The primary goal of this dissertation is to improve the quality of nuclear data available to the nuclear science community. We propose to accomplish this by applying machine learning algorithms to the large number of available benchmark experiments and simulations, with the goal of determining which nuclear data have strong...
In order to address the energy needs of developing countries and remote communities, Oregon State University has proposed the Multi-Application Small Light Water Reactor (MASLWR) design. In order to achieve five years of operation without refueling, use of 8% enriched fuel is necessary.
This dissertation is focused on core design...
We present a method for deterministically solving the consistent temperature and frequency dependent phonon radiative transport problem. We use the single relaxation time (SRT) approximation in the Self-Adjoint Angular Flux (SAAF) form with discrete ordinates (S_N) angular discretization method and continuous finite element method (CFEM) for spatial discretization. Included are...
We present a deterministic spectral method to predict equilibrium temperature distributions, heat flux, and thermal conductivity in homogeneous and heterogeneous media. We solve the Boltzmann transport equation in a second order spatial, self-adjoint angular flux formulation. We implemented this method into the radiation transport code Rattlesnake, built using the MOOSE...
The implementation of advanced hybrid (Monte Carlo/Deterministic) transport methods for realistic test problems has been a challenge due to the overhead efforts associated with interfacing a solution generated by a deterministic solver with a Monte Carlo based radiation transport code. In this work, with the help of Transpire, Inc., a...
Nuclear fuel management is an optimization problem on many levels. Finding “viable” solutions for the core reload design problem is difficult without expert knowledge and software automation. Small modular reactors with a shared used fuel pool demonstrate a novel opportunity for fuel cycle optimization.
A Python package was developed and...
In this dissertation, we attempt to overcome the "curse of dimensionality" inherent to radiation diffusion kinetics problems by employing a novel reduced order modeling technique known as proper generalized decomposition (PGD). After verifying a proposed PGD algorithm and associated solvers through various tests, we explore its performance for computing reduced-order...
In this dissertation, we derive and implement a new transport-diffusion hybrid algorithm for solving thermal radiative transfer (TRT) problems. Using the method of nonlinear elimination (NLEM), the TRT system of equations can be written in terms of a transport equation with the absence of scattering and a diffusion equation. The...
We present a novel multi-objective optimization methodology built upon a multi-agent blackboard framework. This multi-agent blackboard system (MABS) synthesizes blackboard architectures, multi-agent environments, and optimization theory. The blackboard architecture creates the framework for initializing, storing, and solving a multi-objective optimization problem. Multiple agents allow for an optimization problem to be...
Circulating fuel reactor (CFR) kinetics are characterized by delayed neutron precursor (DNP) drift in addition to the neutronic and thermal hydraulic phenomena typical of other reactor types. This environment can be computationally challenging to model, given that the multiphysics phenomena generally have non-linear interdependencies requiring the use of iterative solution...