Advanced reactor concepts span the spectrum from heat pipe-cooled microreactors, through thermal and fast molten-salt reactors, to gas-and salt-cooled pebble bed reactors. The modeling and simulation of each of these reactor types comes with their own geometrical complexities and multiphysics challenges. However, the common theme for all nuclear reactors is the necessity to be able to accurately predict neutron distribution in the presence of multiphysics feedback. We argue that the current standards of modeling and simulation, which couple single-physics, single-reactor-focused codes via ad hoc methods, are not sufficiently flexible to address the challenges of modeling and simulation for advanced reactors. In this work, we present the Multiphysics Object Oriented Simulation Environment (MOOSE)-based radiation transport application Rattlesnake. The use of Rattlesnake for the modeling and simulation of nuclear reactors represents a paradigm shift away from makeshift data exchange methods, as it is developed based on the MOOSE platform with its very natural form of shared data distribution. Rattlesnake is well equipped for addressing the geometric and multiphysics challenges of advanced reactor concepts because it is a flexible finite element tool that leverages the multiphysics capabilities inherent in MOOSE. This paper focuses on the concept and design of Rattlesnake. We also demonstrate the capabilities and performance of Rattlesnake with a set of problems including a microreactor, a molten-salt reactor, a pebble bed reactor, the Advanced Test Reactor at the Idaho National Laboratory, and two benchmarks: a multiphysics version of the C5G7 benchmark and the LRA benchmark.
SummaryIn this paper, a proper generalized decomposition (PGD) approach is employed for uncertainty quantification purposes. The neutron diffusion equation with external sources, a diffusion‐reaction problem, is used as the parametric model. The uncertainty parameters include the zone‐wise constant material diffusion and reaction coefficients as well as the source strengths, yielding a large uncertain space in highly heterogeneous geometries. The PGD solution, parameterized in all uncertain variables, can then be used to compute mean, variance, and more generally probability distributions of various quantities of interest. In addition to parameterized properties, parameterized geometrical variations of three‐dimensional models are also considered in this paper. To achieve and analyze a parametric PGD solution, algorithms are developed to decompose the model's parametric space and semianalytically integrate solutions for evaluating statistical moments. Varying dimensional problems are evaluated to showcase PGD's ability to solve high‐dimensional problems and analyze its convergence.
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