A study on deterministic truncation of Monte Carlo transport solution

Abstract

This paper presents a systematic way to truncate the high-fidelity Monte Carlo (MC) solution in order to reduce the computational cost without compromising the essential reliability of the solutions. Based on the conventional CMFD-assisted MC process, a deterministic truncation of the MC solution (DTMC) is investigated in detail for a systematic approximation to the costly MC solution of the reactor eigenvalue problem. Once the one-energy-group CMFD parameters are obtained from the high-fidelity MC calculation, the deterministic eigenvalue problem is independently solved at every cycle. These CMFD solutions are used for the acceleration of the MC simulation as well as the solution prediction by themselves. The main purpose of this paper is to suggest the variance reduction techniques applicable to the Monte Carlo eigenvalue calculation. The performances of the newly proposed DTMC method are evaluated in view of the neutron multiplication factor and pin-wise power distribution. The stochastic features of the DTMC solutions are discussed in terms of the apparent and real variance, and several schemes were studied to minimize the associated stochastic uncertainties of the DTMC solution. A number of sensitivity tests were conducted to minimize the uncertainties of CMFD parameters and improve the boundary condition treatment by an albedo correction.