6th World Congress on Integrated Computational Materials Engineering (ICME 2022): Multi-Scale Modeling III
Program Organizers: William Joost; Kester Clarke, Los Alamos National Laboratory; Danielle Cote, Worcester Polytechnic Institute; Javier Llorca, IMDEA Materials Institute & Technical University of Madrid; Heather Murdoch, U.S. Army Research Laboratory; Satyam Sahay, John Deere; Michael Sangid, Purdue University
Wednesday 10:30 AM
April 27, 2022
Room: Regency Ballroom AB
Location: Hyatt Regency Lake Tahoe
Session Chair: Benjamin Begley, University of Florida
10:30 AM Invited
An Integrated Recursive Framework for Arbitrarily Multiscale and Multi-fidelity Modeling: Evan Pineda1; Trenton Ricks1; Brett Bednarcyk1; Steven Arnold1; 1NASA Glenn Research Center
The NASA Multiscale Analysis Tool (NAMSAT) serves as a state-of-the-art, “plug and play,” massively multiscale modeling (M3) platform for hierarchical materials and structures. The development of NASMAT has focused on modularity, upgradability and maintainability, interoperability, and utility. Application program interfaces (APIs) have been developed to facilitate the integration NASMAT into other programs (commercial, research, and user-defined) as well as the integration of other codes into NASMAT.NASMAT is an integrated, recursive platform that seamlessly allows for an arbitrary number of scales, and a variety of modeling fidelity, in a single, non-linear analysis. Thus, the macroscopic response of a material can be directly linked to the behavior of the microstructure(s), including subcale defects, making it a suitable tool for integrated computational materials engineering (ICME) because . Examples using NASMAT for simulation of polycrystalline metals at elevated temperatures will be presented.
11:00 AM
Phonon Based Universal Sampling Method for Machine Learning Interatomic Potentials: Nathan Wilson1; Xiaofeng Qian1; Raymundo Arroyave1; 1Texas A&M University
Recent advances in machine learning have accelerated the development of interatomic force fields using first-principles density functional theory calculations. Currently, the predominant sampling method is obtained directly from ab initio molecular dynamics. Such a brute force approach requires multiple sets of ab initio calculations with many heating/cooling, equilibration, and production trajectories using small time steps, which becomes computationally expensive and limits the number of generated structures. In this talk, we will propose a phonon-based sampling method using high order phonons to resolve this bottleneck by efficiently sampling the structure space. The higher order phonons combined with the self-consistent phonon approach can be used to accurately sample structures at higher temperatures, including phonon-stabilized structures, while avoiding brute force sampling of the vast structural space.
11:20 AM
Machine Learning Feature Selection for Predicting Corrosion Rates In High-Entropy Alloys: Mohammad Fuad Nur Taufique1; Ankit Roy2; Ganesh Balasubramanian2; Gaoyuan Ouyang3; Duane Johnson3; Ram Devanathan1; 1Pacific Northwest National Laboratory; 2Lehigh University; 3Ames Laboratory
More than $270 billion is spent on combatting corrosion annually in the USA alone. This work uses machine learning to meet the urgent need for the development of highly corrosion resistant alloys. The focus is on a new class of alloys called high-entropy alloys (HEAs) as a potential solution. Some HEAs have exhibited excellent mechanical properties even at high temperatures but the caveat associated is that their search space is almost up to half a trillion combinations. To counter this challenge, we employ machine learning tools to develop a model that predicts the corrosion resistance of any given HEA, based upon the existing corrosion data available on HEAs. Such a model reveals important features that determine the corrosion resistance of a given alloy and serves as a swift tool for screening a vast number of HEAs and selecting the best corrosion resistant HEAs.
11:40 AM
Bridging High-Fidelity and Macroscopic Simulations of the Laser Powder Bed Fusion Processes: Raeita Mehraban Teymouri1; Chinnapat Panwisawas2; Bahram Ravani1; 1University of California, Davis; 2University of Leicester
Porosity, undesired microstructure, and residual stresses are among the major challenges in Laser Powder Bed Fusion (LPBF) processes that can be potentially mitigated by optimization of process parameters. Reduced Order Models (ROMs) can be used to study these defects while developing a link between micro-scale and macro-scale behavior of the materials. In this work, a ROM of the multi-layer deposition process is developed to model the thermal field during the LPBF process while accounting for lower-length scale phenomena. To improve the accuracy of modeling predictions, temperature-dependency of the thermo-physical properties of the powder materials is considered. The ROM can be used to develop process maps for specific materials in metal AM. Furthermore, it can be used to develop a Machine Learning (ML) tool with the benefit of allowing for near real-time analysis that can ultimately be used as a building block of a digital twin for the LPBF process.
12:00 PM
Multi-Scale Simulations of Crystallographic Facet-Orientation Dependent Corrosion Behavior in Metallic Alloys: Rongpei Shi1; Stephen Weitzner1; Tim Hsu1; Xiao Chen1; Tae Wook Heo1; Tuan Pham1; Christine Orme1; Morris Wang1; Brandon Wood1; 1Lawrence Livermore National Laboratory
The development of successful metal corrosion mitigation strategies is impeded by an incomplete mechanistic understanding of the connection between corrosion performance and microstructure; for instance, crystallographic facet dependent morphology and kinetics of pitting corrosion have not been fully addressed. Here, we present a meso-scale phase field model for pitting corrosion that takes into account interfacial electrochemical reactions, mass transport of electrolytes and morphological evolution of the corroding interface. The model is parameterized using facet-orientation dependent dissolution rates obtained by combining electron backscatter diffraction (EBSD) with atomic force microscopy (AFM). The model is then employed to quantify the individual and combine effect of different microstructure attributes and environment conditions on corrosion behaviors. The model can also be parameterized using facet-orientation dependent equilibrium dissolution potentials computed by density functional theory and then validated by EBSD and AFM measurements. Overall, the findings improve our understanding of the crystallographic controls of corrosion processes.