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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Rapid Refreezing of a Marginal Ice Zone Across a Seafloor Distributed Acoustic Sensor

Abstract Seismic instruments such as broadband seismometers and distributed acoustic sensors (DAS) have a demonstrated potential for wide‐scale and continuous in situ monitoring of near‐surface environmental and anthropogenic processes. DAS is attractive for development as a multi‐geophysical observatory due to the prevalence of existing fiber infrastructure in regions with environmental, cultural, or strategic significance. Here we present results from a multi‐seasonal acquisition of DAS data on a seafloor cable in the Beaufort Sea, Alaska. During a November 2021 data collect we captured the rapid transition of ambient noise characteristics from an “ice‐free” state to an “ice‐bound” state. A sea ice formation front was plainly visible on the DAS record and was observed to propagate 20 km seaward over a period of 8 hr. Satellite‐based instrumentation were unable to record this event due to cloud cover, low light conditions, and orbital frequency.

Baker, Michael G.↗
DOE OSTI2022

Outage Cause Classification of Power Distribution Systems with Machine Learning and Real-World Data

Power distribution systems are geographically dispersed by nature. It may be affected by various factors, such as vegetation, weather, animal and human behaviors. Present response procedures to an outage event massively rely on expert experience and thus tend to be time-consuming. Automatic outage event detection and classification will help to reduce the responding and restoration time. However, this issue is less addressed with existing research done in this area. In this applied research, a set of waveform pre-processing techniques are first proposed to prepare the waveform data for being used as inputs to the classification algorithm. Further, a machine learning-based algorithm is proposed to classify the outage events according to their root causes, e.g. tree contact, animal contact, lightning, etc. Available data include three phase current & voltage waveforms and contextual information during the distribution system outages. The proposed machine learning algorithm takes the current and voltage waveforms as direct inputs in search of features that humans are unable to capture. Real data provided by a distribution company in the East Tennessee region is used to test the proposed pre-processing techniques and the classification algorithm.

Sun, Haoyuan↗
DOE OSTI2021

Validation of Maui PSCAD Model: Motivation, Methodology, and Lessons Learned: Preprint

As power systems become increasingly dependent on inverter-based resources, the dominant physical and control interactions that determine system stability may no longer be accurately captured in positive sequence dynamic simulation tools; electromagnetic transient (EMT) simulations may be needed. This paper describes the process of creating and validating an EMT model of the entire Maui island transmission system in PSCAD software. The model is validated against the utility's PSSE model and against field data for an event that consisted of a single-phase fault followed by a generation trip. The methodology and lessons learned are summarized here.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2023

Solvent Dynamics Are Critical to Understanding Carbon Dioxide Dissolution and Hydration in Water

Simulations of carbon dioxide (CO 2 ) in water may aid in understanding the impact of its accumulation in aquatic environments and help advance technologies for carbon capture and utilization (via, e.g., mineralization). Quantum mechanical (QM) simulations based on static molecular models with polarizable continuum solvation poorly reproduce the energetics of CO 2 hydration to form carbonic acid in water, independent of the level of QM theory employed. Only with density-functional-theory-based molecular dynamics and rare-event sampling, followed by energy corrections based on embedded correlated wavefunction theory (in conjunction with density functional embedding theory), can a close agreement between theory and experiment be achieved. Such multilevel simulations can serve as benchmarks for simpler, less costly models, giving insight into potential errors of the latter. The strong influence of sampling/averaging over dynamical solvent configurations on the energetics stems from the difference in polarity of both the transition state and product (both polar) versus the reactant (nonpolar). When a solute undergoes a change in polarity during reaction, affecting its interaction with the solvent, careful assessment of the energetic contribution of the solvent response to this change is critical. Here, we show that static models (without structural sampling) that incorporate three explicit water molecules can yield far superior results than models with more explicit water molecules because fewer water molecules yield less configurational artifacts. Static models intelligently incorporating both explicit (molecules directly participating in the reaction) and implicit solvation, along with a proper QM theory, e.g., CCSD(T) for closed-shell systems, can close the accuracy gap between static and dynamic models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Three-flavor collective neutrino conversions with multi-azimuthal-angle instability in an electron-capture supernova model

We investigate the multi-azimuthal angle (MAA) effect on collective neutrino oscillation by considering the three-dimensional neutrino momentum distribution in a realistic electron-capture supernova model with an 8.8 M ⊙ progenitor. We find that the MAA effect induces collective flavor conversions at epochs when it is completely suppressed under the axial-symmetric approximation. This novel activity is switched on/off by the growth of the MAA instability and imprints additional time evolution in the expected neutrino event rate. We validate our results by extending the linear stability analysis into the three-flavor scheme including mixing angles, and confirm that the onset of collective neutrino oscillation matches the steep growth of flavor instability. Furthermore, we discuss how the MAA effect alters neutrino detection at Super-Kamiokande and DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Validation of Oregon State University High Temperature Test Facility Experiments Using Pronghorn

The OSU High Temperature Test Facility is a quarter-scale diameter, 1/64 scale volume test facility meant to replicate thermophysical phenomena in the prototypical General Atomics Modular High Temperature Gas Reactor. Tests pertaining to conduction cooldown events were performed from 2016-2019, providing a large database by which computational methods that are applicable to different length scales can be validated. One of these codes is Pronghorn, which is a coarse-meshed, porous-based subchannel thermal hydraulics code based on the MOOSE application, with the intention of better capturing the physics of both conduction and convection heat transfer within the OSU HTTF core. The goal of this summer project is to develop the framework by which Pronghorn can perform validation exercises of the HTTF core for benchmarking, by generating a mesh appropriate to the geometry of the HTTF core, developing input decks that accurately capture the initial and boundary conditions, materials, and relevant equations to the physics seen in the HTTF core, and using a postprocessor to compare simulation results to various experimental data. While validation of codes is a multi-year project, a mesh has been generated and tested in Pronghorn that meets mass conservation and basic heat transfer principles. The next step is to accurate depict the fluid inlet and outlet boundary conditions, which will be performed using computational fluid dynamics software.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Precision measurement of positron decay modes of Xe-125 in the LUX-ZEPLIN experiment

The radioisotope $^{125}\text{Xe}$ is a short-lived ($T_{1/2}\sim16.9 h$) activation product of the neutron calibrations performed in the LUX-ZEPLIN experiment. Subsequently, $^{125}$Xe decays primarily ($>99\%$) via electron capture, but positron emission has been confirmed by direct measurement to at least the 243 keV level of $^{125}\text{I}$. An additional decay to the 188keV level is expected from triple-coincident measurements of the annihilation and relaxation $γ$ rays, but has not been directly confirmed. By utilizing multiple-scatter event analysis and the pre-activation data to constrain backgrounds, this work reports positron emission with a statistical significance of 5.5$σ$. This corresponds to a total branching ratio of $0.29\pm0.08_{\text{stat.}}\pm0.04_{\text{sys.}}$%, and is the first constraint to the individual branching levels of $^{125}\text{I}$.

Akerib, D. S. [SLAC; KIPAC, Menlo Park]↗
DOE OSTI2020

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1-D diffusion-reaction model to calculate depth-resolved CO 2 production rates from soil CO 2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016-2018. While soil respiration correlated with temperature on diel timescales (p<0.05), seasonal variability was dominated by soil moisture and plant phenology (p<0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore-summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Lastly, we observed significant late-season CO 2 fluxes from the deep sub-soil (>165 cm) that support growing evidence for the importance of sub-soil processes in driving integrated respiration fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. Here, this paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

17 WIND ENERGY↗
DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗
DOE OSTI2023

Probing pH-Dependent Dehydration Dynamics of Mg and Ca Cations in Aqueous Solutions with Multi-Level Quantum Mechanics/Molecular Dynamics Simulations

The dehydration of aqueous calcium and magnesium cations is the most fundamental process controlling their reactivity in chemical and biological phenomena, such as the formation of ionic solids or passing through ion channels. It holds particular relevance in light of recent advancements in the development of carbon capture techniques that rely on mineralization for long-term carbon storage. Specifically, dehydration of Ca 2+ and Mg 2+ is a key step in proposed carbon capture processes aiming to exploit the relatively high concentration of dissolved carbon dioxide in seawater via the formation of carbonate minerals from solvated Ca 2+ and Mg 2+ cations for sequestration and storage. Nevertheless, atomic-scale understanding of the dehydration of aqueous Ca 2+ and Mg 2+ cations remains limited. Here, we utilize rare event sampling via density functional theory molecular dynamics and embedded wavefunction theory calculations to elucidate the dehydration dynamics of aqueous Ca 2+ and Mg 2+ . Emphasis is placed on the investigation of the effect pH has on the stability of the different coordination environments. Here, our results reveal significant differences in the dehydration dynamics of the two cations and provide insight into how they may be modulated by pH changes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics

One of the prominent effects of space weather is the formation of rapid geomagnetic field variations on Earth's surface driven by the magnetosphere-ionosphere system. These geomagnetic disturbances (GMDs) cause geomagnetically induced currents to run through ground conducting systems. In particular, localized GMDs (LGMDs) can be high amplitude and can have an effect on scale sizes less than 100 km, making them hazardous to power grids and difficult to predict. In this study, we examine the ability of the Space Weather Modeling Framework (SWMF) to reproduce LGMDs in the 7 September 2017 event using both existing and new metrics to quantify the success of the model against observation. We show that the high-resolution SWMF can reproduce LGMDs driven by ionospheric sources, but struggles to reproduce LGMDs driven by substorm effects. We calculate the global maxima of the magnetic fluctuations to show instances when the SWMF captures LGMDs at the correct times but not the correct locations. To remedy these shortcomings we suggest model developments that will directly impact the ability of the SWMF to reproduce LGMDs, most importantly updating the ionospheric conductance calculation from empirical to physics-based.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Extracellular DNA Alters Detection of Subtle Bacterial Responses to Soil Rewetting

Microbial communities are often characterized using DNA-based sequencing, but these approaches also capture extracellular DNA (exDNA) released from dead cells, potentially altering inference about microbial responses to environmental change. This may be especially important during pulse disturbances, such as soil drying–rewetting, which can increase microbial mortality and transient necromass pools. We assessed whether exDNA altered inference about bacterial responses to drying–rewetting (an 80 mm simulated rainfall event following a 28-day drought) in conventionally tilled corn and perennial switchgrass soils. We quantified bacterial abundance (16 S rRNA gene copies), alpha diversity, and community composition in paired soil samples with exDNA included (+ exDNA) and in samples treated with propidium monoazide (PMAxx) to reduce amplification of exDNA (− exDNA). At our level of replication (n = 4), PMAxx treatment did not significantly alter overall temporal response patterns (i.e., no significant main effect of DNA treatment or DNA × time interaction). However, PMAxx treatment increased sensitivity to detect some pairwise temporal changes in bacterial abundance and community composition in corn soils following rewetting. exDNA pools were proportionally highest immediately after rewetting in corn soils, suggesting transient extracellular DNA may contribute to masking during disturbance recovery. In contrast, PMAxx treatment had comparatively small effects in switchgrass soils, which exhibited weaker temporal responses overall. Inclusion of exDNA also changed which taxa appeared most responsive to rewetting. Together, our results suggest that exDNA does not uniformly bias soil microbial inference, but may reduce detectability of subtle disturbance-driven shifts in certain soils. Future studies should advance knowledge of microbial turnover and necromass dynamics, particularly using multiple complementary methods, to help predict when exDNA is most likely to influence ecological inference.

drying-rewetting↗
DOE OSTI2023

Pre-equilibrium evolution of conserved charges with initial conditions in the ICCING Monte Carlo event generator

Heavy-ion collisions can be well described through relativistic viscous hydrodynamics, but questions still remain when hydrodynamics is applicable because the initial state may begin very far from equilibrium. Thus, a pre-equilibrium evolution phase is used to bridge the gap between the initial state and hydrodynamics. KøMPøST is one such pre-equilibrium model that propagates the energy-momentum tensor by decomposing it into the background and fluctuations around that background, whose evolution is captured by Green's functions. We extend this formalism to include conserved charges and calculate the corresponding nonequilibrium Green's functions in the relaxation-time approximation. The ICCING algorithm initializes conserved charges in the initial state by sampling $g$ → $q$$\overline{q}$ splitting probabilities and is, thus, perfectly positioned to implement Green's functions for charge propagation. As a result, we show that this method alters the initial-state charge geometries and is applicable in central to mid-central collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗
DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Airborne Infrasound Makes a Splash

Natural and anthropogenic events may create low frequency sound waves, or infrasound, that can travel for vast distances in planetary atmospheres. They permit the remote monitoring of geophysical activity over local to global scales. Most studies have utilized ground-based recorders, but it is possible to deploy acoustic sensors to altitudes of over 50 km. Such elevated platforms can capture sounds that their surface analogs cannot access. High altitude balloons and low altitude aerostats are filling this observation gap, but key environments remain out of reach of both of these. Furthermore, recent work by den Ouden, Smets et al. (2021) addressed this with a new instrumentation platform—a large seabird flying just above the ocean's surface. Their work demonstrates that, infrasound sensing using heavier-than-air platforms in windy environments is possible, which has implications both terrestrially (e.g., extending sensor networks over the oceans) and extraterrestrially (proposed or planned missions to Venus and Titan).

47 OTHER INSTRUMENTATION↗