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Meeting MS&T22: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Author(s) Mengnan Li, Vimal Ramanuj, Ying She, Ramanan Sankaran
On-Site Speaker (Planned) Mengnan Li
Abstract Scope With the rapid advancement of computing capability, pore-resolved direct numerical simulations (DNS) have become feasible for studying the transport and kinetic regimes encountered in chemical vapor infiltration (CVI). We present DNS simulations using a level set approach to capture the complex topology between the vapor and solid phases during the densification process. The simulations were performed using a finite rate kinetic model for the deposition of silicon carbide (SiC) from a methyltrichlorosilane (MTS) precursor. We present the computational model and the simulation approach. The model was applied to simulate densification of 3D printed preforms and conduct a parametric study to investigate the role of competing transport and reaction processes. Results are presented to show the evolving non-uniform porosity, structure functions and the distribution of residual voids.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics Informed Machine Learning Approach to Predict Glass Forming Ability
D-7: Development of Structural Descriptors to Predict Dissolution Rate of Volcanic Glasses: Molecular Dynamic Simulations
D-8: Molecular Dynamic Simulations of Polymer Derived Ceramics
Data-driven Prediction of Room Temperature Density of Multicomponent Silicate-based Glasses
Data Driven Design and Enhancement of Machinable Glass Ceramics
Developing ReaxFF for Simulation of Silicon Carbonitride Polymer-derived Ceramics
In-Silico Simulations of Polymer Pyrolysis
Machine Learning-Derived Atomistic Potentials for Y2Si2O7 and Yb2Si2O7
Machine Learning Defect Properties of Semiconductors
Machine Learning to Design and Discover Sustainable Cementitious Binders: Learning from Small Databases and Developing Closed-form Analytical Models
Molecular Dynamics Simulation of Tellurite Glasses
Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Natural Language Processing Aided Understanding of Material Science Literature
Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Predicting and Accessing Metastable Phases
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces
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