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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title A Computationally Scalable Time-parallel Approach for Melt Pool Resolved Simulations of Additive Manufacturing
Author(s) John Coleman, Matt Bement, Alex Plotkowski, Benjamin Stump
On-Site Speaker (Planned) John Coleman
Abstract Scope A major challenge in simulating the thermal behavior of an entire part made by additive manufacturing processes is the disparate length and time scales between transport phenomena occurring in the melt pool and the component. A common approach for the concurrent solution of partial differential equations on multiple computational resources is spatial parallelization by means on mesh decomposition. Ideally, the speed up from spatial parallelization would be linear, however, communication between resources eventually limits the speed up to a constant value, known as the strong scaling limit. Once spatial parallelization becomes saturated, additional parallelism by means of time-domain decomposition is needed to take advantage of high performance computing (HPC) resources. In this talk, a time-parallel method is proposed to improve the computational scalability of additive manufacturing simulations on HPC systems. Example results are shown for the NIST AM benchmark, and the tradeoff between accuracy and speedup is investigated.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Computational Materials Science & Engineering, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Combined Kinetic Monte Carlo and Phase Field Approach to Modeling Thermally Activated Dislocation Motion
A Computationally Scalable Time-parallel Approach for Melt Pool Resolved Simulations of Additive Manufacturing
A Finite Difference Analysis of the Effect of Graphene Additions on the Electrical Conductivity of Polycrystalline Copper
A Monte Carlo Model for the Formation and Evolution of Line Compound Systems Such as SiC
Accurate First-principles Predictions of Organic Molecular Crystal Polymorphism
Addressing Variability in Atomistic Predictions
An Entropy-maximization Approach for the Generation of Training Sets for Machine-learned Potentials
An Examination of the Dislocation Orientation Distribution Function as Test for CDD Theories
An Orientation-field Phase Field Model for Anisotropic Grain Growth
Application of Information Theory in Molecular Dynamics Simulations
Application of Vision Transformers in Tomography Image Segmentations of AM Parts
Calculating Cluster Diffusion Coefficients from Interatomic Potentials
Calibrating Uncertain Parameters in Melt Pool Simulations of Additive Manufacturing
Capturing Nanoscale Lattice Variations by Applying AI-powered Computer Vision Techniques on Synthetic X-ray Diffraction Data
Chemistry and Processing History Prediction from Materials Microstructure by Deep Learning
Clustering Algorithms for Nanomechanical Property Mapping and Resultant Microstructural Constituent Quantification
Coarse-graining Techniques for Migration of Solutes around Defects
Combining Discrete and Continuous in Time Stochastic Simulations in a Solid-solid Phase Field Simulation
Comparing Transfer Learning to Feature Optimization in Microstructure Classification
Computational Modeling of Dual Phase Titanium Armor
Conventional Ti Alloys for Aircraft Landing Gear Beams and Aeroengines—A Data-driven Analysis for Selection of Ti-based Alloys and Future Directions
Designing Thin Film Microstructures via Neuroevolution Guided Time-dependent Processing Protocols
Development of a New Shape Descriptor for Modeling and Uncertainty Quantification of Microstructures
Development of Interatomic Potential to Study Elementary Dislocation Properties in High Entropy Alloys: The FeNiCrMn Model Alloy
Development of Segregation Energy Predictions Utilizing Advanced Descriptors of Local Atomic Environments
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Exascale-capable Graph Convolutional Neural Network Surrogates for Atomic Property Prediction
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FFT-based Polycrystal Plasticity Modelling: New Implementations and Integration with 3-D Imaging Techniques
Highly Accurate Prediction of Material Microstructure Using High-performance Phase-field Simulation and Data Assimilation
Improving Autonomous Data Collection by Iterative Learning Control as Applied to a Robomet.3D Mechanical Serial-sectioning System
Line Free 3D Dislocation Dynamics in Finite Domains
M-12: Discrete Stochastic Model of Point Defect-dislocation Interaction for Simulating Dislocation Climb
M-13: Finite Element Level-set Methods to Study Microstructural Evolution during Recrystallization and Grain Growth
M-14: Smoke Detection in Ladle Hot Repair Process Based on Convolution Neural Network
Machine Learning Models for Predictive Materials Science from Fundamental Physics: An Application to Titanium and Zirconium
Massive Parallelism for Theoretical Spectroscopy and Femtosecond Electron Dynamics First-principles Simulations
Materials Design, Model Calibration, and Multi-fidelity Modeling with Latent Map Gaussian Processes
Measuring Individual Grain Boundary Diffusivity Measurements in Polycrystal Molecular Dynamics Simulations
Microstructure and Porosity Predictions in Additively Manufactured Ti-6Al-4V Alloys Using a Hierarchical Modeling Approach
Modelling Solidification with Phase-field on High-performance Computers
Multiscale Modeling of Ni Alloys Selective Laser Melting Combining CalPhaD, Finite Elements, and Phase-field Methods
Neural Network Models of Phase Field Simulations
NOW ON-DEMAND ONLY - A Generalization of the Universal Equation of States to Develop Magnetic Interatomic Potentials
NOW ON-DEMAND ONLY - Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials
OpenMP GPU Offloading for Cellular Automaton Solidification Microstructural Model
Parameter Estimation of Phase-field Model Based on Microstructure Data and Its Uncertainty Quantification by the Adjoint Method
Physics-based Data-driven Discovery of Continuum Equations
Predicting Temperature-dependent Oxide Redox Reactions with Machine-learning Augmented First-principles Calculations
Quantum Computation for Predicting Solids-state Material Properties
Random Forest Regressor Models for the Prediction of Novel Alloy Corrosion Performance
Refinements to the Production of Machine Learning Interatomic Potentials
Simulating Dislocation Transport at Experimental Time Scales Using a Time-explicit Runge-Kutta Discontinuous Galerkin Finite Element Scheme
Statistical Predictions of Failure in Hydrided Zirconium Materials
Statistics of the Lattice Distortion of Dislocated Crystals
Using 2D, Dendrite-resolved, Melt-pool-scale Phase-field Simulations of Solidification as Reference Solutions for a Multiscale Model
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