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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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At least 19 records

Fusion Energy Sciences Network Requirements Review. Final Report, April - October 2021

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Visioning Energy: Science Fiction Author-Energy Researcher Collaboration Workshop Recap

The Visioning Energy: Science Fiction Author-Energy Researcher Collaboration Workshop brought together speculative fiction authors and NREL researchers to examine possible scenarios of the future. The goals of the workshop were to support out-of-the-box thinking and creative future visioning for the researchers and to provide authors with insights into the latest clean energy technologies and their potential. This report outlines the proceedings of the workshop and insights from the collaborative brainstorming activity, highlighting themes and opportunities for further exploration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Basic Energy Sciences Roundtable: Foundational Science to Accelerate Nuclear Energy Innovation

Energy security, availability, and reliability are among the greatest challenges facing the nation and the planet. An abundant potential source of energy resides in the fundamental atomic building blocks of the universe in the form of nuclear fission and fusion reactions. In fact, energy from nuclear fission currently provides the majority of the world’s zero-carbon electricity, and future fusion energy systems offer great promise; carbon-free nuclear energy technologies can be key to the world’s decarbonized energy future. Although contemporary fission systems use well-established technologies to supply safe and efficient baseload power, they could be more fuel efficient and less costly. Moving beyond massive light-water fission reactors to a variety of advanced nuclear systems—which will vary in size and operate in extremes of temperature, corrosivity, and other parameters—will place stringent conditions on materials and chemical systems. New demands will be placed on the coolants and solvents, the materials, and the monitoring tools used in these reactors. Fusion-based nuclear energy will require superior materials to withstand extremely high temperatures, plasma exposure, radiation damage, and implanted gases. The advantages associated with these new fission and fusion technologies will be realized only through continued advancements in the fundamental science underpinning our knowledge of the physics and chemistry of nuclear systems gained via improved experimental and computational methods. In July 2022, the U.S. Department of Energy’s Office of Basic Energy Sciences—in coordination with the Offices of Nuclear Energy, Fusion Energy Sciences, and Advanced Scientific Computing Research—held a virtual roundtable titled “Foundational Science to Accelerate Nuclear Energy Innovation” to discuss the scientific and technical barriers for advanced nuclear energy systems. Five priority research opportunities were identified to address these scientific and technical challenges and to accelerate progress toward the realization of next-generation fusion and fission energy systems. The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous Basic Energy Sciences workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Using chiral-induced spin selectivity as a tool to improve materials and processes for energy science

Studies relating to electrical energy conversion, storage, and generation, date back to the 19th century, however only in recent years have scientists begun to investigate the impact of the electron spin on these processes. Control and manipulation of the electron spin, an intrinsically quantum property of matter, opens new approaches to addressing energy science challenges. Furthermore, the chiral-induced spin selectivity (CISS) effect is enabling for this effort, because of the control over the transport and generation of both pure spin currents and spin-polarized charge currents. In this review article, we begin with a brief introduction on design strategies for the implementation of CISS in materials and then describe recent studies that demonstrate how CISS can be used to improve electrocatalysis and spintronics. Lastly, we conclude with forward-looking thoughts on the next steps for leveraging CISS in energy science.

Batteries↗

Basic Energy Sciences Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Between March and September 2022, ESnet and the Office of Basic Energy Sciences (BES) of the DOE SC organized an ESnet requirements review of BES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗

Assessment of Sandia's 2021 Pilot Program for Research Traineeships to Broaden and Diversify Fusion Energy Science: Development and Rapid Screening of Refractory Multi-Principal Elemental Composites for Plasma Facing Components

The Fusion Energy Sciences office supported “A Pilot Program for Research Traineeships to Broaden and Diversify Fusion Energy Sciences” at Sandia National Laboratories during the summer of 2021. This pilot project was motivated in part by the Fusion Energy Sciences Advisory Committee report observation that “The multidisciplinary workforce needed for fusion energy and plasma science requires that the community commit to the creation and maintenance of a healthy climate of diversity, equity, and inclusion, which will benefit the community as a whole and the mission of FES”. The pilot project was designed to work with North Carolina A&T (NCAT) University and leverage SNL efforts in FES to engage underrepresented students in developing and accessing advanced material solutions for plasma facing components in fusion systems. The intent was to create an environment conducive to the development of a sense of belonging amongst participants, foster a strong sense of physics identity among the participants, and provide financial support to enable students to advance academically while earning money. The purpose of this assessment is to review what worked well and lessons that can be learned. We reviewed implementation and execution of the pilot, describe successes and areas for improvement and propose a no-cost extension of the pilot project to apply these lessons and continue engagement activities in the summer of 2022.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Report on New and Upgraded National User Facilities in Basic Energy Sciences

On December 1, 2023, the Director of the of the Office of Science (SC) charged all the Department of Energy Office of Science Federal Advisory Committees to respond to look toward the scientific horizon and identify what new or upgraded facilities will best serve the SC community needs in the next ten years (2024-2034). This report by the Basic Energy Sciences Advisory Committee (BESAC) assess facilities for the Basic Energy Sciences (BES) program.

07 ISOTOPE AND RADIATION SOURCES↗

Center for Bio-Inspired Energy Science (CBES) (Final Report)

The goal of the EFRC on Bio-Inspired Energy Science (CBES) has been to create and understand materials and systems inspired by biology, seeking to innovate on energy utilization and its environmental implications. In the most recent two-year funding cycle (2022–2024), we extended strategic projects from each of the five tasks from the previous cycle. This work offers important advances in how soft matter can be used to convert and store energy. We briefly summarize selected key accomplishments from each task below.

10 SYNTHETIC FUELS↗

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend on the science goals. We illustrate the utility of this suite by showing examples for the validation of cosmoDC2, the extragalactic catalog recently released for the LSST DESC second Data Challenge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards Resilient Near Real-Time Analysis Workflows in Fusion Energy Science

Nuclear fusion holds the promise of an endless source of energy. Several research experiments across the world and joint modeling and simulation efforts between the nuclear physics and high performance computing communities are actively preparing the operation of the International Thermonuclear Experimental Reactor (ITER). Both experimental reactors and their simulated counterparts generate data that must be analyzed quickly and in a resilient way to support decision making for the configuration of subsequent runs or prevent a catastrophic failure. However, the cost if the traditional techniques used to improve the resilience of analysis workflows, i.e., replicating datasets and computational tasks, becomes prohibitive with explosion of the volume of data produced by modern instruments and simulations. Therefore, we advocate in this paper for an alternate approach based on data reduction and data streaming. The rationale is that by allowing for a reasonable, controlled, and guaranteed loss of accuracy it becomes possible to transfer smaller amounts of data, shorten the execution time of analysis workflows, and lower the cost of replication to increase resilience. We develop our research and development roadmap towards resilient near real-time analysis workflows in fusion energy science and present early results showing that data streaming and data reduction is a promising way to speed up the execution and improve the resilience of analysis workflows.

Suter, Fred↗

Basic Energy Sciences Roundtable: Foundational Science for Carbon-Neutral Hydrogen Technologies

Basic research to identify and understand the fundamental principles governing hydrogen processes is essential for achieving a carbon-neutral, hydrogen-based energy and chemical infrastructure. In August 2021, the Office of Basic Energy Sciences (BES)—in coordination with the US Department of Energy (DOE) technology Offices of Energy Efficiency and Renewable Energy, Fossil Energy and Carbon Management, and Nuclear Energy—held a roundtable titled, “Foundational Science for Carbon-Neutral Hydrogen Technologies,” to discuss the scientific and technical barriers for carbon-neutral hydrogen production, storage, and utilization. Four priority research opportunities (PROs) were identified to address these scientific and technical challenges and accelerate progress toward the realization of energy-efficient, carbon-neutral cycles for hydrogen processes. The PROs are as follows: Discover and Control Materials and Chemical Processes to Revolutionize Electrolysis Systems; Manipulate Hydrogen Interactions to Harness the Full Potential of Hydrogen as a Fuel; Elucidate the Structure, Evolution, and Chemistry of Complex Interfaces for Energy- and Atom-Efficiency; and Understand and Limit Degradation Processes to Enhance the Durability of Hydrogen Systems.

08 HYDROGEN↗

Quantum Information for Fusion Energy Sciences (Final Technical Report)

The simulation of plasma dynamics is a critical area of Fusion Energy Sciences (FES) due to it’s usefulness in predicting, controlling, and confining plasmas in the context of potential fusion reactors. The simulation of plasmas is a computationally difficult problem in both classical and quantum physics, motivating investigation into the potential of quantum computers to simulate these systems. This project took several concrete steps towards this goal by developing tools for improving the control, characterization, and calibration of quantum gates on a superconducting quantum computer, developing error suppression and mitigation tools to reduce errors on the quantum computer, and utilizing these advancements to simulate reduced models of plasma dynamics on the quantum computer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Quantum Information for Fusion Energy Sciences (Final Technical Report)

The simulation of plasma dynamics is a critical area of Fusion Energy Sciences (FES) due to it’s usefulness in predicting, controlling, and confining plasmas in the context of potential fusion reactors. The simulation of plasmas is a computationally difficult problem in both classical and quantum physics, motivating investigation into the potential of quantum computers to simulate these systems. This project took several concrete steps towards this goal by developing tools for improving the control, characterization, and calibration of quantum gates on a superconducting quantum computer, developing error suppression and mitigation tools to reduce errors on the quantum computer, and utilizing these advancements to simulate reduced models of plasma dynamics on the quantum computer. In order to efficiently simulate plasma physics, an optimal control method which synthesizes, directly at the pulse level, any quantum gate on qubit and qutrit systems was developed. Using four superconducting transmon quantum processors at Rigetti and LLNL, it was demonstrated that any arbitrary quantum gate on qubits and qutrits could be implemented with high fidelity, leading to a significantly reduced length of a gate sequence. A problem of interest in FES is the nonlinear optical process of laser pulse compression within a plasma. Since quantum physics is linear, simulating nonlinear operations is not naturally feasible on a quantum computer, however it is possible to simulated a quantized version of the nonlinear process. A quantization approach to convert nonlinear wave-wave interaction problems to Hamiltonian simulation problems was developed and demonstrated using two qubits on a Rigetti device. In this experiment, a number of error suppression and mitigation techniques were investigated to determine how best to utilize the finite quantum resources. This study provides an example of how plasma problems may be solved on near-term, noisy quantum computing platforms and identified a promising set of techniques. Building on the insights of these experiments, the investigation turned to linear electron-plasma wave physics. A connection was identified between a local one-dimensional lattice spin model and linear wave phenomena, allowing a plasma physics problem to be efficiently mapped to the quantum computer. In this framework, reflection and transmission of plasma waves at a sharp boundary was studied, as well as the propagation of waves through an inhomogeneous plasma medium. In addition to the suite of error suppression and mitigation techniques developed, this experiment introduced the use of a digital-analog gate scheme designed to efficiently simulate the plasma Hamiltonian. With hardware available at the conclusion of the project, simulation at the scale of 9 qubits and 15 timesteps (60 entangling layers) was achieved.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Basic Energy Sciences Roundtable: Foundational Science for Carbon Dioxide Removal Technologies (Brochure)

Basic research to identify and understand the fundamental principles governing carbon dioxide removal processes of capture, conversion, and storage is essential for achieving zero carbon emissions by 2050. In March 2022, the Office of Basic Energy Sciences (BES)—in coordination with the US Department of Energy (DOE) technology Offices of Energy Efficiency and Renewable Energy, Fossil Energy and Carbon Management, and Nuclear Energy—held a roundtable titled, “Foundational Science for Carbon Dioxide Removal Technologies,” to discuss the scientific and technical barriers for CO 2 capture, conversion, and storage. Five priority research opportunities were identified to address these scientific and technical challenges and accelerate progress toward the realization of zero carbon emissions. The PROs are as follows: 1) Master Interfacial Processes of CO2 Transport and Reactivity Across Multiple Length and Time Scales; 2) Create Materials that Simultaneously Exhibit Multiple Properties for CO 2 Capture and Release or Conversion; 3) Discover Unconventional Pathways for Energy-Efficient CO 2 Capture, Release, and Conversion; 4) Control Multiphase Interactions Required for CO 2 Conversion into Minerals and Materials; and 5) Achieve Predictive Understanding of Coupled Processes in Complex Subsurface Geologic Systems for Secure Carbon Storage.

36 MATERIALS SCIENCE↗

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↗

PANDA-FES: Portable and Adaptable Neutron Diagnostics for Advancing Fusion Energy Science

Nuclear fusion is a potential source of carbon-free electricity with many concepts in development. The Portable and Adaptable Neutron Diagnostics for Advancing Fusion Energy Science (PANDA-FES) suite has been deployed since 2021 to measure neutron yield, energy, and spatiotemporal source location at two different Z-pinch fusion devices. This diagnostic can be used at a variety of facilities pursuing fusion in the magnetic, inertial, and magneto-inertial regimes. These different regimes have a wide range of time scales from less than 100 ns to a few μ s, neutron yields from 10 6 to 10 11 , and noise environments. Neutron yield is measured through activation of 79 Br and 89 Y with calibrated detectors. Temporal, spatial, and energy dependence of neutrons is measured with scintillators coupled to photomultiplier tubes (PMTs). Experimental setups and data analysis methods have been developed for these conditions. Finally, neutron yield, neutron energy anisotropy, and spatiotemporal evolution of the source have been measured.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Quantum computing for fusion energy science applications

This is a review of recent research exploring and extending present-day quantum computing capabilities for fusion energy science applications. We begin with a brief tutorial on both ideal and open quantum dynamics, universal quantum computation, and quantum algorithms. Then, we explore the topic of using quantum computers to simulate both linear and nonlinear dynamics in greater detail. Because quantum computers can only efficiently perform linear operations on the quantum state, it is challenging to perform nonlinear operations that are generically required to describe the nonlinear differential equations of interest. In this work, we extend previous results on embedding nonlinear systems within linear systems by explicitly deriving the connection between the Koopman evolution operator, the Perron–Frobenius evolution operator, and the Koopman–von Neumann evolution (KvN) operator. We also explicitly derive the connection between the Koopman and Carleman approaches to embedding. Extension of the KvN framework to the complex-analytic setting relevant to Carleman embedding, and the proof that different choices of complex analytic reproducing kernel Hilbert spaces depend on the choice of Hilbert space metric are covered in the appendixes. Finally, we conclude with a review of recent quantum hardware implementations of algorithms on present-day quantum hardware platforms that may one day be accelerated through Hamiltonian simulation. We discuss the simulation of toy models of wave–particle interactions through the simulation of quantum maps and of wave–wave interactions important in nonlinear plasma dynamics.

Joseph, I. (ORCID:0000000255400840)↗

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗