Monte Carlo variance reduction with deterministic importance functions

Recent trends in Monte Carlo code development have reflected a recognition of the benefits of using deterministic importance functions for Monte Carlo variance reduction. This paper offers a review of the use of deterministic importance functions for variance reduction of Monte Carlo simulations. Adjoint methodology and the concept of "importance" are presented, along with an explanation of their use for variance reduction. Relevant works from a number of different researchers are briefly reviewed. The authors' CADIS methodology for calculating consistent source biasing and weight window parameters based on deterministic importance functions is presented. Efforts to automate the generation and use of deterministic importance functions are briefly described, including an overview of the A ³MCNP code. Finally, aspects of interest, including computational benefits, associated with using deterministic importance functions for Monte Carlo simulation of real-world problems are demonstrated.