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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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DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance (Q2 FY2021 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance FY21 Q3 Progress Report

42 ENGINEERING↗

DOE OSTI2021

Tribal Colleges and Universities/Advanced Manufacturing Network Initiative: Phase II Sandia Technical Assistance (FY21 Q4 Progress Report)

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) 1) to align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

42 ENGINEERING↗

DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios.To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

Aykac, Deniz↗

DOE OSTI2023

Laboratory Study of Collisionless Magnetic Reconnection

A concise review is given on the past two decades’ results from laboratory experiments on collisionless magnetic reconnection in direct relation with space measurements, especially by the Magnetospheric Multiscale (MMS) mission. Highlights include spatial structures of electromagnetic fields in ion and electron diffusion regions as a function of upstream symmetry and guide field strength, energy conversion and partitioning from magnetic field to ions and electrons including particle acceleration, electrostatic and electromagnetic kinetic plasma waves with various wavelengths, and plasmoid-mediated multiscale reconnection. Combined with the progress in theoretical, numerical, and observational studies, the physics foundation of fast reconnection in collisionless plasmas has been largely established, at least within the parameter ranges and spatial scales that were studied. Immediate and long-term future opportunities based on multiscale experiments and space missions supported by exascale computation are discussed, including dissipation by kinetic plasma waves, particle heating and acceleration, and multiscale physics across fluid and kinetic scales.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Panel Session 2: Hot Topics in US DOE Environmental Management

The well-attended Panel Session was chaired by Martin Schneider. The four panelists did not provide presentations. Rather the session was a discussion focusing on pressing issues facing US DOE Environmental Management (EM) sites and how the program is moving forward. The session began by momentarily reflecting on a success: the three Tank Waste Treatment Facilities will soon be coming online. These three facilities will treat as much waste in two years as was treated in the past 30 years. That is a significant milestone in progress. Also, safety enhancements at WIPP are a big success. The panelists noted that there will be challenges with the victories of the startup of the Waste Treatment Facilities. Treating tank waste is the last EM mission and will help with the relationships with the stakeholders, regulators and Congress. One of the biggest challenges will be moving back to full operation mode. Although the operation will increase productivity of reducing/managing the tank waste, it will decrease life cycle cost and lessen years of cleanup. The challenge will be having people understand that EM will be operating 24/7 and that maintenance will be key in keeping the operations. EM will be working together with the regulators to show tangible progress that will be effective and efficient. By working together, they can find opportunities/technologies for streamlining and accelerating cleanup. Recent changes to the regulatory framework between DOE-EM Los Alamos Field Office and New Mexico Environmental Department have helped cleanup in Los Alamos. Every year EM and the agencies review progress and come to agreements on the next work priorities. At Savannah River Site, the Federal Facility Agreement among EM, South Carolina Department of Health and Environmental Control, and Environmental Protection Agency (EPA) has developed a small core team that work are shared by together in resolving issues that come up. The discussion moved on to how progress can be made by sharing lessons learned among the contractors. An example of how these learned lessons can be shared is that key representatives from projects across the EM sites participate in various forums to review/contribute to the work planned. Other opportunities can be by temporary assignments of DOE staff to gain experience and by monthly EM Headquarters report that provide the lessons learned across the EM sites. The panelists discussed how skyline changes are the best examples of tangible progress. Decontamination and Demolition (D and D) of structures are the easiest tangible progress that can be seen. Whereas tank waste cleanup is the hardest to see. Disposal of waste onsite versus offsite is another challenge, and timely discussions are necessary to get to the best value to the government.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Progress on Pu-238 Production at INL From February 2022 to December 2022

Idaho National Laboratory (INL) has continued to qualify irradiation positions in the Advanced Test Reactor (ATR) for Pu-238 production to support NASA deep space missions. Over the past year, INL qualified Np-237 targets for ATR’s North East Flux Trap (NEFT), inner A, and H positions. Work has begun to requalify the South Flux Trap (SFT) and qualify the East Flux Trap (EFT) for the ATR GEN I target and is midway through the qualification process. This paper gives an overview of operational and technical activities from February 2022 to December 2022.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

ORNL Infrastructure: Status Update on BEA Research Reactor Cask Planning Activities Completed During FY 2025

This report documents Oak Ridge National Laboratory’s (ORNL’s) FY 2025 progress toward establishing readiness to use the Battelle Energy Alliance (BEA) Research Reactor (BRR) shipping cask in support of the Nuclear Science User Facilities (NSUF). The BRR cask will provide a new shared infrastructure capability for transporting irradiated fuels and materials between ORNL and Idaho National Laboratory (INL), thereby supporting NSUF’s mission of enabling user access to advanced nuclear research facilities. In FY 2025, ORNL advanced the regulatory, contractual, safety review, and planning activities necessary to qualify its facilities and staff for BRR cask handling. Although loaded shipments were delayed due to fabrication lead times for Orano Federal Services LLC’s internal basket hardware, the program progressed to the point where an empty cask dry run is scheduled for October 2025. This dry run represents a critical step in demonstrating ORNL’s ability to receive, handle, and return the BRR cask.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Progress on Pu-238 production at Idaho National Laboratory from February 2022 to July 2023

Idaho National Laboratory (INL) has continued to qualify irradiation positions in the Advanced Test Reactor (ATR) for Pu-238 production to support NASA deep space missions. Over the past year, INL qualified Np-237 targets for ATR’s North East Flux Trap (NEFT), Inner-A and H positions. Work has begun to requalify the South Flux Trap (SFT) and to qualify the East Flux Trap (EFT) for the ATR GEN I target and is midway through the qualification process. This paper gives an overview of operational and technical activities from February 2022 to July 2023.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2022

Deployment of the HFIRCON transport and depletion tool for plutonium-238 production studies

Irradiation of {sup 237}Np-bearing targets in Oak Ridge National Laboratory's (ORNL) High Flux Isotope Reactor (HFIR) results in the efficient production of {sup 238}Pu, which, in the form of heat source PuO{sub 2}, is used as a reliable power source for deep-space and planetary NASA missions. A technology demonstration subproject was initiated at ORNL in 2011 to develop and implement the technology required to establish a {sup 238}Pu supply chain. A systematic progression of NpO{sub 2}/Al cermet (20 vol.% NpO{sub 2}) activities to date has successfully demonstrated target fabrication, irradiation, and chemical recovery processes. Recent program tasks have included the development of the HFIRCON transport and depletion tool for efficient reactor physics analyses and the evaluation of increased NpO{sub 2} loadings (i.e., beyond 20 vol.%) and NpN-based targets. This paper documents the deployment of the HFIRCON code to assess various Np concentrations in NpO{sub 2}- and NpN-based targets in HFIR's inner small vertical experiment facilities. Results indicate that {sup 238}Pu production and quality can be enhanced with increased Np loadings; however, target conversion rates are reduced. The results recorded in this paper, thermal and material balance evaluations, and testing requirement planning will be used to determine whether increased NpO{sub 2} loadings or NpN-based targets will be further considered. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Efficient, Predictive Tomography of Multi-Qubit Quantum Processors

After decades of R&D, quantum computers comprising more than 2 qubits are appearing. If this progress is to continue, the research community requires a capability for precise characterization (“tomography”) of these enlarged devices, which will enable benchmarking, improvement, and finally certification as mission-ready. As world leaders in characterization -- our gate set tomography (GST) method is the current state of the art – the project team is keenly aware that every existing protocol is either (1) catastrophically inefficient for more than 2 qubits, or (2) not rich enough to predict device behavior. GST scales poorly, while the popular randomized benchmarking technique only measures a single aggregated error probability. This project explored a new insight: that the combinatorial explosion plaguing standard GST could be avoided by using an ansatz of few-qubit interactions to build a complete, efficient model for multi-qubit errors. We developed this approach, prototyped it, and tested it on a cutting-edge quantum processor developed by Rigetti Quantum Computing (RQC), a US-based startup. We implemented our new models within Sandia’s PyGSTi open-source code, and tested them experimentally on the RQC device by probing crosstalk. We found two major results: first, our schema worked and is viable for further development; second, while the Rigetti device is indeed a “real” 8-qubit quantum processor, its behavior fluctuated significantly over time while we were experimenting with it and this drift made it difficult to fit our models of crosstalk to the data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

International Collaboration Activities in Geologic Disposal Research (FY24 Progress Report)

This report describes the FY24 status of international collaboration regarding geologic disposal research in the Office of Spent Fuel and Waste Science and Technology (SFWST) in the Department of Energy’s (DOE) Office of Nuclear Energy (NE). The mission of SFWST is to provide confidence in the safe long-term management of the nation’s spent nuclear fuel and high-level radioactive waste by reducing uncertainty and advancing technology for extended storage, transportation, and geologic disposal.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Solving Hard Problems with AI: Dramatically Accelerating Drug Discovery Through A Unique Public-Private Partnership

Dramatic disruptions in technologies can result in the creation of new markets, the emergence of new paradigms, and the displacement of entrenched approaches and business models. This can be especially pronounced when multiple technologies converge to create something new. Today, artificial intelligence (“AI”) is acting as an accelerant for such technology transformations. Still, the scope and depth of its impact will depend in part on our ability to address challenging problems that are surfacing without solutions. Industries will pursue AI for their corporate missions and to create shareholder value. But the timescales and applications can be incongruent with important public needs and with the longer research horizons required to make real progress. Public-Private partnerships are an important mechanism for tackling these rapidly emerging new, complex, challenging problems. Finally, this paper examines our experience gained by creating a unique public-private partnership to apply AI to one example of a hard problem that is particularly timely today-- dramatically accelerating drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Progress on Pu-238 Production at Idaho National Laboratory From February 2022 to December 2022

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2023

Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute

At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. The International Neuroinformatics Coordinating Facility (INCF) was inaugurated in 2007 as an initiative to improve this situation. The INCF has since pursued its mission to help the development of standards and best practices. In a community paper published this very same year, Brette et al. tried to assess the state of the field and to establish a scientific approach to simulation technology, addressing foundational topics, such as which simulation schemes are best suited for the types of models we see in neuroscience. In 2015, a Frontiers Research Topic “Python in neuroscience” by Muller et al. triggered and documented a revolution in the neuroscience community, namely in the usage of the scripting language Python as a common language for interfacing with simulation codes and connecting between applications. The review by Einevoll et al. documented that simulation tools have since further matured and become reliable research instruments used by many scientific groups for their respective questions. Open source and community standard simulators today allow research groups to focus on their scientific questions and leave the details of the computational work to the community of simulator developers. A parallel development has occurred, which has been barely visible in neuroscientific circles beyond the community of simulator developers: Supercomputers used for large and complex scientific calculations have increased their performance from ~10 TeraFLOPS (10 13 floating point operations per second) in the early 2000s to above 1 ExaFLOPS (10 18 floating point operations per second) in the year 2022. This represents a 100,000-fold increase in our computational capabilities, or almost 17 doublings of computational capability in 22 years. Moore's law (the observation that it is economically viable to double the number of transistors in an integrated circuit every other 18–24 months) explains a part of this; our ability and willingness to build and operate physically larger computers, explains another part. It should be clear, however, that such a technological advancement requires software adaptations and under the hood, simulators had to reinvent themselves and change substantially to embrace this technological opportunity. It actually is quite remarkable that—apart from the change in semantics for the parallelization—this has mostly happened without the users knowing. The current Research Topic was motivated by the wish to assemble an update on the state of neuroscientific software (mostly simulators) in 2022, to assess whether we can see more clearly which scientific questions can (or cannot) be asked due to our increased capability of simulation, and also to anticipate whether and for how long we can expect this increase of computational capabilities to continue.

biophysically detailed models↗

DOE OSTI2024

Electronic structure simulations in the cloud computing environment

The transformative impact of modern computational paradigms and technologies, such as high-performance computing, quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages, such as NWChem, NWChemEx, SPEC, ExaChem, and FLOSIC codes on the Azure Quantum Element (AQE) Microsoft cloud services. We pay particular attention to the intricate workflows for performing composite chemistry simulations, associated data curation, and mechanisms for accuracy assessment, as defined by the enabling cloud Computational Chemistry as a Service (CCaaS). Our focus also extends to Arrows' automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities (LCFs).

computational chemistry, electronic structure, Clo↗