Fatigue damage in metals manifests itself as irreversible dislocation motion followed by crack initiation and propagation. Characterizing the transition from a crack-free to a cracked metal remains one of the most challenging problems in fatigue. Persistent slip bands (PSBs) form in metals during cyclic loading and are one of the most important aspects of this transition. We used in situ microfatigue experiments to investigate PSB formation and evolution mechanisms, and we discovered that PSBs are prevalent at the micrometer scale. Dislocation accumulation rates at this scale are smaller than those in bulk samples, which delays PSB nucleation. Our results suggest the need to refine PSB and crack-initiation models in metals to account for gradual and heterogeneous evolution. These findings also connect micrometer-scale deformation mechanisms with fatigue failure at the bulk scale in metals.

Magnesium (Mg) and its alloys hold great potential as an energy-saving structural material for automative, aerospace applications. However, the use of Mg alloys has been limited due to poor ductility and formability. Poor mechanical properties of Mg alloys origin from the insufficient number of slip systems, and deformation twinning plays an important role to accommodate plastic deformation. Here, we report a comprehensive experimental and modeling study to understand crystal size effect on the transformation in deformation modes in twin oriented Mg single crystals. The experiments reveal two regimes of size effects: (1) single twin propagation, where a typical "smaller the stronger" behavior was dominant in pillars ≤ 18 µm in diameter, and (2) twin-twin interaction, which results in anomalous strain hardening in pillars > 18 µm. Molecular dynamics simulations further indicate a transition from twinning to dislocation mediated plasticity for crystal sizes below a few hundred nanometers. Our results provide new understanding of the fundamental deformation modes of twin oriented Mg from nano-scale to bulk, and insights to design Mg alloys with superior mechanical properties through dimensional refinement. This subsequently can materialize into more utilization of Mg alloys as a structural material in technologically relevant applications.

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This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations.