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Meeting Materials Science & Technology 2019
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Peridynamics Modeling of Impact-induced Crack Patterns in Glass
Author(s) N. M. Anoop Krishnan, Jonathan Berjikian, Mathieu Bauchy, Jared Rivera
On-Site Speaker (Planned) N. M. Anoop Krishnan
Abstract Scope Brittle materials, such as glasses, are constantly exposed to high velocity impact. This leads to the formation of cracks and failure of the material. Understanding the role of geometric and material properties on the formation of crack patterns is essential for the design of novel impact resilient materials. Here, using peridynamics, we model the damage in a brittle glass plate due to projectile impact. Simulations are carried out using copper and glass bullets with velocities varying from 5 m/s to 100 m/s. We investigate the role of geometric properties such as plate thickness and radius, and material properties such as elastic modulus and fracture energy on the overall damage. We observe an interesting power law dependence of the overall damage with respect to the fracture energy. The origin of this power-law dependence is found to be associated with the velocity of crack propagation and crack-branching behavior in the material.


Atomistic Modeling of Fundamental Deformation Mechanisms in MAX Phases
Development of Boron Oxide Potentials for Computer Simulations of Multi-component Oxide Glasses
Embedding Machine Learning in the Physics of Disordered Solids
Exploring Molecular Dynamics Descriptors to Improve Machine Learning Predictions of Glass Forming Ability
Force-Enhanced Refinement of the Atomic Structure of Silicate Glasses
Genesis of “Free” Carbon in Silicon Oxycarbide Ceramics
Impact of Carbon Morphology on Mechanical Properties of SiCO Ceramics
Machine Learning-aided Development of Empirical Force-fields for Glassy Materials
Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons
Machine Learning Applied to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Machine Learning to Predict the Elastic Properties of Glasses
Peridynamics Modeling of Impact-induced Crack Patterns in Glass
Physics-Based Machine Learning Models for High Throughput Screening of Novel Scintillator Chemistries
Predicting Nuclear Magnetic Resonance Parameters in Ceramics Using Density Functional Theory
Prediction of Compressive Strength and Modulus of Elasticity of Concrete Using Machine Learning Models
Reactive MD Simulations of Polysiloxanes: Modeling the Polymer-to-Ceramic Route towards Silicon Oxycarbide Ceramics
Role of Multi-state Hydrogen during Mayenite Electride Formation by First-principles Calculation
The Stability, Structure and Properties of the Zeta Phase in the Transition Metal Carbides
The Thermophysical Properties of TcO2
Thermal Conductivity of a Glass Material by First-principles Molecular Dynamics: The Case of GeTe4
Tuning Electronic Properties in II-IV-V2 Semiconductors via Sub-lattice Configurational Disorder

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