Deterministically estimated fission source distributions for Monte Carlo k-eigenvalue problems. (September 2018) - Biondo, Elliott D.; Davidson, Gregory G.; Pandya, Tara M.; Hamilton, Steven P.; Evans, Thomas M.
Highlights: Deterministic initial guesses can be used for Monte Carlo k-eigenvalue problems. This reduces the number of inactive cycles for reactor/cask problems, except AP1000. Method often only effective when a large number of histories per cycle are required. Abstract: The standard Monte Carlo (MC) k -eigenvalue algorithm involves iteratively converging the fission source distribution using a series of potentially time-consuming inactive cycles before quantities of interest can be tallied. One strategy for reducing the computational time requirements of these inactive cycles is the Sourcerer method, in which a deterministic eigenvalue calculation is performed to obtain an improved initial guess for the fission source distribution. This method has been implemented in the Exnihilo software suite within SCALE using the SP N or S N solvers in Denovo and the Shift MC code. The efficacy of this method is assessed with different Denovo solution parameters for a series of typical k -eigenvalue problems including small criticality benchmarks, full-core reactors, and a fuel cask. It is found that, in most cases, when a large number of histories per cycle are required to obtain a detailed flux distribution, the Sourcerer method can be used to reduce the computational time requirements of the inactive cycles.