The \ac{doe}’s \ac{neams} program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In \ac{fy}-23, we initiated the \ac{uq} work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on \ac{triso} nuclear fuel. This year, we further expanded on that \ac{uq} work by investigating an approach to quantifying model inadequacy and accounting for \ac{lls} effects in \ac{triso} \ac{ag} release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing \ac{uq}. Specifically, we utilized \ac{koh} to account for model inadequacy in \ac{triso} \ac{ag} release predictions made by BISON. The \ac{koh} framework represents an improvement over the standard Bayesian framework used in \ac{fy}-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the \ac{agr} data. We compared the inverse \ac{uq} results obtained from both the standard Bayesian and \ac{koh} frameworks in light of the \ac{agr}-2/3/4 data, and also compared the predictive \ac{uq} results obtained from these two frameworks in light of the \ac{agr}-1 data. Next, we investigated the impact of considering \ac{lls} effects in the \ac{ag} release simulations. We developed an expanded database of \ac{lls} simulated effective diffusivities for \ac{ag}, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating \ac{lls} effects into the engineering-scale \ac{ag} release \ac{uq}. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse \ac{uq} results in light of the \ac{agr}-2/3/4 data and the predictive \ac{uq} results in light of the \ac{agr}-1 data, and compared the \ac{lls}-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the \ac{moose}/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian \ac{uq}. For verification purposes, we first tested these new capabilities on a species interaction problem. We then demonstrated them on the \ac{triso} \ac{ag} release application, showing that parallel active learning capabilities can enhance the accuracy of \ac{uq} while also substantially reducing the computational cost in comparison to the reference methods developed in \ac{fy}-23.
