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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

High-Resolution Near-Infrared Spectroscopy of an Equivalent Width-Selected Sample of Starbursting Dwarf Galaxies

Spectroscopic observations from the Large Binocular Telescope and the Very Large Telescope reveal kinematically narrow lines (approx. 50 km/s) for a sample of 14 Extreme Emission Line Galaxies (EELGs) at redshifts 1.4 < zeta < 2.3. These measurements imply that the total dynamical masses of these systems are low ( 3 × 10(exp 9) M). Their large [O III]5007 equivalent widths (500 − 1100 A) and faint blue continuum emission imply young ages of 10−100 Myr and stellar masses of 10(exp 8)−10(exp 9) M, confirming the presence of a violent starburst. The stellar mass formed in this vigorous starburst phase thus represents a large fraction of the total (dynamical) mass, without a significantly massive underlying population of older stars. The occurrence of such intense events in shallow potentials strongly suggests that supernova-driven winds must be of critical importance in the subsequent evolution of these systems.

Spectroscopy↗
DOE OSTI2020

Frequency Event Categorization in Power Distribution Systems Using Micro PMU Measurements

Frequency is an important metric for power stability. To enhance the situational awareness in power distribution systems, this article proposes a data-driven approach that utilizes the data from micro phasor measurement units (μPMU) to categorize frequency events observed in distribution feeders. The proposed approach has three layers, each of which analyzes if a frequency event is propagated from the transmission system, formed inside a distribution feeder or falsely produced by instantaneous frequency (IF) estimation algorithms, respectively. In the first layer, the Granger causality is utilized to compare the causal relations from measurements across different locations to decide if an event is propagated from the transmission side. In the second layer, a trained sparse coding dictionary is utilized to unmix the physical causes of a frequency event. In the third layer, a moving z-score based event-pair detector is applied to identify if a frequency event is algorithm-induced. The proposed approach is tested using the μPMU measurements collected from Lawrence Berkeley National Laboratory (LBNL) and Riverside Public Utility (RPU).

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1985

Beam-driven ion cyclotron harmonic resonances in the terrestrial foreshock

A terrestrial upstream wave event which demonstrates multiple, ion cyclotron harmonic resonances between the interplanetary wave population and an observed proton beam is analyzed. The techniques and parameters employed in the data analysis are discussed, including the use of differential and band-pass filters. An upstream wave event demonstrating multiple harmonic waves is examined, and the instability analysis relevant to the ion beam observations thought to be responsible for that event is discussed. It is shown that an observed bi-Maxwellian ion beam is capable of generating right and left-hand polarized waves through ion cyclotron harmonic resonance.

Smith, C. W.↗
NASA NTRS2022

The First Gradual Solar Energetic Particle Event With an Enhanced He-3 Abundance on Solar Orbiter

The origin of 3 He abundance enhancements in gradual solar energetic particle (SEP) events remains largely unexplained. Two mechanisms have been suggested: the reacceleration of remnant flare material by coronal mass ejection (CME)-driven shocks in interplanetary space, and concomitant activity in the corona. We explore the first gradual SEP event with enhanced 3 He abundance that was observed by Solar Orbiter. The event started on 2020 November 24 and was associated with a relatively fast halo CME. During the event, the spacecraft was at 0.9 au from the Sun. The event-averaged 3 He/ 4 He abundance ratio is 24 times higher than the coronal or solar wind value, and the timing of the 3He intensity was similar to that of other species. We inspected available imaging, radio observations, and the spacecraft magnetic connection to the CME source. The most probable cause of the enhanced 3He abundance apparently are residual 3He ions remaining from a preceding long period of 3 He-rich SEPs on 2020 November 17–23.

Solar particle emission↗
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 OSTI2025

A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.

Earth Systems Modeling↗
NASA NTRS1999

What We Don't Understand About Ion Acceleration Flares

There are now strong associations between the (3)He-rich, Fe-rich ions in "impulsive" solar energetic particle (SEP) events and the similar abundances derived from gamma-ray lines from flares. Compact flares, where wave energy can predominate, are ideal sites for the study of wave-particle physics. Yet there are nagging questions about the magnetic geometry, the relation between ions that escape and those that interact, and the relative roles of cascading Alfven waves and the EMIC waves required to enhance He-3. There are also questions about the relative timing of ion and electron acceleration and of heating; these relate to the variation of ionization states before and during acceleration and during transport out of the corona. We can construct a model that addresses many of these issues, but problems do remain. Our greatest lack is realistic theoretical simulations of element abundances, spectra, and their variations. By contrast, we now have a much better idea of the acceleration at CME-driven shock waves in the rare but large "gradual" SEP events, largely because of their slow temporal evolution and great spatial extent.

Reames, Donald V.↗
NASA NTRS2017

Current Status and Future Challenges in Risk-Based Radiation Engineering

This presentation covers the basis and challenges for radiation effects in electronic systems. The three main types of radiation effects in electronics are: 1) total ionizing dose (TID), 2) total non-ionizing dose (TNID) / displacement damage dose (DDD), and 3) single-event effect (SEE). Some content on relevant examples of effects, current concerns, and possible environmental model-driven solutions are also included.

Total Ionizing Dose (TID)↗
NASA NTRS2021

Severity of Drought and Heatwave Crop Losses Tripled Over the Last Five Decades in Europe

Extreme weather disasters (EWDs) can jeopardize domestic food supply and disrupt commodity markets. However, historical impacts on European crop production associated with droughts, heatwaves, floods, and cold waves are not well understood—especially in view of potential adverse trends in the severity of impacts due to climate change. Here, we combine observational agricultural data (FAOSTAT) with an extreme weather disaster database (EM-DAT) between 1961 and 2018 to evaluate European crop production responses to EWD. Using a compositing approach (superposed epoch analysis), we show that historical droughts and heatwaves reduced European cereal yields on average by 9% and 7.3%, respectively, associated with a wide range of responses (inter-quartile range +2% to −23%; +2% to −17%). Non-cereal yields declined by 3.8% and 3.1% during the same set of events. Cold waves led to cereal and non-cereal yield declines by 1.3% and 2.6%, while flood impacts were marginal and not statistically significant. Production losses are largely driven by yield declines, with no significant changes in harvested area. While all four event frequencies significantly increased over time, the severity of heatwave and drought impacts on crop production roughly tripled over the last 50 years, from −2.2% (1964–1990) to −7.3% (1991–2015). Drought-related cereal production losses are shown to intensify by more than 3% yr−1. Both the trend in frequency and severity can possibly be explained by changes in the vulnerability of the exposed system and underlying climate change impacts.

Extreme weather disasters↗
DOE OSTI2025

Achilles and GENIE Model Comparisons Between Quasi-Elastic Proton Exclusive Events in DUNE PRISM ND-LAr

In this study, we analyze simulated CCQE proton exclusive events in DUNE s ND-LAr using Achilles and GENIE Monte Carlo event generators. We generate events using muon neutrino exclusive flux files of DUNE s on-axis and 20m off-axis PRISM positions and compare various kinematics of proton exclusive events between the event generators. Events are further analyzed in categories according to the number of final-state protons. This study was motivated by QE s dominance in statistics for off-axis events over other channels in which off-axis events are very useful for characterizing DUNE s flux. Furthermore, our motivation for comparing results between generators is driven by off-axis QE neutrino spectra peaking below 1 GeV. In this region, many low-energy proton events are produced in which models have been shown to have disagreements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A Closed Bay-Breeze Circulation and Its Lifecycle From TRACER With a New Orienteering Tape Recorder Diagram

A closed bay-breeze circulation (BBC) followed by a gulf-breeze front (GBF) was observed on 10 September 2022 during the Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) TRACER campaign. Using high-resolution X-band Scanning Cloud Radar (XSACR) and a newly developed orienteering tape recorder diagram, the study analyzed radar reflectivity and Doppler velocity to identify anomalies and track the evolution of these circulations. The BBC, a mesoscale system approximately 30-km long, 30-km wide, and 2-km deep, formed from enhanced horizontal convective rolls along the Galveston Bay coast, progressing northwestward under a 6 m s−1 onshore flow anomaly and reaching 1.5 km depth with return flow aloft. The inland penetration speed was 2 m s−1, driven by an observed 6-9 K land-water temperature contrast. The GBF, coupled to the BBC, intensified with additional southerly flow, penetrating further inland after the BBC collapsed. Passing over the TRACER field site, both fronts significantly impacted boundary layer thermodynamics, dynamics, and aerosol concentration. The BBC event exhibited four lifecycle stages-formation, development, maturation, and dissipation-driven by solar heating, wind field rotation, and interactions with convective eddies and the GBF. This study provides insights into the inland evolution of coastal breeze circulations and their interactions with environmental processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mean-field limit of a hybrid system for multi-lane multi-class traffic

This article aims to study coupled mean-field equation and ODEs with discrete events motivated by vehicular traffic flow. Precisely, multi-lane traffic flow in presence of human-driven and autonomous vehicles is considered, with autonomous vehicles possibly influenced by external policymakers. First, a finite-dimensional hybrid system is developed based on the continuous Bando-Follow-the-Leader dynamics coupled with discrete events due to lane-change maneuvers. Then the mean-field limit of the finite-dimensional hybrid system is rigorously derived for the dynamics of the human-driven vehicles. The microscopic lane-change maneuvers of the human-driven vehicles generate a source term for the mean-field PDE. This leads to an infinite-dimensional hybrid system described by coupled Vlasov-type PDE, ODEs, and discrete events.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Solar flares and avalanches in driven dissipative systems

The contention of Lu and Hamilton (1991) that the energy release process in solar flares can be understood as avalanches of many small reconnection events is further developed. The dynamics of the complex magnetized plasma of solar active regions is modeled with a simple driven dissipative system, consisting of a vector field with local instabilities that cause rapid diffusion of the field. It is argued that the avalanches in this model are analogous to solar flares. The distributions of avalanches in this model are compared with the solar flare frequency distributions obtained from ISEE 3/ICE satellite observations. Quantitative agreement is found with the energy, peak luminosity, and duration distributions over four orders of magnitude in flare energy, from the largest flares down to the completeness limit of the observations. It is predicted that the power-law solar flare frequency distributions will be found to continue downward with the same logarithmic slopes to an energy of about 3 x 10 exp 25 ergs and duration of about 0.3 s, with deviations from power-law behavior below these values.

Lu, Edward T.↗
NASA NTRSDate not supplied

Can Large Scale Volcanism Explain the Heat-Death of Venus (and Venus-like worlds?)

One of the fundamental questions about Venus is whether it ever had a habitable period in its distant past. If it did have such a period how did Venus get from a temperate state to its present day hot-house? Recent studies have demonstrated that increasing solar luminosity through time is an unlikely pathway for Venus even if that is one way for Earth to end up in a runaway greenhouse state. Large Igneous Provinces (LIPs; like that of the Siberian Traps) have been responsible for the End Permian mass extinction and other dramatic climate change events throughout Earth’s history. We investigate the hypothesis that overlapping Large Igneous Provinces could have driven Venus from a temperate to a runaway greenhouse state. We look at the LIP record in Earth history and the probability for overlapping LIP events as a proxy.

Venus↗
DOE OSTI2021

Seasonal changes in the drivers of water physico-chemistry variability of a small freshwater tidal river

Where rivers meet the sea, tides can exert a physical and chemical influence on the lower reaches of a river. How tidal dynamics in these tidal river reaches interact with upstream hydrological drivers such as storm rainfall, which ultimately determines the quantity and composition of material transferred from watersheds to estuaries, is currently unknown. We monitored a small freshwater tidal river in the Pacific Northwest, USA in high resolution over one year to evaluate the relative importance of tides versus upstream hydrological flows (i.e., base flow and precipitation events) on basic physico-chemical parameters (pH, dissolved oxygen, turbidity, specific conductivity, and temperature), and how these interactions relate to the downstream estuary. Tidal variability and diurnal cycles (i.e. solar radiation) dominated water physico-chemical variability in the summer, but the influence of these drivers was overshadowed by storm-driven sharp pulses in river physico-chemistry during the remainder of the year. Within such events, we found incidences of counterclockwise hysteresis of pH, counterclockwise hysteresis of dissolved oxygen, and clockwise hysteresis of turbidity, although systematic trends were not observed across events. The dominance of storm rainfall in the river’s physico-chemistry dynamics, and similar pulses of decreased pH observed in adjacent estuarine waters, suggest that the linkage between tidal streams and the broader system is variable throughout the year. High-frequency monitoring of tidal river biogeochemistry is therefore crucial to enable the assessment of how the relative strength of these drivers may change with future sea level rise and altered precipitation patterns to modulate biogeochemical dynamics across the land-ocean-atmosphere continuum.

aquatic↗
DOE OSTI2020

Improving Primary Frequency Response in Networked Microgrid Operations using Multilayer Perceptron-Driven Reinforcement Learning

Individual microgrids can improve the reliability of power grids during extreme events, and networked microgrids can further improve efficiency through resource sharing and increase the resilience of critical end-use loads. However, networked microgrid operations are subject to large switching transients, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of inverter-connected renewable energy resources, which do not provide the system inertia needed to mitigate the transients. Existing generator controls can be modified to invoke a drop in terminal voltage in response to a frequency deviation, thereby reducing load and improving frequency response. This paper investigates the use of a reinforcement-learning--based controller trained over several switching transient scenarios to modify generator controls during large frequency deviations. Compared to previously used proportional integral controllers, the proposed controller can improve primary frequency response while adapting to changes in system topologies and events.

Radhakrishnan, Nikitha↗
NASA NTRS2022

Turbulent Heat Flux, Downward Longwave Radiation, and Large-Scale Atmospheric Circulation Associated with Wintertime Barents–Kara Sea Extreme Sea Ice Loss Events

We investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents–Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents–Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows that strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, although turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6–10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden–Julian oscillation (MJO) or El Nino–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

Arctic↗
DOE OSTI2025

A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗