte Carlo (RMC) method for estimating the importance function distribution in three-dimensional systems, intended for importance sampling applications, is developed. The method consists of dividing the system into relatively thin geometrical regions and solving the inhomogeneous forward transport equation for each of the regions. The RMC method is found to possess a number of unique features, including the ability to infer the importance function distributions pertaining to many different detectors from essentially a single Monte Carlo run. Various technical questions concerned with the practical application of the RMC method, including the questions of the accumulation of statistical and systematic errors and their dependence on the details of the system division and source batch size, are investigated. A promising algorithm for the application of the method is formulated. The practicality and efficiency of the RMC method is investigated for a number of monoenergetic problems.

• Research Organization: Israel Atomic Energy Commission, Nuclear Research Center-Negev P.O. Box 9001, Beer Sheva
• OSTI ID: 6810742
• Journal Information: Nucl. Sci. Eng.; (United States), Vol. 76:3
• Country of Publication: United States
• Language: English

Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
NEUTRON IMPORTANCE FUNCTION
DISTRIBUTION
MONTE CARLO METHOD
ALGORITHMS
DATA ACQUISITION
DIFFUSION LENGTH
EFFICIENCY
ERRORS
HETEROGENEOUS EFFECTS
NEUTRON DETECTORS
NEUTRON DIFFUSION EQUATION
NEUTRON TRANSPORT THEORY
SYSTEMS ANALYSIS
THREE-DIMENSIONAL CALCULATIONS
DIFFERENTIAL EQUATIONS
DIMENSIONS
EQUATIONS
FUNCTIONS
LENGTH
MATHEMATICAL LOGIC
MEASURING INSTRUMENTS
RADIATION DETECTORS
TRANSPORT THEORY
654003* - Radiation & Shielding Physics- Neutron Interactions with Matter