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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 OSTI2026

HydroGEN Consortium: Advancements in Hydrogen Production

HydroGEN Energy Materials Network (EMN) is an U.S. Department of Energy (DOE) EERE Hydrogen and Fuel Cell Technologies Office (HFTO)-funded consortium that aims to accelerate the discovery and development of advanced water splitting materials (AWSM) for clean, low-cost hydrogen production. Materials innovations are key to enhancing performance, durability, and cost of hydrogen generation technologies. HydroGEN is focused on low technology readiness level AWS technologies, including low- (alkaline exchanged membrane electrolysis) and high-temperature electrolysis (proton-conducting solid oxide electrolysis), photoelectrochecmical (PEC) and thermochemical (TCH) water splitting. The AWS technologies in this consortium study proton conduction in solid oxide electrolysis and hydroxide conduction in polymer electrolysis, and proton transport in photoelectrochemical water splitting. This presentation will provide an overview of the HydroGEN EMN and technical highlights of a few lab-led and DOE-awarded "seedling" R&D projects. HydroGEN continues to grow its community of industry, university, and national laboratories, forming a national innovation ecosystem focused on renewable hydrogen production.

08 HYDROGEN↗
DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper we discuss recent advances in deep convolutional neural networks (CNN) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory (LSTM) networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F 1 -score of ~91% on individual time-slices using only the ECEi data. This indicates the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales not only for mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including impact of recent upgrades to the ECEi diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Interim Service ISDN Satellite (ISIS) hardware experiment design for advanced ISDN satellite design and experiments

The Interim Service Integrated Services Digital Network (ISDN) Satellite (ISIS) Hardware Experiment Design for Advanced Satellite Designs describes the design of the ISDN Satellite Terminal Adapter (ISTA) capable of translating ISDN protocol traffic into time division multiple access (TDMA) signals for use by a communications satellite. The ISTA connects the Type 1 Network Termination (NT1) via the U-interface on the line termination side of the CPE to the V.35 interface for satellite uplink. The same ISTA converts in the opposite direction the V.35 to U-interface data with a simple switch setting.

Pepin, Gerard R.↗
NASA NTRS1992

Interim Service ISDN Satellite (ISIS) hardware experiment development for advanced ISDN satellite designs and experiments

The Interim Service Integrated Service Digital Network (ISDN) Satellite (ISIS) Hardware Experiment Development for Advanced Satellite Designs describes the development of the ISDN Satellite Terminal Adapter (ISTA) capable of translating ISDN protocol traffic into Time Division Multiple Access (TDMA) signals for use by a communications satellite. The ISTA connects the Type 1 Network Termination (NT1) via the U-interface on the line termination side of the CPE to the RS-499 interface for satellite uplink. The same ISTA converts in the opposite direction the RS-499 to U-interface data with a simple switch setting.

Pepin, Gerard R.↗
DOE OSTI2023

Toward radiative-limited coherence of erbium dopants in a nanophotonic resonator

Atomic-like emitters in the solid state serve as important resources in the advancement of future quantum networks. In particular, intra-4f optical transitions of rare earth ions exhibit excellent coherence properties thanks to the shielding effect of outer electrons. Still, the presence of various dephasing channels in solid state hosts introduces additional decoherence beyond the radiative decay, causing the coherence time of most rare earth doped materials to be over an order of magnitude lower than the radiative limit. Such obstacle prevents the emission of indistinguishable photons from rare earth ions, which is an essential requirement for various quantum applications. In this work, we perform optical coherence study on erbium ions doped in thin-film lithium niobate by patterning photonic crystal resonators with quality factor around 100 k and sub-λ3 mode volume. Leveraging the combination of long coherence and strong Purcell enhancement in the cavity, we show that the coherence time measured with photon echo approaches the radiative limit (80%), representing a 50-fold improvement compared to the waveguide case. Our results present promising prospects toward utilizing rare earth doped materials as quantum repeaters and sources of indistinguishable photons.

Physics↗
DOE OSTI2014

Short gamma-ray burst formation rate from BATSE data using E{sub p} -L{sub p} correlation and the minimum gravitational-wave event rate of a coalescing compact binary

Using 72 short gamma-ray bursts (SGRBs) with well determined spectral data observed by BATSE, we determine their redshift and luminosity by applying the E{sub p} -L{sub p} correlation for SGRBs found by Tsutsui et al. For 53 SGRBs with an observed flux brighter than 4 × 10{sup –6} erg cm{sup –2} s{sup –1}, the cumulative redshift distribution up to z = 1 agrees well with that of 22 Swift SGRBs. This suggests that the redshift determination by the E{sub p} -L{sub p} correlation for SGRBs works well. The minimum event rate at z = 0 is estimated as R{sub on−axis}{sup min}=6.3{sub −3.9}{sup +3.1}× 10{sup −10} events Mpc{sup −3} yr{sup −1}, so that the minimum beaming angle is 0.°6-7.°8 assuming a merging rate of 10{sup –7}- 4 × 10{sup –6} events Mpc{sup –3} yr{sup –1} suggested from the binary pulsar data. Interestingly, this angle is consistent with that for SGRB 130603B of ∼4°-8°. On the other hand, if we assume a beaming angle of ∼6° suggested from four SGRBs with the observed beaming angle value, then the minimum event rate including off-axis SGRBs is estimated as R{sub all}{sup min}=1.15{sub −0.66}{sup +0.56} × 10{sup −7} events Mpc{sup −3} yr{sup −1}. If SGRBs are induced by the coalescence of binary neutron stars (NSs) and/or black holes (BHs), then this event rate leads to a minimum gravitational-wave detection rate of 3.8{sub −2.2}{sup +1.8} (146{sub −83}{sup +71}) events yr{sup −1} for an NS-NS (NS-BH) binary, respectively, by a worldwide network with KAGRA, advanced-LIGO, advanced-VIRGO, and GEO.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

High performance FPGA embedded system for machine learning based tracking and trigger in sPhenix and EIC

We present a comprehensive end-to-end pipeline to classify triggers versus background events in this paper. This pipeline makes online decisions to select signal data and enables the intelligent trigger system for efficient data collection in the Data Acquisition System (DAQ) of the upcoming sPHENIX and future EIC (Electron-Ion Collider) experiments. Starting from the coordinates of pixel hits that are lightened by passing particles in the detector, the pipeline applies three-stage of event processing (hits clustering, track reconstruction, and trigger detection) and labels all processed events with the binary tag of trigger versus background events. The pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the message-passing graph neural network to predict links between hits and reconstruct tracks and a hierarchical pooling algorithm (DiffPool) to make the graph-level trigger detection. We obtain an impressive performance (≥70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline. We deploy the end-to-end pipeline into a field-programmable gate array (FPGA) and accelerate the three stages with speedup factors of 1152, 280, and 21, respectively.

Instruments & Instrumentation↗
DOE OSTI2010

Testing gravitational parity violation with coincident gravitational waves and short gamma-ray bursts

Gravitational parity violation is a possibility motivated by particle physics, string theory, and loop quantum gravity. One effect of it is amplitude birefringence of gravitational waves, whereby left and right circularly polarized waves propagate at the same speed but with different amplitude evolution. Here we propose a test of this effect through coincident observations of gravitational waves and short gamma-ray bursts from binary mergers involving neutron stars. Such gravitational waves are highly left or right circularly polarized due to the geometry of the merger. Using localization information from the gamma-ray burst, ground-based gravitational wave detectors can measure the distance to the source with reasonable accuracy. An electromagnetic determination of the redshift from an afterglow or host galaxy yields an independent measure of this distance. Gravitational parity violation would manifest itself as a discrepancy between these two distance measurements. We exemplify such a test by considering one specific effective theory that leads to such gravitational parity violation, Chern-Simons gravity. We show that the advanced LIGO-Virgo network and all-sky gamma-ray telescopes can be sensitive to the propagating sector of Chern-Simons gravitational parity violation to a level roughly 2 orders of magnitude better than current stationary constraints from the LAGEOS satellites.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

AI-based Cyber Event OSINT via Twitter Data

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.

Dale, Dakota↗
DOE OSTI2023

Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense With High Penetration of DERs

With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Ultrahigh strength, modulus, and conductivity of graphitic fibers by macromolecular coalescence

Theoretical considerations suggest that the strength of carbon nanotube (CNT) fibers be exceptional; however, their mechanical performance values are much lower than the theoretical values. To achieve macroscopic fibers with ultrahigh performance, we developed a method to form multidimensional nanostructures by coalescence of individual nanotubes. The highly aligned wet-spun fibers of single- or double-walled nanotube bundles were graphitized to induce nanotube collapse and multi-inner walled structures. These advanced nanostructures formed a network of interconnected, close-packed graphitic domains. Their near-perfect alignment and high longitudinal crystallinity that increased the shear strength between CNTs while retaining notable flexibility. The resulting fibers have an exceptional combination of high tensile strength (6.57 GPa), modulus (629 GPa), thermal conductivity (482 W/m·K), and electrical conductivity (2.2 MS/m), thereby overcoming the limits associated with conventional synthetic fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

NEONiso R Package, versions greater than 0.5.2

Carbon fluxes in terrestrial ecosystems and their response to environmental change are a major source of uncertainty in the modern carbon cycle. The National Ecological Observatory Network (NEON) presents the opportunity to merge eddy covariance (EC)-derived fluxes with CO2 isotope ratio measurements to gain insights into carbon cycle processes. Collected continuously and consistently across >40 sites, NEON EC and isotope data facilitate novel integrative analyses. However, currently provisioned atmospheric isotope data are uncalibrated, greatly limiting ability to perform cross-site analyses. Here, we present two approaches to calibrating NEON CO2 isotope ratios, along with an R package to calibrate NEON data. We find that calibrating CO2 isotopologues independently yields a lower d13C bias (<0.05‰) and higher precision (<0.40‰) than directly correcting d13C with linear regression (bias: <0.11‰, precision: 0.42‰), but with slightly higher error and lower precision in calibrated CO2 mole fraction. The magnitude of the corrections to d13C and CO2 mole fractions vary substantially by site, underscoring the need for users to apply a consistent calibration framework to data in the NEON archive. Post-calibration data sets show that site mean annual d13C correlates negatively with precipitation, temperature, and aridity, but positively with elevation. Forested and agricultural ecosystems exhibit larger gradients in CO2 and d13C than other sites, particularly during the summer and at night. The overview and analysis tools developed here will facilitate cross-site analysis using NEON data, provide a model for other continental-scale observational networks, and enable new advances leveraging the isotope ratios of specific carbon fluxes.

Fiorella, Richard↗
DOE OSTI2016

General Line Ampacity State Solver

INL’s General Line Ampacity State Solver (GLASS) is a high performance software platform that enables advanced grid system operations and planning efforts to be computed in real time. It enables the combination of advanced sensors and sensor networks, communication and data transfer channels, secure and reliable data management, and real-time processing through a flexible system architecture, with the ability to support and integrate with any utility enterprise system. GLASS is designed to help resolve the problems associated with thermally limited transmission and distribution networks, namely bare overhead power transmission lines. GLASS enables the methodology of Concurrent Cooling (Dynamic Line Rating) by reducing the need of using a cost-prohibited number of weather stations (reduce the number of weather stations per mile of overhead power line) to achieve the resolution necessary for individual electric transmission line span capacity calculations. GLASS achieves this by coupling real-time grid operations data and infrastructure architecture information with real-time field deployed weather station measurements and pre-computed Computational Fluid Dynamics (CFD)-based wind modeling interpolation parameters (look-up tables). GLASS uses mathematical models based on the IEEE 738 standard and builds off of the provided iterative solution using modern software paradigms and programming styles to provide a more robust and modular software system. GLASS also interfaces in real-time with utility companies using known transmission line spans and structures. GLASS provides real-time ampacity calculations based on recent weather station data as well as transmission line specifications. GLASS also allows the user to interact graphically with internal databases, which include information such as transmission line conductor types and thermal rating limits, weather station names and locations, specific transmission line span and structure locations, as well as recent ampacity calculations.

Gentle, Jake↗
DOE OSTI2018

Embedded 100 Gbps Photonic Components

This innovation to fiber optic component technology increases the performance, reduces the size and reduces the power consumption of optical communications within dense network systems, such as advanced distributed computing systems and data centers. VCSEL technology is enabling short-reach (< 100 m) and >100 Gbps optical interconnections over multi-mode fiber in commercial applications.

42 ENGINEERING↗
DOE OSTI2019

Open Watershed Science by Design: Leveraging Distributed Research Networks to Understand Watershed Systems: Workshop Report

The workshop upon which this report is based was convened in response to a community-recognized need for greater openness and coordination of multiwatershed distributed research efforts that integrate across capabilities within the U.S. Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) and link to those of other agencies. The science enabled by such an approach targets challenges and strategic directions articulated in the Biological and Environmental Research Advisory Committee’s (BERAC) 2017 grand challenges report (BERAC 2017) and the 2018 Climate and Environmental Sciences Division (CESD) strategic plan (U.S. DOE 2018). The BERAC report and CESD strategic plan both focus heavily on the need for integrative research to connect environmental microbes, multiomics, plant system dynamics, biogeochemical interactions, and hydrological processes to understand ecosystem function. The need to develop such connections across scales of space, time, and biological complexity is framed in the context of improving predictions of the Earth system in response to disturbance, including extreme events. This report summarizes the discussions and ideas that came from the watershed systems research community on how to use integrated, coordinated, open, and community-networked watershed science to advance BER’s efforts to link fundamental processes to emergent properties of watershed structure, function, and evolution. The ultimate goal is to enhance predictive capacity across scales up to the integrated Earth system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Demonstration of Algorithm for Sensor Placement Optimization using Simulation Data

This deliverable reports FY26 progress in advancing a neural-network-based Green’s-function framework for reconstructing neutron-flux distributions from ex-core measurements and for translating reconstruction requirements into a practical detector-layout strategy. Building on the FY25 formulation, the present work had two main objectives: (1) refine and re-evaluate the reconstruction methodology on an updated Purdue University Reactor Number One (PUR-1) model, and (2) develop a systematic, Green’s-function-guided procedure for boundary detector placement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Effectively Considering the Distribution System in Integrated Resource Plans

While electricity planning practices vary by state and utility based on utility type and market structure, integrated resource planning (IRP) remains a prominent vehicle — even in states with centrally-organized wholesale electricity markets. IRP focuses on meeting forecasted long-term electricity needs. Typically, utilities have not considered impacts of design and operation of the low-voltage distribution network in IRP. With advanced capabilities of grid-edge technologies to generate and store electricity and provide load flexibility, and large utility investments in distribution systems, it's increasingly important to consider at least some distribution planning elements in IRP. This report considers the value proposition for doing so, such as reducing utility costs through resource co-optimization and strategic siting of grid-edge resources, and idenfities the most important touchpoints between planning for bulk power and distribution systems and provide a range of tactics for integrating these two processes.

Relf, Grace [Lawrence Berkeley National Laboratory↗
DOE OSTI2016

ELECTROMAGNETIC EMISSION FROM LONG-LIVED BINARY NEUTRON STAR MERGER REMNANTS. II. LIGHT CURVES AND SPECTRA

Recent observations indicate that in a large fraction of binary neutron star (BNS) mergers a long-lived neutron star (NS) may be formed rather than a black hole. Unambiguous electromagnetic (EM) signatures of such a scenario would strongly impact our knowledge on how short gamma-ray bursts (SGRBs) and their afterglow radiation are generated. Furthermore, such EM signals would have profound implications for multimessenger astronomy with joint EM and gravitational-wave (GW) observations of BNS mergers, which will soon become reality thanks to the ground-based advanced LIGO/Virgo GW detector network. Here we explore such EM signatures based on the model presented in a companion paper, which provides a self-consistent evolution of the post-merger system and its EM emission up to ∼10{sup 7} s. Light curves and spectra are computed for a wide range of post-merger physical properties. We present X-ray afterglow light curves corresponding to the “standard” and the “time-reversal” scenario for SGRBs (prompt emission associated with the merger or with the collapse of the long-lived NS). The light curve morphologies include single and two-plateau features with timescales and luminosities that are in good agreement with Swift observations. Furthermore, we compute the X-ray signal that should precede the SGRB in the time-reversal scenario, the detection of which would represent smoking-gun evidence for this scenario. Finally, we find a bright, highly isotropic EM transient peaking in the X-ray band at ∼10{sup 2}–10{sup 4} s after the BNS merger with luminosities of L{sub X} ∼ 10{sup 46}–10{sup 48} erg s{sup −1}. This signal represents a very promising EM counterpart to the GW emission from BNS mergers.

79 ASTRONOMY AND ASTROPHYSICS↗