An improved convergence rate for the prompt α eigenvalue calculation in α-k iteration methods

The α-k iteration method is a common approach for calculating the fundamental α- or time-eigenvalue. The bottleneck of the method lies in how to estimate or adjust the amount of α value in each iteration. Prolonged convergence as well as the need for a proper initial guess for the α-eigenvalue are two main deficiencies of commonly employed α adjusting techniques. This article proposes a direct physical relation to adjust the α-eigenvalue in the Monte Carlo (MC) α-k iteration method, lifting the need for an initial guess along with an improved convergence rate. To do that, a link is established between the actual physical parameters of the system and the α-eigenvalue in each iteration. Also, it is shown that the combination of currently used methods and our proposed algorithm would end to a reduced variance in the final result. The MC3 Monte Carlo code is empowered via several modules enabling us to perform a comparative analysis on the performance of α adjusting techniques. Several test cases are examined for the assessment of suggested scheme proving efficiency and robustness of the approach.