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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Particle acceleration in solar flares - Observations

Contrary to our historical understanding, the energetic particles in most major solar proton events do not come from the flare itself. The particle abundances, ionization states, time evolution, and longitude distributions all indicate that the particles are accelerated from the ambient plasma by a shock wave driven by a coronal mass ejection in these events. In contrast, the particles that do come from impulsive solar flares are unique in character. These particles are electron rich, have He-3/He-4 enhancements of up to 10,000, and enhancements in heavy elements such as Fe/C by factors of 10. The high ionization state of Fe, +20 indicates that the material has been heated to temperatures of about 2 x 10 exp 7 K. It is generally believed that preferential heating by selective absorption of plasma waves is combined with stochastic acceleration in these events. Recent studies of the broad gamma-ray lines emitted by energetic particles within the flare loops indicate that they are also Fe-rich, He-3 rich and proton-poor like the particles seen at 1 AU. In large impulsive events, particles from the impulsive phase may be reaccelerated by a coronal blast-wave shock.

Reames, Donald V.↗
DOE OSTI2025

Techno-Economic Analysis of Data-Driven and Transactive Approaches for Resilience Enhancement

As extreme weather events lead to more frequent power outages, understanding and enhancing grid resilience is critical to mitigating economic losses and non-energy impacts from service disruptions. Here, this study introduces a novel techno-economic analysis framework for evaluating resilience enhancement mechanisms. The framework combines grid response modeling with a co-simulation approach and valuation methodology to provide a comprehensive assessment. We apply this framework to a realistic case study of the Texas grid during Winter Storm Uri in February 2021. Two advanced resilience strategies are analyzed: a data-driven rolling outage mechanism and a transactive energy (TE) based allocation scheme. The rolling outage scheme selectively serves customers based on real-time curtailment needs, while the TE scheme allows customers to trade energy allocations according to their preferences. Our findings show that both the rolling outage and TE schemes significantly outperform conventional methods (i.e. controlled outages) by reducing the amount of energy not supplied to customers by 41% and 64%, respectively. These approaches also enhance flexibility and customer satisfaction, while improving energy utilization for greater resilience. Additionally, they maintain thermal comfort about 3.5 times better and substantially lower customer risk exposure. A key contribution of this study is addressing both utility and customer perspectives while considering both energy and non-energy impacts. The techno-economic analysis indicates that implementing these resilience enhancement strategies would incur an additional 1.1Bto1.6B in utility costs but has the potential to avoid 17.3Bto18B of customer losses as compared to existing solutions, thereby underscoring the value of investing in advanced resilience, as it provides significant societal benefits to customers.

42 ENGINEERING↗
NASA NTRS1999

The Role of Self-Organized Criticality in the Substorm Phenomenon and its Relation to Localized Reconnection in the Magnetospheric Plasma Sheet

Evidence is presented that suggests there is a significant self-organized criticality (SOC) component in the dynamics of substorms in the magnetosphere. Observations of BBFs, fast flows, localized dipolarizations, plasma turbulence, etc. are taken to show that multiple localized reconnection sites provide the basic avalanche phenomenon in the establishment of SOC in the plasma sheet. First results are presented from a continuing plasma physical study of this avalanche process. A one-dimensional resistive MHD model of a magnetic field reversal is discussed. Resistivity, in this model, is self-consistently generated in response to the excitation of an idealized current-driven instability. When forced by convection of magnetic flux into the field reversal region, the model yields rapid magnetic field annihilation through a dynamic behavior that is shown to exhibit many of the characteristics of SOC. Over a large range of forcing strengths, the annihilation rate is shown to self-adjust to balance the rate at which flux is convected into the reversal region. Several analogies to magnetotail dynamics are discussed: (1) It is shown that the presence of a localized criticality in the model produces a remarkable stability in the global configuration of the field reversal while simultaneously exciting extraordinarily dynamic internal evolution. (2) Under steady forcing, it is shown that a loading-unloading cycle may arise that, as a consequence of the global stability, is quasi-periodic and, therefore, predictable despite the presence of internal turbulence in the field distribution. Indeed, it is shown that the global loading-unloading cycle is a consequence of the internal turbulence. (3) It is shown that, under steady, strong forcing the loading-unloading cycle vanishes. Instead, a recovery from a single unloading persists indefinitely. The field reversal is globally very steady while internally it is very dynamic as field annihilation goes on at the rate necessary to match the strong forcing. From this result we speculate that steady magnetospheric convection events result when the plasma sheet has been driven close to criticality over an extended spatial domain. During these events, we would expect to find localized reconnection sites distributed over the spatial domain of near criticality and we would expect to find plasma sheet transport in that domain to be closely related to that of BBF and fast flow events.

Klimas, Alex J.↗
NASA NTRSDate not supplied

A Knowledge Graph Framework for Organizing Heterogeneous Datasets for Utilization in Classical and Quantum Computing: Current Challenges and Future Directions

"The escalating impact of climate change induced extreme weather events in urban, suburban, and rural environments demands a rethink of how we have been using the single event-based or use-case-based knowledge graph models. The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the interconnectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environmental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause mudslides, landslides, etc., The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc, can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a “common-denominator-variable” in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). We use the insights gained from EIKG to conduct classical and Quantum Machine Learning (QML) based data analysis on the research questions developed. Our preliminary study shows how the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC) are used along with the classical machine learning models to compare the model accuracies. Our study elaborates on how a quantitative analysis uses state-of-the-art machine learning techniques that include implementing both classical and quantum machine learning models and developing the knowledge graph. The EIKG is used to organize heterogeneous datasets and integrate the relations to case-specific extreme weather events such as floods. The study uses datasets such as county-to-country residential mobility data, socioeconomic datasets from the US Census Bureau, climate and weather-related Earth Observational data from NASA, and critical infrastructure data from the Homeland Infrastructure datasets."

Knowledge Graphs, Quantum Computing, Heterogenous ↗
NASA NTRS2008

Ion Anisotropy and High-Energy Variability of Large Solar Particle Events: A Comparative Study

We have made comparative studies of ion anisotropy and high-energy variability of solar energetic particle (SEP) events previously examined by the Solar, Heliospheric, and Interplanetary Environment (SHINE) Workshop campaign. We have found distinctly different characteristics of SEPs between two large "gradual" events having very similar solar progenitors (the 2002 April 21 and August 24 events). Since the scattering centers of SEPs are approximately frozen in the solar wind, we emphasize work in the solar-wind frame where SEPs tend to be isotropized, and small anisotropies are easier to detect. While in the August event no streaming reversal occurred, in the April event the field-aligned anisotropy of all heavy ions showed sign of streaming reversal. The difference in streaming reversal was consistent with the difference in the presence of the outer reflecting boundary. In the April event the magnetic mirror, which was located behind the interplanetary shock driven by the preceding coronal mass ejection (CME), could block the stream of SEPs, while in the August event SEPs escaped freely because of the absence of nearby boundary. The magnetic mirror was formed at the bottleneck of magnetic field lines draped around a flank of the preceding CME. In the previous SHINE event analysis the contrasting event durations and Fe/O ratios of the both events were explained as the interplay between shock geometry and seed population. Our new findings, however, indicate that event duration and time as well as spectral variation are also affected by the presence of a nearby reflecting boundary.

Tan, Lun C.↗
DOE OSTI2026

Exploring Wildfire & Energy data toward State Prioritization Index (WESPI)

Energy infrastructure can both induce and suffer risks from wildfires ranging from direct damage to energy assets such as substations and power lines to Public Safety Power Shutoffs. Recent wildfire events underscore the need for data-driven approaches that help states and utilities proactively plan for wildfire risk. Existing national tools such as Federal Emergency Management Agency (FEMA)’s National Risk Index (NRI) are valuable for community hazard planning. However, they are less suited for energy infrastructure, as they emphasize population and building exposure rather than system vulnerabilities. In this paper, we explore relationships between energy and wildfire data and present a Wildfire-Energy State Prioritization Index (WESPI). Our methodology combines data from the US Forest Service’s Fire Simulation (FSIM) dataset with energy resilience metrics, historical fire incidents, and geospatial data on transmission lines and fire stations. Correlation analyses suggest that FSIM burn probability is more strongly associated with power outage metrics (ρ = 0.32) than NRI wildfire frequency, and counties with a greater density of fire stations experience more frequent, but less intense wildfires. We further leverage data for burn probability, transmission line density, and fire station density to develop a Wildfire-Energy State Prioritization Index (WESPI) to highlight counties where wildfire hazard, infrastructure exposure, and limited suppression capacity converge. The index provides a consistent, scalable framework for state energy offices and utilities to screen counties for vegetation management, optimization of outage management system deployment, and to inform wildfire mitigation plans.

Critical infrastructure↗
DOE OSTI2025

Designing Peptide Fossils That Model the Evolution of the Bacterial Ferredoxin Fold

Electron transfer coupled to redox chemistry is at the heart of metabolism. The proteins responsible for moving electrons (protein electron carriers) must have emerged at the origin of life. The small iron–sulfur-binding bacterial ferredoxins were likely among these first proteins. Embedded within the ferredoxin sequence and structure is a symmetry that points to an ancient gene duplication event. Little is understood about the nature of ferredoxins prior to this duplication event or what environmental factors may have driven the selection for more complex forms. The deep-time molecular history of ferredoxins goes back billions of years and cannot be reconstructed by phylogenetic analyses based on amino acid sequences. Here, we use structure-guided protein design to model a fossil half-ferredoxin stage in the evolution of this fold, the semidoxins, and their symmetric full-length counterparts, the symdoxins. Semidoxin designs homodimerize, exhibiting structural, thermodynamic, and electrochemical behaviors in most cases identical to cognate symdoxins. However, the semi- and symdoxin fossil stages behave differently when incorporated into an in vivo electron transfer complementation assay. Both can support bacterial growth dependent on protein expression. Growth rates of bacteria expressing the semidoxins are much more sensitive to oxygen than those of bacteria expressing symdoxins. Motivated by the in vivo functionality of designed semidoxins, we identified putative naturally occurring semidoxins in extant anaerobic microorganisms. This is consistent with the observed in vivo oxygen sensitivity of the semidoxin designs. One natural semidoxin is shown to be folded and redox active. However, it exists as a mixture of monomers and dimers, suggesting a potential connection between semidoxins and even simpler single iron–sulfur cluster-binding peptides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Energetic Electron Precipitation: Multievent Analysis of Its Spatial Extent During EMIC Wave Activity

Electromagnetic ion cyclotron (EMIC) waves can drive precipitation of tens of keV protons and relativistic electrons, and are a potential candidate for causing radiation belt flux dropouts. In this study, we quantitatively analyze three cases of EMIC-driven precipitation, which occurred near the dusk sector observed by multiple Low-Earth-Orbiting (LEO) Polar Operational Environmental Satellites/Meteorological Operational satellite programme (POES/MetOp) satellites. During EMIC wave activity, the proton precipitation occurred from few tens of keV up to hundreds of keV, while the electron precipitation was mainly at relativistic energies. We compare observations of electron precipitation with calculations using quasi-linear theory. For all cases, we consider the effects of other magnetospheric waves observed simultaneously with EMIC waves, namely, plasmaspheric hiss and magnetosonic waves, and find that the electron precipitation at MeV energies was predominantly caused by EMIC-driven pitch angle scattering. Interestingly, each precipitation event observed by a LEO satellite extended over a limited L shell region (ΔL ~ 0.3 on average), suggesting that the pitch angle scattering caused by EMIC waves occurs only when favorable conditions are met, likely in a localized region. Furthermore, we take advantage of the LEO constellation to explore the occurrence of precipitation at different L shells and magnetic local time sectors, simultaneously with EMIC wave observations near the equator (detected by Van Allen Probes) or at the ground (measured by magnetometers). Our analysis shows that although EMIC waves drove precipitation only in a narrow ΔL, electron precipitation was triggered at various locations as identified by POES/MetOp over a rather broad region (up to ~4.4 hr MLT and ~1.4 L shells) with similar patterns between satellites.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Differentiating Between Simultaneous Loss Drivers in Earth's Outer Radiation Belt: Multi‐Dimensional Phase Space Density Analysis

Abstract We analyzed the contribution of electromagnetic ion cyclotron (EMIC) wave driven electron loss to a flux dropout event in September 2017. The evolution of electron phase space density (PSD) through the dropout showed the formation of a radially peaked PSD profile as electrons were lost at high L* , resembling distributions created by magnetopause shadowing. By comparing 2D Fokker Planck simulations of pitch angle diffusion to the observed change in PSD, we found that the μ and K of electron loss aligned with maximum scattering rates at dropout onset. We conclude that, during this dropout event, EMIC waves produced substantial electron loss. Because pitch angle diffusion occurred on closed drift paths near the last closed drift shell, no radial PSD minimum was observed. Therefore, the radial PSD gradients resembled solely magnetopause shadowing loss, even though the local pitch angle scattering produced electron losses of several orders of magnitude of the PSD.

Staples, F. A.↗
DOE OSTI2025

Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in 76 Ge to achieve a half-life sensitivity of 10 27 (10 28 ) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$ after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

artificial intelligence↗
DOE OSTI2019

A Review of Smart Grid Restoration to Enhance Cyber-Physical System Resilience

The growth in electricity demand and the development of grid modernization have increased the complexity of power grid and driven the grid more vulnerable to extreme events like environmental disasters or man-made attacks. Those disruptions can cause outages, cascading failures, and blackouts. The objectives of this paper are to 1) review the recent development addressing the aforementioned challenges with the focus on the impact of extreme events on power system resilience, and 2) discuss the application of smart grid technologies to provide faster restoration techniques and enhance power system resilience. Moreover, challenges and opportunities for future work to enhance smart grid resilience have also been discussed in this paper. The ultimate goal is to present a summary of state-of-the-art and stimulate interests in the area of cyber-physical- human resilience of smart grid.

Man-made attacks↗
DOE OSTI2024

A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems

Accurate and quick identification of high-impedance faults (HIFs) is critical for the reliable operation of distribution systems. Unlike other faults in power grids, HIFs are very difficult to detect by conventional overcurrent relays due to the low fault current. Although HIFs can be affected by various factors, the voltage-current characteristics can substantially imply how the system responds to the disturbance and thus provides opportunities to effectively localize HIFs. In this work, we propose a data-driven approach for the identification of HIF events. To tackle the nonlinearity of the voltage-current trajectory, first, we formulate optimization problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations. Numerical studies on the IEEE 123-node test feeder demonstrate the validity and accuracy of the proposed approach for real-time HIF identification.

explainable artificial intelligence↗
DOE OSTI2019

Evolution of Induced Fracture Networks Using Machine Learning Correlation Image Analysis

Enhanced geothermal systems (EGS) can enrich the Nation’s energy portfolio by increasing the amount of renewable baseload power available to the electrical grid. Microseismic activity, due to fluid resource injection and production, provides critical insight into the processes that occur within EGS subsurface fracture networks. To more precisely characterize these fracture networks, we leverage recent advances in deep neural network (DNN) image analysis techniques. Using seismic spectrogram data collected during a meso scale EGS field experiment as image inputs, we developed a new approach to correlate microseismic events. Our results indicate that our chosen DNN is able to estimate the correlation value between highly correlated events (i.e., cross-correlation coefficients greater than approximately 0.7), but has difficulty estimating correlation values between poorly correlated events. In physical systems however, the highly correlated microseismic events are the events of interest. Therefore, this data-driven method has shown potential for improving domestic Energy and Resource Security, advancing the current methods available to image subsurface EGS thermal reservoirs to enhance the availability of clean and reliable energy to the national electric grid.

58 GEOSCIENCES↗
DOE OSTI2025

MuSIKAL: Multiphysics Simulations and Knowledge Discovery through AI/ML Technologies

Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks

Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Experimental Comparison of Hydrogen Refueling with Directly Pressurized vs. Cascade Method

This paper presents a comparative analysis of two hydrogen station configurations during the refueling process: the conventional “directly pressurized refueling process” and the innovative “cascade refueling process.” The objective of the cascade process is to refuel vehicles without the need for booster compressors. The experiments were conducted at the Hydrogen Research and Fueling Facility located at California State University, Los Angeles. In the cascade refueling process, the facility buffer tanks were utilized as high-pressure storage, enabling the refueling operation. Three different scenarios were tested: one involving the cascade refueling process and two involving compressor-driven refueling processes. On average, each refueling event delivered 1.6 kg of hydrogen. Although the cascade refueling process using the high-pressure buffer tanks did not achieve the pressure target, it resulted in a notable improvement in the nozzle outlet temperature trend, reducing it by approximately 8 °C. Moreover, the overall hydrogen chiller load for the two directly pressurized refuelings was 66 Wh/kg and 62 Wh/kg, respectively, whereas the cascading process only required 55 Wh/kg. This represents a 20% and 12% reduction in energy consumption compared to the scenarios involving booster compressors during fueling. The observed refueling range of 150–350 bar showed that the cascade process consistently required 12–20% less energy for hydrogen chilling. Additionally, the nozzle outlet temperature demonstrated an approximate 8 °C improvement within this pressure range. These findings indicate that further improvements can be expected in the high-pressure region, specifically above 350 bar. This research suggests the potential for significant improvements in the high-pressure range, emphasizing the viability of the cascade refueling process as a promising alternative to the direct compression approach.

08 HYDROGEN↗