Skip to main content

Response Matrix Reloaded

for Monte Carlo Simulations in Reactor Physics

Time: Tue 2019-12-10 14.00

Location: FB54, AlbaNova universitetscentrum, Roslagstullsbacken 21, Stockholm (English)

Subject area: Physics, Nuclear Engineering

Doctoral student: Mickus Ignas , Kärnenergiteknik

Opponent: Dr Jaakko Leppänen, VTT Technical Research Centre of Finlad Ltd, Espoo, Finland

Supervisor: Jan Dufek,


This thesis investigates Monte Carlo methods applied to criticality and time-dependent problems in reactor physics. Due to their accuracy and flexibility, Monte Carlo methods are considered as a “gold standard” in reactor physics calculations. However, the benefits come at a significant computing cost. Despite the continuous rise in easily accessible computing power, a brute-force Monte Carlo calculation of some problems is still beyond the reach of routine reactor physics analyses. The two papers on which this thesis is based try to address the computing cost issue, by proposing methods for performing Monte Carlo reactor physics calculations more efficiently. The first method addresses the efficiency of the widely-used k-eigenvalue Monte Carlo criticality calculations. It suggests, that the calculation efficiency can be increased through a gradual increase of the neutron population size simulated during each criticality cycle, and proposes a way to determine the optimal neutron population size. The second method addresses the application of Monte Carlo calculations to reactor transient problems. While reactor transient calculations can, in principle, be performed using only Monte Carlo methods, such calculations take multiple thousands of CPU hours for calculating several seconds of a transient. The proposed method offers a middle-ground approach, using a hybrid stochastic-deterministic scheme based on the response matrix formalism. Previously, the response matrix formalism was mainly considered for steady-state problems, with limited application to time-dependent problems. This thesis proposes a novel way of using information from Monte Carlo criticality calculations for solving time-dependent problems via the response matrix.