Parallelized Hybrid Monte Carlo Simulation of Stress-Induced Texture Evolution

A parallelized hybrid Monte Carlo (HMC) methodology is devised to quantify the microstructural evolution of polycrystalline material under elastic loading. The approach combines a time explicit material point method (MPM) for the mechanical stresses with a calibrated Monte Carlo (cMC) model for grain boundary kinetics. The computed elastic stress generates an additional driving force for grain boundary migration. The paradigm is developed, tested, and subsequently used to quantify the effect of elastic stress on the evolution of texture in nickel polycrystals. As expected, elastic loading favors grains which appear softer with respect to the loading direction. The rate of texture evolution is also quantified, and an internal variable rate equation is constructed which predicts the time evolution of the distribution of orientations.Comment: 20 pages, 8 figure

A Tutorial on Advanced Dynamic Monte Carlo Methods for Systems with Discrete State Spaces

Advanced algorithms are necessary to obtain faster-than-real-time dynamic simulations in a number of different physical problems that are characterized by widely disparate time scales. Recent advanced dynamic Monte Carlo algorithms that preserve the dynamics of the model are described. These include the $n$-fold way algorithm, the Monte Carlo with Absorbing Markov Chains (MCAMC) algorithm, and the Projective Dynamics (PD) algorithm. To demonstrate the use of these algorithms, they are applied to some simplified models of dynamic physical systems. The models studied include a model for ion motion through a pore such as a biological ion channel and the metastable decay of the ferromagnetic Ising model. Non-trivial parallelization issues for these dynamic algorithms, which are in the class of parallel discrete event simulations, are discussed. Efforts are made to keep the article at an elementary level by concentrating on a simple model in each case that illustrates the use of the advanced dynamic Monte Carlo algorithm.Comment: 53 pages, 17 figure

Grain growth behavior and efficient large scale simulations of recrystallization with the phase-field method

This book summarizes the found insights of grain growth behavior, of multidimensional decomposition for regular grids to efficiently parallelize computing and how to simulate recrystallization by coupling the finite element method with the phase-field method for microstructure texture analysis. The frame of the book is created by the phase-field method, which is the tool used in this work, to investigate microstructure phenomena

Grain growth behavior and efficient large scale simulations of recrystallization with the phase-field method

This book summarizes the found insights of grain growth behavior, of multidimensional decomposition for regular grids to efficiently parallelize computing and how to simulate recrystallization by coupling the finite element method with the phase-field method for microstructure texture analysis. The frame of the book is created by the phase-field method, which is the tool used in this work, to investigate microstructure phenomena

Modeling, Characterizing and Reconstructing Mesoscale Microstructural Evolution in Particulate Processing and Solid-State Sintering

abstract: In material science, microstructure plays a key role in determining properties, which further determine utility of the material. However, effectively measuring microstructure evolution in real time remains an challenge. To date, a wide range of advanced experimental techniques have been developed and applied to characterize material microstructure and structural evolution on different length and time scales. Most of these methods can only resolve 2D structural features within a narrow range of length scale and for a single or a series of snapshots. The currently available 3D microstructure characterization techniques are usually destructive and require slicing and polishing the samples each time a picture is taken. Simulation methods, on the other hand, are cheap, sample-free and versatile without the special necessity of taking care of the physical limitations, such as extreme temperature or pressure, which are prominent issues for experimental methods. Yet the majority of simulation methods are limited to specific circumstances, for example, first principle computation can only handle several thousands of atoms, molecular dynamics can only efficiently simulate a few seconds of evolution of a system with several millions particles, and finite element method can only be used in continuous medium, etc. Such limitations make these individual methods far from satisfaction to simulate macroscopic processes that a material sample undergoes up to experimental level accuracy. Therefore, it is highly desirable to develop a framework that integrate different simulation schemes from various scales to model complicated microstructure evolution and corresponding properties. Guided by such an objective, we have made our efforts towards incorporating a collection of simulation methods, including finite element method (FEM), cellular automata (CA), kinetic Monte Carlo (kMC), stochastic reconstruction method, Discrete Element Method (DEM), etc, to generate an integrated computational material engineering platform (ICMEP), which could enable us to effectively model microstructure evolution and use the simulated microstructure to do subsequent performance analysis. In this thesis, we will introduce some cases of building coupled modeling schemes and present the preliminary results in solid-state sintering. For example, we use coupled DEM and kinetic Monte Carlo method to simulate solid state sintering, and use coupled FEM and cellular automata method to model microstrucutre evolution during selective laser sintering of titanium alloy. Current results indicate that joining models from different length and time scales is fruitful in terms of understanding and describing microstructure evolution of a macroscopic physical process from various perspectives.Dissertation/ThesisDoctoral Dissertation Materials Science and Engineering 201

Solution Anneal Heat Treatments to Enhance Mechanical Performance of Additively Manufactured Inconel 718

The nickel-based superalloy Inconel 718 (IN718) is an excellent candidate among aerospace alloys for laser powder-bed fusion (LPBF) manufacturing. As-built LPBF IN718 has a vertically aligned columnar (001) microstructure which translates into orthotropic mechanical behavior. The post-process heat treatments for IN718 were developed 60 years ago for wrought and cast processes and do not mitigate the columnar microstructure of the LPBF process. Recrystallization is required to remove the columnar microstructure, which would allow for parts to be fabricated on different machines or in different orientations but still achieve the same properties. This research investigated the microstructure of LPBF IN718 as it evolved under a solution treatment of 1160 °C. It was shown that this higher solution temperature mitigated the scan strategy effects and anisotropy resulting from the fabrication process. The grain size, shape, and recrystallization were measured and compared throughout the evolution. Additionally, the X–Y and X–Z planes were compared to find the point in time at which the annealing process resulted in equiaxed, isotropic grains. An equiaxed microstructure was successfully achieved through recrystallization and grain growth. Isotropic tensile properties were achieved following a modified solution treatment at 1160 °C for 4 hours and validated via nanoindentation and tensile testing. Rupture life was not improved by the equiaxed microstructure. Microstructural evolution was simulated in a kinetic Monte Carlo simulation using a novel approach of combining the stored energy of the as-built LPBF IN718 with the boundary energy and pinning particles within SPPARKS. The resulting models accurately approximated the experimental results of recrystallized area and JMAK model constants

Accelerate Microstructure Evolution Simulation Using Graph Neural Networks with Adaptive Spatiotemporal Resolution

Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.Comment: 28 pages, 11 figure

Historical review of computer simulation of radiation effects in materials

In this Article, I review the development of computer simulation techniques for studying radiation effects in materials from 1946 until 2018. These developments were often closely intertwined with associated experimental developments, which are also briefly discussed in conjunction with the simulations. The focus is on methods that either deal directly with the primary radiation damage generation event, or with such defects or phase changes that typically occur due to radiation. The methods discussed at some length are, in order of historical appearance: Reaction rate theory or rate equations (RE), Monte Carlo neutronics calculations (MCN), Metropolis Monte Carlo (MMC), Molecular Dynamics (MD), Binary Collision Approximation (BCA), Kinetic Monte Carlo (KMC), Discrete Dislocation Dynamics (DDD), Time-Dependent Density Functional Theory (TDDFT), and Finite Element Modelling (FEM). For each method, I present the origins of the methods, some key developments after this, as well as give some opinions on possible future development paths. (C) 2019 The Author. Published by Elsevier B.V.Peer reviewe