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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials

Optical metamaterial device design has enjoyed a long track of success over the past 50 years leading to the manipulation of light over a wide range of wavelengths spanning the ultraviolet to the far infrared. The manipulation of light over such wavelengths has already led to many technological advancements such as the design of selective radiative absorbers for solar energy, daytime passive cooling using deep space, and optical invisibility cloaks for defense applications. Further disruptive advancements in energy, defense, computing, and biomedical fields could be enabled or enhanced by future optical metamaterial devices. These technologies could lead to increased energy efficiency and hence reduced national primary energy consumption, cheap long duration energy storage, and next generation solid-state heat engines. But historically the methods to invent and develop all of these devices have been time- consuming and based mostly on intuition and iteration. Finding an optimal design can take years.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials

Optical metamaterial device design has enjoyed a long track of success over the past 50 years leading to the manipulation of light over a wide range of wavelengths spanning the ultraviolet to the far infrared. The manipulation of light over such wavelengths has already led to many technological advancements such as the design of selective radiative absorbers for solar energy, daytime passive cooling using deep space, and optical invisibility cloaks for defense applications. Further disruptive advancements in energy, defense, computing, and biomedical fields could be enabled or enhanced by future optical metamaterial devices. These technologies could lead to increased energy efficiency and hence reduced national primary energy consumption, cheap long duration energy storage, and next generation solid-state heat engines. But historically the methods to invent and develop all of these devices have been time- consuming and based mostly on intuition and iteration. Finding an optimal design can take years. In this project we developed a machine learning-based algorithm capable of automatically generating device designs to produce desired optical properties, reducing the design cycle life in certain situations to be almost instantaneous.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗
DOE OSTI2021

Future Battery Material Demand Analysis Based on U.S. Department of Energy R&D Targets

The U.S. Department of Energy’s Vehicle Technologies Office (VTO) supports research, development, and deployment of efficient, sustainable transportation technologies that will improve energy efficiency and fuel economy, and enable America to use less petroleum. To accelerate the development and adoption of new technologies, VTO has developed specific targets for a wide range of powertrain components, including the energy storage system. In this study, we use Autonomie, Argonne National Laboratory’s (Argonne’s) vehicle system simulation tool to evaluate future energy storage requirements (power, energy, etc.) for different vehicle classes, powertrains, component technologies and timeframes. BatPac, Argonne’s tool dedicated to energy storage pack design and costs, is then used to quantify the materials required for each pack. Market penetrations are then used to estimate the overall material demand worldwide and in the United States, with or without recycling. The results demonstrate that the positive impact of VTO research and development will lead to significant reduction in material compared to business-as-usual due to new anode and cathode designs, along with acceleration in battery cell chemistry penetrations. In terms of material demands, it is observed that lithium demand reaches about 80,000 tons (by a factor of 42–45), nickel demand reaches about 500,000 tons (by a factor of 47–56), manganese demand reaches about 30,000–50,000 tons (by a factor of 20–34), and cobalt demand reaches about 30,000 tons (by a factor of 13–28) in the future by 2050. The individual material demand per unit energy, however, decreases significantly in the future due to advances in VTO research and development activities. The increase in battery material demands is mostly driven by increased electrified vehicle fleet penetration in the markets.

25 ENERGY STORAGE↗
DOE OSTI2020

A charge-density-based general cation insertion algorithm for generating new Li-ion cathode materials

Future lithium (Li) energy storage technologies, in particular solid-state configurations with a Li metal anode, opens up the possibility of using cathode materials that do not necessarily contain Li in its as-made state. To accelerate the discovery and design of such materials, we develop a general, chemically, and structurally agnostic methodology for identifying the optimal Li sites in any crystalline material. For a given crystal structure, we attempt multiple Li insertions at symmetrically in-equivalent positions by analyzing the electronic charge density obtained from first-principles density functional theory. In this report, we demonstrate the effectiveness of this procedure in successfully identifying the positions of the Li ion in well-known cathode materials using only the empty host (charged) material as guidance. Furthermore, applying the algorithm to over 2000 candidate cathode empty host materials we obtain statistics of Li site preferences to guide future developments of novel Li-ion cathode materials, particularly for solid-state applications.

25 ENERGY STORAGE↗
DOE OSTI2024

Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning

Knowing the rate at which particle radiation releases energy in a material, the “stopping power,” is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the “Bragg Peak,” varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multimodal In Situ Characterization Uncovers Unexpected Stability of a Cobalt Electrocatalyst for Acidic Sustainable Energy Technologies

An accelerated development of durable and affordable sustainable energy technologies is often hindered by a limited understanding of how non-precious materials within these systems degrade. In acidic proton exchange membrane fuel cells and water electrolyzers, metallic cobalt (Co) is considered an unstable component that is often combined with precious metals or other stabilizers. To understand the mechanisms behind Co instability, we employ an experimental platform that quantifies dissolution with on-line inductively coupled plasma mass spectrometry and product formation with electrochemical mass spectrometry during electrochemical testing, along with ex- situ characterization. Under varied conditions (electrocatalysis, time, gas-type saturation, and ion concentration), windows of Co stability are observed that are different than predicted with classical chemical thermodynamics, suggesting new stabilization and degradation mechanisms than previously understood. Notably, Co is active for the hydrogen evolution reaction (HER), with prolonged stability that is ~300 mV different than thermodynamically projected. Additionally, in an oxygenated environment, Co concurrently performs the HER and oxygen reduction reaction (ORR) yet undergoes different morphology changes and dissolution mechanisms. Interestingly, in the absence of electrocatalysis, there is a 22x decrease in dissolution in an oxygen-free environment, proposing a route to decrease Co losses during device shutdown protocols. Lastly, under more extreme operating conditions, Co becomes stable after a substantial amount of dissolution, suggesting that high concentrations of Co 2+ ions in the microenvironment induce the formation of a stable CoHO 2 surface. Altogether, these results can be leveraged to improve the design and development of more robust and cost-effective sustainable energy technologies, as well as promote strategic strategies for prolonged material utilization.

Dissolution↗
DOE OSTI2025

NSTTF Voucher Program RPPR-1 (Final Report)

Sandia issued a Request for Proposals (RFP) to solicit proposals from industry, academia, research laboratories, government agencies, and individuals on the use of the National Solar Thermal Test Facility (NSTTF) to increase CSP technology market adoption across the United States. The voucher funds will be used to cover the cost of NSTTF test facilities usage and technical staff support for analysis, design and test planning and execution. Sandia will collect submitted proposals, coordinate their review through DOE SETO, and work in partnership or under contract with the applicants to complete the funded research. Through this program, participants will be supported in their use of the world class facilities and expertise available at the NSTTF at Sandia in Albuquerque, NM to accelerate the advancement of CST technologies toward meeting 2030 SETO goals for CSP. The goals of this semi-annual reporting period were to complete all administrative tasks and contracting, begin testing on three of the vouchers, and report on initial findings. The fourth voucher (University of Michigan) is predicated on the results of an ongoing heat exchanger test that is expected to conclude by the end of FY22.

14 SOLAR ENERGY↗
DOE OSTI2025

High-Power Targetry for Muon Production

The production of high-intensity muon beams is crucial for advancing particle and accelerator physics, both now and in the future. Achieving these high-intensity goals requires overcoming significant challenges in high-power targetry. Here, we will outline the key challenges and explore the selection of target materials, focusing on muon production and susceptibility to radiation damage -- key factors for optimal target designs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Reconstruction of neutrino events in the Accelerator Neutrino Neutron Interaction Experiment. Part I

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) was designed to reconstruct neutrino events from the Fermilab Booster Neutrino Beam (BNB) with the parallel goals of measuring neutron production in interactions with oxygen and serving as a testbed for new technology. The ANNIE detector consists of a 26-ton water Cherenkov target tank instrumented with conventional photomultiplier tubes (PMTs), a downstream tracking muon spectrometer, and an upstream double wall of plastic scintillator to serve to veto charged particles incoming from neutrino events that occur upstream of the experimental setup. ANNIE has also deployed multiple Large-Area Picosecond PhotoDetectors (LAPPDs) and a test vessel of water-based liquid scintillator (WbLS). This paper describes the event reconstruction performance of the detector before implementation of these novel technologies, which will serve as a baseline against which their impact can be measured. That said, even the techniques used for event reconstruction using only the conventional PMT array and muon spectrometer are significantly different than those used in other water Cherenkov detectors due to the small size of ANNIE (which makes nanosecond-scale timing not as useful as in a large detector) and the availability of reconstruction information from the tracking muon spectrometer. We demonstrate that combining the information from these two elements into a single fit using only pattern recognition yields a muon vertex uncertainty of 60 cm, a directional uncertainty of 13.2 degrees, and energy reconstruction uncertainty of about 10% for BNB muon neutrino Charged Current Zero Pion (CC0π) events.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Higher order mode analysis of a 915 MHz 2-cell cavity for a prototype industrial accelerator

A possible solution to reduce the complexity posed by the cryogenic systems in a superconducting RF accelerator for industrial applications, is to capitalize on the advances achieved by the Nb3Sn superconducting RF technology, as well as the feasibility of a reliable 4 K cooling system, based on commercial cryocoolers. Following this philosophy, the conceptual design for a prototype, conduction-cooled, 4 MeV, 20 kW SRF electron linac, is being developed at Jefferson Lab. Such design is based on a 915 MHz two-cell Nb3Sn cavity. In this contribution, we present the proposed cavity design, including the fundamental power coupler, and the analysis of the Higher Order Modes, using numerical simulations to estimate the potentially dangerous modes and to provide the requirements for damping for reliable operations with a cryocooler.

Rimmer, Robert↗
DOE OSTI2026

Exploring Offshore Regional Atlantic Carbon Storage and Infrastructure for Eastern USA (Supporting Communities and Industry for Mid-Atlantic Offshore Carbon Storage Hub)

This final technical report presents a summary of work completed for the project Supporting Communities and Industry for Mid-Atlantic Offshore Carbon Storage Hub (FE0032407), which has been designated EXPLORE-ACS (Exploring Potential Long-Term Offshore Regional Atlantic Carbon Storage). The project is part of the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL) funding opportunity announcement 2799 for Regional Initiatives to Accelerate Carbon Capture, Utilization, and Storage (CCUS) Deployment. The objective of the DOE 2799 program is to identify and address the challenges facing commercial deployment of carbon capture, transport, and storage in specific regions of the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Miniaturize the Redox Flow Battery for Accelerated Materials Discovery and Development

Redox flow batteries are a promising technology for grid-scale energy storage. The aqueous organic redox flow battery is of particular interest for its potentially low material cost and sustainability. Developing novel organic active material for flow battery electrolytes typically entails molecular engineering toward desired properties, necessitating organic synthesis. In a research laboratory setting, the synthesis of specifically designed organic molecules featuring targeted functional groups is time and resources intensive. In the past, synthesizing materials required for battery testing has often required gram-scale production, presenting considerable constraints on the pace of novel organic material discovery. In this report, we introduce a miniaturized cell design that mandates only milligram-scale material synthesis while yielding testing outcomes equivalent or superior to those reported with other commercially available or homemade flow cells in the literature. The test results under various pH conditions validate the scale-down strategy to accelerate the flow battery material discovery and development using the newly designed mini cell. This approach offers researchers an efficient means to notably reduce the time and resources required to develop novel materials for flow batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Discovery of chalcogenides structures and compositions using mixed fluxes

Advancements in many modern technologies rely on the continuous need for materials discovery. However, the design of synthesis routes leading to new and targeted solid-state materials requires understanding of reactivity patterns. Advances in synthesis science are necessary to increase efficiency and accelerate materials discovery. We present a highly effective methodology for the rational discovery of materials using high-temperature solutions or fluxes having tunable solubility. This methodology facilitates product selection by projecting the free-energy landscape into real synthetic variables: temperature and flux ratio. We demonstrate the effectiveness of this technique by synthesizing compounds in the chalcogenide system of A(Ba)-Cu-Q(O) (Q = S or Se; A = Na, K or Rb) using mixed AOH/AX (A = Li, Na, K or Rb; X = Cl or I) fluxes. We present 30 unreported compounds or compositions, including more than ten unique structural types, by systematically varying the temperature and flux ratios without requiring changing the proportions of starting materials. Additionally, we found that the structural dimensionality of the compounds decreases with increasing reactant solubility and temperature. This methodology serves as an effective general strategy for the rational discovery of inorganic solids.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Laser Powder Bed Fusion Additive Manufacture Nb1Zr Development

Next generation fission and fusion nuclear reactors require materials that can withstand operating temperatures greater than 500 °C, neutron irradiation doses of up to 200 displacements per atom (dpa), and potentially corrosive coolants such as the alkali liquid metals sodium, lithium, and NaK (Na33K eutectic alloy). Refractory alloys, such as Nb1Zr (Nb-1wt%Zr) and Molybdenum alloy TZM (Mo-0.5wt%Ti-0.08wt%Zr) have been traditionally considered viable candidates for advanced fission and fusion reactor concepts. However, it is relatively difficult to generate complex geometries of interest from these alloys using traditional manufacturing methods. In addition, there needs to be a concentrated effort to address refractory metal challenges at elevated temperature operation. In order to generate complex geometries of interest, modern manufacturing techniques are considered to increase the technological readiness level (TRL), cost-effectiveness, and schedule savings. This work focused on the continued development of laser powder bed fusion (L-PBF) additive manufacturing (AM) to improve both design flexibility, evaluate microstructure and properties, and ultimately accelerate the TRL and qualification of these processes and alloys for components to potentially be put into service. Niobium alloy Nb1Zr was identified through a down-selection process outlined in previous reports as a candidate to develop in L-PBF AM. Historically, Nb1Zr had been explored for high temperature fast spectrum fission reactors for both terrestrial and space applications. Molybdenum alloy TZM has also been considered for these reactor concepts due to exceptional high-temperature strength, creep resistance, and stability under irradiation. L-PBF AM of TZM has previously been investigated at LANL under the Microreactor program, NASA, ORNL, and in academia. However, due to the crack prone nature of TZM, L-PBF AM of TZM resulted in significant microcracking and additional development is required to pursue viable maturation. Other AM methods have been found to be more successful in printing TZM, and those alternatives approaches are discussed in this effort. The efforts detailed in this report focused on continued development of Nb1Zr through L-PBF and development of TZM via L-PBF and electron powder bed fusion (E-PBF). The objective of this work was to further the development of these AM techniques for the chosen refractory alloys, elucidating and addressing associated challenges through characterization of several demonstration builds. At LANL, Nb1Zr builds were completed using an EOS M290 and M400 machines, and a refractory alloy-dedicated L-PBF system, the Xact Metal XM200G, was installed. The XM200G primary purpose was to do the Nb1Zr parameter development process; however, due to difficulties associated with the machine installation and qualification process, it was decided to pivot development to the larger M400 and M290 machines. Although the supply of Nb1Zr powder was limited, it was sufficient to generate sub-scale metallographic specimens for the purpose of parameter development. This was first accomplished on the EOS M400 then the M290 due to machine schedule availability. Further development of TZM has been initiated at the University of Texas El Paso (UTEP) under contract with LANL to use both a heated build envelope L-PBF machine and E-PBF machine that have been found in the literature to mitigate microcracking. UTEP was provided with TZM powder and build plates to support parallel TZM parameter development across both machines. As part of the contract, UTEP will also be conducting microstructural characterization once optimized process parameters have been identified. The optimized process parameters for each machine will be used to generate a series of metallographic, mechanical, and surface finish specimens for subsequent characterization and testing. In the next section, we provide a detailed discussion of the methodology used for investigating the feasibility of leveraging these alloys for use in advanced reactor applications.

36 MATERIALS SCIENCE↗