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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Infusion of AI/ML Technology into Operational NASA Data Systems

NASA has been developing a variety of Artificial Intelligence / Machine Learning technologies related to Earth Observations. In most cases, the full value of such a technology is realized when it is infused into an operational system. NASA’s Earth Science Data Systems program has been formulating repeatable methods to execute technology infusion. These efforts include the Advancing Collaborative Connections for Earth System Science (ACCESS) program, a Technology Infusion Playbook, and an assemblage of working groups investigating methods for infusion collaboration, community development, and capacity building. ESDS has also been executing a pathfinder activity to infuse a machine-learning-driven recommender of science keywords for Earth Observation datasets, which is intended to be used for metadata curation in the Earth Observation System Data and Information System.

C Lynnes↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Flame Chemistry Workshop: a perspective on challenges and strategic actions in combustion experiments and chemical kinetics modeling

Continued progress in the development of predictive models for combustion chemistry—including ignition and flame behavior, species evolution, and combustion system performance—relies on overcoming enduring and emerging challenges in experimental measurements, theoretical formulations, and chemical kinetics mechanism construction. As combustion science continues to coincide with advances in sustainable fuels development, plasma technologies, and automated modeling capabilities, the need for coordinated, community-driven strategies is essential. The Flame Chemistry Workshop (FCWS), held biennially before the International Symposium on Combustion, serves as a dedicated platform to identify, consolidate, and address these challenges in a structured and collaborative manner. This perspective arises from discussions at the 7th FCWS in Milan, Italy (2024), and presents a collective view of the critical barriers currently limiting progress. Across the five technical domains discussed during the 7th FCWS – sustainable fuels combustion, advanced diagnostics for combustion measurements, experiments and modeling in plasma combustion, artificial intelligence and automated methods for theory and mechanisms generation, and chemical kinetic models—a series of persistent and emerging scientific challenges were identified, highlighting the need for deeper integration between three areas: theory, experiments, and modeling. In conclusion, the present article concisely describes present challenges that were identified in each of the technical domains in an effort to streamline and coordinate solutions to accelerate progress in combustion science.

Chemical kinetics↗
DOE OSTI2025

Large language models for batteries

Large Language Models (LLMs) are advanced artificial intelligence systems capable of solving diverse tasks using language, reasoning, and external tools. Despite their growing deployment in academia and industry, their potential remains underexplored in battery research. This review presents a comprehensive overview of existing and emerging applications of LLMs in batterie field, addressing two critical questions: What can LLMs offer to support battery-related tasks, and how to develop more effective models for this purpose. We begin by outlining the principles of LLMs and criteria for selecting appropriate models and tools for battery research and development. We then explore their roles in text-mining, data interpretation, and the development of intelligent battery systems. In parallel, we discuss technical challenges, such as data standardizing and sharing, model evaluation, and tool integration. Lastly, we propose future research directions with short-, medium-, and long-term goals and highlight more broad perspectives for connecting experts and cross-disciplinary collaborations.

SoC↗
NASA NTRS1998

Case-Based Capture and Reuse of Aerospace Design Rationale

The goal of this project is to apply artificial intelligence techniques to facilitate capture and reuse of aerospace design rationale. The project applies case-based reasoning (CBR) and concept mapping (CMAP) tools to the task of capturing, organizing, and interactively accessing experiences or "cases" encapsulating the methods and rationale underlying expert aerospace design. As stipulated in the award, Indiana University and Ames personnel are collaborating on performance of research and determining the direction of research, to assure that the project focuses on high-value tasks. In the first five months of the project, we have made two visits to Ames Research Center to consult with our NASA collaborators, to learn about the advanced aerospace design tools being developed there, and to identify specific needs for intelligent design support. These meetings identified a number of task areas for applying CBR and concept mapping technology. We jointly selected a first task area to focus on: Acquiring the convergence criteria that experts use to guide the selection of useful data from a set of numerical simulations of high-lift systems. During the first funding period, we developed two software systems. First, we have adapted a CBR system developed at Indiana University into a prototype case-based reasoning shell to capture and retrieve information about design experiences, with the sample task of capturing and reusing experts' intuitive criteria for determining convergence (work conducted at Indiana University). Second, we have also adapted and refined existing concept mapping tools that will be used to clarify and capture the rationale underlying those experiences, to facilitate understanding of the expert's reasoning and guide future reuse of captured information (work conducted at the University of West Florida). The tools we have developed are designed to be the basis for a general framework for facilitating tasks within systems developed by the Advanced Design Technologies Testbed (ADTT) project at ARC. The tenets of our framework are (1) that the systems developed should leverage a designer's knowledge, rather than attempting to replace it; (2) that learning and user feedback must play a central role, so that the system can adapt to how it is used, and (3) that the learning and feedback processes must be as natural and as unobtrusive as possible. In the second funding period we will extend our current work, applying the tools to capturing higher-level design rationale.

Leake, David B.↗
DOE OSTI2025

Modernizing Accelerator Responsiveness and Controls in Operations

Accelerators increasingly use artificial intelligence (AI) and machine learning (ML) software and workflows for a variety of tasks, from optimization to fault detection and recovery. Efficient and sustainable application of these technologies necessitates specialized and facility-specific infrastructure commitments. Accelerator facilities also introduce unique radiation and security hazards, placing additional demands on operational infrastructure. These needs further escalate the prioritization of effective collaboration models and associated funding mechanisms and legal frameworks.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Introduction to the special section on learning and security for multi-agent systems

This report features six articles that provide an overview of the emerging research topics at the intersection learning, security, and multi-agent systems. Recent years have witnessed a surge in the number of works at their intersections, and they have appeared in system and control communities as well as many other communities in artificial intelligence, cyber–physical systems, and economics. The articles in this special section give accessible and comprehensive tutorials and surveys for a broad systems and control audience, covering topics including adversarial machine learning, multi-agent reinforcement learning, cyber resilience, resilient control systems, and game design. It is hopeful that this special section will spawn future interest and cross-disciplinary collaborations in this emerging transdisciplinary research area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Are humans still necessary? Expanding the discussion

The rise of automation, artificial intelligence (AI), and autonomous systems raises important questions about the future role of humans and the field of human factors/ergonomics in workplaces. This paper builds on Dr. Peter Hancock’s 2023 ‘Are Humans Still Necessary?’ article published in the Ergonomics journal. Using a multi-method approach that included a debate, opinion polling, roundtable discussions, and AI queries, the current effort examined the necessity of human involvement in future work environments. Debate team members presented arguments for and against the need for human workers, considering human factors, technology, and socioeconomic factors. Observations indicate that while AI may handle routine tasks, humans will likely remain essential for complex decision making, creativity, and ethical considerations. The paper advocates for viewing workplace dynamics as collaborative human-AI partnerships rather than competition, highlighting the need for a transdisciplinary approach in which human factors/ergonomics professionals play a vital role in enhancing these relationships.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Opportunities for human factors in machine learning

Introduction The field of machine learning and its subfield of deep learning have grown rapidly in recent years. With the speed of advancement, it is nearly impossible for data scientists to maintain expert knowledge of cutting-edge techniques. This study applies human factors methods to the field of machine learning to address these difficulties. Methods Using semi-structured interviews with data scientists at a National Laboratory, we sought to understand the process used when working with machine learning models, the challenges encountered, and the ways that human factors might contribute to addressing those challenges. Results Results of the interviews were analyzed to create a generalization of the process of working with machine learning models. Issues encountered during each process step are described. Discussion Recommendations and areas for collaboration between data scientists and human factors experts are provided, with the goal of creating better tools, knowledge, and guidance for machine learning scientists.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ReVise: A Human-AI Interface for Incremental Algorithmic Recourse

The recent adoption of artificial intelligence in socio-technical systems raises concerns about the black-box nature of the resulting decisions in fields such as hiring, finance, admissions, etc. If data subjects—such as job applicants, loan applicants, and students—receive an unfavorable outcome, they may be interested in algorithmic recourse, which involves updating certain features to yield a more favorable result when re-evaluated by algorithmic decision-making. Unfortunately, when individuals do not fully understand the incremental steps needed to change their circumstances, they risk following misguided paths that can lead to significant, long-term adverse consequences. Existing recourse approaches focus exclusively on the final recourse goal but neglect the possible incremental steps to reach the goal with real-life constraints, user preferences, and model artifacts. To address this gap, we formulate a visual analytic workflow for incremental recourse planning in collaboration with AI/ML experts and contribute an interactive visualization interface that helps data subjects efficiently navigate the recourse alternatives and make an informed decision. We also present one of the many usage scenarios, developed during exploratory feedback sessions with twelve graduate students using a real-world dataset, which demonstrates that our approach can be instrumental for data subjects in choosing a suitable recourse path.

algorithmic recourse↗
DOE OSTI2024

Wearable Technology and Its Influence on Motor Development and Biomechanical Analysis

The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.

Morouço, Pedro (ORCID:0000000259569790)↗
DOE OSTI2024

NSTX-U National Research Program: White Paper in Response to Call from FESAC Sub-Committee

Both scientific and technical innovation is needed for the realization of an attractive engineering solution for a timely and cost-effective Pilot Plant, the design and construction of which is the overarching recommendation of the FESAC Long Range Plan, and the 2021 NASEM Pilot Plant reports, which underpin the Bold Decadal Vision. The two most significant plasma physics gaps to close for a Compact Pilot Plant (CPP) are core confinement improvement and heat flux mitigation, neither of which have been closed in an integrated fashion for any planned fusion power production device. High core confinement and stability are essential for producing majority self-driven plasmas in CPPs with reduced size and auxiliary heating power requirements, with an improvement in confinement being the major driver for cost reduction of a CPP. The National Spherical Tokamak Experiment - Upgrade (NSTX-U) is a unique low aspect ratio research facility that will address the fundamental challenge of developing the science and technology basis for a CPP design that integrates high core and edge confinement with the ability to mitigate very high incident heat fluxes. NSTX-U capabilities will enable the high performance, already achieved on NSTX, to extend into physics regimes much closer to those anticipated in Spherical Tokamak (ST)-based CPPs. These confinement and stability properties will be assessed by a full complement of diagnostics and analysis tools, which will also aid in the development of the underlying theory and predictive models needed for further optimization. Both conventional and transformative heat flux mitigation methods, such as liquid lithium plasma-facing components, will be developed and tested in-situ in NSTX-U at incident heat fluxes of ~100 MW/m 2 , and will inform plans and reduce risk for a subsequent major upgrade to the device to fully heated, high-Z wall and full liquid lithium divertor capability, a technology that potentially could then be implemented on any magnetic confinement device at any aspect ratio. NSTX-U research is fully complementary to programs performed on other STs, nationally and internationally. Furthermore, NSTX-U research has a direct connection to the private sector by informing design choices for future power production facilities being developed by these companies. The NSTX-U program will operate as a national User Facility, with collaborating researchers, engineers, and graduate students from 19 outside institutions, and open to participation and experiments led by researchers from both public and private entities. The research program will advance workforce development through training of young scientists, engineers, and technicians, and it will also serve for further diagnostic innovation, especially for high heat flux and high-Z wall environments, and implementation of advanced artificial intelligence (AI) for plasma and heat flux control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
NASA NTRS1989

Proceedings of the NASA Conference on Space Telerobotics, volume 5

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center.

Rodriguez, Guillermo↗
DOE OSTI2024

An Ethics-Based Review of Generative Artificial Intelligence: Assuring Responsible Use (Version 1.0)

The rapid expansion of generative artificial intelligence (GenAI) has generated excitement regarding its potential benefits and concern over its ethical implications. Governments, corporations, and standards organizations have described ethical principles to direct GenAI's development and use; however, practical guidance for implementing these principles is limited. Addressing this gap is critical, especially considering the array of risks associated with GenAI, such as legal liabilities, privacy concerns, security threats, and potential misuse. Robust policies and procedures are critical to support responsible deployment of GenAI. This report examines Pacific Northwest National Laboratory (PNNL)’s approach to promoting responsible GenAI use. Proposed initiatives include developing policies based on ethical principles, creating a governance process to review projects relative to those principles, and implementing onboarding processes for training staff. The governance framework described in this report adapts the structure and principles of Institutional Review Boards (IRBs), traditionally used in human subjects research, for GenAI ethical review, providing oversight. Ethical principles guiding responsible GenAI usage include transparency and accountability, privacy, fairness, safety, security, and validity and reliability. To operationalize these principles, we propose forming a GenAI Assurance Council (GAC) that mirrors the IRB's structure. The GAC will evaluate GenAI projects across privacy, accountability, transparency, safety, security, fairness, and validity dimensions. Complementing policy and governance is AI literacy training to support staff understanding of GenAI's ethical implications. An initial training effort for AI Incubator Chat—a GenAI tool deployed at PNNL—showed promising results, underscoring the importance of clear guidelines and user accountability. Collaborative efforts and the dissemination of best practices are also discussed. The proposed GAC model and AI literacy training provide a blueprint for establishing ethical GenAI use and governance, offering practical tools to bridge the gap between ethical principles and real-world applications. The responsible integration of GenAI at PNNL entails a multifaceted approach involving policy development, ethical governance, and AI literacy training. The positive initial feedback and collaborative opportunities position PNNL to lead by example in GenAI's responsible use, reflecting a proactive stance in addressing the ethical, legal, and societal challenges associated with this emerging technology. PNNL's systematic and ethical approach to GenAI offers a model for other institutions to emulate, promoting safe and responsible technological advancements in the AI domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Preliminary Feasibility of Printing, Microstructure Analysis and Mechanical Performance of a Down Selected Ni Alloy

This report is submitted as completion of a Milestone 3 deliverable under work package ORNL CT-23OR1304051 in support of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is aiming at the faster incorporation of new materials and manufacturing technologies into complex nuclear-related systems. An integrated approach, combining advanced characterization, high-throughput and accelerated testing, modeling and simulation including machine learning and artificial intelligence will be employed. While 316H (Fe-16-18Cr-10-14Ni-2-3Mo-0.04-0.1C) has been identified as a key alloy to be integrated into the AMMT accelerated alloy qualification approach due its relevance for many current and future nuclear energy reactors, many other alloys could be considered for the advanced fabrication of innovate high-performance nuclear components. ANL, INL, ORNL and PNNL are collaborating on identifying the most promising alloy candidates relevant for the AMMT program. A selection criteria matrix was established to evaluate the alloys considering their relative importance and technological readiness levels for nuclear energy applications, with a focus on laser powder bed fusion (LPBF). Due to the broad range of potential candidate alloys, ORNL and INL focused on Ni-based alloys, while ANL and PNNL mainly evaluated Fe-based alloys. PNNL previously published materials scorecards reports on several key alloys and this report is providing a broader overview of Ni-based candidate alloys, expending beyond alloys well-known to the nuclear community. Of particular interest are alloys that are currently commercially available in powder form due to the growing demand from other industries that have invested heavily in additive manufacturing. Among these alloys, Haynes 282 (Fe-20Cr-20Co-8Mo) was selected due to its superior strength at high temperature compared to the code-qualified alloy 617 (Ni-20-24Cr-10-15Co-8-10Mo). To assess the integration of this new alloy into the AMMT digital manufacturing framework, we performed the rapid optimization of alloy 282 printing parameters on a Renishaw 250 machine, the fabrication of sufficient materials for extensive characterization and mechanical testing both at ORNL and INL, and added the printing data into the digital platform via the Peregrine software. A detailed analysis of the LPBF 718 alloy was also conducted, with creep specimens being tested at 600-650°C and characterized by advanced electron microscopy. The alloy superior mechanical strength and great printability associated with the extensive database that has already been generated highlight the promising potential of LPBF 718 as a candidate alloy for the AMMT program. INL, ANL and PNNL have generated similar reports and all the information will be compiled into a final M2 milestone report to provide the AMMT leadership team with clear recommendations on the down selection of reactor materials, as well as establish a roadmap for the qualification of these selected alloys.

36 MATERIALS SCIENCE↗