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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Temperature-dependent collective magnetization reversal in a network of ferromagnetic nanowires

The collective behavior of geometrically frustrated magnetization in connected networks of ferromagnetic nanowires, known as artificial spin ice, leads to complex magnetotransport behavior in those structures. Here, we present temperature- and current-dependent magnetotransport studies on a connected square artificial spin-ice system and correlate our observations to micromagnetic simulations. We find that the field at which the lattice magnetization collectively switches increases as the temperature is lowered. Our experimental findings highlight the importance of the global and local temperatures for the onset of a collective magnetization reversal in the connected system. These studies may also provide useful insights into novel storage concepts and applications in neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Synchrotron Radiation Beam Diagnostics for the Integrable Optics Test Accelerator

The Integrable Optics Test Accelerator (IOTA) is a research electron and proton storage ring currently being commissioned at Fermilab's Accelerator Science and Technology (FAST) facility. An extensive beam physics research program is planned, including tests of novel techniques for improving beam intensity, stability, and emittance. A key part of IOTA beam diagnostics suite are synchrotron light beam monitors, mounted onto each dipole. In this paper, we present the hardware and software design of this system. Mechanical layout and actuator control electronics are described. High throughput image acquisition and analysis architecture is outlined, and its preliminary performance is explored. Integration of the system within accelerator control network and possible user applications, such as camera auto-focusing, are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

User-level File Systems Specialized for HPC Workloads Year-End Report

High-performance computing (HPC) clusters attract the attention of Deep Learning (DL) training users due to the clusters' powerful computation capabilities. While there have been many existing efforts to enable deep neural works to leverage the powerful CPU and GPU processors from leadership high-performance computing (HPC) systems, large-scale deep learning with larger datasets requires efficient I/O support from the underlying file and storage systems. As some current and upcoming HPC clusters have large on-node memory or are equipped with NVMe SSD on compute nodes, more distributed DL trainings are considered to leverage those storages to store dataset for efficient dataset access. Our project goal is to design a specialized DL-oriented file system to improve datasets loading performance of any DL training applications. Over the past year, the team at Florida State University has performed research activities in three different aspects, including 1) completing a specialized memory-based I/O framework (DeepIO) for improving dataset loading performance on DL applications; 2) proposing a more generalized file system (DLFS) for DL applications on node-local SSDs; 3) completing the BeeGFS performance evaluation project for Deep Neural Networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Comparisons of global ozone trends inferred from the BUV experiment on Nimbus 4 and the ground-based network

Preliminary comparisons between global ozone burdens derived from the backscattered ultraviolet (BUV) experiment on Nimbus 4 and those inferred from an analysis of ground-based network data seem to indicate significant differences in the inter-annual variability of ozone. Some of the observed differences may be due to improper weighting of the ground-based network data, slowly changing planetary wave structure over the fixed station, of small inter-annual changes in meridional transport parameters. There is also some evidence which indicates that the polar stratosphere at high latitudes may represent an important ozone storage resevoir which tends to compensate for large scale changes observed in the regions outside of the polar stratosphere. Possible consequences of this are that the global trends derived from ground based ozone measurements may not be valid and furthermore that the current satellite techniques by themselves may be sufficient. An ozone monitoring system which includes observations from satellites, ground-based stations, balloons and rockets may be necessary.

Heath, D. F.↗
NASA NTRS2004

ICESat Observations of Inland Surface Water Stage, Slope, and Extent: a New Method for Hydrologic Monitoring

River discharge and changes in lake, reservoir and wetland water storage are critical terms in the global surface water balance, yet they are poorly observed globally and the prospects for adequate observations from in-situ networks are poor (Alsdorf et al., 2003). The NASA-sponsored Surface Water Working Group has established a framework for advancing satellite observations of river discharge and water storage changes which focuses on obtaining measurements of water surface height (stage), slope, and extent. Satellite laser altimetry, which can achieve centimeter-level elevation precision for single, small laser footprints, provides a method to obtain these inland water parameters and contribute to global water balance monitoring. Since its launch in January, 2003 the Ice, Cloud, and land Elevation Satellite (ICESat), a NASA Earth Observing System mission, has achieved over 540 million laser pulse observations of ice sheet, ocean surface, land topography, and inland water elevations and cloud and aerosol height distributions. By recording the laser backscatter from 80 m diameter footprints spaced 175 m along track, ICESat acquires globally-distributed elevation profiles, using a 1064 nm laser altimeter channel, and cloud and aerosol profiles, using a 532 nm atmospheric lidar channel. The ICESat mission has demonstrated the following laser altimeter capabilities relevant to observations of inland water: (1) elevation measurements with a precision of 2 to 3 cm for flat surfaces, suitable for detecting river surface slopes along long river reaches or between multiple crossings of a meandering river channel, (2) from the laser backscatter waveform, detection of water surface elevations beneath vegetation canopies, suitable for measuring water stage in flooded forests, (3) single pulse absolute elevation accuracy of about 50 cm (1 sigma) for 1 degree sloped surfaces, with calibration work in progress indicating that a final accuracy of about 12 cm (1 sigma) will be achieved for clear atmosphere conditions, suitable for detection of stage changes through time, (4) ability to precisely point the spacecraft so as to position the laser profile on the Earth the surface with a cross-track accuracy of 50 m (1 sigma), enabling small water bodies and specific locations to be targeted and re-observed through time, (5) adequate signal levels from specular water surfaces up to 5 degrees off-nadir, enabling complete global access to any location on the Earth's surface from the ICESat repeat orbit by off-nadir pointing, and (6) day and night operation with successful laser ranging to the Earth's surface through thin to moderate cloud cover, enabling more frequent measurements than can be achieved by passive optical sensors. Here we illustrate these capabilities by showing ICESat observations through time for selected river and lake locations.

Harding, David J.↗
DOE OSTI2022

Advancing Vision-based Feedback and Convolutional Neural Networks for Visual Outlier Detection

Machine learning has matured into a technology that has immediate applicability to the surveillance needs of nuclear material storage containers. These containers at LANL are the barrier preventing release of radioactive material to the workers, public, and environment during the storage period of the material. Annual surveillance activities can only provide coverage on a handful of containers. There is a significant need for surveillance tools to identify potential issues and precursors to containment failure that can be used during opportunistic inspections and, more generally, outside of annual surveillance activities. In this report we provide details on the advancement of our proposed embodiment that combines an automation system for taking pictures and a high-accuracy machine learning-driven object detection software. We further showcase the improvements on the software side with progress on extracting unique identification features and advances in detecting damage. The current state of the system captures subject matter expert training and a space-conscious design whose implementation is envisioned in the near future.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2003

Analysis of Moessbauer Data from Mars: A Database and Artificial Neural Network for Identification of Iron-bearing Phases

The exploration of the planet Mars is one of the major goals within the Solar system exploration programs of the US-American space agency NASA and the European Space Agency ESA. In particular the search for water and life and understanding of the history of the surface and atmosphere will be the major tasks of the upcoming space missions to Mars. The miniaturized Moessbauer spectrometer MIMOS II has been selected for the NASA Mars-Exploration-Rover twin-mission to Mars in 2003 and the ESA 2003 Mars-Express Beagle 2 mission. Reduced in size and weight, in comparison to ordinary laboratory setup, the sensor head just weights approximately 400 g, with a volume of (50x50x90) cu mm, and holds two gamma-ray sources: the stronger for experiments and the weaker for calibrations. The collimator (in sample direction) also shields the primary radiation off the detectors. Around the drive four detectors are mounted. The detectors are made of Si-PIN-photodiodes in chip form (100 sq mm, thickness of 0.5 mm). The control unit is located in a separate electronics board. This board is responsible for the power supply, generation of the drive's velocity reference signal, read of the detector pulses to record the spectrum, data storage and communication with the host computer. After more than four decades from the discovery of the Moessbauer effect, more than 400 minerals were studied at different temperatures. Their Moessbauer parameters were reported in the literature, and have been recently collected in a data bank. Previous Mars-missions, namely Viking and Mars Pathfinder, revealed Si, Al, Fe, Mg, Ca, K, Ti, S and Cl to be the major constituents in soil and rock elemental composition of the red planet. More than 200 minerals already studied by Moessbauer spectroscopy contain significant amounts of these elements. A considerable number of Moessbauer studies were also carried out on meteorites and on Moon samples. Looking backward in the studies of the whole Moessbauer community, we have built a specific library containing Moessbauer parameters of those possible Mars minerals. The selected minerals, their Moessbauer parameter values (min. max. s.d and number of available data), main site substitution, behavior as a function of temperature and a ranking as expected to be found on Mars were organized. Mars-analogue Fe-bearing minerals not studied by Moessbauer spectroscopy are being collected and investigated. In addition, it an identification system based on Artificial Neural Networks (ANN) was implemented which enables fast and precise mineral identification from the experimental Moessbauer parameters at a given temperature.

P A de Souza Jr.↗
DOE OSTI2024

Breakthrough Conductivity Enhancement in Deep Eutectic Solvents via Grotthuss–Type Proton Transport

There is an increasing demand for the development of ion-conducting electrolytes for energy storage systems. Much attention is directed toward deep eutectic solvents as potential candidates. In the search for highly conductive systems, the possibility of designing deep eutectic solvents with Grotthuss-type proton transport is widely overlooked. Herein, ethaline, a mixture of choline chloride and ethylene glycol is used in a 1:2 molar ratio, to induce a significant conductivity increase with the addition of water and sulfuric acid (H 2 SO 4 ). The achieved breakthrough conductivity is analyzed experimentally and simulated with ab initio molecular dynamics (AIMD). At sufficient water content, an H-bonding network is formed that leads to a significant breakthrough conductivity based on H 2 SO 4 -derived proton transfer following the long-established Grotthuss proton transport mechanism. This result is substantiated by the positive deviation from the ideal KCl line in the Walden plot. Specifically, the data series positioned above the reference line indicates a Grotthuss mechanism in action. The AIMD simulations demonstrate proton transfer between water and ethylene glycol, supported by simulation frames captured at various times.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Authentication of smart grid communications using quantum key distribution

Smart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid’s reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks. Here we report the first use of quantum key distribution (QKD) keys in the authentication of smart grid communications. In particular, we make such demonstration on a deployed electric utility fiber network. The developed method was prototyped in a software package to manage and utilize cryptographic keys to authenticate machine-to-machine communications used for supervisory control and data acquisition (SCADA). This demonstration showcases the feasibility of using QKD to improve the security of critical infrastructure, including future distributed energy resources (DERs), such as energy storage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Optimizing Asynchronous Multi-Level Checkpoint/Restart Configurations with Machine Learning

With the emergence of versatile storage systems, multi-level checkpointing (MLC) has become a common approach to gain efficiency. However, multi-level checkpoint/restart can cause enormous I/O traffic on HPC systems. To use multilevel checkpointing efficiently, it is important to optimize check-point/restart configurations. Current approaches, namely modeling and simulation, are either inaccurate or slow in determining the optimal configuration for a large scale system. In this paper, we show that machine learning models can be used in combination with accurate simulation to determine the optimal checkpoint configurations. We also demonstrate that more advanced techniques such as neural networks can further improve the performance in optimizing checkpoint configurations.

checkpoint-restart↗
DOE OSTI2021

Scalable filesystem enumeration and metadata operations

Systems, apparatus, and methods are disclosed for performing scalable operations in a file system, including POSIX-like file systems. Metadata entries in a namespace or directory tree are sharded across multiple file metadata servers. An enumeration operation, such as listing a directory, is parallelized across the multiple file metadata servers, while retaining standard functionality transparently to clients. Other enumeration operations include no-output operations such as changing file attributes or deleting a file, and cumulative operations such as counting total disk space usage. The parallelization is compatible with tree-level parallelization and storage-level parallelization. Disclosed technologies can be applied to other fields requiring scalable enumeration, such as database and network applications.

Grider, Gary A.↗
DOE OSTI2018

Power Hardware-in-the-Loop Testing of a Smart Distribution System

This paper presents the results of the third and final phase of the National Renewable Energy Lab (NREL) INTEGRATE demonstration: Smart Distribution. For this demonstration, high penetrations of solar PV and wind energy systems were simulated in a power hardware-in-the-loop set-up using a smart distribution test feeder. Simulated and real DERs were controlled by a real-time control platform, which manages grid constraints under high clean energy deployment levels. The power HIL testing, conducted at NREL's ESIF smart power lab, demonstrated how dynamically managing DER increases the grid's hosting capacity by leveraging active network management's (ANM) safe and reliable control framework. Results are presented for how ANM's real-time monitoring, automation, and control can be used to manage multiple DERs and multiple constraints associated with high penetrations of DER on a distribution grid. The project also successfully demonstrated the importance of escalating control actions given how ANM enables operation of grid equipment closer to their actual physical limit in the presence of very high levels of intermittent DER.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Dynamic Boundary Microgrids Under Privatization Considerations

Microgrids have physical, electrical, and logical (data, network, and ownership) boundaries. To power unserved customer loads during an outage, microgrids can extend the traditional operational boundaries. This can become complex when considering microgrid-to-microgrid (M2M) interactions where sensitive information such as competitive microgrid operational data is not shared. This work proposes an optimization method coordinated between microgrid controllers and distribution management systems that limits data sharing. The method involves a competitive bidding strategy that maximizes unserved load coverage while minimizing resource utilization and sensitive operational data sharing among entities. The work is validated on a two-microgrid system with photovoltaic and energy storage systems and curves of load derived from real world residential buildings datasets. Results show that the proposed method, when applied for three distinct use cases of energy storage sufficiency to cover the predefined boundary and/or the expanded boundary, can successfully select and bid the available load coverage.

Starke, Michael [ORNL] (ORCID:0000000221211195)↗
NASA NTRS1994

Telecommunications issues of intelligent database management for ground processing systems in the EOS era

Future NASA earth science missions, including the Earth Observing System (EOS), will be generating vast amounts of data that must be processed and stored at various locations around the world. Here we present a stepwise-refinement of the intelligent database management (IDM) of the distributed active archive center (DAAC - one of seven regionally-located EOSDIS archive sites) architecture, to showcase the telecommunications issues involved. We develop this architecture into a general overall design. We show that the current evolution of protocols is sufficient to support IDM at Gbps rates over large distances. We also show that network design can accommodate a flexible data ingestion storage pipeline and a user extraction and visualization engine, without interference between the two.

Touch, Joseph D.↗
DOE OSTI2025

Rheo–Electric Foundations and Engineering of Carbon Slurry Flow Electrodes

Flowable carbon slurry electrodes promise continuous, scalable electrochemical desalination and energy storage when their rheology (flow and stability) and electronic connectivity are jointly optimized. This report integrates six complementary thrusts that we executed under the Laboratory Directed Research and Development (LDRD) project: (i) a fast, ex situ, centrifuge-compatible method to quantify pellet (packed slurry) electronic conductivity, with a validated cell constant (“shape factor”) and quantitative structure–property insights for carbon mixtures; (ii) a controlled circulation study showing that peristaltic pumping can irreversibly alter particle size and morphology over laboratory-relevant durations; (iii) development of a miniature jet pump that preserves particle integrity while achieving entrainment ratios of 2–4 at practical flow rates; (iv) a comprehensive mapping of how a representative non-ionic surfactant (Tween 20) collapses the carbon gel network, separating network-percolation losses from intrinsic particle contact resistance; (v) in-situ visualization revealing how gravitational settling and flow-driven mixing compete to control charge transport, with dynamic bed layers preventing saturation while achieving 30-fold local concentration enhancement; and (vi) a simple, widely adoptable fabrication route to structured, gas-nitrided titanium nitride (TiN) titanium current collectors that dramatically lower interfacial losses and enable geometry-driven performance gains. Together, these results provide a coherent toolchain and design rules to accelerate formulation screening, de-risk device prototyping, and standardize characterization for carbon slurry electrodes in flow-electrode capacitive deionization (FCDI), electrochemical flow capacitor (EFC), and related systems.

25 ENERGY STORAGE↗
DOE OSTI2019

A Quantitative Study of Deep Learning Training on Heterogeneous Supercomputers

Deep learning (DL) has become a key technique for solving complex problems in scientific research and discovery. DL training for science is substantially challenging because it has to deal with massive quantities of multi-dimensional data. High- performance computing (HPC) supercomputers are increasingly being employed for meeting the exponentially growing demand for DL. Multiple GPUs and high-speed interconnect network are needed for supporting DL on HPC systems. However, the excessive use of GPUs without considering effective benefits leads to inefficient resource utilization of these expensive setups. In this paper, we conduct a quantitative analysis to gauge the efficacy of DL workloads on the latest HPC system and identify viability of next-generation DL-optimized heterogeneous supercomputers for enabling researchers to develop more efficient resource management and distributed DL middleware. We evaluate well- known DL models with large-scale datasets using the popular TensorFlow framework, and provide a thorough evaluation in- cluding scalability, accuracy, variability, storage resource, GPU- GPU/GPU-CPU data transfer, and GPU utilization. Our analysis reveals that the latest heterogeneous supercomputing cluster shows varying performance trend as compared to the existing literature for single- and multi-node training. To the best of our knowledge, this is the first work to conduct such a quantitative and comprehensive study of DL training on a supercomputing system with multiple GPUs.

Han, Jingoo↗
DOE OSTI2021

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) for Monitoring and Control of Medium Voltage Distribution Systems with High Penetration of Intermittent Distributed Energy Resources

The primary focus of the SIMPLE project was to develop, commission, test and introduce intelligent voltage and current sensor platform with enhanced characteristics (accuracy, bandwidth and harmonic range) and high measurement granularity for medium voltage distribution system monitoring, protection and control. The enhancements incorporated existing (commercially available) sensors and made them suitable for applications such as voltage regulation, frequency support, fault detection and location, distribution system state estimation, power quality measurements and electrical distribution network topology processing. The development of the intelligent sensor platform was driven by the need for high-fidelity monitoring and control of medium voltage distribution feeder to enable higher penetration of renewable resources and microgrid operation.

14 SOLAR ENERGY↗