R. Schafer, “Uncertainty Quantification of Fission Product Concentration Estimation in MCNP Fuel Depletion Simulations”, M.S. Thesis, Nuclear Engineering, Texas A&M University, College Station, TX (2019).
Monte Carlo N-Particle Transport Code (MCNP) is a Monte Carlo computational neutron transport code with multi core parallel simulation functionality developed by Los Alamos National Laboratory and is widely used in nuclear reactor modeling and nuclear fuel burnup/depletion simulations. In burnup simulations, MCNP calculates neutron reaction rates and their corresponding stochastic uncertainties at each differential time step of fuel depletion, however these reaction rate uncertainties are not currently propagated through multiple depletion time steps. Moreover, these reaction rate uncertainties are not propagated into the concentrations of fission products and actinides produced in depleted nuclear fuel. The objective of this thesis research is to develop a methodology to quantify the stochastic uncertainties in actinide and fission product concentration estimates from performing nuclear fuel burnup simulations using MCNP. A mathematical methodology using the reaction rates given by an MCNP burnup simulation was developed to estimate the concentrations of various nuclides over the entirety of the depletion simulation. A Monte Carlo sampling procedure of reaction rates using the predicted stochastic uncertainty was implemented into the model, where each necessary reaction rate was sampled for each depletion time step. This procedure was run multiple times and a statistical analysis was performed to estimate the overall stochastic uncertainty in selected fission product and actinide concentrations predicted at the end of multiple time step depletion simulations by MCNP. rn The results from the Multi Stage Monte Carlo Methodology were then compared to an MCNP data set, containing data from over 100 identical MCNP burnup simulations and results from new methodology were found effective in predicting overall stochastic uncertainties.