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Microstructure geometry for thermal barrier coatings to mitigate CMAS attack

Thermal barrier coatings and methods to make such coatings present improved resistance to CMAS infiltration. The method for forming a thermal barrier coating includes applying a layer of the thermal barrier coating to a component having a surface, forming a plurality of first channels in the thermal barrier coating, and forming a plurality of second channels in the thermal barrier coating. The first channels extend through a thickness of the thermal barrier coating from an interface with the surface of the component to a free surface opposite the interface. The second channels are disposed between the free surface and the interface and extending lengthwise generally parallel to the free surface of the thermal barrier coating, wherein the thermal barrier coating comprises a material comprising yttrium aluminum garnet (YAG) or yttria stabilized zirconia (YSZ).

Jordan, Eric↗

Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages

Damage in the microstructures of energetic materials (EMs), such as propellants and plastic bonded explosives (PBXs), can significantly alter their response to external loads. Both sensitization and desensitization can occur, causing concerns with safety and performance in the field; predictive models that connect damage and the sensitivity of EMs can enable design and provide confidence in their robustness and reliability. However, modeling of damage evolution is challenging for real microstructures of EMs; samples of damaged EMs are difficult to obtain, thereby hindering experiments and direct numerical simulations to determine the sensitivity of EMs at various stages of damage. Here, we develop an approach to generate synthetic, i.e., in silico produced, damaged microstructures for use in simulations to connect damage levels to sensitivity. The development of the present workflow to generate and impose varying levels of damage in microstructures, known as HEDS (Heterogeneous Energetic Material Damage Simulator), begins with a small set of images of damaged PBXs and combines a collection of deep neural network techniques to generate microstructures with varying levels of damage. By making the synthetic microstructures conform closely to those observed in available real, imaged microstructures, we develop an ensemble of damaged microstructures that can be used for in silico shock experiments. HEDS develops these microstructure ensembles as level set fields, which are directly employed in a sharp interface Eulerian hydrocode where shock simulations are performed to quantify the energy release rate from hotspot fields generated in the microstructure. These capabilities can be useful for the analysis and assessment of changes in the sensitivity of EMs and to design formulations that are less susceptible to damage-induced changes in sensitivity and performance.

Fang, Irene (ORCID:0009000844557122)↗