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Meeting MS&T23: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title D-9: Discrete Element Simulation of Delamination in Thermal Barrier Coating
Author(s) Yafeng Li, Zhengzhao Ji, Jing Zhang
On-Site Speaker (Planned) Yafeng Li
Abstract Scope In this work, we present a discrete element (DE) model to simulate the coating delamination behaviors in thermal barrier coatings (TBCs) during indentation test. In the DE model, the linear parallel bond model is first calibrated with the experimental properties from the literature. Then both the 2D Vickers and Rockwell micro-indentation tests were simulated with layered coating microstructures. The model results reveal that the coating interface delimitation has two distinctive stages, i.e., crack initiation and propagation. Additionally, TBC systems with a thicker top are more susceptible to delamination due to less strain compliance.

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A B-C Story, Investigated by A.I. and CALPHAD
An ICME Approach for Short Fiber Reinforced Ceramic Matrix Composite via Direct Ink Writing
Atomistic Perspectives in Characterizing Crystalline Defect Formation in Amorphous Silicon Nitride
Combining Experimental and Simulation Datasets in Machine Learning for Glass Properties Prediction
Comparison of Core Level Chemical Shift in CH3NH3PbBr3 Perovskite Due to Surface Terminations and Orientations of CH3NH3 Ion
D-10: Unraveling the structure and mechanical properties ZIFs and its topological equivalents: Large scale simulations
D-9: Discrete Element Simulation of Delamination in Thermal Barrier Coating
Decoding the Structural Genome of Silicate Glasses
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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