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Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning

This project report presents the results from the AU31 project that is entitled “Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning” performed between FY2021 and FY2022. The technical objective of this project is to enhance risk analysis of accidental release of radioactive materials and provide novel risk forecasting and capabilities for assessing unplanned hazardous chemical releases for the U.S. Department of Energy (DOE). The approach includes a cross-platform finite element analysis of airborne chemical release, machine learning (ML) for simulation and forecasting, and a big data approach to data management, transformation and analysis capable of handling petabyte-scale unstructured and structured data. The outcome of these findings were assembled and accessible as a final output via an intuitive web hosted geospatial-based user interface for demonstration and general dissemination. These new results also provide a mechanism of combining this knowledge in a risk analysis framework tool. The geospatial analysis and output can utilize 5 years of qualified meteorological data to illustrate projected plume footprints that are generated from computational aspects of this study. Specifically, we show the development of a geospatial risk analysis capabilities to respond to and plan for accidental release of radioactive materials and to provide a risk forecasting tool for assessing hazardous chemical releases for the DOE. The focus of this effort is germane to common practices in risk analysis; and it is highly relevant to the timely and transparent release of information related to materials at risk including hazardous materials and chemical vapors and rapid assessment of the potential impacts on the workforce at active sites across the DOE complex.

42 ENGINEERING↗

A Guide to Case Studies of Grid Enhancing Technologies

This document has been developed as a resource for the electric power industry to rapidly understand the scale and history with which certain new technologies have been installed on or otherwise simulated to impact the transmission system. This document is attempting to address the findings of the 2022 U.S. Department of Energy (DOE) Report "Grid Enhancing Technologies: A Case Study on Ratepayer Impact", which identified the need for workforce development and training materials to transition new technologies from a state of “pilots” and “case studies” to “business-as-usual.” The authors go beyond typical survey-style literature reviews in that the key details and notes about each project are identified. Still, as will become evident, there is a need for more work to be done to increase the industry’s public knowledge on important topics related to the implementation of grid enhancing technology on the transmission system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Los Angeles 100% Renewable Energy Study (LA100): Chapter 11. Economic Impacts and Jobs

The City of Los Angeles has set ambitious goals to transform its electricity supply, aiming to achieve a 100% renewable energy power system by 2045, along with aggressive electrification targets for buildings and vehicles. To reach these goals, and assess the implications for jobs, electricity rates, the environment, and environmental justice, the Los Angeles City Council passed a series of motions directing the Los Angeles Department of Water and Power (LADWP) to determine the technical feasibility and investment pathways of a 100% renewable energy portfolio standard. The Los Angeles 100% Renewable Energy Study (LA100) is a first-of-its-kind objective, rigorous, and science-based power systems analysis to determine what investments could be made to achieve these goals. The LA100 final report is presented as a collection of 12 chapters and an executive summary, each of which is available as an individual download. This chapter reviews economic impacts, including local net economic impacts and gross workforce impacts.

100% Renewable↗

The Case for an EIC Theory Alliance

This documents outlines the case for the creation of an EIC Theory Alliance. The EIC will be a unique and versatile facility that will enable the understanding of some of the most compelling questions in the physics of the strong nuclear force. To fully exploit the potential of the EIC, a focused theory effort will be required. The goal of the EIC Theory Alliance is to provide support and stewardship of the theory effort in EIC physics, broadly defined, over the lifetime of the facility. It will promote EIC theory and contribute to workforce development through: support of graduate students; EIC Theory Fellowships for postdocs; bridge positions at universities; and short and long term visitor programs to enhance collaboration between groups. In addition, the alliance will organize topical schools and workshops. The EIC Theory Alliance will be a decentralized organization, open to participation by anyone in the community who is interested in EIC physics, i.e., it will be a membership organization, where members elect an executive board which will effectively run the alliance. The executive board will determine the major scientific thrusts of the theory alliance, make decisions regarding at which universities bridge faculty positions will be created, and serve as a search committee for EIC-related positions. Furthermore, the executive board will coordinate the organization of workshops and schools related to the research activities of the alliance. In addition, the EIC theory alliance will seek out and nurture international cooperation to maximally leverage the available funding. The EIC theory alliance has a wider range of physics goals and longer lifetime, commensurate with that of the EIC research program, than individual nuclear theory topical collaborations. The structure of the EIC Theory Alliance will build on previous examples of successful alliances in nuclear theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Nuclear Security Mission Growth: A regional strategy for meeting operational challenges [Slides]

The nation's growing nuclear security requirements have yielded three major conclusions: (1) LANL possesses an exceptionally diversified workforce, multidimensional infrastructure and mission-focused strategy to fulfill the responsibilities entrusted to us; (2) However, amidst significant opportunity and growth, regional considerations have exposed several operational challenges; and (3) Given these challenges and the Lab's many strengths, assistance is needed to successfully execute our mission.

99 GENERAL AND MISCELLANEOUS↗

Monitoring and Documentation of Forest Management Activities at Los Alamos National Laboratory

Forest management treatments at Los Alamos National Laboratory (LANL) are administered by the Emergency Management Division (EMD-DO) Wildland Fire Program within the Wildland Fire and Forest Management Program (WFFM Program or the Program). Open space forest inventory monitoring is conducted by Forest Health Program staff and students within the Environmental Stewardship Group (EPC-ES). The goals of the integrated Wildland Fire and Forest Management Program are to reduce impacts to Laboratory operations from climate driven events by implementing forest management and healthy forest initiatives such as forest thinning and erosion control. The LANL Wildland Fire Mitigation and Forest Health Plan (EMD PLAN-200, LA-UR-19-25122; LANL 2019) presents treatment standards for LANL property to meet the following goals: 1) Restore and maintain landscapes: LANL landscapes are resilient to disturbances; 2) Develop a fire-adapted community: LANL workforce, neighbors, and infrastructure can withstand a wildland fire without loss of life and property; 3) Ensure wildland fire mitigation implementation: All wildland fire mitigation working group organizations participate in making and implementing safe, effective, efficient risk-based wildland fire management decisions.

54 ENVIRONMENTAL SCIENCES↗

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2021: Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals of supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high-performance computing (HPC) needs; contributing to the community to build the next generation HPC workforce, and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2021 saw continued excellence in research supported by the OLCF’s leadership-class computing resources, including Summit (the nation’s most powerful supercomputer), the global scratch file system Alpine, the Scalable Protected Infrastructure (SPI), the Exploratory Visualization Environment for Research in Science and Technology (EVEREST), and the archival mass-storage resource High-Performance Storage System (HPSS). While maintaining access and exceptional user support for Summit, the OLCF continued to make progress on the installation and deployment of Frontier, which will be the nation’s first exascale system when it comes online at the start of CY 2023. Users have already begun running and optimizing scientific codes on Crusher, the OLCF test and development system equipped with Frontier’s architecture. Throughout the year, the OLCF maintained a strong culture of operational excellence, including risk management, workplace safety, and cybersecurity. The OLCF’s rigorous risk management strategy anticipated and mitigated risks, and at this time there are no high-priority operational risks. Similarly, ORNL and the OLCF were committed to operating under the US Department of Energy’s (DOE’s) safety regulations that ensure a safe workplace. Technical staff tracked and monitored existing threats and vulnerabilities within the OLCF while continually developing tools and practices to enhance operations without increasing the facility’s risk. CY 2021 was filled with outstanding results and accomplishments, including a very high rating from users on overall satisfaction for the eighth consecutive year; a tremendous number of node hours delivered to 1,671 researchers on Summit; and the successful delivery of the allocation split of roughly 60%, 20%, and 20% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director’s Discretionary (DD) programs, respectively (Section 2). COVID-19 research remained a focus in 2021, and the ALCC and DD programs allocated over 1 million Summit hours to the COVID-19 High Performance Computing Consortium. These accomplishments, coupled with the high utilization rates (i.e., overall and capability usage), represent the fulfillment of the promise of leadership class machines: efficient facilitation of leadership-class computational applications.

97 MATHEMATICS AND COMPUTING↗

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗

LDRD 2022 Annual Report: Laboratory Directed Research and Development Program Activities

Each year, Brookhaven National Laboratory (BNL) is required to provide a report of its completed Laboratory Directed Research and Development Program (LDRD) projects to the Department of Energy (DOE) Office of Scientific and Technical Information in accordance with DOE Order 413.2C Chg1 (MinChg) dated August 2, 2018. This report provides a detailed look at the scientific and technical activities for each of the LDRD projects funded by BNL in FY 2022, in fulfillment of that requirement. In FY 2022, the BNL LDRD Program funded 70 projects, 30 of which were new starts, at a total cost of $17.2M. The investments that BNL makes in its LDRD program support the Laboratory’s strategic goals. BNL has identified seven scientific initiatives that define the Laboratory’s scientific future and that will enable it to realize its overall vision. This requires simultaneous excellence in all aspects of BNL’s work – from science and operations, to external partnerships with the local, state, and national communities, and beyond. This is enabled by safe, efficient, and secure operations; by an unwavering commitment to a diverse, equitable, and inclusive environment, including workforce development, both with staff and reaching out to the community; and by a strong focus on renewed infrastructure. The seven scientific initiatives are: 1) Nuclear Physics: uncover the structure of visible matter by constructing and operating the Electron-Ion Collider at BNL to maintain international leadership in nuclear physics for decades; 2) Clean Energy and Climate: support a net-zero U.S. economy through fundamental research in basic energy and climate sciences to revolutionize grid-scale storage, renewable integration, and the study of atmospheric processes with a new facility to improve climate predictability; 3) Quantum Information Science and Technology: discover new quantum materials to enhance quantum computers and develop an entanglement sharing quantum network as a prototype for the first quantum internet; 4) Discovery Science Driven by the Human-AI Facility Integration: revolutionize the operation of experiments across the sciences at user facilities and in core programs; 5) High Energy Physics: understand the origin of space and time with the ATLAS high luminosity upgrade at CERN and the future Long Baseline Neutrino Facility/Deep Underground Neutrino Experiment; 6) Isotope Production: accelerate and expand isotope production to ensure the security of the Nation’s supply; 7) Accelerator Science and Technology: harness the cross-cutting accelerator science expertise at BNL to develop new facilities, improve and expand its user facilities, and promote the use of accelerators in industry. The funded projects support BNL’s seven scientific initiatives and priority programs as well as new areas of research and competencies at the Laboratory that are consistent with the Laboratory’s vision and mission. In total, these LDRD investments supported 43 postdoctoral researchers in whole or in part and resulted in 138 publications and 7 awards. This Program Activities Report represents the future of BNL science; it is an impressive body of exploratory work that investigates many scientific and technical directions in support of the DOE and BNL missions.

99 GENERAL AND MISCELLANEOUS↗

Corrosion Testing Trade Study on Testing Methods in Molten Salts

In assessing molten salt compatibility with structural materials, static testing is often reported because it is easy to conduct. However, in order to assess mass transfer, where material dissolves in the salt in the hot section and may precipitate out of the liquid in colder regions, a flowing salt experiment in a temperature gradient is needed. While a forced convection or pumped loop produces prototypic flowing conditions, it also is a multi-million dollar investment. Among the less expensive dynamic testing options, the thermal convection loop (TCL) was favored during the 1960’s development of molten salt reactor technology. Because the workforce and core facilities are currently available to conduct TCL experiments, it is the least expensive strategy to evaluate candidate structural alloys in flowing molten salt conditions.

36 MATERIALS SCIENCE↗

The case for an EIC Theory Alliance: Theoretical Challenges of the EIC

We outline the physics opportunities provided by the Electron Ion Collider (EIC). These include the study of the parton structure of the nucleon and nuclei, the onset of gluon saturation, the production of jets and heavy flavor, hadron spectroscopy and tests of fundamental symmetries. We review the present status and future challenges in EIC theory that have to be addressed in order to realize this ambitious and impactful physics program, including how to engage a diverse and inclusive workforce. In order to address these many-fold challenges, we propose a coordinated effort involving theory groups with differing expertise is needed. We discuss the scientific goals and scope of such an EIC Theory Alliance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Extraction, Separation, and Production of High Purity Rare Earth Elements and Critical Minerals from Coal-Based and Related Resources

The general objectives of this project are to develop concepts for rare earth metal and critical mineral production from coal-based and related (minerals associated with coal) resources and incorporate them into a Technical Research Plan with an associated overall flow sheet. The project team has extensive expertise in market evaluation, mineral separation, leaching, chemical separations, alternative metallothermic reaction technology, electrowinning, and electrorefining that was critical to the success of the proposed project. The project included critical industrial partners needed for project success. The project team has extensive expertise in market evaluation, mineral separation, leaching, chemical separations, alternative reduction technology, electrowinning, and electrorefining as well as appropriate pilot-scale facilities to enable this project. The project encompassed broader opportunities for developing domestic resources of REE/CM materials for a more resilient, diverse, and secure supply chain for REE/CM materials with built-in redundancies and appropriate resource stockpiles. The production of REE/CM materials will help to revitalize and rebuild world-class American manufacturing capacity and the related workforce through new jobs and infrastructure. Furthermore, due to the nature and location of the production sites, economic growth in diverse communities of color and economically distressed areas will be cultivated. Finally, this technology can be applied to reuse and remediate coal waste tailings for REE/CM production.

01 COAL, LIGNITE, AND PEAT↗

LANL/UTEP Metallurgical Science Center of Excellence Planning

LANL seeks research assistance and skills development in three distinct subject areas over the next three years. Individualized scope and detail implementation schedules will be the first deliverable for these subject areas and will represent a joint determination between LANL SMEs and UTEP faculty based on LANL priority (schedule) and UTEP staffing (faculty and student- graduate and undergraduate) and capability of immediate availability per schedule. LANL will endeavor to make summer internships available to UTEP students within these subject areas: Chemical Sciences, Additive Manufacturing, and Metallurgy: casting and solidification, characterization, and metal purification. Unarticulated but generalized scoping tasks follow within each topical area to indicate that students and technicians familiar with these skill sets are of value and interest in LANL workforce pipeline and the tasks that seek to develop these skill sets are intentional on LANL's part. Consequently, LANL will sponsor 2-3 senior (capstone) projects within these subject areas per year over the three year time period within these task areas and within separations technologies, wet chemical analysis, and analytical characterization and quantification as required by all tasks.

36 MATERIALS SCIENCE↗

Advanced Simulation and Computing: ASC FY24 Implementation Plan

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, security, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including previous nuclear tests, stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent. The ASC program is a cornerstone of the SSP, providing simulation capabilities and computational resources to support the annual stockpile assessment and certification process, study advanced nuclear weapons design and manufacturing processes, analyze accident scenarios and weapons aging, and provide the tools to enable stockpile Life Extension Programs (LEPs) and the resolution of Significant Finding Investigations (SFIs). This work requires a balance of resources, including technical staff, hardware, simulation software, and computer science solutions. The ASC program focuses on increasing the predictive capabilities in a three-dimensional (3D) simulation environment while maintaining support to the SSP. The Program continues to improve its unique tools for understanding and solving progressively more difficult stockpile problems (sufficient resolution, dimensionality, and scientific details), and quantifying critical margins and uncertainties. Resolving each issue requires increasingly difficult analyses because the aging process has progressively moved the stockpile further from the original test base. While the focus remains on the U.S. nuclear weapons program, where possible, the Program also enables the use of high-performance computing (HPC) and simulation tools to address broader national security needs, such as foreign nuclear weapon assessments and nuclear counterterrorism. The 2022 Nuclear Posture Review (NPR) calls for NNSA to “deliver a modern, adaptive nuclear security enterprise based on an integrated strategy for risk management, production-based resilience, science and technology innovation, and workforce initiatives.” Furthermore, “NNSA will establish a Science and Technology Innovation Initiative to accelerate the integration of science and technology (S&T) throughout its activities.” Executing this strategy necessitates the continued emphasis on developing and sustaining high-quality scientific and engineering staff, as well as supporting computational and experimental capabilities. These components constitute the foundation of the nuclear weapons program. The continued success of the SSP and LEPs is predicated upon the ability to credibly certify the stockpile, without a return to underground nuclear tests (UGTs). Shortly after the nuclear test moratorium entered into force in 1992, the Accelerated Strategic Computing Initiative (ASCI) was established to provide an extensive simulation capability to underpin stockpile certification. While computing and simulation have always been essential to the success of the nuclear weapons program, the program goal of ASCI was to execute NNSA’s vision of using these tools in support of the stockpile stewardship mission. The ASCI program was essential to the successful demonstration of the SSP, providing critical nuclear weapons simulation and modeling capabilities. ASCI officially evolved into the ASC program in fiscal year (FY) 2005, but the mission remains essentially the same: provide the simulation and computational capabilities that underpin the ability to maintain a safe, secure, effective nuclear weapon stockpile, without returning to underground nuclear testing. The capabilities that the ASC program provides at the national laboratories play a vital role in the nuclear security enterprise and are necessary for fulfilling the stockpile stewardship and life extension requirements outlined for NNSA. The Program develops modern simulation tools that provide insights into stockpile aging issues, provide the computational and simulation tools that enable designers and analysts to certify the current stockpile and life-extended nuclear weapons, and inform the decision-making process when any modifications in nuclear warheads or the associated manufacturing processes are deemed necessary. Furthermore, ASC is enhancing the predictive simulation capabilities that are essential to evaluate weapons effects, design experiments, and ensure test readiness. The ASC program continues to improve its unique tools to solve stockpile problems— with a focus on sufficient resolution, dimensionality, and scientific detail—to enable Quantification of Margins and Uncertainties (QMU) and to resolve the increasingly difficult analyses needed for stockpile stewardship. The needs of the Stockpile Management and Production Modernization programs (formerly Directed Stockpile Work) also drive the requirements for simulation and computational resources. These requirements include planned LEPs, stockpile support activities, and mitigation efforts against the potential for technical surprise. All of the weapons within the current stockpile are in some stage of the life extension process. The simulation and computational capabilities are crucial for successful execution of these life extensions and for ensuring NNSA can certify these life-extended weapons without conducting a UGT.

97 MATHEMATICS AND COMPUTING↗

Towards Automated Design of Polymer Precursors for Rational Design of Porous Carbon-Materials [Slides]

Overview: Developed capability enables easy building of realistic polymer structures and a straightforward evaluation of polymer properties using different computational approaches. Topics of relevance to LANL mission: fundamental understanding of gas, vapor, ion, and liquid transport in polymer materials with applications in carbon capture, clean energy, hydrogen production (e.g., fuel cells and electrolyzes); Toxic element removal; Design of polymer binders in explosives. Workforce Development: Project provided a valuable opportunity to for a PI to manage a LANL research effort, expand collaborations (e.g., MPA division), and establish research topic that aligns with the LANL mission; The capability built was highlighted in the proposal "Versatile Polyolefins-Based Ionomer Platform for Next Generation Polymer Electrolyte Membrane and Hydrogen Production" lead by InnoSense LLC in collaboration with MPA-1 and T-1 as a response to DOE's call DE-FOA-0003003: Science Foundations for Energy Earthshots.

36 MATERIALS SCIENCE↗

A New Era of Discovery: The 2023 Long-Range Plan for Nuclear Science (V.1.2)

Nuclear science is the investigation of how protons and neutrons are formed from elementary particles and how the forces between those particles produce both nuclei and the vast variety of nuclear phenomena that occur in the universe. It has evolved into a broad field that addresses profound scientific questions: Where does the mass of visible matter come from? How do stars ignite, live, and die? How do nuclei illuminate the search for new laws of nature? This science points the way to using nuclei to build new technologies that benefit society. The 2015 Nobel Prize in physics was shared by nuclear physicists Art McDonald and Takaaki Kajita for the discovery of neutrino oscillations, which confirmed that neutrinos have mass. Our progress on big questions like this one since 2015 has been remarkable owing to new experimental tools, theoretical breakthroughs, powerful computational techniques, and the talented people who make these innovations possible. Focusing on these new tools, the Facility for Rare Isotope Beams (FRIB) at Michigan State University is already producing exciting results on decays of never-before-produced isotopes a year after it was completed on time and on budget. The energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) was also completed on schedule and on budget—new data from this facility are revealing the spectrum, structure, and dynamics of protons, neutrons, nuclei, and mesons. On the theory front, we can now calculate the distribution of quarks inside the proton from first principles. The implementation of artificial intelligence (AI) and machine learning (ML) techniques has led to improved data analysis and increased efficiency in running experiments and theoretical calculations. The impact of nuclear science goes beyond expanding the frontiers of knowledge about matter in the universe. We simultaneously develop a STEM work force that advances the security, technology, health, and wealth of our nation. Some connections are obvious. Expert scientists trained to work with radioactive nuclei are in demand in nuclear security arenas and are highly sought after by various government agencies and private industries. Graduate students and postdoctoral fellows (postdocs) obtain extensive computational, modeling, and data science skills that are similarly in high demand. Less obvious but equally important is the connection between these trained scientists and success in other professions, including medicine, energy, and entrepreneurial pursuits. The workforce that enables discovery in nuclear science also makes breakthroughs in technologies with tremendous impact on the nation’s economic advancement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

AMES: MS and MENG in Advanced Manufacturing for Energy at the University of Connecticut (Final Technical Report)

The objective of the project is to develop and implement an advanced degree program (MS and MENG in Advanced Manufacturing for Energy Systems, AMES) responding to the long-term workforce and technology requirements of the nation’s advanced energy products manufacturing industry. The program provided an industry relevant research experience by leveraging existing energy (e.g. fuels, power electronics, electrochemical power sources) and advanced manufacturing (e.g. additive manufacturing, composites, sensing) research at UConn funded by federal and state agencies, and industry, as well as through our industry partnerships. The trainees have joined research teams, advised by faculty with relevant research interests and expertise and were co-advised by industrial mentors. The AMES program have developed a truly interdisciplinary curriculum, first graduate degree program at UConn School (now College) of Engineering not housed in an academic department, with concentrations focusing on various challenges in advanced manufacturing for energy systems, e.g. advanced materials and processing. The project also developed new courses focusing on common technical and professional skills, and integrated various components for an industry relevant training. The program has admitted 29 Master of Science (MS) students since inception in January 2019. Twenty six of these students were AMES fellows, who have received partial funding from DoE through this project. The program far exceeded the goal of admitting at least five new MS (with thesis) students. All students were required to complete a thesis (M.S.) or a capstone (M.Eng.) project that are defined in collaboration with industry partners to ensure industrial relevancy, addressing a current industrial challenges. Industrial mentors also participated in advising the students in their research. AMES fellows, in addition were also required to complete an industrial internship for further industrial experience.

36 MATERIALS SCIENCE↗