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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Insights on the Study of Nafion Nanoscale Morphology by Transmission Electron Microscopy

Nafion is one of the most common materials used for polyelectrolyte membranes and is the standard to which novel materials are compared. In spite of great interest in Nafion’s nanostructure, it is still a subject of controversy. While multiple research efforts have addressed Nafion’s morphology with Transmission Electron Microscopy, the results of these efforts have often been inconsistent and cannot satisfactorily describe the membrane structure. One of the reasons for differences in the reported results is the lack of sufficient control over the damage caused by electron beam irradiation. In this work, we describe some aspects of damage in the material that have a strong influence on the results. We show that irradiation causes mass loss and phase separation in the material and that the morphologies that have been observed are, in many cases, artifacts caused by damage. We study the effect of the sample temperature on damage and show that, while working at low temperature does not prevent damage and mass loss, it slows formation of damage-induced artifacts to the point where informative low-dose images of almost undamaged material may be collected. We find that charging of the sample has a substantial effect on the damage, and the importance of charge neutralization under irradiation is also seen by the large reduction of beam induced movement with the use of an objective aperture or a conductive support film. To help interpret the low-dose images, we can apply slightly higher exposures to etch away the hydrophobic phase with the electron beam and reveal the network formed by the hydrophilic phase. Energy loss spectroscopy shows evidence that fluorine removal governs the beam damage process.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

Here we present a fully analytical, heuristic model – the “Analytical Transport Network Model” – for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5–1.5 and 5–6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.

25 ENERGY STORAGE↗
DOE OSTI2018

A Remarkable Return On Investment In Fundamental Research: 40 Years of Basic Energy Sciences at the Department of Energy

In 1977, the Department of Energy established an office of Basic Energy Sciences (BES) to support fundamental research in areas pertinent to DOE missions. In the 40 years since its founding, BES has grown substantially, supporting both individual researchers and interdisciplinary teams of researchers at universities and national laboratories. It engages with the scientific community through an extensive network of advisory committees that help BES to identify high priority problems or areas of opportunity. BES has also created unique shared research facilities, open to all scientists, which have become a distinctive feature of the U.S. research effort and have greatly increased the productivity of university, federal, and industrial research efforts. BES collaborates closely with other federal research agencies, including other DOE offices focused on applied research. Such research often builds on new scientific knowledge generated by BES and advances it toward commercial technologies. In all, BES represents a bet on the future—a significant federal investment in new knowledge.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Synthesis of polyaniline shell on nickel oxide nanoflake arrays for enhanced lithium ion storage

Graphical abstract: Self-supported NiO/PANI core/shell arrays are prepared by successive electrodeposition methods and exhibit good lithium ion storage with high capacity & stable cycling life. - Highlights: • Construct self-supported porous NiO/PANI core/shell arrays. • Porous NiO/PANI composite arrays show high capacity and good cycling life. • Hierarchical porous structure is favorable for fast ion/electron transfer. - Abstract: Developing conducting polymer/oxide composites are of great importance to construct advanced electrochemical devices. In this work, we report a successive electrodeposition methods to fabricate polyaniline (PANI) on nickel oxide (NiO) nanoflake forming composite core-shell arrays. A thin layer of PANI of ∼10 nm is successfully coated on the surface of NiO nanoflake core. High porosity and enhanced conductivity are obtained in this composite electrode. As anode materials for lithium ion batteries, the NiO/PANI core-shell arrays exhibit weaker polarization and better cycling performance as compared to the bare NiO film. The second discharge capacity of NiO/PANI composite arrays is about 780 mAh g{sup −1} at 0.1 A g{sup −1}, higher than that of the bare NiO film (679 mAh g{sup −1}). The improvements of the electrochemical properties are attributed to the PANI, which forms a uniform conductive network leading to improved electrochemical performance.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Sample EP Flow Analysis of Severely Damaged Networks

These are slides for a presentation at the working group meeting of the WESC SREMP Software Product Integration Team on sample EP flow analysis of severely damaged networks. The following topics are covered: ERCOT EP Transmission Model; Zoomed in to Houston and Overlaying StreetAtlas; EMPACT Solve/Dispatch/Shedding Options; QACS BaseCase Power Flow Solution; 3 Substation Contingency; Gen. & Load/100 Optimal Dispatch; Dispatch Results; Shed Load for Low V; Network Damage Summary; Estimated Service Areas (Potential); Estimated Outage Areas (potential).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

SDN-NGenIA, a Software Defined Next Generation Integrated Architecture for HEP and Data Intensive Science

This project aims to enable the LHC and other leading programs in high energy physics and other global science domains funded by the DOE to operate with a new level of efficiency and control, through the development of a Next Generation Integrated Architecture (NGenIA) based on intelligent software defined network (SDN)-driven network systems coupled to high throughput applications. While the initial focus is on the challenging LHC use case, the systems and products being developed are general, and apply to many fields of data intensive science ranging from astrophysical sky surveys to bioinformatics and earth observation, as well as other organizations facing the challenges of extracting knowledge from distributed multi-petabyte data stores. A central concept in this development program is a new paradigm ‘consistent network operations’ among widely distributed computing and storage facilities, where stable high throughput flows, at set rates, cross load-balanced network paths, up to flexible high water marks that are adjusted in real time to accommodate other network traffic. The large smooth flows are launched and managed by SDN services that act in concert with the experiments’ site-resident data distribution and management systems, to meet the expanding needs of the science programs. The technical goals include the construction of autonomous, intelligent site-resident services that interact dynamically with network-resident services, and with the science programs’ principal data distribution and management tools, to request or command network resources in support of high throughput petascale to exascale workflow Specific work items include: 1. Developing compact Data Transfer Nodes (DTNs) with auto-tuning functions that support data transfer rates in the 100 Gbps to the 1 Tbps range when used with high throughput data transfer applications 2. Deep site orchestration among virtualized clusters, storage subsystems and subnets to successfully coschedule CPU, storage and network resources 3. Science-program designed site architectures, operational modes, and policy and resource usage priorities, adjudicated across multiple network domains and virtual organizations, using the orchestration functions and methods 4. Seamlessly extending end-to-end operation across both extra-site and intra-site boundaries through the use of Open vSwitch (OVS), FireQoS + next generation Science DMZs 5. Novel methods of system integration that enable granular control of extreme scale long distance transfers through flow matching of scattered source-destination address pairs to multi-domain dynamic circuits 6. Funneling massive sets of streams to DTNs at the site edge hosting petascale buffer pools configured for flows of 100 Gbps and up, exploiting state of the art data transfers 7. Adaptive scheduling based on pervasive end-to-end monitoring, including DTN or compute-node resident agents providing comprehensive end-system profiling 8. Developing unsupervised and supervised machine learning and modeling methods to optimize the workflow involving terabyte to multi-petabyte datasets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Elucidating Pore Network Evolution in Laboratory‐ and Shaft‐Furnace Hydrogen‐Reduced Iron Pellets Using Nanotomographic Characterization

Direct reduced iron (DRI) is an increasingly important feedstock for modern steelmaking. Fundamental research into DRI properties is limited by the discrepancy between the behavior of industrially produced pellets and laboratory‐produced pellets, leading to nongeneralizable conclusions from laboratory work. Here, in this study, a detailed nano‐computed tomographic characterization of ore pellets, hydrogen DRI reduced in a pilot‐scale shaft furnace, and laboratory‐reduced DRI is presented to better understand the microstructurally influenced property differences between the two. The shaft‐furnace‐reduced pellets show lower overall porosity but larger average pore volume thickness and solid volume thickness than the laboratory‐reduced pellets. This effect is attributed to increased sintering behavior in the shaft furnace case. All pellet types show almost entirely connected pore volumes. The tortuosity of the pores is shown to increase with a degree of reduction, though the shaft furnace pellets show lower tortuosity than the laboratory‐reduced pellets. Again, this difference is attributed to the larger pore volume thickness for shaft‐furnace‐reduced pellets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

5G Energy FRAME: The Design and Implementation of Data, Model, and Use Case (Year 2 Report)

This report summarizes the Year 2 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research (ASCR) Program. In this report, newest 5G equipment testing results are presented, along with two 5G-enabled AI/ML examples for grid applications; in addition, the work flow of grid edge, cloud, and High Performance Computing (HPC) platform is introduced, to support and interface the cross-domain simulation for power system transmission, distribution, and communication networks. Last but not least, the outlook for Year 3 work and the overarching impact of 5G Energy FRAME work to a multitude of stakeholders are provided. Additional 5G performance data now is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 2

Topics covered include: Ringwoodite-olivine assemblages in Dhofar L6 melt veins; Amorphization of forsterite grains due to high energy heavy ion irradiation: Implications for grain processing in ISM; Validation of AUTODYN in replicating large-scale planetary impact events; A network of geophysical observatories for mars; Modelling catastrophic floods on the surface of mars; Impact into coarse grained spheres; The diderot meteorite: The second chassignite; Galileo global color mosaics of Io; Ganymede's sulci on global and regional scales; and The cold traps near the south pole of the moon.

Source record↗
DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles

The latest generation of geostationary satellites provide Earth observations similar to widely used polar-orbiting sensors but at intervals as frequently as every 5–10 min, making them ideal for studying the diurnal dynamics of land–atmosphere interactions. The NASA Earth Exchange (NEX) group created the GeoNEX datasets by collating data from several geostationary platforms, including GOES-16/17/18, Himawari-8/9, and GK-2A, and placing them on a common grid to facilitate use by the Earth science community. Here, we document the GeoNEX Coincident Ground Observations (GeCGO) dataset for terrestrial ecosystem studies and provide examples for its use. Currently, GeCGO provides GOES-16 Advanced Baseline Imager (ABI) data over a 10 km × 10 km area surrounding 1586 network sites across the Americas. GeCGO makes it easy to compare the time series of geostationary data with the diurnal ground observations, including carbon/water fluxes and aerosol optical depth, and is extensible to other regions. We also develop GeoNEXTools to facilitate analyses that require both GeoNEX data and other NASA satellite data. The objectives of this paper are to introduce GeCGO and GeoNEXTools and demonstrate their applications. First, we describe the details of GeCGO and GeoNEXTools. Second, we explain how GeCGO can be integrated with other satellite data. Finally, we showcase comparisons between GeCGO and observations from three ground-based networks. GeCGO is available at https://doi.org/10.25966/y5pe-xp41 (Hashimoto et al., 2025).

Hashimoto, Hirofumi [NASA Ames Research Center (AR↗
DOE OSTI2020

Arabidopsis SnRK1 negatively regulates phenylpropanoid metabolism via Kelch domain-containing F-box proteins

Phenylpropanoid metabolism represents a substantial metabolic sink for the photosynthetically fixed carbon. The evolutionarily conserved Sucrose Non-Fermenting Related Kinase 1 (SnRK1) is a major metabolic sensor that reprograms metabolism upon carbon deprivation. However, it is not clear if and how SnRK1-mediated sugar signaling pathway controls phenylpropanoid metabolism. Here, we show that Arabidopsis SnRK1 negatively regulates phenylpropanoid biosynthesis via a group of Kelch domain-containing F-Box (KFB) proteins that are responsible for the ubiquitination and degradation of phenylalanine ammonia lyase (PAL). Down-regulation of AtSnRK1 significantly promoted the accumulation of soluble phenolics and lignin polymers and drastically increased PAL cellular accumulation but only slightly altered its transcription level. Co-expression of SnRK1α with PAL in Nicotiana benthamiana leaves resulted in the severe attenuation of the latter's protein level, but protein interaction assays suggested PAL is not a direct substrate of SnRK1. Furthermore, up- or down-regulation of AtSnRK1 positively affected KFB PALs gene expression, and energy starvation upregulated KFB PAL expression, which partially depends on AtSnRK1. Collectively, our study reveals that SnRK1 negatively regulates phenylpropanoid biosynthesis, and KFB PALs act as regulatory components of the SnRK1 signaling network, transcriptionally regulated by SnRK1 and subsequently mediating proteasomal degradation of PAL in response to the cellular carbon availability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

An Economic Evaluation Framework for Assessing Renewable Energy Projects

It is becoming increasingly imperative to integrate renewable energy, such as solar and wind, into electricity generation due to increased regulations on air and water pollution and a sociopolitical desire to develop more clean energy sources. This increased spotlight on renewable energy requires evaluating competing projects using either conventional economic analysis techniques or other economics-based models and approaches in order to select a subset of the projects to be funded. Even then, there are reasons to suspect that techniques applied to renewable energy projects may result in decisions that will reject viable projects due to the use of a limited number of quantifiable and tangible attributes about the projects. This paper presents a framework for economic evaluation of renewable energy projects. The framework is based on a systems approach in which the processes within the entire network of the system, from generation to consumption, are accounted for. Furthermore, the framework uses the concept of fuzzy system to calculate the value of information under conditions of uncertainty.

Assessment analysis↗
DOE OSTI2023

Efficient human activity recognition with spatio-temporal spiking neural networks

In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

dCache: The Storage System of Choice for Data-Intensive Applications

The ever-increasing volumes of data produced by modern scientific facilities like EuXFEL and LHC put significant stress on data management infrastructure operated by laboratories and research centers. The challenges to be addressed span the entire data life cycle, from ingest and efficient data analysis to long-term preservation, typically involving large tape libraries. dCache, a storage system developed in collaboration between the Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory, and Nordic e-Infrastructure Collaboration (NeIC), is designed to manage a large number of disk servers and to facilitate transparent data migration to and from archival storage. Its multifaceted approach offers a unified method to support a variety of scientific use cases with the same storage infrastructure, including high-throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters, and long-term data preservation on tertiary storage. Initially developed for high energy physics (HEP) experiments, dCache is now used by various scientific communities, including astrophysics, biomedical research, and life sciences, each having specific requirements. This paper presents architecture, deployment strategies, performance and scalability enhancements, and recent advancements in dCache addressing the needs of scientific communities. Finally, we touch on the development and release process, ensuring the software’s high quality.

DCache↗
DOE OSTI2021

Pollution inequality 50 years after the Clean Air Act: the need for hyperlocal data and action

Fifty years ago the Clean Air Act amendments of 1970 were the first major US legislation that authorized regulation of air pollutants, creating National Ambient Air Quality Standards (NAAQSs) to protect public health and the environment. While US air quality has improved, with average PM 2.5 concentrations in 2016 a third of 1981 levels, air pollution remains a major health risk in the US and globally. Moreover, air pollution impacts are still uneven, with the most polluted US communities of 50 years ago still so today. Air pollution “hot spots” result in disproportionate exposure at neighborhood scales within cities, particularly in disadvantaged communities, but an in-depth understanding at these scales is lacking. This policy perspective discusses how trends in sensor technology, spatial data collection, analytics and retrieval are converging to enable the production of hyperlocal air pollution data, and that this needs to be done in a manner that enables marginalized communities to shape decision making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗