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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Providing Experimental Infrastructure for Accelerating Advanced Reactor Demonstrations through the National Reactor Innovation Center

A suite of experimental infrastructure projects has been developed by the National Reactor Innovation Center to accelerate advanced reactor demonstrations and facilitate their development, addressing crucial gaps in data, materials characterization, and modeling. First, the Molten Salt Thermophysical Examination Capability (MSTEC) provides a specialized platform for post-irradiation characterization of molten salt reactor fuel, coolant salts, and structural materials, essential for supporting the design and operation of advanced reactors and future commercial molten salt reactor development and licensing. The Virtual Test Bed (VTB) complements these efforts by leveraging advanced modeling and simulation tools to evaluate reactor performance and safety. Serving as a library of reference models, the VTB offers a database of multiphysics reactor models, facilitating rapid safety evaluations and includes continuous software quality assurance, crucial for accelerating deployment while maintaining reliability. Additionally, the Helium Component Test Facility (HeCTF) addresses the need for high-temperature helium-cooled reactor component testing. As the first-of-its-kind facility in the United States, HeCTF emulates high-temperature gas reactor conditions, reducing time and cost associated with component validation, thereby accelerating reactor development. Finally, In-cell Thermal Creep Frames provide a unique solution for obtaining thermal creep data from irradiated materials, critical for materials qualification and licensing. Developed by the National Reactor Innovation Center, these compact frames enable the examination of previously irradiated materials, overcoming traditional limitations and enhancing the understanding of mechanical properties crucial for reactor development. Collectively, these experimental infrastructure projects form a comprehensive framework aimed at expediting advanced reactor demonstrations, fostering innovation, and ensuring the viability of next-generation nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Frontiers and opportunities in bioenergy crop microbiome research networks

Researchers from across the four U.S. Department of Energy Bioenergy Research Centers engaged in a microbiome workshop that focused on identifying challenges and collaboration opportunities to better understand bioenergy-relevant plant–microbe interactions. The virtual workshop included hands-on educational sessions and a keynote address on current best practices in microbiome science and community microbiome standards, as well as breakout sessions aimed at identifying microbiome-related data and measurements that should be prioritized, opportunities for and barriers to integrating plant metabolites to microbiome research, and strategies for more effectively integrating microbiome data and processes into existing models. Based on participant discussion, key findings of the workshop were the need to prioritize scaling data sharing across BRCs and the broader research community and securing collaborative infrastructure in the areas of microbiome-ecosystem modeling and molecular plant-microbe interactions. This workshop review highlights additional main findings from this event, to encourage cross-site and more holistic meta-analyses while promoting wide scientific community engagement across plant microbiome sciences.

09 BIOMASS FUELS↗
DOE OSTI2025

Radsource Mr: Mixed Reality Planning Tool For Radioactive Recovery

The RadSource MR system leverages Meta Quest 3's advanced mixed reality capabilities to create a comprehensive spatial planning platform for end-of-life sealed radioactive source recovery operations. The application utilizes the Quest 3's high-resolution passthrough cameras and spatial mapping algorithms to generate accurate 3D environmental models. Core technical components include: (1) Real-time spatial measurement algorithms calculating distances, angles, slopes, and surface areas with sub-centimeter accuracy; (2) Virtual object placement system allowing users to position digital representations of recovery equipment (trailers, containment vessels, protective barriers) within the real environment; (3) Voice recording and annotation system for hands-free documentation in protective equipment; (4) 3D mesh capture and storage capabilities for post-operation analysis and regulatory documentation. (5) Procedure documentation is available for viewing in Mixed Reality, providing an innovative and convenient way to access the information during pre-visit and inspection activities. (6) Support for screen capture for the view for real world and virtual objects together to use it later for planning. The system integrates computer vision techniques for environmental understanding, spatial mathematics for precise measurements, and human-computer interaction principles optimized for hazardous environment operations. Data persistence allows teams to save and share planning sessions across multiple stakeholders while maintaining operational security requirements.

Khadka, Rajiv [Idaho National Laboratory (INL), Id↗
DOE OSTI2020

Traversing large graphs on GPUs with unified memory

Due to the limited capacity of GPU memory, the majority of prior work on graph applications on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware and software advances make it possible to address much larger host memory transparently as a part of a feature known as unified virtual memory. While accessing host memory over an interconnect is understandably slower, the problem space has not been sufficiently explored in the context of a challenging workload with low computational intensity and an irregular data access pattern such as graph traversal. We analyse the performance of breadth first search (BFS) for several large graphs in the context of unified memory and identify the key factors that contribute to slowdowns. Next, we propose a lightweight offline graph reordering algorithm, HALO (Harmonic Locality Ordering), that can be used as a pre-processing step for static graphs. HALO yields speedups of 1.5x-1.9x over baseline in subsequent traversals. Our method specifically aims to cover large directed real world graphs in addition to undirected graphs whereas prior methods only account for the latter. Additionally, we demonstrate ties between the locality ordering problem and graph compression and show that prior methods from graph compression such as recursive graph bisection can be suitably adapted to this problem.

Gera, Prasun↗
DOE OSTI2022

Winter School: Applications of Artificial Intelligence to Topics in Nuclear Physics

The virtual topical Winter School on Artificial Intelligence in Nuclear Physics aimed at giving the participants a deeper understanding on what Artificial Intelligence and Machine Learning are and how they can be used to analyze data, design new detectors, controls, and calibration systems for nuclear physics experiments and perform theoretical calculations of nuclear many-body systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Measurement of the Higgs boson width using the ZZ final state

A measurement of the Higgs boson width via its decay to two Z bosons is presented using data from the CMS experiment. A combination of the on- and off-shell Higgs boson production decaying to four leptons is used to determine the Higgs boson width. An assumption that no new virtual particles affect the production is tested by adding new heavy particles in the gluon fusion loop model. This result is combined with a previous CMS analysis of the off-shell Higgs boson production with decay to two leptons and two neutrinos, giving a measured Higgs boson width of 3.0^{+2.0}_{−1.5} MeV, in agreement with the standard model prediction of 4.1 MeV. The strength of the off-shell Higgs boson production is also tested. The scenario of no off-shell Higgs boson production is excluded at a confidence level corresponding to 3.8 standard deviations.

Srivastav, Mohit [Johns Hopkins U.]↗
DOE OSTI2026

Structure–Property Relationships of Recycled Lithium-Ion Battery Cathodes: Microstructure Optimization Using Virtual Materials Testing

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.

25 ENERGY STORAGE↗
DOE OSTI2023

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-hour time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Digital Twin Technology for Safety, Security, and Training in Spent Nuclear Fuel Handling

The increasing complexity of spent nuclear fuel handling requires significant resources to ensure safety, security, and personnel training. As nuclear facilities have continued to advance in scale and technology, the integration of digital tools has become indispensable. Among these tools, digital twins, which are virtual models of physical systems, are emerging as invaluable tools for enhancing safety protocols, security measures, and training in the nuclear sector. These models were conceptualized in the Industry 4.0 revolution. Digital twins can process data from physical systems in real time (by using sensors), include multiple code packages to enable simulations of different physics applications, and even implement artificial intelligence or machine learning techniques for advanced data processing. Despite the advantages that digital twins provide, challenges still exist regarding their widespread implementation. For instance, data used by a digital twin must be accurate to ensure that the digital twin is accurately tuned. Furthermore, if insecure digital twins are targeted by hackers, then they can pose serious risks to the security and safety of nuclear facilities.

Digital twins↗
DOE OSTI2023

Final Report in Response to ARPA-E Contract Award Number DE-AR0001157

Autonomous operations and maintenance (O&M) by robots has been identified as a key technology to facilitate both the safe operation, and reduced operational costs associated with future nuclear reactors. Radiation and thermal environments, particularly associated with molten salt reactor designs, excludes the use of human proximity and so there is a need to train robots for tasks that have not yet been fully identified, or for accidents that may occur in the future. This program sought to develop a generic methodology that could be applied to train ‘any’ robot, to perform ‘any’ task, through the use of machine learning (ML) training performed in a virtual reality (VR) environment that simulates the physically perceived task(s). The VR environment allows us to construct ‘any’ future task and the ML approach, which included reinforcement learning (RL), allowed us to generate extensive data sets that can be used to establish control algorithms to thereby control the physical robot in the physical environment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.

AI↗
DOE OSTI2024

Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters - CRADA 625 (Abstract)

Commercial heat pump water heater (CHPWH) systems significantly decarbonize the commercial and multifamily sectors by eliminating the reliance on gas-fired water heating. CHPWH systems are also well suited to include load shift controls that enable load-up and shed commands for supporting grid reliability and time-of-use pricing structure. However, they have not had wide adoption due to factors including price, complexity, and perceived risk. Although CHPWHs have been available in the US for decades, they have not made significant market gains in part because the systems have required significant and costly engineering design expertise and proved lackluster performance. Successful widespread market adoption requires a different approach; a shift from the current custom specialized expertise project design and installation to a repeatable approach that requires little specialized knowledge or expertise and can deliver persistent performance. Using this type of holistic systems approach requires effectively integrating four CHPWH system key components: primary air-to-water heat pumps; primary thermal storage tanks, a temperature maintenance system, and a control system which has capabilities to manage the primary heat pump cycles, any back-up, supplemental, or temperature maintenance heating, alarms, and grid connectivity allowing for demand response (DR), and/or load shifting. The project team has developed and will implement a suite of tools to support faster, less expensive, and more reliable field installations of CHPWH technology and with the resulting data used to further improve the tool set. These tools include: (1) A tool for optimizing system size and costs. (2) A tool that predicts annual energy use and overall system efficiency. (3) The Advanced Water Heater Specification (AWHS 8.0) defining the components of a full CHPWH system addressing performance requirements by climate zone. (4) The Qualified Products List: (QPL) of approved products that meet the specifications requirements. (5) Training materials including online on-demand modules, instructor-led training, and virtual interactive video tours of CHPWH installations in multifamily buildings. Demonstration site identification in low-income buildings in underserved communities is currently underway. Preliminarily, the team anticipates having three demonstrations in the Pacific Northwest and three in the Northeast for a total of six sites. After the demonstration sites are finalized, and M&V instrumentation installations are complete, the team will gather performance data and confirm whether the CHPWH systems perform as predicted and use the data to improve the existing tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Constraining baryon annihilation in the hadronic phase of heavy-ion collisions via event-by-event fluctuations

We point out that the variance of net-baryon distribution normalized by the Skellam distribution baseline, κ 2 [$B-\overline{B}$]/$\langle B+\overline{B} \rangle$, is sensitive to the possible modification of (anti)baryon yields due to $B\overline{B}$ annihilation in the hadronic phase. The corresponding measurements can thus place stringent limits on the magnitude of the $B\overline{B}$ annihilation and its inverse reaction. We perform Monte Carlo simulations of the hadronic phase in Pb-Pb collisions at the LHC via the recently developed subensemble sampler + UrQMD afterburner and show that the effect survives in net-proton fluctuations, which are directly accessible experimentally. The available experimental data of the ALICE Collaboration on net-proton fluctuations disfavors a notable suppression of (anti)baryon yields in $B\overline{B}$ annihilations predicted by the present version of UrQMD if only global baryon conservation is incorporated. On the other hand, the annihilations improve the data description when local baryon conservation is imposed. The two effects can be disentangled by measuring κ 2 [$B-\overline{B}$]/$\langle B+\overline{B} \rangle$, which at the LHC is notably suppressed by annihilations but virtually unaffected by baryon number conservation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Evidence of off-shell Higgs boson production from ZZ leptonic decay channels and constraints on its total width with the ATLAS detector

This Letter reports on a search for off-shell production of the Higgs boson using 139 fb —1 of $pp$ collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS detector at the Large Hadron Collider. The signature is a pair of $\overline{Z}$ bosons, with contributions from both the production and subsequent decay of a virtual Higgs boson and the interference of that process with other processes. The two observable final states are $\overline{Z}$ $\overline{Z}$ → 4$\ell$ and $\overline{Z}$ $\overline{Z}$ → 2$\ell$2$ν$ with $\ell$ = $e$ or $μ$. In the $\overline{Z}$ $\overline{Z}$ → 4$\ell$ final state, a dense Neural Network is used to enhance analysis sensitivity with respect to matrix element-based discrimination. The backgroundonly hypothesis is rejected with an observed (expected) significance of 3.3 (2.2) standard deviations, representing experimental evidence for off-shell Higgs boson production. Assuming that no new particles enter the production of the virtual Higgs boson, its total width can be deduced from the measurement of its off-shell production cross-section. The measured total width of the Higgs boson is ${4.5}_{—2.5}^{+3.3}$ MeV, and the observed (expected) upper limit on the total width is found to be 10.5 (10.9) MeV at 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds

The prerequisite of therapeutic drug design is to identify novel molecules with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to obtain molecules with desired target properties is the preservation of critical scaffolds in the generation process. To this end, we propose a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of a desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We show that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific datasets, we generate covalent and non-covalent antiviral inhibitors. Therefore, to measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease and non-structural protein endoribonuclease (NSP15) targets. Most importantly, our model performs well with relatively small volumes of training data and generalizes to new scaffolds, making it applicable to other domain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Multidimensional measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic charged-kaon electroproduction off protons in the valence region

Measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic electron scattering (SIDIS) with positively charged kaons off protons have been performed with 10.6 and 10.2 GeV incident electron beams using the CLAS12 spectrometer at Jefferson Lab. We report an analysis of the electroproduction of positively charged kaons over a large kinematic range of fractional energy, Bjorken 𝑥, transverse momentum, and photon virtualities 𝑄 2 ranging from 1 GeV 2 up to 6 GeV 2 . This is the first published multidimensionally binned CLAS12 measurement of a kaon SIDIS single-spin asymmetry in the valence quark regime. The data provide constraints on the structure function ratio $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$⁡/$𝐹_{𝑈⁢𝑈}$, where $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$ is a quantity with a leading twist of twist-3 that can reveal novel aspects of the quark-gluon correlations within the nucleon. The impact of the data on understanding the underlying reaction mechanisms and their kinematic variation is explored using theoretical models for the different contributing twist-3 parton-distribution functions (PDFs) and fragmentation functions (FFs).

form factors↗
DOE OSTI2025

𝜂 and 𝜂′ meson production in 𝐽/𝜓 radiative decays from lattice QCD

We report on the computation of amplitudes describing the radiative decay processes 𝐽/𝜓 →𝛾⁢𝜂 and 𝐽/𝜓 →𝛾⁢𝜂′ from first principles via lattice quantum chromodynamics (QCD). Using lattices with two heavier than physical degenerate flavors of light quark where 𝑚 𝜋 ∼391 MeV, and a strange quark tuned to approximately the physical value, we compute three-point correlation functions using optimized meson operators with a range of momenta. The use of optimized operators allows access to the 𝜂′, even though it appears as an excited state, lying above the ground-state 𝜂 in the isoscalar pseudoscalar channel. Statistically precise signals are obtained in this disconnected process by averaging over large numbers of kinematically equivalent correlators. We determine transition form factors as a function of photon virtuality across the timelike region, which describe also the Dalitz processes 𝐽/𝜓 →𝑒 + ⁢𝑒 − ⁢𝜂 (′) . Significantly lower magnitudes of transition form factor for both the 𝜂 and 𝜂′ are found than those extracted from experimental data, and possible explanations for this observation are presented.

form factors↗
DOE OSTI2023

Spatially Adaptive Tunable Lighting Control System with Expanded Wellness and Energy Saving Benefits

Lighting design is becoming increasingly complex, including active dimming for energy savings and spectral tuning for human wellbeing. Modern commercial lighting control systems are already difficult to use, and maintain, and even with so-called Smart Lighting, optimizing light settings is rapidly exceeding the capabilities of direct human control, limiting the adoption of lighting control systems. The growing importance of Occupancy Centric Controls (OCC) for advanced building management systems can be applied to the development of autonomous lighting system controls needed to drive the adoption of advanced lighting controls for improved adaptive “sculpted illumination” for both greater energy savings and broader human wellbeing perspectives. This project, led by Rensselaer Polytechnic Institute, entitled “Spatially adaptive tunable lighting control system with expanded wellness and energy saving benefits” will develop and test an entirely new platform for automated optimized lighting design and control that requires little or no human engagement, yet contours lighting profiles automatically to minimize lighting energy use. We call this approach to lighting control “dynamic light sculpting” since the right amount of illumination is automatically delivered to occupants in real time. Since the system uses new, privacy-preserving occupant position and pose detection technologies developed by Rensselaer for broad OCC building applications, the control system will analyze how to deliver the right amount of the right type of illumination only where and when it is needed based these OCC platforms. To create these powerful autonomous lighting control platforms, the project will integrate evolving augmented reality (AR) and virtual reality (VR) tools with sophisticated lighting and interior design toolkits to create interactive lighting design and control simulators. Using the quickly growing paradigm of digital twins, these tools will integrate light fixture properties, occupancy sensor data, interior design data, and lighting specifications to accurately visualize how various design concepts interact with simulated yet realistic human activities that occur in commercial office buildings of various types. These advanced digital twin design and simulation tools will be combined with a new class of digitally-programmable LED lighting fixtures that can dynamically change the spectral content and direction of light emission. When fully integrated, the new autonomous lighting control system will take all of the guesswork out of optimizing light quality while minimizing energy consumption. Digital twin tools will simplify the design, installation, commissioning, operation, and maintenance of future energy-efficient lighting systems. It should be possible to reduce lighting energy costs from 40% to 70% with OCC based dynamic light sculpting systems. The work will be led by the Center for Lighting Enabled Systems and Applications (LESA) and the Center for Architectural Science and Ecology (CASE), both at Renssealer Polytechnic Institute; Lumileds, a global leader in the development of advanced LED systems; and HKS, a leading global architecture design firm. This interdisciplinary project team’s experience includes all of the design simulation tools, VR/AR digital twin visualization technology, advanced occupancy sensing technology, and complex control system design capabilities that will revolutionize lighting design and control technology to autonomously deliver high-quality lighting that improves human health and wellbeing while simultaneously maximizing lighting energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗