Estimating consistent biased source probabilities over arbitrary bins - 25621

Abstract

This paper describes computational algorithms for estimating consistent biased source probabilities for the CADIS and FW-CADIS methods that are compatible with most of the general fixed-source capabilities of the MCNP continuous-energy transport code. The basic algorithm is a novel Monte Carlo sampling technique that enables the straightforward estimation of biased source probabilities over arbitrarily complex distribution trees. In recent work, a method has been developed for generating correct biased spatial source probabilities in the presence of rejection from an arbitrary number of geometry cells. Two numerical test problems that demonstrate the correctness of this new algorithm are presented, including a simplified model of a cooling water system with an N-17 neutron source. (authors)