FORWARD-WEIGHTED CADIS METHOD FOR VARIANCE REDUCTION OF MONTE CARLO CALCULATIONS OF DISTRIBUTIONS AND MULTIPLE LOCALIZED QUANTITIES

This paper presents a new hybrid (Monte Carlo/deterministic) method for increasing the efficiency of Monte Carlo calculations of distributions, such as flux or dose rate distributions (e.g., mesh tallies), as well as responses at multiple localized detectors and spectra. This method, referred to as Forward-Weighted CADIS (FW-CADIS), is a variation on the Consistent Adjoint Driven Importance Sampling (CADIS) method, which has been used for some time to very effectively improve the efficiency of Monte Carlo calculations of localized quantities, e.g., flux, dose, or reaction rate at a specific location. The basis of this method is the development of an importance function that represents the importance of particles to the objective of uniform Monte Carlo particle density in the desired tally regions. Implementation of this method utilizes the results from a forward deterministic calculation to develop a forward-weighted source for a deterministic adjoint calculation. The resulting adjoint function is then used to generate consistent space- and energy-dependent source biasing parameters and weight windows that are used in a forward Monte Carlo calculation to obtain approximately uniform statistical uncertainties in the desired tally regions. The FW-CADIS method has been implemented in the ADVANTG/MCNP framework and has been fully automated within the MAVRIC sequence of SCALE 6. Results of the application of the method to enabling the calculation of dose rates throughout an entire fullscale pressurized-water reactor facility are presented and discussed.

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