Neutronic Analysis of Stochastic Distribution of Fuel Particles in Very High Temperature Gas-Cooled Reactors.

Neutronic Analysis;Monte Carlo Simulation;Chord Length Sampling;Very High Temperature Gas-Cooled Reactors;Stochastic Transport;Nuclear Engineering and Radiological Sciences;Engineering;Nuclear Engineering & Radiological Sciences

Ji, WeiLarsen, Edward W. ;

University of Michigan

关键词: Neutronic Analysis;    Monte Carlo Simulation;    Chord Length Sampling;    Very High Temperature Gas-Cooled Reactors;    Stochastic Transport;    Nuclear Engineering and Radiological Sciences;    Engineering;    Nuclear Engineering & Radiological Sciences;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/58523/wjiz_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The Very High Temperature Gas-Cooled Reactor (VHTR) is a promising candidate for Generation IV designs due to its inherent safety, efficiency, and its proliferation-resistant and waste minimizing fuel cycle. A number of these advantages stem from its unique fuel design, consisting of a stochastic mixture of tiny (0.78mm diameter) microspheres with multiple coatings. However, the microsphere fuel regions represent point absorbers for resonance energy neutrons, resulting in the ;;double heterogeneity;; for particle fuel. Special care must be taken to analyze this fuel in order to predict the spatial and spectral dependence of the neutron population in a steady-state reactor configuration. The challenges are considerable and resist brute force computation: there are over 1010 microspheres in a typical reactor configuration, with no hope of identifying individual microspheres in this stochastic mixture. Moreover, when individual microspheres ;;deplete;; (e.g., burn the fissile isotope U-235 or transmute the fertile isotope U-238 (eventually) to Pu-239), the stochastic time-dependent nature of the depletion compounds the difficulty posed by the stochastic spatial mixture of the fuel, resulting in a prohibitive computational challenge. The goal of this research is to develop a methodology to analyze particle fuel randomly distributed in the reactor, accounting for the kernel absorptions as well as the stochastic depletion of the fuel mixture. This Ph.D. dissertation will address these challenges by developing a methodology for analyzing particle fuel that will be accurate enough to properly model stochastic particle fuel in both static and time-dependent configurations and yet be efficient enough to be used for routine analyses. This effort includes creation of a new physical model, development of a simulation algorithm, and application to real reactor configurations.