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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Microstructures in type III events in the solar wind

A model is presented for the generation and evolution of bump-in-tail driven Langmuir waves in the solar wind during type III emission which removes a number of apparent inconsistencies between theory and observations. Growth rates and energy densities of Langmuir waves are considerably enhanced, permitting growth to overcome linear scattering losses, and also allowing nonlinear decay into ion-acoustic waves, in line with observations. Estimates are made of the probability distribution p(E), of wave field strengths E, based on linear and nonlinear wave-packet evolution, yielding p(E) approximately equal to E exp -alpha, alpha greater than or equal to 3. This helps explain why very high values of E are rarely found in the measured spiky wave turbulence.

Melrose, D. B.↗
NASA NTRS1992

Transient events in the outer magnetosphere - Boundary waves of flux transfer events?

Simultaneous solar wind and magnetospheric observations are presented for a series of flux transfer events (FTEs) observed by the UKS and IRM spacecraft in the outer prenoon magnetosphere on October 28, 1984. The CCE satellite, located near local noon, observed an enhancement in the magnetospheric magnetic field strength 4 min prior to each IRM/UKS event, suggesting the antisunward propagation of a compression in the magnetopause surface. It is suggested that these compressions resulted from wavy magnetopause motion driven by variations in the solar wind dynamic pressure applied to the magnetosphere, rather than the motion of FTEs formed by magnetic merging. This hypothesis is confirmed by the fact that the events previously identified as FTEs occurred during a sequence of quasi-periodic (5-6 min) oscillations in the magnetospheric plasma velocity and magnetic field.

Sibeck, D. G.↗
DOE OSTI2020

Untangling the sequence of events during the S 2 → S 3 transition in photosystem II and implications for the water oxidation mechanism

In oxygenic photosynthesis, light-driven oxidation of water to molecular oxygen is carried out by the oxygen-evolving complex (OEC) in photosystem II (PS II). Recently, we reported the room-temperature structures of PS II in the four (semi)stable S-states, S 1 , S 2 , S 3 , and S 0 , showing that a water molecule is inserted during the S 2 → S 3 transition, as a new bridging O(H)-ligand between Mn1 and Ca. To understand the sequence of events leading to the formation of this last stable intermediate state before O 2 formation, we recorded diffraction and Mn X-ray emission spectroscopy (XES) data at several time points during the S 2 → S 3 transition. At the electron acceptor site, changes due to the two-electron redox chemistry at the quinones, Q A and Q B , are observed. At the donor site, tyrosine Y Z and His190 H-bonded to it move by 50 µs after the second flash, and Glu189 moves away from Ca. This is followed by Mn1 and Mn4 moving apart, and the insertion of O X (H) at the open coordination site of Mn1. This water, possibly a ligand of Ca, could be supplied via a “water wheel”-like arrangement of five waters next to the OEC that is connected by a large channel to the bulk solvent. XES spectra show that Mn oxidation (τ of ∼350 µs) during the S 2 → S 3 transition mirrors the appearance of O X electron density. This indicates that the oxidation state change and the insertion of water as a bridging atom between Mn1 and Ca are highly correlated.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Alfvenically driven slow shocks in the solar chromosphere and corona

The nonlinear evolution of an Alfvenic impulse launched from the photosphere and its dynamical effects on the chromosphere, transition region (TR), and corona are investigated using a simple 1D model. It is found that the leading edge of the torsional pulse can steepen into a fast shock in the chromosphere if the pulse is of sufficiently large amplitude and short duration. A slow shock which develops behind the Alfvenic pulse can reflect downgoing Alfven waves back up to the corona. The upgoing reflected wave can induce a significant upward ejection of the TR. Nonlinear dynamics are found to lead to very impulsive behavior at later times. It is suggested that impulsive events occurring in the TR or corona need not be interpreted in terms of reconnection-driven microflares. It is also found that B(0) in the chromosphere can be amplified when the TR and chromosphere fall.

Hollweg, Joseph V.↗
NASA NTRSDate not supplied

Hourly Stream Heights – A Short-term Deep Learning Prediction Model

River flooding can have a detrimental impact on a community by causing loss of life, loss or damage to property, and damage to infrastructure. Having the capability to forecast flooding events can prevent the loss of life and mitigate damage to property. A programmatic, data-driven approach using deep learning to forecast a stream’s gauge height every 6 hours has been developed by NASA’s Short-term Prediction Research and Transition Center (SPoRT) and is currently in operation. Based on our end user engagement and feedback, this medium-term product has been successfully adopted by several National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC). SPoRT is currently researching and developing a short-term hourly deep learning model which will be useful in forecasting flash flood events.

Michael Antia↗
DOE OSTI2021

Progress Towards Interpretable Machine Learning-based Disruption Predictors Across Tokamaks

In this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently missing for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. Thanks to the univariate analysis on the features used in such data-driven applications on both tokamaks, we are able to statistically highlight differences in the dominant disruption precursors: JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge cooling mechanisms that destabilize dangerous MHD modes. Even though the analyzed datasets are characterized by such intrinsic differences, we show how data-driven algorithms trained on one device can be used to predict and interpret disruptive scenarios on the other. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be used to explain a disruptive behavior on another device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Gender Gaps in Mode Usage, Vehicle Ownership, and Spatial Mobility When Entering Parenthood: A Life Course Perspective

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention.

Yang, Hung-Chia (ORCID:0000000161439904)↗
DOE OSTI2018

Adaptive Sampling of Large Deviations

We introduce and test an algorithm that adaptively estimates large deviation functions characterizing the fluctuations of additive functionals of Markov processes in the long-time limit. These functions play an important role for predicting the probability and pathways of rare events in stochastic processes, as well as for understanding the physics of nonequilibrium systems driven in steady states by external forces and reservoirs. The algorithm uses methods from risk-sensitive and feedback control to estimate from a single trajectory a new process, called the driven process, known to be efficient for importance sampling. Its advantages compared to other simulation techniques, such as splitting or cloning, are discussed and illustrated with simple equilibrium and nonequilibrium diffusion models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Preface: special issues on photosystem II

Here, the appearance of oxygenic photosynthesis 3–4 billion years ago is arguably one of the most important evolutionary developments which radically transformed both the biotic and abiotic landscapes of Earth. The presence of molecular oxygen enabled the evolution of complex lifeforms requiring large amounts of chemical energy which could only be produced by aerobic respiration. Additionally, molecular oxygen remodeled the geochemistry of the planet during its transition from a reducing to an oxidizing atmosphere during the Great Oxidation Event. Central to these processes is Photosystem II (PS II), the light energy-driven, water-plastoquinone oxidoreductase which initially evolved in cyanobacteria and subsequently was incorporated, along with Photosystem I (PS I), the cytochrome b 6 f complex and the Ndh complex, into all algae and higher plants by the process of endosymbiosis. This membrane protein complex releases molecular oxygen and protons as the byproducts of water splitting and produces plastoquinol, which provides reducing equivalents that are used sequentially by the cytochrome b 6 /f complex, PS I, and ultimately, as NADPH + H + , by the Calvin–Benson–Bassham cycle. Additionally, the protons released by PS II (and those pumped by the cytochrome b 6 /f complex and the NDH complex (during cyclic electron transfer)) form the proton motive force that drives ATP synthesis via the ATP Synthase, thus providing the chemical energy source for carbon fixation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Wildfire and power grid nexus in a changing climate

Global wildfire events have had increasingly severe impacts in recent years, particularly in the western USA, driven by extreme fire-weather conditions, fuel accumulation and multiple ignition sources. Wildfires sparked by power lines tend to be larger and more destructive, as they often occur during high winds, which accelerate the spread of fires. Moreover, efforts to contain wildfires frequently result in power outages, causing considerable economic disruption. Here, in this Review, we examine wildfire risks related to power-line-induced ignitions, infrastructure damage, climate-induced environmental impacts, grid operational risks, real-time grid management risks, vegetation management risks, and financial and funding risks in the context of a changing climate and their interdependence with power grid infrastructures. We then explore the resilience of power grids under wildfire threats, looking at risk analysis, prediction and mitigation strategies. The Review also shares practical insights and experiences in the USA to inform researchers, policymakers and industry professionals.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches

Evaluating the response of the land carbon sink to the anomalies in temperature and drought imposed by El Niño events provides insights into the present-day carbon cycle and its climate-driven variability. It is also a necessary step to build confidence in terrestrial ecosystems models' response to the warming and drying stresses expected in the future over many continents, and particularly in the tropics. Here we present an in-depth analysis of the response of the terrestrial carbon cycle to the 2015/2016 El Niño that imposed extreme warming and dry conditions in the tropics and other sensitive regions. First, we provide a synthesis of the spatio-temporal evolution of anomalies in net land–atmosphere CO 2 fluxes estimated by two in situ measurements based on atmospheric inversions and 16 land-surface models (LSMs) from TRENDYv6. Simulated changes in ecosystem productivity, decomposition rates and fire emissions are also investigated. Inversions and LSMs generally agree on the decrease and subsequent recovery of the land sink in response to the onset, peak and demise of El Niño conditions and point to the decreased strength of the land carbon sink: by 0.4–0.7 PgC yr –1 (inversions) and by 1.0 PgC yr –1 (LSMs) during 2015/2016. As a result, LSM simulations indicate that a decrease in productivity, rather than increase in respiration, dominated the net biome productivity anomalies in response to ENSO throughout the tropics, mainly associated with prolonged drought conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Joint Chance Constraints Reduction Through Learning in Active Distribution Networks

Due to an increase in distributed generation and controllable loads, distribution networks are frequently operating under high levels of uncertainty. Joint chance constraints, which seek to satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints for optimization and control of these networks. Due to the complexity of evaluating these constraints directly, sampling approaches or approximations can be used to transform the joint chance constraint into deterministic constraints. However, sampling techniques may be extremely computationally expensive and not suitable for physical networks operating on fast timescales, and conservative approximations may needlessly result in a much higher cost of system operation. The proposed framework aims to provide a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and uses this additional information to accurately and efficiently approximate the joint chance constraint directly.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Graph Convolutional Network for Active Distribution System Anomaly Detection Considering Measurement Spatial-Temporal Correlations

The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measure-ments and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measure-ment correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.

active distribution system↗
DOE OSTI2019

Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning

This paper considers distribution systems with a high penetration of distributed, renewable generation and addresses the problem of incorporating the associated uncertainty into the optimal operation of these networks. Joint chance constraints, which satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints, leading to a chance-constrained optimal power flow problem. Departing from the computationally-heavy scenario-based approaches or approximations that transform the joint constraint into conservative deterministic constraints, this paper develops a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and accurately and efficiently approximates bounds on the joint chance constraint iteratively. In particular, the proposed framework improves upon the classic methods based on the union bound (or Boole's inequality) by generating a much less conservative set of single chance constraints that also guarantees the satisfaction of the original joint constraint. The proposed framework is evaluated numerically using the IEEE 37-node test feeder, focusing on the problem of voltage regulation in distribution grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019)

Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and temporal characteristics of fire, or the classic fire regime concept, we need to critically define fire events from remote sensing data. Events, fundamentally a geographic concept with delineated spatial and temporal boundaries around a specific phenomenon that is homogenous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe Fire Events Delineation (FIRED), an event-delineation algorithm, that has been used to derive fire events (N = 51,871) from the MODIS MCD64 burned area product for the coterminous US (CONUS) from January 2001 to May 2019. The optimized spatial and temporal parameters to cluster burned area pixels into events were an 11-day window and a 5-pixel (2315 m) distance, when optimized against 13,741 wildfire perimeters in the CONUS from the Monitoring Trends in Burn Severity record. The linear relationship between the size of individual FIRED and Monitoring Trends in Burn Severity (MTBS) events for the CONUS was strong (R2 = 0.92 for all events). Importantly, this algorithm is open-source and flexible, allowing the end user to modify the spatio-temporal threshold or even the underlying algorithm approach as they see fit. We expect the optimized criteria to vary across regions, based on regional distributions of fire event size and rate of spread. We describe the derived metrics provided in a new national database and how they can be used to better understand US fire regimes. The open, flexible FIRED algorithm could be utilized to derive events in any satellite product. We hope that this open science effort will help catalyze a community-driven, data-integration effort (termed OneFire) to build a more complete picture of fire.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

The mean ionic charge state of solar energetic Fe ions above 200 MeV per nucleon

We have analyzed the geomagnetic transmission of solar energetic Fe ions at approximately 200-600 MeV per nucleon during the great solar energetic particle (SEP) events of 1989 September-October. By comparing fluences from the Chicago charged-particle telescope on IMP-8 in interplanetary space and from NRL's Heavy Ions in Space (HIIS) experiment aboard the Long Duration Exposure Facility (LDEF) in low-Earth orbit, we obtain a mean ionic charge (Q(sub 3)) = 14.2 +/- 1.4. This result is significantly lower than (Q) observed at approximately 1 MeV per nucleon in impulsive, He-3 rich SEP events, indicating that neither acceleration at the flare site nor flare-heated plasma significantly contributes to the high-energy Fe ions we observe. But it agrees well with the (Q) observed in gradual SEP events at approximately 1 MeV per nucleon, in which ions are accelerated by shocks driven by fast coronal mass ejections, and hence shows that particles are accelerated to very high energies in this way. We also note apparent differences between solar wind and SEP charge state distributions, which may favor a coronal (rather than solar wind) seed population or may suggest additional ionization in the ambient shock-region plasma.

Tylka, A. J.↗
NASA NTRS1999

Microflaring in Low-Lying Core Fields and Extended Coronal Heating in the Quiet Sun

We have previously reported analyses of Yohkoh SXT data examining the relationship between the heating of extended coronal loops (both within and stemming from active regions) and microflaring in core fields lying along neutral lines near their footpoints (J. G. Porter, D. A. Falconer, and R. L. Moore 1998, in Solar Jets and Coronal Plumes, ed. T. Guyenne, ESA SP-421, and references therein). We found a surprisingly poor correlation of intensity variations in the extended loops with individual microflares in the compact heated areas at their feet, despite considerable circumstancial evidence linking the heating processes in these regions. Now, a study of Fe XII image sequences from SOHO EIT show that similar associations of core field structures with the footpoints of very extended coronal features can be found in the quiet Sun. The morphology is consistent with the finding of Wang et al. (1997, ApJ 484, L75) that polar plumes are rooted at sites of mixed polarity in the magnetic network. We find that the upstairs/downstairs intensity variations often follow the trend, identified in the active region observations, of a weak correspondence. Apparently much of the coronal heating in the extended loops is driven by a type of core field magnetic activity that is "cooler" than the events having the coronal signature of microflares, i.e., activity that results in little heating within the core fields themselves. This work was funded by the Solar Physics Branch of NASA's Office of Space Science through the SR&T Program and the SEC Guest Investigator Program.

Porter, Jason G.↗