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At least 145 records · Page 8

Scan strategies in EBM-printed IN718 and the physics of bulk 3D microstructure development

Three-dimensional (3D) characterization provides opportunities for understanding processing-structure relationships in additively manufactured (AM) materials. Bulk samples of Inconel 718 were fabricated via electron beam melting (EBM) in order to study microstructural development as a function of energy input and beam scan strategy. TriBeam tomography of bulk Inconel 718 microstructures built under steady-state growth conditions reveals the sensitivity of microstructure formation and evolution to machine process parameters. Here, samples manufactured using a narrow range of energy input per unit build area result in varied grain morphologies and crystallographic textures. Using TRUCHAS, a thermal simulation software, the thermal history of bulk scan strategies was predicted, and combined with a calibrated microstructure-processing map to accurately predict bulk grain morphologies. The solidification parameters and the 3D measured nucleation density are used to predict the transition between columnar and equiaxed grain morphologies, providing a process map to guide AM parameter choices to locally control as-printed microstructure. A two-dimensional metric for characterizing bulk grain morphology was also found to agree well with predictions from the process map calibrated by 3D data. Combined with 3D tomography and thermal modelling, the physics of structure development were understood at a new level of detail with respect to the competing processes of grain nucleation and epitaxial growth.

36 MATERIALS SCIENCE↗

Study on Texture Evolution and Shear Behavior of an Al/Ni/Cu Composite

The microstructure, texture, and mechanical properties of the Al/Ni/Cu composite during various accumulative roll bonding (ARB) cycles were studied using optical microscopy, scanning electron microscopy, x-ray diffraction, shear punch test, and hardness test. In addition, ImageJ software and Rietveld software were used in order to study microstructure and dislocation density variations, respectively. It was found that Ni and Cu layers were fractured and distributed in the Al matrix due to differences in their mechanical properties. Fracture and distribution of Cu and Ni particles after cycle five led to the alteration of the composite structure from a layered to a particle-reinforced structure. ARB process leads to the formation of strong orientation along the β-fiber and also to pronounced copper and dillamore components in both Al and Cu phases. Furthermore, the shear yield stress and ultimate shear strength of the composite increased as the ARB process advanced; however, shear elongation presented a non-uniform variation. Investigation of the fracture surfaces revealed that the mechanical properties of the composite are affected not only by the strain hardening of the Cu layer, but also by the structural change in the composite during the initial ARB cycles. During the last stages of the process, however, changes in mechanical properties were mostly governed by reinforcement particles serving as strain concentration zones and the strain hardening of the Al matrix.

36 MATERIALS SCIENCE↗

Atomate2: modular workflows for materials science

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.

97 MATHEMATICS AND COMPUTING↗

ArQTiC: A Full-stack Software Package for Simulating Materials on Quantum Computers

ArQTiC is an open-source, full-stack software package built for the simulations of materials on quantum computers. It currently can simulate materials that can be modeled by any Hamiltonian derived from a generic, one-dimensional, time-dependent Heisenberg Hamiltonian. ArQTiC includes modules for generating quantum programs for real- and imaginary-time evolution, quantum circuit optimization, connection to various quantum backends via the cloud, and post-processing of quantum results. By enabling users to seamlessly design, execute, and analyze materials simulations on quantum computers, ArQTiC opens this field to a broader community of scientists from a wider range of scientific domains.

97 MATHEMATICS AND COMPUTING↗

Nuclear safety Enhanced: A Deep dive into current and future RAVEN applications

As the horizon of nuclear energy expands with the advent of small modular reactors, IV generation reactors, and fusion reactors, there is a growing perspective that the licensing process could benefit from a more comprehensive approach. Moving beyond traditional deterministic and PRA analysis might pave the way for a novel safety analysis paradigm propelled by the increasing computational power at our disposal. This paper explores different methodologies that can improve the outcomes of nuclear safety analysis. These range from uncertainty quantification techniques, aimed at enhancing the precision of safety margins, to deploying dynamic event trees by driving system code simulations, capturing the potential evolutions of severe accidents. These methodologies introduce innovative dimensions to safety analysis, considering the consequences of postulated events and the dynamics of accident sequences. However, they also bring forth challenges, especially in managing the complexity and sheer volume of potential scenarios. The paper touches upon some strategies to counter these challenges, emphasizing the importance of adaptability and continuous evolution in the face of emerging nuclear safety concerns. Additionally, the paper sheds light on the need for advanced tools to apply these methodologies. Among these tools is RAVEN, an open-source software designed for parametric and probabilistic analyses. Its core components, including distribution, sampler, and reduced order model, enable various applications, from risk assessment and mitigation to dynamic learning and plant control logic simulations.

97 - MATHEMATICS AND COMPUTING↗

A experimental research program on chirality at the LHC

Heavy-ion collisions provide a unique opportunity to investigate the fundamental laws of physics of the strong force. The extreme conditions created by the collisions within a finite volume are akin to the properties of the deconfined partonic state which existed very shortly after the Big Bang and just prior to visible matter formation in the Universe. In this state massless quarks and gluons (partons) are ``quasi free" particles, the so-called Quark Gluon Plasma (QGP). By following the expansion and cooling of this state, we will map out the process of nucleonic matter formation, which occurs during the phase transition. The fundamental properties of this early partonic phase of matter are not well understood, but they are essential for confirming QCD (Quantum Chromo-Dynamics) and the Standard Model. The specific topic, chiral symmetry restoration, has been called ``the remaining puzzle of QCD.'' This puzzle can only be studied in the dense partonic medium generated in heavy-ion collisions. The research objectives of this proposal are the development and application of new analysis strategies to study chirality and the properties of the medium above the QGP phase transition using hadronic resonances detected with the ALICE experiment at the Large Hadron Collider (LHC) at the CERN research laboratory in Switzerland. This grant funded a new effort at the University of Texas at Austin (UT Austin) to investigate the Quark Gluon Plasma (QGP) at the highest possible energy of 2.76 TeV per nucleon at the Large Hadron Collider (LHC) at CERN via the ALICE experiment. The findings added to our knowledge of the dynamical evolution and the properties of the hot, dense matter produced in heavy-ion collisions, and provided a deeper understanding of multi-hadron interactions in these extreme nuclear matter systems. Our group contributed as well to the hardware and software for the ALICE USA-funded Calorimeter Detector (EMCal). The LHC research program and its connection to fundamental questions in high energy, nuclear and astrophysics has triggered the imagination of many young students worldwide. The studies also promoted the early involvement of students and young postdocs in a large, multi-national research effort abroad, which provided them with substantial experience and skills prior to choosing their career path. The undergraduate program, in conjunction with the Freshman Research Initiative at UT Austin, allowed the students to complete a research project within the field of Nuclear Physics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Computational modeling of drug-resistant bacteria. Final report

Initial proposal summary: The evolution of antibiotic-resistant mutants among bacteria (superbugs) is a persistent and growing threat to public health. In many ways, we are engaged in a war with these microorganisms, where the corresponding arms race involves chemical weapons and biological targets. Just as advances in microelectronics, imaging technology and feature recognition software have turned conventional munitions into smart bombs, the long-term objectives of this proposal are to develop highly effective antibiotics using next-generation biomolecular modeling capabilities in tandem with novel subatomic feature detection software. Using model compounds and targets, our design methodology will be validated with correspondingly ultra-high resolution structure-determination methods at premier DOE facilities (single-crystal X-ray diffraction at Argonne National Laboratory, and neutron diffraction at Oak Ridge National Laboratory). The objectives and accomplishments are summarized.

60 APPLIED LIFE SCIENCES↗

ASPCAP: THE APOGEE STELLAR PARAMETER AND CHEMICAL ABUNDANCES PIPELINE

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) has built the largest moderately high-resolution ( R ≈ 22,500) spectroscopic map of the stars across the Milky Way, and including dust-obscured areas. The APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) is the software developed for the automated analysis of these spectra. ASPCAP determines atmospheric parameters and chemical abundances from observed spectra by comparing observed spectra to libraries of theoretical spectra, using χ {sup 2} minimization in a multidimensional parameter space. The package consists of a fortran90 code that does the actual minimization and a wrapper IDL code for book-keeping and data handling. This paper explains in detail the ASPCAP components and functionality, and presents results from a number of tests designed to check its performance. ASPCAP provides stellar effective temperatures, surface gravities, and metallicities precise to 2%, 0.1 dex, and 0.05 dex, respectively, for most APOGEE stars, which are predominantly giants. It also provides abundances for up to 15 chemical elements with various levels of precision, typically under 0.1 dex. The final data release (DR12) of the Sloan Digital Sky Survey III contains an APOGEE database of more than 150,000 stars. ASPCAP development continues in the SDSS-IV APOGEE-2 survey.

79 ASTRONOMY AND ASTROPHYSICS↗

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Engineering↗

Understanding Microstructural Evolution During Rapid Heat Treatment of Microalloyed Steels Through Computational Modeling, Advanced Physical Simulation, and Multiscale Characterization Techniques

An AISI 1045 steel modified with vanadium (V) and niobium (Nb) was studied to evaluate microstructural conditioning prior to and throughout a rapid heat treat process. In order to accomplish this, both computational and physical simulation techniques have been employed with the goal of assessing the microstructural evolution in a medium-carbon bar steel during the rapid austenitization and quenching procedures involved in an induction hardening process. The appropriate thermal profiles for induction hardening were obtained through finite element modeling using Flux 2D software. Physical simulations of the induction hardening process were carried out using a Gleeble{sup ®} 3500. Analysis of prior austenite grain size is complemented by observation of nanoscale carbonitride precipitation via transmission electron microscopy, scanning transmission electron microscopy, and high-energy synchrotron small-angle x-ray scattering. Through a combination of characterization techniques, this study presents a deeper understanding of nano- and microstructural changes occurring in a microalloyed steel during an induction hardening process.

36 MATERIALS SCIENCE↗

X-ray tomography of feed-to-glass transition of simulated borosilicate waste glasses

The feed composition of a high level nuclear waste (HLW) glass melter affects the overall melting rate by influencing the chemical, thermophysical, and morphological properties of a relatively insulating cold cap layer over the molten phase where the primary feed vitrification reactions occur. Data from X ray computed tomography imaging of melting pellets comprised of a simulated high-aluminum HLW feed heated at a rate of 10°C/min reveal the distribution and morphology of bubbles, collectively known as primary foam, within this layer for various SiO 2 /(Li 2 CO 3 +H 3 BO 3 +Na 2 CO 3 ) mass fractions at temperatures between 600°C and 1040°C. To track melting dynamics, cross-sections obtained through the central profile of the pellet were digitally segmented into primary foam and a condensed phase. Pellet dimensions were extracted using Photoshop CS6 tools while the DREAM.3D software package was used to calculate pellet profile area, average and maximum bubble areas, and two-dimensional void fraction. The measured linear increase in the pellet area expansion rates – and therefore the increase in batch gas evolution rates – with SiO 2 /(Li 2 CO 3 +H 3 BO 3 +Na 2 CO 3 ) mass fraction despite an exponential increase in viscosity of the final waste glass at 1050°C and a lower total amount of gas-evolving species suggest that the retention of primary foam with large average bubble size at higher temperatures results from faster reaction kinetics rather than increased viscosity. However, viscosity does affect the initial foam collapse temperature by supporting the growth of larger bubbles. Because the maximum bubble size is limited by the pellet dimensions, larger scale studies are needed to understand primary foam morphology at high temperatures. This temperature-dependent morphological data can be used in future investigations to synthetically generate cold cap structures for use in models of heat transfer within a HLW glass melter.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

FutureTense

Protective vaccines and reliable diagnostics are essential tools for controlling viral diseases. However, the efficacy of these tools can be diminished by mutations in viral genomes. The delay between the emergence of new viral strains and the redesign of vaccines and diagnostics allows for continued viral transmission. Is it possible to address this challenge by computationally predicting viral genome sequence evolution? Can we “future-proof” vaccines and diagnostics by targeting both current and anticipated future sequence variants? While predicting viral evolution is still an unsolved, “grand challenge” problem in biology, the large, and rapidly growing, number of SARS-CoV-2 genome sequences provide an opportunity to quantify the ability of machine learning to predict viral genome sequence evolution. Towards this end, we have developed a simple computational model for predicting viral evolution at the level of individual nucleotides. The key metric for quantifying the per-base, prediction accuracy for viral evolution is the Mann-Whitney U statistic (or, equivalently, the area under the receiver operator curve). Since the Mann-Whitney U statistic is not a differentiable function, existing deep leaning packages (like Pytorch and Keras/TensorFlow) are not useful, as they require that the accuracy metric/objective function be analytically differentiable with respect to the model parameters. To overcome this challenge, we have implemented custom software, “FutureTense”, that can train a machine learning model by maximizing the non-differentiable Mann-Whitney U statistic. This software trains a machine learning model by exploring along the direction of the discrete gradient of the Mann-Whitney U statistic in the model parameter space. Parallel computing and genome sequence-specific optimizations are used to accelerate model training. The resulting machine learning model learns the observed high C->U mutation rates in the SARS-CoV-2 genome (which are potentially induced by host defenses) and provides prediction accuracies that are significantly better than one would expect from random chance. While predicting viral evolution is still quite far from a solved problem, the surprising performance of this simple model gives hope that the accuracy of predicting viral genome evolution can be further increased by more sophisticated approaches.

Gans, Jason↗

Science Uses Deployment Operations-Advanced Wireless: Exploring Open Radio Access Network Technologies for Energy Science

Open Radio Access Network is emerging as a solution to the increasing demand for more flexible, cost-effective, and advanced mobile network infrastructures. This evolution is driven by advancements in wireless technologies and the growing complexity of deploying and managing these networks. O-RAN represents a significant shift in wireless technology, building upon the 3rd Generation Partnership Project framework to foster openness, flexibility, and interoperability. By decoupling hardware and software components, Open Radio Access Network enables a multi-vendor ecosystem that encourages innovation and diverse solutions. Open Radio Access Network's potential extends beyond traditional wireless applications, with growing interest in its role in advancing energy systems, particularly in the context of smart grids, microgrids, and the integration of renewable energy sources. While the role of open-wireless technologies in driving energy transformation is increasingly recognized, further exploration is needed. Vendors and utilities are investigating how Open Radio Access Network technologies can optimize energy use cases and improve the performance of 5G and beyond applications. This report outlines efforts under the Science Uses Deployment Operations Advance Wireless project, a collaboration between the National Laboratory of the Rockies' Cybersecurity Research Center, Argonne National Laboratory, Lawrence Berkeley National Laboratory, and the Department of Energy's Energy Science Network research and operations staff. The focus of this project is on due diligence, through testing and evaluation, preparing for the deployment of advanced wireless infrastructure for scientific use cases, with an emphasis on Open Radio Access Network technology, its components, integrations, and its ability to support vertical stack application across the energy sector. Additionally, the report highlights the value cases for utilities, underscoring how adopting open wireless standards can accelerate the evolution of energy systems, foster innovation, and improve the integration of critical energy technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Recent evolutions in the LTDP CDF project

In the latest years, CNAF (the national center of the Italian Institute for Nuclear Physics INFN dedicated to Research and Development on Information and Communication Technologies) has been working on the Long Term Data Preservation (LTDP) project for the CDF experiment, active at Fermilab from 1990 to 2011. The main aims of the project are to protect the most relevant part of the CDF RUN-2 data collected between 2001 and 2011 and already stored on tape at CNAF (4 PB), as well as to ensure the availability and the access to the analysis facility to those data over time. Lately, the CDF database, hosting information about CDF datasets such as their structure, file locations and metadata, has been imported from Fermilab to CNAF. Also, the Sequential Access via Metadata (SAM) station data handling tool for CDF data management, that allows to manage data transfers and to retrieve information from the CDF database, has been properly installed and configured at CNAF. This was a fundamental step in the perspective of a complete decommissioning of CDF services on Fermilab side. An access system has been designed and tested to submit CDF analysis jobs, using CDF software distributed via CERN Virtual Machine File System (CVMFS) and requesting delivery of CDF files stored on CNAF tapes, as well as data present only on Fermilab storage archive. Moreover, the availability and the correctness of all CDF data stored on CNAF tapes has been verified. This paper describes all these recent evolutions in detail, presenting the future plans for the LTDP project at CNAF.

Fornari, Federico↗

Cryogenic System Preliminary Design for a 0.5m-Long, Conduction-Cooled Nb3Sn Undulator Magnet Prototype

There are several NbTi superconducting undulator (SCU) magnets currently in operation at the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). The development on Nb3Sn-based superconducting undulator magnets at APS is underway due to the potential to further enhance the performance of the SCUs. Superconducting undulator magnets need to keep temperature gradients minimized in order to retain thermal and operating current margin. We have designed the thermal links for efficient heat conduction using 3D finite element analysis (FEA) simulation in COMSOL Multiphysics software, which was later used for the calculation of the temperature distribution across a 0.5 m long, conduction-cooled, Nb3Sn undulator magnet prototype that includes both conductive heat transfer and radiative heating components. We have modelled the evolution of the thermal properties of the magnet winding as well as other cold parts during cool-down from ambient temperature with the operation of a SHI RDK-415D cryocooler to examine the estimated time that is needed for the cooldown as well as the baseline temperature we could achieve. A key result was that a maximum coil delta T = 0.11 K along the designed thermal links and a temperature range of 3.42 K-3.53 K for the winding were predicted at steady state. A pair of G10 support rods for carrying the magnet as well as a pair of current leads made of metals were also designed and optimized for the testing system, targeting for an operation temperature of 4 K and a coil current of 950 A DC.

Zhang, Danlu↗

APOGEE-2S Mg–Al anti-correlation of the metal-poor globular cluster NGC 2298

We present detailed elemental abundances and radial velocities of stars in the metal-poor globular cluster (GC) NGC 2298, based on near-infrared high-resolution ($R$ ~ 22 500) spectra of 12 members obtained during the second phase of the Apache Point Observatory Galactic Evolution Experiment (APOGEE-2) at Las Campanas Observatory as part of the seventeenth Data Release (DR 17) of the Sloan Digital Sky Survey IV (SDSS-IV). We employed the Brussels Automatic Code for Characterizing High accuracy Spectra (BACCHUS) software to investigate abundances for a variety of species including α elements (Mg, Si, and Ca), the odd-Z element Al, and iron-peak elements (Fe and Ni) located in the innermost regions of NGC 2298. We find a mean and median metallicity [Fe/H] = -1.76 and -1.75, respectively, with a star-to-star spread of 0.14 dex, which is compatible with the internal measurement errors. Thus, we find no evidence for an intrinsic [Fe/H] abundance spread in NGC 2298. The typical $α$-element enrichment in NGC 2298 is overabundant relative to the Sun, and it follows the trend of other metal-poor GCs. We confirm the existence of an Al-enhanced population in this cluster, which is clearly anti-correlated with Mg, indicating the prevalence of the multiple-population phenomenon in NGC 2298.

79 ASTRONOMY AND ASTROPHYSICS↗

Materials Challenges and Opportunities for Energy Generation, Conversion, Delivery, and Storage (Applied Energy Tri-Laboratory Consortium Workshop Report)

This report documents the outcomes of the Tri-Laboratory Materials Workshop that was held July 31 and August 1, 2019 to begin addressing the needs, opportunities, and challenges associated with the development, fabrication, and testing of the needed materials and components for integrated hybrid energy systems (i.e., incorporating nuclear, fossil, and renewables for electric and thermal applications). This was accomplished by assembling the research program leads and principal investigators at Idaho National Laboratory (INL), National Energy Technology Laboratory (NETL), and National Renewable Energy Laboratory (NREL), who support the research and development of new technology and system integration. The team then identified and prioritized key materials development needs. This effort was intended to enhance communications and synergy among the Tri-Lab partners. Advanced functional and structural materials are central to transformative energy technologies for energy generation, conversion, delivery, and storage. With that in mind, the workshop focused on identifying and assessing the foundational materials research needs at both the basic and applied levels. Materials challenges include the ability to withstand harsh environments, such as high temperatures and pressures, corrosion, oxidation, or irradiation while maintaining flexible mission profiles and long service lifespans. Advanced energy system material challenges and needs range from materials for the capture, upgrading/concentration, storage, and delivery of low-grade heat to materials for high temperature environments that involve liquid metals, molten salt, and very high temperature gas heat delivery and storage systems. Material improvements are needed for hybrid energy systems due to accelerated corrosion and stress-fatigue failure of materials and equipment, which results from increased frequency and amplitude of thermal, mechanical, and electrical cycling of systems components. Multifunctional materials are needed for high temperature solid-oxide fuel cells, advanced electrochemical reactors, and in-process separation. Relative to materials manufacturing, application of advanced additive and subtractive methods need to be understood to develop both thin-layer homogenous materials and materials of graded composition. Materials modeling and machine learning will be critical to accelerate the design and production of power electronics, and nuclear reactor materials and fuel, as well as to gain an understanding of beneficial materials phenomena or deleterious microstructure evolution. There is also a need for standardized models, computational structures, data reporting protocols and modeling tools across the three laboratories. This would allow consistent results, analysis, and data sharing. Combining computational capabilities between the three laboratories (e.g., hardware, software) would greatly increase computational capabilities and throughput. The workshop identified the need for laboratories to anticipate and address problems that will occur during scale-up. Laboratory work must connect with industry to ensure that research focuses on processes that are scalable and marketable. Industry input and perspective are essential to guide laboratory research to meet these requirements and deploy new technology in industrial demonstrations. Another aspect of scale-up is the integration of multiple systems since new challenges often arise at the subsystem interfaces. Establishing a scale-up manufacturing demonstration/pilot plant, potentially as an industrial user facility, would be beneficial to the laboratories and industry. That modular scale-up manufacturing demonstration/pilot plant would allow researchers to find and resolve interface problems that cannot be identified by focusing only on individual parts. Communication exchanges among the organizers, attendees, and workshop survey responses indicate that the workshop was successful in achieving its goal to identify key technology gaps and research needs. Strong positive feedback was received on the sharing of ideas, capabilities, talent, and passion to move forward on the materials-related action items.

36 MATERIALS SCIENCE↗

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗