The mechanical degradation of all-solid-state Li-ion batteries (ASSLBs) is expected to be more severe than that in traditional Li-ion batteries with liquid electrolytes due to the additional mechanical constraints imposed by the solid electrolyte on the deformation of electrodes. Cracks and fractures could occur both inside the solid electrolyte (SE) and at the SE/electrode interfconce. A coupled electrochemical-mechanical model was developed and solved by the Finite Element Method (FEM) to evaluate the stress development in ASSLBs. Two sources of volume change were considered, namely the expansion/shrinkage of electrodes due to lithium concentration change and the interphase formation at the SE/electrode interface due to the decomposition of SEs. The most plausible solid electrolyte decomposition reactions and their associated volume change were predicted by density functional theory (DFT) calculations. It was found that the stress associated with a volume change due to solid electrolyte decomposition can be much more significant than that of electrode volumetric changes associated with Li insertion/extraction. This model can be used to design 3D ASSLB architectures to minimize their internal stress generation.

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to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure while only incurring a reasonable computational expense. Both of these approaches have different strengths and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-ofconcept uncertainty quantification analyses of a simple component under prototypical conditions. The builtin stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

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