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Meeting MS&T23: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Fracture Resistance of Rare-earth Phosphates as Environmental Barrier Coatings under CMAS Corrosion
Author(s) Subrato Sarkar, Rahul Rahul, Kartik Josyula, Keith Bryce, Jie Lian, Liping Huang, Lucy Zhang, Suvranu De
On-Site Speaker (Planned) Subrato Sarkar
Abstract Scope Environmental barrier coatings (EBCs) are essential to protect SiC-based ceramic matrix composite components in the hot section of gas turbine engines from water vapor and molten CMAS (calcium-magnesium aluminosilicate) corrosion. Recent studies suggest that rare-earth phosphates are promising EBC materials with better corrosion resistance against CMAS than the third-generation rare-earth silicate EBCs. This work investigates the fracture behavior of EBCs using mesoscale cohesive-zone finite element method. The model accounts for anisotropic material properties and different fracture strengths for grain, grain boundary and CMAS. The fracture resistance of EBCs with and without CMAS corrosion is estimated using experimentally obtained microstructures. A quantitative comparison of fracture resistance at different levels of CMAS corrosion indicates that the rare-earth phosphates are more fracture resistant than the rare-earth silicates. The mesoscale model can be further extended to design microstructural attributes that improve the fracture resistance of EBCs under CMAS corrosion.

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Comparison of Core Level Chemical Shift in CH3NH3PbBr3 Perovskite Due to Surface Terminations and Orientations of CH3NH3 Ion
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Defect Chemistry and Electrical Properties of Doped BaTiO3
Development of a Machine Learned Interatomic Potential for Shock Simulations of Boron Carbide
First-Principles Modeling of Thermodynamics and Kinetics of Thin-Film Tungsten Carbides
Fracture Resistance of Rare-earth Phosphates as Environmental Barrier Coatings under CMAS Corrosion
Generation of Spectral Neighbor Analysis Potentials for Alpha Boron and Comparison of the Results with the Angular Dependent Potential
Lithium Dopant and Surface Effects on the Band Gap of Calcium Hexaboride (CaB6) Using DFT Methods
Machine Learning Prediction of Heat Capacity for Solid Mixtures of Pseudo-binary Oxides
Using Deep Learning to Develop a Smart and Sustainable Cement Manufacturing Process

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