We perform multilevel Bayesian calibration of a model for fission gas diffusivity in UO2 nuclear fuel. Specifically, we use a two-level delayed acceptance method that couples a machine learning surrogate for Xe and U diffusivities with a …
Mechanistic models informed by lower-length-scale simulations have a role to play in accelerating fuel qualification by enabling the use of separate effects tests to reduce uncertainty on model parameters that impact the predictions of in-reactor …
Taking a nuclear fuel concept through the research, development, and qualification stages has historically taken on the order of 20 to 25 years because of extensive irradiation tests required for a variety of conditions. The concept of …
The evolution and release of fission gas impacts the performance of UO2 nuclear fuel. We have created a Bayesian framework to calibrate a novel model for fission gas transport that predicts diffusion rates of uranium and xenon in UO2 under both …
We construct a global sensitivity analysis framework for a coupled multiphysics model used to predict the changes in material properties and surface morphology of helium plasma-facing components in future fusion reactors. The model combines the …
A Bayesian inference strategy has been used to estimate uncertain inputs to global impurity transport code (GITR) modeling predictions of tungsten erosion and migration in the linear plasma device, PISCES-A. This allows quantification of GITR output …
In this work, the surface response of a tungsten plasma-facing component was simulated by a cluster-dynamics code, Xolotl, with a focus on quantifying the impact of uncertainty in one of the input parameters to Xolotl, namely, the incident helium …