Algorithm Development in Materials Science and Engineering: Nano, Micro and Macro Scale Algorithms and Their Applications
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee, TMS: Phase Transformations Committee, TMS: Solidification Committee
Program Organizers: Mohsen Asle Zaeem, Colorado School of Mines; Mikhail Mendelev, NASA ARC; Garritt Tucker, Colorado School of Mines; Ebrahim Asadi, University of Memphis; Bryan Wong, University of California, Riverside; Sam Reeve, Oak Ridge National Laboratory; Enrique Martinez Saez, Clemson University; Adrian Sabau, Oak Ridge National Laboratory

Wednesday 2:00 PM
March 2, 2022
Room: 253A
Location: Anaheim Convention Center

Session Chair: Adrian Sabau, Oak Ridge National Laboratory; Ebrahim Asadi, University of Memphis

2:00 PM
An Examination of the Dislocation Orientation Distribution Function as Test for CDD Theories: Jose Manuel Torres Lopez¹; Joseph Anderson²; Anter El-Azab²; ¹University of Rochester; ²Purdue University
    Line defects known as dislocations are the carriers of plastic deformation in crystalline materials. Because of the computational limitations of discrete dislocation simulations, the evolution of mesoscale plasticity is often studied using continuum dislocation dynamics (CDD) theories. In this presentation, we contemplate the approximations made by two common CDD models: the vector-density theory assumes the underlying dislocations to be parallel to the continuum vector-density, while higher-order models rely on parallel dislocations having equal curvature. This paper numerically evaluates these postulates by means of local probability density functions for orientations and curvatures. To do this, we introduce a partition of the material sample into sub-volumes, and examine statistical measures of the dislocation distribution within each sub-volume. It is found that the probability functions are concentrated around the directions of the mean fields (vector-density and curvature density). In particular, this validates the vector-density approach for suitably small scales.

2:20 PM
FFT-based Polycrystal Plasticity Modelling: New Implementations and Integration with 3-D Imaging Techniques: Ricardo Lebensohn¹; Miroslav Zecevic¹; ¹Los Alamos National Laboratory
    Crystal Plasticity models are increasingly used in engineering applications to obtain microstructure-sensitive mechanical response of polycrystalline materials. These models require a proper consideration of the single crystal deformation mechanisms, a representative description of the microstructure, and an appropriate scheme to connect the microstates with the macroscopic response. The latter can be based on homogenization, which relies on a statistical description of the microstructure, or on full-field solutions, which requires a spatial description of the microstructure. Full-field Fast Fourier Transform (FFT)-based methods are attractive due their relative higher efficiency and direct use of voxelized microstructural images. In this talk, we will report recent progress on FFT-based polycrystal plasticity, with emphasis in novel implementations for advanced constitutive regimes, including large-strain elasto-visco-plasticity, strain-gradient plasticity, field dislocation mechanics, and dynamic deformation regimes including inertia, along with integration with emerging 3-D characterization methods in Experimental Mechanics.

2:40 PM
Simulating Dislocation Transport at Experimental Time Scales Using a Time-explicit Runge-Kutta Discontinuous Galerkin Finite Element Scheme: Manas Upadhyay¹; Jérémy Bleyer²; Vincent Taupin³; Stéphane Berbenni³; ¹LMS, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris; ²Navier Laboratory, CNRS, École des Ponts ParisTech; ³LEM3, CNRS, Université de Lorraine
     Dislocation dynamics simulations are very useful in advancing our knowledge on plastic deformation at the intragranular level. Recent advances in non-destructive space/time-resolved characterization techniques e.g., dark-field X-ray microscopy, open the possibility of comparing and validating dislocation dynamics simulation predictions. However, the fine temporal-resolution required to run most dislocation dynamics models often prevents a one-to-one comparison with experiments running at time scales that are several orders of magnitude higher than those achievable by simulations.Bearing this challenge in mind, we have developed a time-explicit Runge-Kutta Discontinuous Galerkin Finite Element (RKDG-FE) scheme to solve the Field Dislocation Mechanics (FDM) problem. FDM is a continuum theory that rigorously (mathematically and thermo-mechanically) connects dislocation transport and interactions to the static/dynamic response of single/poly-crystals. By implementing FDM numerically with RKDG-FE, we show that it is possible to model transport and interaction of individual dislocations at experimental time scales while keeping a “compact dislocation core”.

3:00 PM
Computational Modeling of Dual Phase Titanium Armor : Collin Roberts¹; Cameron McElfresh¹; Sicong He¹; Sergey Prikhodko¹; Jaime Marian¹; ¹University of California Los Angeles
    Titanium alloys are superior structural materials based on high specific strength, fracture toughness, and have potential as armor in defense applications. Our method to understand Ti-6Al-4V as armor material is to computationally model dislocation motion and microstructural evolution of a dual-phase lamellar alloy under a varied parametric space consisting of beta phase volume fraction, grain size, loading orientation, and strain rate, ultimately considering ballistic strain rate conditions such as dε/dt ≥ 100 s^-1. The underlying model is capable of describing intra-grain plasticity while accounting for the geometrically necessary dislocations at grain boundaries. This model has been modified to account for strengthening and plasticity per phase, anisotropy effects from the hexagonal close-packed alpha phase, and grain texture from the lamellar packets. Future considerations include temperature effects on microstructure evolution, as well as machine-learning for prediction of mechanical properties such as Young's modulus, yield strength, and total elongation based on microstructural components.