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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI), v4

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2025) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2025) – Power plants that are listed in the 2025 U.S. Hydropower Development Pipeline Data or were listed in previous versions of the dataset These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) – EPA SuRGE sampling locations Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

Hansen, Carly [ORNL] (ORCID:0000000193280838)↗
DOE OSTI2025

Electronic and thermal properties of the phase-change memory material, Ge 2 Sb 2 Te 5 , and results from spatially resolved transport calculations

Here, we report new insights into the electronic, structural, and transport (heat and charge) properties of the phase-change memory material amorphous Ge 2 Sb 2 Te 5 . Using realistic structural models of Konstantinou et al., (2019), we analyze the topology, electronic states, and lattice dynamics with density functional methods, including hybrid-functional calculations and machine-learned interatomic potentials. The Kohn–Sham orbitals near the Fermi level display a strong electron–phonon coupling, and exhibit large energy fluctuations at room temperature. The conduction tail states exhibit larger phonon-induced fluctuations than the valence tail states. To resolve transport at the atomic scale, we employ space-projected electronic conductivity and site-projected thermal conductivity methods. Local analysis of heat transport highlights the role of filamentary networks dominated by Te, with Sb and Ge making progressively smaller contributions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Deterministic Fabrication of Large-Area, High-Crystallinity Oxide Moiré Superlattices

Oxide twistronics extends moiré engineering beyond van der Waals materials, offering a promising platform for accessing emergent interfacial phenomena arising from the strong coupling of lattice, charge, and orbital degrees of freedom in complex oxides. However, deterministic fabrication of high-crystallinity oxide moiré superlattices over large lateral dimensions remains challenging due to the three-dimensional bonding network of oxides. Here, we demonstrate a scalable, generalized fabrication strategy that enables the formation of high-crystallinity oxide moiré superlattices with clean, chemically bonded interfaces and precisely controlled twist angles down to nominal values of 0.1°, achieving subdegree twist-angle accuracy across large contiguous lateral dimensions approaching the millimeter scale. Using NaNbO3 as a model system, we show that the resulting interlayer coupling drives pronounced structural reconstruction that modifies both the phase structure and ferroelectric domain configuration. Synchrotron-based X-ray 3D reciprocal space mapping reveals the emergence of a single-phase state in twisted bilayers, in contrast to the mixed-phase structure observed in single-layer membranes prior to twist assembly. The structural signatures are further consistent with gradual lattice rotation distributed along the thickness direction that may accommodate interfacial shear strain, distinct from reconstruction observed in van der Waals moiré systems which primarily occurs through in-plane stacking rearrangement. This collective lattice response is correlated with twist-dependent nanoscale electromechanical modulations observed by piezoresponse force microscopy. These results establish a scalable materials platform for oxide twistronics and support the implementation of twist-engineered functionalities in practical, macroscale device architectures.

Ghanbari, Reza [North Carolina State University (N↗
DOE OSTI2022

Width-Based Discharge Partitioning in Distributary Networks: How Right We Are

River deltas are home to large populations and can be composed of complex channel networks which convey flows of matter to the shoreline. Knowledge of flow within individual channels is needed to quantify the distribution of discharge across the delta, and thus its sustainability over time. Due to a lack of field measurements at the local channel scale, researchers leverage remote sensing data to estimate the partitioning of flow. We compare data from 15 river deltas to discharge partitioning estimates based on channel network graphs derived from remote sensing imagery. We quantify errors in the common width-based method and test alternative partitioning techniques to find that width-based discharge partitioning is universally applicable, suggesting that absent any site-specific information, discharge partitioning by average channel width is an appropriate approach. We also provide networks, streamflow measurements, and flux partitioning estimates for 28 delta networks as the Discharge In Distributary NeTworks (DIDNT) dataset.

58 GEOSCIENCES↗
DOE OSTI2025

Continuous operation of a coherent 3,000-qubit system

Neutral atoms are a promising platform for quantum science, enabling advances in areas ranging from quantum simulations and computation to metrology, atomic clocks and quantum networking. Although atom losses typically limit these systems to a pulsed mode, continuous operation could substantially enhance cycle rates, remove bottlenecks in metrology and enable deep-circuit quantum evolution through quantum error correction. Here we demonstrate an experimental architecture for high-rate reloading and continuous operation of a large-scale atom-array system while realizing coherent storage and manipulation of quantum information. Our approach utilizes a series of two optical lattice conveyor belts to transport atom reservoirs into the science region, where atoms are repeatedly extracted into optical tweezers without affecting the coherence of qubits stored nearby. Using a reloading rate of 300,000 atoms in tweezers per second, we create over 30,000 initialized qubits per second, which we leverage to assemble and maintain an array of over 3,000 atoms for more than 2 hours. Furthermore, we demonstrate persistent refilling of the array with atomic qubits in either a spin-polarized or a coherent superposition state while preserving the quantum state of stored qubits. Our results pave the way for the realization of large-scale continuously operated atomic clocks, sensors and fault-tolerant quantum computers.

atomic and molecular physics↗
DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Synthetic Aperture Radar Initial Development and Project Scope

Development of a proof-of-concept solution consisting of a network of radars to identify and discriminate an object based on its fixed position and reports on its movement with the resolution of a few centimeters. This includes scaling the solution, addressing other critical requirements (multi-look radar to overcome occlusions and obstructions, interference), and also the ability to scale up to large warehouses. Finally, improved higher-dimensional resolution will be developed.

47 OTHER INSTRUMENTATION↗
NASA NTRS1975

Solar microclimatology

It has become apparent in recent years that solar energy can be used for electric power production by several methods. Because of the diffuse nature of the solar insolation, the area involved in any central power plant design can encompass several square miles. A detailed design of these large area collection systems will require precise knowledge of the local solar insolation. Detailed information will also be needed concerning the temporal nature of the insolation and the local spatial distribution. Therefore, insolation data was collected and analyzed for a network of sensors distributed over an area of several square kilometers in Arizona. The analyses of this data yielded probability distributions of cloud size, velocity, and direction of motion which were compared with data obtained from the National Weather Service. Microclimatological analyses were also performed for suitable modeling parameters pertinent to large scale electric power plant design. Instrumentation used to collect the data is described.

Mckenney, D. B.↗
NASA NTRS1984

A kinetic energy study of the meso beta-scale storm environment during AVE-SESAME 5 (20-21 May 1979)

Kinetic energy of the near storm environment was analyzed by meso beta scale data. It was found that horizontal winds in the 400 to 150 mb layer strengthen rapidly north of the developing convection. Peak values then decrease such that the maximum disappears 6 h later. Southeast of the storms, wind speeds above 300 mb decrease nearly 50% during the 3 h period of most intense thunderstorm activity. When the convection dissipates, wind patterns return to prestorm conditions. The mesoscale storm environment of AVE-SESAME 5 is characterized by large values of cross contour generation of kinetic energy, transfers of energy to nonresolvable scales of motion, and horizontal flux divergence. These processes are maximized within the upper troposphere and are greatest during times of strongest convection. It is shown that patterns agree with observed weather features. The southeast area of the network is examined to determine causes for vertical wind variations.

Printy, M. F.↗
DOE OSTI2023

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

Abstract We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ( κ ) from photometric measurements of galaxies along a given line of sight (LOS). The method is of particular interest in strong gravitational time-delay cosmography (TDC), where characterizing the “external convergence” ( κ ext ) from the lens environment and LOS is necessary for precise Hubble constant ( H 0 ) inference. Starting from a large-scale simulation with a κ resolution of ∼1′, we introduce fluctuations on galaxy–galaxy lensing scales of ∼1″ and extract random sight lines to train our BGNN. We then evaluate the model on test sets with varying degrees of overlap with the training distribution. For each test set of 1000 sight lines, the BGNN infers the individual κ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population. For a test field well sampled by the training set, the BGNN recovers the population mean of κ precisely and without bias (within the 2 σ credible interval), resulting in a contribution to the H 0 error budget well under 1%. In the tails of the training set with sparse samples, the BGNN, which can ingest all available information about each sight line, extracts a stronger κ signal compared to a simplified version of the traditional method based on matching galaxy number counts, which is limited by sample variance. Our hierarchical inference pipeline using BGNNs promises to improve the κ ext characterization for precision TDC. The code is available as a public Python package, Node to Joy ⏬ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

R Coronae Borealis Star Evolution: Simulating 3D Merger Events to 1D Stellar Evolution Including Large-scale Nucleosynthesis

R Coronae Borealis (RCB) stars are rare hydrogen-deficient carbon-rich variable supergiants thought to be the result of dynamically unstable white dwarf mergers. In this article, we attempt to model RCB stars through all the relevant timescales by simulating a merger event in Octo-tiger, a 3D adaptive mesh refinement (AMR) hydrodynamics code, and mapping the post-merger object into MESA, a 1D stellar evolution code. We then post-process the nucleosynthesis on a much larger nuclear reaction network to study the enhancement of s-process elements. We present models that match observations or previous studies in most surface abundances, isotopic ratios, early evolution, and lifetimes. We also observe similar mixing behavior to previous modeling attempts that result in the partial He-burning products visible on the surface in observations. However, we do note that our subsolar models lack any enhancement in s-process elements, which we attribute to a lack of hydrogen in the envelope. We also find that the 16 O/ 18 O isotopic ratio is very sensitive to initial hydrogen abundance and increases outside of the acceptable range with a hydrogen mass fraction greater than 10 -4 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs

Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors, such as the MicroBooNE detector at Fermilab, feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to LArTPC event images is challenging, due to the large size of the events. Events in these detectors are typically two orders of magnitude larger than images found in classical challenges, like recognition of handwritten digits contained in the MNIST database or object recognition in the ImageNet database. Ideally, training would occur on many instances of the entire event data, instead of many instances of cropped regions of interest from the event data. However, such efforts lead to extremely long training cycles, which slow down the exploration of new network architectures and hyperparameter scans to improve the classification performance. We present studies of scaling a LArTPC classification problem on multiple architectures, spanning multiple nodes. The studies are carried out on simulated events in the MicroBooNE detector. We emphasize that it is beyond the scope of this study to optimize networks or extract the physics from any results here. Institutional computing at Pacific Northwest National Laboratory and the SummitDev machine at Oak Ridge National Laboratory’s Leadership Computing Facility have been used. To our knowledge, this is the first use of state-of-the-art Convolutional Neural Networks for particle physics and their attendant compute techniques onto the DOE Leadership Class Facilities. We expect benefits to accrue particularly to the Deep Underground Neutrino Experiment (DUNE) LArTPC program, the flagship US High Energy Physics (HEP) program for the coming decades.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

Abstract Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System , thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, Shining↗
DOE OSTI2022

Neural network–based pore flow field prediction in porous media using super resolution

Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry of the microstructure of porous media to predict the velocity fields therein based on neural networks. However, such trained neural networks do not perform well for unseen porous media with a large degree of heterogeneity. In this study we propose that incorporating a coarse velocity field in the input of neural networks is an effective way to improve the prediction performance. The coarse velocity field can be simulated with a low computational cost and provides global information to regularize the ill-posedness of the learning problem, which is usually caused by the use of local geometries due to the computational resource constraints. We show that incorporating the coarse-mesh velocity field significantly improves the prediction accuracy of the fine-mesh velocity field by comparison to the prediction that relies on geometric information alone, especially for the porous medium with a large interior vuggy pore space. We also show the flexibility of training the network in using coarse velocity fields with various resolutions. The results suggest that even using coarse velocity field with a very low resolution, the predictions are still enhanced and close to the ground truths. The feasibility of the method is further demonstrated by testing the trained network on real rocks. This study highlights the merits of incorporating a coarse-mesh velocity field into the input for neural networks, which provides global, physics-based information for the model, thereby improving the model's generalization capability.

42 ENGINEERING↗
DOE OSTI2021

Self-Healing Ceramics and Mechanical Metamaterials

We performed large scale molecular dynamics (MD) simulations and reactive & quantum molecular dynamics simulations to investigate: (1) faceting, grain growth, and crack healing in ceramics; (2) stress-release mechanism and healing in fibers; and (3) mechanical properties of two-dimensional metamaterials. We also designed highly accurate interatomic potentials and forces using artificial neural networks (ANN) and first-principles molecular dynamics simulation data for use in our MD and reactive MD simulations. We demonstrated the efficacy of modeling quantum materials atomistic simulation data using unsupervised deep learning approaches on a quantum computer. These achievements are elaborated in the following sections.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Binary–Host Connection: Astrophysics of Gravitational-Wave Binaries from Host Galaxy Properties

Gravitational waves from the merger of binary neutron stars (BNSs) are accompanied by electromagnetic counterparts, making it possible to identify the associated host galaxy. In this work, we explore how properties of the hosts relate to the astrophysical processes leading to the mergers. It is thought that the BNS merger rate within a galaxy at a given epoch depends primarily on the galaxy's star formation history, as well as the underlying merger time-delay distribution of the binary systems. The stellar history of a galaxy, meanwhile, depends on the cosmological evolution of the galaxy through time, and is tied to the growth of structure in the universe. We study the hosts of BNS mergers in the context of structure formation by populating the UniverseMachine simulations with gravitational wave (GW) events, based on a simple time-delay model. We find that different time-delay distributions predict different properties of the associated host galaxies, including the distributions of stellar mass, star formation rate, halo mass, and local and large-scale clustering of hosts. Moreover, BNSs merging today with short delay times occur preferentially in hosts with high star formation rates, while those with long delay times live in dense regions within massive halos that have low star formation. Here, we show that with ${ \mathcal O }(10)$ events from current GW detector networks, it is possible to make preliminary distinctions between formation channels which trace stellar mass, halo mass, or star formation rate. We also find that strategies to follow-up GW events with electromagnetic telescopes can be significantly optimized using the clustering properties of their hosts.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Networking ground-based images of Comet Halley during the Giotto encounter

During the period immediately before and after the European, Russian, and Japanese spacecraft encounters with Comet Halley in early March 1986, sequences of ground-based electronic images of the comet, obtained at Table Mountain Observatory (TMO), CA, were transmitted via the Space Physics Analysis Network (SPAN) to the European Space Operations Centre (ESOC), and to University College London (UCL). During the 48-h period when the European Space Agency spacecraft Giotto was within the extended coma of Comet Halley, the ground-based images revealed that the comet displayed several spectacular near-nuclear and large-scale features. The TMO images provided a format for the interpretation of the unique in situ results obtained during the closest of the five spacecraft encounters with Comet Halley.

Rees, David↗
NASA NTRS1991

Video data compression using artificial neural network differential vector quantization

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

Krishnamurthy, Ashok K.↗