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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Thermal and Chemical Characterization of Non-Metallic Materials Using Coupled Thermogravimetric Analysis and Infrared Spectroscopy

Thermogravimetric analysis (TGA) is widely employed in the thermal characterization of non-metallic materials, yielding valuable information on decomposition characteristics of a sample over a wide temperature range. However, a potential wealth of chemical information is lost during the process, with the evolving gases generated during thermal decomposition escaping through the exhaust line. Fourier Transform-Infrared spectroscopy (FT-IR) is a powerful analytical technique for determining many chemical constituents while in any material state, in this application, the gas phase. By linking these two techniques, evolving gases generated during the TGA process are directed into an appropriately equipped infrared spectrometer for chemical speciation. Consequently, both thermal decomposition and chemical characterization of a material may be obtained in a single sample run. In practice, a heated transfer line is employed to connect the two instruments while a purge gas stream directs the evolving gases into the FT-IR. The purge gas can be either high purity air or an inert gas such as nitrogen to allow oxidative and pyrolytic processes to be examined, respectively. The FT-IR data is collected realtime, allowing continuous monitoring of chemical compositional changes over the course of thermal decomposition. Using this coupled technique, an array of diverse materials has been examined, including composites, plastics, rubber, fiberglass epoxy resins, polycarbonates, silicones, lubricants and fluorocarbon materials. The benefit of combining these two methodologies is of particular importance in the aerospace community, where newly developing materials have little available data with which to refer. By providing both thermal and chemical data simultaneously, a more definitive and comprehensive characterization of the material is possible. Additionally, this procedure has been found to be a viable screening technique for certain materials, with the generated data useful in the selection of other appropriate analytical procedures for further material characterization.

Huff, Timothy L.↗
NASA NTRS2001

Thermal and Chemical Characterization of Non-metallic Materials Using Coupled Thermogravimetric Analysis and Infrared Spectroscopy

Thermogravimetric analysis (TGA) is widely employed in the thermal characterization of non-metallic materials, yielding valuable information on decomposition characteristics of a sample over a wide temperature range. However, a potential wealth of chemical information is lost during the process, with the evolving gases generated during thermal decomposition escaping through the exhaust line. Fourier Transform-Infrared spectroscopy (FT-IR) is a powerful analytical technique for determining many chemical constituents while in any material state, in this application, the gas phase. By linking these two techniques, evolving gases generated during the TGA process are directed into an appropriately equipped infrared spectrometer for chemical speciation. Consequently, both thermal decomposition and chemical characterization of a material may be obtained in a single sample run. In practice, a heated transfer line is employed to connect the two instruments while a purge gas stream directs the evolving gases into the FT-IR, The purge gas can be either high purity air or an inert gas such as nitrogen to allow oxidative and pyrolytic processes to be examined, respectively. The FT-IR data is collected real-time, allowing continuous monitoring of chemical compositional changes over the course of thermal decomposition. Using this coupled technique, an array of diverse materials has been examined, including composites, plastics, rubber, fiberglass epoxy resins, polycarbonates, silicones, lubricants and fluorocarbon materials. The benefit of combining these two methodologies is of particular importance in the aerospace community, where newly developing materials have little available data with which to refer. By providing both thermal and chemical data simultaneously, a more definitive and comprehensive characterization of the material is possible. Additionally, this procedure has been found to be a viable screening technique for certain materials, with the generated data useful in the selection of other appropriate analytical procedures for further material characterization.

Huff, Timothy L.↗
DOE OSTI2024

Quantifying the Correlation between Coordination Chemistry, Interfacial Formation, and Electrochemical Performances for Mg Battery Electrolytes

Here, the rise of magnesium batteries as promising post-Li-ion energy storage technologies has sparked considerable attention toward understanding the fundamental aspects of coordination chemistry concerning Mg cations in multivalent electrolytes. This exploration includes investigating how coordination influences crucial electrolyte properties like solubility, electroreduction stability, and the formation of the interphase, all of which are pivotal for practical battery applications. Despite recent progress in developing a few functional electrolytes, a comprehensive understanding of the solvation structure that can facilitate efficient Mg deposition performance and the formulation of general design rules based on the solvation structure is still lacking. In our study, we endeavor to establish a connection between solvent and anion interactions with Mg 2+ , interface formation, and cycling performance through a series of organic ether solvents (tetrahydrofuran, glyme, diglyme, and triglyme) and amine solvents (dimethylamine, 3-methoxypropylamine, and dimethoxyethylamine). Our findings reveal a distinct coordination trend for solvent/Mg 2+ and (Mg-TFSI):solvent across various solvents, which dictates the extent of ion pairing for TFSI salts with increasing solvent molecule size and denticity. The solvated species in the bulk electrolyte across different solvents lead to diverse interfacial chemistries with varying decomposition components. We also explore the cycling efficiency as well as Mg deposition overpotentials for different solvents. A correlation analysis was conducted to assess the interplay between the structure and performance. Lastly, we apply the insights gained from these results to tailor the relative anion/Mg 2+ coordination structures using cosolvent systems, aiming for improved cell performance.

25 ENERGY STORAGE↗
DOE OSTI2023

Chemical Kinetic Method for Active-Site Quantification in Fe-N-C Catalysts and Correlation with Molecular Probe and Spectroscopic Site-Counting Methods

Mononuclear Fe ions ligated by nitrogen (FeN x ) dispersed on nitrogen-doped carbon (Fe-N-C) serve as active centers for electrocatalytic O 2 reduction and thermocatalytic aerobic oxidations. Despite their promise as replacements for precious metals in a variety of practical applications, such as fuel cells, the discovery of new Fe-N-C catalysts has relied primarily on empirical approaches. In this context, the development of quantitative structure–reactivity relationships and benchmarking of catalysts prepared by different synthetic routes and by different laboratories would be facilitated by the broader adoption of methods to quantify atomically dispersed FeN x active centers. In the present study, we develop a kinetic probe reaction method that uses the aerobic oxidation of a model hydroquinone substrate to quantify the density of FeN x centers in Fe- N-C catalysts. The kinetic method is compared with low-temperature Mössbauer spectroscopy, CO pulse chemisorption, and electrochemical reductive stripping of NO derived from NO 2 – on a suite of Fe-N-C catalysts prepared by diverse routes and featuring either the exclusive presence of Fe as FeN x sites or the coexistence of aggregated Fe species in addition to FeN x . The FeN x site densities derived from the kinetic method correlate well with those obtained from CO pulse chemisorption and Mössbauer spectroscopy. The broad survey of Fe-N-C materials also reveals the presence of outliers and challenges associated with each site quantification approach. Furthermore, the kinetic method developed here does not require pretreatments that may alter active-site distributions nor specialized equipment beyond reaction vessels and analytical instrumentation (e.g., NMR).

36 MATERIALS SCIENCE↗
NASA NTRS2019

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds - NASA FluidCam, MiDAR, and NeMO-Net

We highlight three emerging NASA optical technologies that enhance our ability to remotely sense, analyze, and explore ocean worlds–FluidCam and fluid lensing, MiDAR, and NeMO-Net. Fluid lensing is the first remote sensing technology capable of imaging through ocean waves without distortions in 3D at sub-cm resolutions. Fluid lensing and the purpose-built FluidCam CubeSat instruments have been used to provide refraction-corrected 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs). Results from repeat 2013 and 2016 airborne fluid lensing campaigns over coral reefs in American Samoa present a promising new tool for monitoring fine-scale ecological dynamics in shallow aquatic systems tens of square kilometers in area. MiDAR is a recently-patented active multispectral remote sensing and optical communications instrument which evolved from FluidCam. MiDAR is being tested on UAVs and autonomous underwater vehicles (AUVs) to remotely sense living and non-living structures in light-limited and analog planetary science environments. MiDAR illuminates targets with high-intensity narrowband structured optical radiation to measure an object’s spectral reflectance while simultaneously transmitting data. MiDAR is capable of remotely sensing reflectance at fine spatial and temporal scales, with a signal-to-noise ratio 10-10(exp 3) times higher than passive airborne and spaceborne remote sensing systems, enabling high-framerate multispectral sensing across the ultraviolet, visible, and near-infrared spectrum. Preliminary results from a 2018 mission to Guam show encouraging applications of MiDAR to imaging coral from airborne and underwater platforms whilst transmitting data across the air-water interface. Finally, we share NeMO-Net, the Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment. NeMO-Net is a machine learning technology under development that exploits high-resolution data from FluidCam and MiDAR for augmentation of low-resolution airborne and satellite remote sensing. NeMO-Net is intended to harmonize the growing diversity of 2D and 3D remote sensing with in situ data into a single open-source platform for assessing shallow marine ecosystems globally using active learning for citizen-science based training. Preliminary results from four-class Q17 coral classification have an accuracy of 94.4%. Together, these maturing technologies present promising scalable, practical, and cost-efficient innovations that address current observational and technological challenges in optical sensing of marine systems.

Ved Chirayath↗
DOE OSTI2025

Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data for "Design of Diverse, Functional Mitochondrial Targeting Sequences Across Eukaryotic Organisms Using Variational Autoencoder"

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

AI/ML↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quick Talk: Resilient Communities, Maryland

Groundswell, Inc., in partnership with the Maryland Energy Administration (MEA), Ayika Solutions, the National Renewable Energy Lab (NREL), and the Baltimore Office of Sustainability, is advancing a vision for resilient, community-centered energy systems in Maryland. This effort aligns with Groundswell's commitment to a clean energy economy that equitably benefits all communities, closing gaps in representation, wages, and access to wealth generation, savings, and local jobs within a diverse energy supply chain. Project Vision and Objectives The project emphasizes "energy resilience," defined as a community’s capacity to anticipate, adapt to, and swiftly recover from power disruptions through inclusive planning and technical solutions. The approach centers on Resilience Hubs—designated community facilities equipped to support residents before, during, and after power-related disruptions. These hubs offer emergency resources, coordinate communications, and deliver essential services while reducing carbon emissions, ultimately enhancing residents' quality of life. Additionally, Resilience Hubs foster social connections, support disaster preparedness, and provide spaces for community programming and everyday use. The goals of Resilient Communities Maryland include: Developing a replicable framework for community energy resilience, applicable beyond Maryland. Defining community-led indicators of social impact and resilience. Establishing equitable decision-making processes in project design and implementation. Informing MEA of practical, community-based resilience strategies to shape future state initiatives. Approach Groundswell and its partners prioritize collaborative engagement with local stakeholders, ensuring that solutions are responsive to each community's unique needs. By integrating feedback from residents, local organizations, and government entities, the team has developed a holistic model for resilience that balances immediate benefits, such as improved emergency response, with long-term gains in social equity and environmental sustainability. Key Outcomes This initiative has produced valuable resources and insights, including: A Community Energy Resilience Framework: Designed to guide other communities in establishing energy resilience strategies tailored to their specific needs. Case Studies: Documenting best practices and lessons learned from pilot projects to serve as models for future resilience efforts. Community-Driven Impact Metrics: Highlighting the positive social impacts that resilience initiatives can bring, such as improved social connectedness and local decision-making power. Recommendations for MEA: Offering evidence-based insights to inform policies and funding opportunities that support resilient energy infrastructure in vulnerable communities. Next Steps Groundswell's final report will detail findings, including the potential for scaling resilience hubs across Maryland and other regions, with a focus on equitable access and community-driven outcomes. This project underscores the importance of aligning energy resilience with social equity, reinforcing community agency, and building systems that withstand and recover from future challenges effectively and sustainably.

14 SOLAR ENERGY↗
DOE OSTI2025

Toward more-robust, AI-enabled subsurface seismic imaging for geotechnical applications

Non-invasive seismic imaging has the potential to cost-effectively evaluate large volumes of subsurface material to inform geotechnical site investigation. However, seismic imaging using full waveform inversion (FWI) requires significant computational time and is dependent on an initial starting model. As a result, FWI has not yet been widely adopted into geotechnical practice. Previous efforts, on relatively simple two-layered models, indicate that data-driven artificial intelligence (AI) models may be as effective as FWI at predicting 2D images of shear wave velocity (V s ). Furthermore, the AI model predictions can be made almost instantaneously after data acquisition and do not require an initial starting model. We examine the generality of these findings by developing a new AI model for subsurface seismic imaging, whereby we make several notable contributions. First, we architect a multimodal AI model that combines time- and frequency-domain representations of the seismic wavefield to predict a 50 m by 20 m subsurface image of V s . Second, we developed a new diverse dataset of 100,000 images with their corresponding seismic wavefields to train the AI model. Third, we propose four physics-informed data augmentations for data-driven seismic imaging. Fourth, we develop two prediction consistency tests to evaluate the model’s performance when the true subsurface is unknown. Our final model, which has been made publicly available, is capable of predicting a subsurface V s image from a single seismic wavefield with an average, mean absolute percent error (MAPE) of 24 %. The predictive model is applied to a field dataset and shown to be consistent with local geology and shear-wave refraction measurements from the same location.

Artificial intelligence↗
DOE OSTI2020

Real-Time Coupling of Geographically Distributed Research Infrastructures: Taxonomy, Overview, and Real-World Smart Grid Applications

Novel concepts enabling a resilient future power system and their subsequent experimental evaluation are experiencing a steadily growing challenge: large scale complexity and questionable scalability. The requirements on a research infrastructure (RI) to cope with the trends of such a dynamic system therefore grow in size, diversity and costs, making the feasibility of rigorous advancements questionable by a single RI. Analysis of large scale system complexity has been made possible by the real-time coupling of geographically separated RIs undertaking geographically distributed simulations (GDS), the concept of which brings the equipment, models and expertise of independent RIs, in combination, to optimally address the challenge. This article presents the outputs of IEEE PES Task Force on Interfacing Techniques for Simulation Tools towards standardization of GDS as a concept. First, the taxonomy for setups utilized for GDS is established followed by a comprehensive overview of the advancements in real-time couplings reported in literature. The overview encompasses fundamental technological design considerations for GDS. The article further presents four application oriented case studies (real-world implementations) where GDS setups have been utilized, demonstrating their practicality and potential in enabling the analysis of future complex power systems.

distributed laboratories↗
NASA NTRS1990

On the determination of iridium in diverse geological samples employing HPGe-coincidence/NaI(Tl)-anticoincidence spectrometry

This paper reports the Ir content of a variety of geological samples determined by the high-purity Ge-coincidence/NaI(Tl)-anticoincidence gamma-ray spectrometry (henceforth referred to as coincidence/anticoincidence technique) and by the conventional INAA. The advantages of this technique are: (1) the Ir content of the samples is obtained (ppm to a fraction of ppb ranges) not only by the 468.1 keV peak as in the conventional INAA but also by the 784.6 keV and 920.9 keV sum peaks, which gives more confidence in the values obtained; and (2) it is well suited for the samples with high Compton background for which it is difficult to measure the Ir content by the conventional INAA technique. The practical sensitivity of this technique depends on the sample matrix. Under present experimental conditions, it varied from 0.1 ng for Mn nodules and 0.004 ng for Libyan Desert Glass. Iridium values obtained on small (about 1 microg) olivine grains demonstrate the potential application of this new technique to microsamples. The principle and methodology of this new technique as well as its advantages and disadvantages over the conventional INAA are discussed.

Murali, A. V.↗
NASA NTRS2024

On the Role of Friction and Particle Size Distribution in Granular Packings

Packing of particles in a disordered arrangement has tremendous significance in both condensed matter physics and engineering applications. The last three decades have seen remarkable progress in our understanding of the physics of granular packings that has been largely facilitated by a rapid growth in the power of modern computers. Although granular packings are ubiquitous in diverse natural settings, from clogging of powders in hoppers to the crowding of living cells, a significant motivation for modeling granular packings has emerged from a proposal that the transition from a fluid-like state to a solid-like state of a granular material upon increasing volume fraction, called jamming, is intimately related to the origins of glass transition in thermal systems. As such, a majority of modeling efforts have focused on the jamming behavior of an idealized granular material: frictionless, monodisperse sphere. While such studies have illuminated the rich physics of jamming, granular materials in nature and engineering practice are rarely frictionless or monodisperse. The analogous research on the packing of these ‘real-world’ granular materials is still not fully developed. Besides requiring the exploration of a huge parameter space, three key computational considerations have inhibited their modeling: (i) traditional computational methods are not adept at simulating mechanically-stable packings of frictional particles near the jamming transition; (ii) standard algorithms of contact detection in discrete element methods are impractical to simulate granular packings with a wide distribution of particle sizes; (iii) a lack of well-established contact mechanics models of friction that can accurately reproduce experimental data. This chapter will review the latest computational advances to simulate the jamming of size-dispersed frictional particles, and describe the rich microstructural diversity that emerges in their packings.

granular↗
NASA NTRS1985

A General-applications Direct Global Matrix Algorithm for Rapid Seismo-acoustic Wavefield Computations

A new matrix method for rapid wave propagation modeling in generalized stratified media, which has recently been applied to numerical simulations in diverse areas of underwater acoustics, solid earth seismology, and nondestructive ultrasonic scattering is explained and illustrated. A portion of recent efforts jointly undertaken at NATOSACLANT and NORDA Numerical Modeling groups in developing, implementing, and testing a new fast general-applications wave propagation algorithm, SAFARI, formulated at SACLANT is summarized. The present general-applications SAFARI program uses a Direct Global Matrix Approach to multilayer Green's function calculation. A rapid and unconditionally stable solution is readily obtained via simple Gaussian ellimination on the resulting sparsely banded block system, precisely analogous to that arising in the Finite Element Method. The resulting gains in accuracy and computational speed allow consideration of much larger multilayered air/ocean/Earth/engineering material media models, for many more source-receiver configurations than previously possible. The validity and versatility of the SAFARI-DGM method is demonstrated by reviewing three practical examples of engineering interest, drawn from ocean acoustics, engineering seismology and ultrasonic scattering.

Schmidt, H.↗
NASA NTRS1998

Research and Technology 1997

NASA Lewis Research Center is responsible for developing and transferring critical technologies that address national priorities in aeropropulsion and space applications in partnership with U.S. industries, universities, and Government institutions. As NASA s designated Lead Center for Aeropropulsion, our role is to develop, verify, and transfer aeropropulsion technologies to U.S. industry. As NASA s designated Center of Excellence in Turbomachinery, our role is to develop new and innovative turbomachinery technology to improve the reliability, performance, efficiency and affordability, capacity, and environmental compatibility of future aerospace vehicles. We also maintain a science and technology development role in aeropropulsion, communications, space power and onboard propulsion, and microgravity fluid physics and combustion. We are committed to enabling non-aerospace U.S. industries to benefit directly from the technologies developed through our programs to maximize the benefit to the Nation and the return on each taxpayer s investment. In addition, we are aggressively pursuing continuous improvement in our management and business practices and striving for diversity in our workforce as together we push the edge of technology in space and aeronautics. The Lewis Research Center is a unique facility located in an important geographical area, the southwest corner of Cleveland, Ohio. Situated on 350 acres of land adjacent to the Cleveland Hopkins International Airport, Lewis comprises more than 140 buildings that include 24 major facilities and over 500 specialized research and test facilities. Additional facilities are located at Plum Brook Station, which is about 50 miles west of Cleveland. Over 3700 people staff Lewis, including civil service employees and support service contractors. Over half of them are scientists and engineers, who plan, conduct or oversee, and report on our research tasks and projects. They are assisted by technical specialists, skilled workers, and an administrative staff. Our end product is knowledge. This report is designed to help us make this knowledge fully available to potential users the aircraft engine industry, the energy industry, the automotive industry, the aerospace industry, and others. It is organized so that a broad cross section of the community can readily use it. Each article begins with a short introductory paragraph that should prove to be a valuable tool for the layperson. These articles summarize the progress made during the year in various technical areas and portray the technical and administrative support associated with Lewis technology programs. We hope that the information is useful to all. If additional information is desired, readers are encouraged to contact the researchers identified in the articles and to visit Lewis on the World Wide Web (http://www.lerc.nasa.gov/). This document is available on the World Wide Web (http://www.lerc.nasa.gov/WWW/RT).

Source record↗
NASA NTRS2016

NASA Earth Observing System Data and Information System (EOSDIS): A U.S. Network of Data Centers Serving Earth Science Data: A Network Member of ICSU WDS

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, and serving a diverse user community around the world with Earth science data from satellites, aircraft, field campaigns and research investigations. The ESDIS Project, responsible for EOSDIS is a Network Member of the International Council for Sciences (ICSU) World Data System (WDS). Nine of the 12 Distributed Active Archive Centers (DAACs), which are part of EOSDIS, are Regular Members of the ICSUWDS. This poster presents the EOSDIS mission objectives, key characteristics of the DAACs that make them world class Earth science data centers, successes, challenges and best practices of EOSDIS focusing on the years 2014-2016, and illustrates some highlights of accomplishments of EOSDIS. The highlights include: high customer satisfaction, growing archive and distribution volumes, exponential growth in number of products distributed to users around the world, unified metadata model and common metadata repository, flexibility provided to uses by supporting data transformations to suit their applications, near-real-time capabilities to support various operational and research applications, and full resolution image browse capabilities to help users select data of interest. The poster also illustrates how the ESDIS Project is actively involved in several US and international data system organizations.

Earth Science↗
DOE OSTI2025

Best Practices for Grid Communications

As the grid evolves, the communications architecture will need to evolve with it. That architecture affords a structured means by which the evolving complexities of the modern electric grid can be managed. This document provides best practices that can be implemented in the grid of today and evolve towards the grid and grid architecture of the future. The evolving grid and its control communications increasingly rely on commercial communications providers and a variety of technologies, from wireless (e.g., 5G, microwave, Wi-Fi) to wireline (fiber, copper) to radio communications (P25, other repeater-based systems), and all these communications systems rely on electric power. A reliable and resilient grid must account for this complex set of interdependencies in its planning activities, especially those involving restoration and recovery. The participation of all relevant parties in both planning and exercising of plans can prevent unexpected conditions that impede the reliable operation and recovery of the grid. Best practices for grid communications include using a Network Management System to document the operational state, define and monitor baselines, detect changes, and accelerate response to abnormalities. If transitioning from SONET to IP/packet-based systems, translating grid requirements into communications requirements for latency, bandwidth and throughput, IP packet delay variation, packet loss, and availability should inform and drive technology planning and selection as well as that communication system’s Quality of Service (QoS) policies and Service Level Agreements (SLAs). Secure and reliable timing is another key component of a reliable and resilient grid that can operate through adverse events. A trusted internal NTP configuration, an integrated and diverse timing delivery system, optimizing the timing architecture based on the transport technologies of the communications system, and using established standards can deliver the level of timing accuracy required by a range of time-sensitive power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

4D structural changes and pore network model of biomass during pyrolysis

Biochar is an engineered carbon-rich substance used for soil improvement, environmental management, and other diverse applications. To date, the understanding of how biomass affects biochar microstructure has been limited due to the complexity of analysis involved in tracing the changes in the physical structure of biomass as it undergoes thermochemical conversion. In this study, we used synchrotron x-ray micro-tomography to visualize changes in the internal structure of biochar from diverse feedstock (miscanthus straw pellets, wheat straw pellets, oilseed rape straw pellets, and rice husk) during pyrolysis by collecting a sequence of 3D scans at 50 °C intervals during progressive heating from 50 °C to 800 °C. The results show a strong dependence of biochar porosity on feedstock as well as pyrolysis temperature, with observed porosity in the range of 7.41–60.56%. Our results show that the porosity, total surface area, pore volume, and equivalent diameter of the largest pore increases with increasing pyrolysis temperature up to about 550 °C. The most dramatic development of pore structure occurred in the temperature range of 350–450 °C. This understanding is pivotal for optimizing biochar’s properties for specific applications in soil improvement, environmental management, and beyond. By elucidating the nuanced variations in biochar’s physical characteristics across different production temperatures and feedstocks, this research advances the practical application of biochar, offering significant benefits in agricultural, environmental, and engineering contexts.

54 ENVIRONMENTAL SCIENCES↗