No abstract

No abstract

No abstract

No abstract

Accurately predicting changes in the thermal conductivity of light water reactor UO 2 fuel throughout its lifetime in reactor is an essential part of fuel performance modeling. However, typical thermal conductivity models from the literature are empirical. In this work, we begin to develop a mechanistic thermal conductivity model by focusing on the impact of gaseous fission products, which is coupled to swelling and fission gas release. The impact of additional defects and fission products will be added in future work. The model is developed using a combination of atomistic and mesoscale simulation, as well as analytical models. The impact of dispersed fission gas atoms is quantified using molecular dynamics simulations corrected to account for phonon-spin scattering. The impact of intragranular bubbles is accounted for using an analytical model that considers phonon scattering. The impact of grain boundary bubbles is determined using a simple model with five thermal resistors that are parameterized by comparing to 3D mesoscale heat conduction results. However, when used in the BISON fuel performance code to model four reactor experiments, it produces reasonable predictions without having been fit to fuel thermocouple data.

No abstract

We present a novel phase-field model development capability in the open source MOOSE finite element framework. This facility is based on the "modular free energy" approach in which the phase-field equations are implemented in a general form that is logically separated from model-specific data such as the thermodynamic free energy density and mobility functions. Free energy terms contributing to a phase-field model are abstracted into self-contained objects that can be dynamically combined at simulation run time. Combining multiple chemical and mechanical free energy contributions expedites the construction of coupled phase-field, mechanics, and multiphase models. This approach allows computational material scientists to focus on implementing new material models, and to reuse existing solution algorithms and data processing routines. A key new aspect of the rapid phase-field development approach that we discuss in detail is the automatic symbolic differentiation capability. Automatic symbolic differentiation is used to compute derivatives of the free energy density functionals, and removes potential sources of human error while guaranteeing that the nonlinear system Jacobians are accurately approximated. Through just-in-time compilation, we greatly reduce the computational expense of evaluating the differentiated expressions. The new capability is demonstrated for a variety of representative applications.

No abstract