h i g h l i g h t sDevelop an advanced microstructure model using enrichment approach. Model multiphysics phenomena among the particle aggregates in the electrode. Explore the impact of microstructures on battery performance. a b s t r a c tPerformance and degradation of a Li-ion battery reflect the transport and kinetics of related species within the battery's electrode microstructures. The variational multi-scale principle is adapted to a Li-ion battery system in order to improve the predictions of battery performance by including multi-scale and multiphysics phenomena among the particle aggregates in the electrode; this physics cannot be addressed by conventional homogenized approaches. The developed model is verified through the direct numerical solutions and compared with the conventional pseudo-2D (P2D) model method. The developed model has revealed more dynamic battery behaviors related to the variation of the microstructuredsuch as particle shape, tortuosity, and material compositiondwhile the corresponding result from P2D shows a monotonous change within different structures.

In this paper, a multi-scale analysis scheme for solidification based on two-scale computational homogenization is discussed. Solidification problems involve evolution of surfaces coupled with flux jump boundary conditions across interfaces. We provide consistent macro-micro transition and averaging rules based on Hill's macrohomogeneity condition. The overall macro-scale behavior is analyzed with solidification at the micro-scale modeled using an enthalpy formulation. The method is versatile in the sense that two different models can be employed at the macroand micro-scales. The micro-scale model can incorporate all the physics associated with solidification including moving interfaces and flux discontinuities, while the macro-scale model needs to only model thermal conduction using continuous (homogenized) fields. The convergence behavior of the tightly coupled macro-micro finite element scheme with respect to decreasing element size is analyzed by comparing with a known analytical solution of the Stefan problem.

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Background Whilst the COVID‐19 diagnostic test has a high false‐negative rate, not everyone initially negative is re‐tested. Michigan Medicine, a primary regional centre, provided an ideal setting for studying testing patterns during the first wave of the pandemic. Objectives To identify the characteristics of patients who underwent repeated testing for COVID‐19 and determine if repeated testing was associated with downstream outcomes amongst positive cases. Methods Characteristics, test results, and health outcomes for patients presenting for a COVID‐19 diagnostic test were collected. We examined whether patient characteristics differed with repeated testing and estimated a false‐negative rate for the test. We then studied repeated testing patterns in patients with severe COVID‐19‐related outcomes. Results Patient age, sex, body mass index, neighbourhood poverty levels, pre‐existing type 2 diabetes, circulatory, kidney, and liver diseases, and cough, fever/chills, and pain symptoms 14 days prior to a first test were associated with repeated testing. Amongst patients with a positive result, age (OR: 1.17; 95% CI: (1.05, 1.34)) and pre‐existing kidney diseases (OR: 2.26; 95% CI: (1.41, 3.68)) remained significant. Hospitalization (OR: 7.88; 95% CI: (5.15, 12.26)) and ICU‐level care (OR: 6.93; 95% CI: (4.44, 10.92)) were associated with repeated testing. The estimated false‐negative rate was 23.8% (95% CI: (19.5%, 28.5%)). Conclusions Whilst most patients were tested once and received a negative result, a meaningful subset underwent multiple rounds of testing. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients.