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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A porcine ligated loop model reveals new insight into the host immune response against Campylobacter jejuni

The symptoms of infectious diarrheal disease are mediated by a combination of a pathogen’s virulence factors and the host immune system. Campylobacter jejuni is the leading bacterial cause of diarrhea worldwide due to its near-ubiquitous zoonotic association with poultry. One of the outstanding questions is to what extent the bacteria are responsible for the diarrheal symptoms via intestinal cell necrosis versus immune cell initiated tissue damage. To determine the stepwise process of inflammation that leads to diarrhea, we used a piglet ligated intestinal loop model to study the intestinal response to C. jejuni. Pigs were chosen due to the anatomical similarity between the porcine and the human intestine. We found that the abundance of neutrophil related proteins increased in the intestinal lumen during C. jejuni infection, including proteins related to neutrophil migration (neutrophil elastase and MMP9), actin reorganization (Arp2/3), and antimicrobial proteins (lipocalin-2, myeloperoxidase, S100A8, and S100A9). The appearance of neutrophil proteins also corresponded with increases of the inflammatory cytokines IL-8 and TNF-a. Compared to infection with the C. jejuni wild-type strain, infection with the noninvasive C. jejuni ?ciaD mutant resulted in a blunted inflammatory response, with less inflammatory cytokines and neutrophil markers. These findings indicate that intestinal inflammation is driven by C. jejuni virulence and that neutrophils are the predominant cell type responding to C. jejuni infection. We propose that this model can be used as a platform to study the early immune events during infection with intestinal pathogens.

disease model, INNATE IMMUNITY, proteomics, intest↗
DOE OSTI2023

Gravitational Wave Eigenfrequencies from Neutrino-driven Core-collapse Supernovae

Abstract Core-collapse supernovae (CCSNe) are predicted to produce gravitational waves (GWs) that may be detectable by Advanced LIGO/Virgo. These GW signals carry information from the heart of these cataclysmic events, where matter reaches nuclear densities. Recent studies have shown that it may be possible to infer the properties of the proto-neutron star (PNS) via GWs generated by hydrodynamic perturbations of the PNS. However, we lack a comprehensive understanding of how these relationships may change with the properties of CCSNe. In this work, we build a self-consistent suite of over 1000 exploding CCSNe from a grid of progenitor masses and metallicities combined with six different nuclear equations of state (EOS). Performing a linear perturbation analysis on each model, we compute the resonant GW frequencies of the PNS, and we motivate a time-agnostic method for identifying characteristic frequencies of the dominant GW emission. From this, we identify two characteristic frequencies, of the early- and late-time signal, that measure the surface gravity of the cold remnant neutron star, and simultaneously constrain the hot nuclear EOS. However, we find that the details of the CCSN model, such as the treatment of gravity or the neutrino transport, and whether it explodes, noticeably change the magnitude and evolution of the PNS eigenfrequencies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Covalency-Driven Differences in the Hydrogenation Chemistry of Lanthanide- and Actinide-Based Frustrated Lewis Pairs

The electronic organization of Frustrated Lewis Pairs (FLPs) allows them to activate strong bonds in mechanisms that are usually free of redox events at the Lewis acidic site. The unique 6d/5f manifold of uranium could serve as an interesting FLP acceptor site, but to date FLP-like catalysis with actinide ions is unknown. In this paper, the catalytic, FLP-like hydrogenation reactivity of trivalent uranium complexes is explored in the presence of base-stabilized silylenes. Comparison to isoelectronic, isostructural lanthanide and thorium complexes lends insight into the electronic factors governing dihydrogen activation. Mechanistic studies of the uranium- and lanthanide-catalyzed hydrogenations are presented, including discussion of likely intermediates. Computational modeling of the f-element complexes, combined with experimental comparison to p-block Lewis acids, elucidates the relevance of steric hindrance to productive reactivity with dihydrogen. As a result, consideration of the complete experimental and theoretical evidence provides a clear picture of the electronic and steric factors governing dihydrogen activation by these FLPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Data-driven Alignment of the Tracker for the Mu2e Experiment

An alignment of the Mu2e Tracker is investigated using the Mu2e Offline Software and Millepede-II. Simulated misalignments of Tracker planes were inserted between Monte-Carlo event generation and before reconstruction using randomly generated $(x,y,z)$-translations and rotations of the rigid-body planes in the detector model. Cosmic ray muons, with no magnetic field present in the Detector Solenoid, will follow a straight-line trajectory. A maximum-likelihood fit is used to reconstruct a track and determine its parameters. Tracks traversing at least four planes, with ten registered straw hits, and a maximum time residual of $20$ ns are selected to be used in an alignment sample. The cuts were chosen to discard tracks with low information content, such as those with high levels of multiple scattering or those with low numbers of measurements contributing to a single track fit. Millepede-II produces the resulting alignment constants according to a simultaneous least-squares fit of the alignment parameters and the track parameters for all input tracks. The improved alignment constants are used to repeat the process and recover an increased number of suitable tracks, further improving the estimate of the alignment constants in subsequent iterations. A performance study of the process was conducted on a misaligned Tracker in the case of translation-only, and translation plus rotational misalignments. In both cases, the correct alignment constants were recovered to within statistical error after three global fit iterations. Additionally, a continuous integration system was developed for the \texttt{Mu2e/Offline} GitHub repository, introducing a convenient automated testing process for all proposed changes (``pull requests"). The tests developed included a full build of the software, followed by tests of the validation routines, geometry overlap checking, and multi-threading mode. Additionally, code quality checks and low-statistics physics comparison checks a re also available.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California

Mesoscale model predictions of wind, turbulence, and wind energy capacity factors are evaluated in the Altamont Pass Wind Resource Area of California (APWRA), where the diurnal regional sea breeze and associated terrain-driven speedup flows drive wind energy production during the summer months. Results from the Weather Research and Forecasting model version 4.4 using a novel three-dimensional planetary boundary layer (3D PBL) scheme, which treats both vertical and horizontal turbulent mixing, are compared to those using a well-established one-dimensional (1D) scheme that treats only vertical turbulent mixing. Each configuration is evaluated over a nearly 3-month-long period during the Hill Flow Study, and due to the recurring nature of the observed speedup flows, diurnal composite averaging is used to capture robust trends in model performance. Both model configurations showed similar overall skill. The general timing and direction of the speedup flows is captured, but their magnitude is overestimated within a typical wind turbine rotor layer. Both also fail to capture a persistent observed near-surface jet-like flow, likely due to the limited grid resolution that is typical of mesoscale models. However, the 3D PBL configuration shows several minor improvements over the 1D PBL configuration, including improved wind speed and turbulence kinetic energy profiles during the accelerating phase of the speedup events, as well as reduced positive wind speed bias at surface stations across the APWRA region. Using a mesoscale wind farm parameterization, modeled capacity factors are also compared to monthly data reported to the US Energy Information Administration (EIA) during the study period. Although the monthly trend in the data is captured, both model configurations overestimate capacity factors by roughly 7 %–11 %. Through model evaluation, this study provides confidence in the 3D PBL scheme for wind energy applications in complex terrain and provides guidance for future testing.

17 WIND ENERGY↗
DOE OSTI2022

Quantifying Carbon Cycle Extremes and Attributing Their Causes Under Climate and Land Use and Land Cover Change From 1850 to 2300

The increasing atmospheric carbon dioxide (CO 2 ) mole fraction affects global climate through radiative (trapping longwave radiation) and physiological effects (reduction of plant transpiration). We use the simulations of the Community Earth System Model (CESM1-BGC) forced with Representative Concentration Pathway 8.5 to investigate climate-vegetation feedbacks from 1850 to the year 2300. Human-induced land use and land cover change (LULCC), through biogeochemical and biogeophysical processes, alter the climate and modify photosynthetic activity. The changing characteristics of extreme anomalies in photosynthesis, referred to as carbon cycle extremes, increase the uncertainty of terrestrial ecosystems to act as a net carbon sink. However, the role of LULCC in altering carbon cycle extremes under business-as-usual (continuously rising) CO 2 emissions is unknown. Here we show that LULCC magnifies the intensity, frequency, and extent of carbon cycle extremes, resulting in a net reduction in expected photosynthetic activity in the future. We found that large temporally contiguous negative carbon cycle extremes are due to a persistent decrease in soil moisture, which is triggered by declines in precipitation. With LULCC and global warming, vegetation exhibits increased vulnerability to hot and dry environmental conditions, increasing the frequency of fire events and resulting in considerable losses in photosynthetic activity. While most regions show strengthening of negative carbon cycle extremes, a few locations show a weakening effect driven by declining vegetation cover or benign climate conditions for photosynthesis. Increasing hot, dry, and fire-driven carbon cycle extremes are essential for improving carbon cycle modeling and estimation of ecosystem responses to LULCC and rising CO 2 mole fractions. Moreover, large aberrations in vegetation productivity represent potential and growing threats to human lives, wildlife, and food security.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The frequency of extreme X-ray variability for radio-quiet quasars

ABSTRACT We analyse 1598 serendipitous Chandra X-ray observations of 462 radio-quiet quasars to constrain the frequency of extreme amplitude X-ray variability that is intrinsic to the quasar corona and innermost accretion flow. The quasars in this investigation are all spectroscopically confirmed, optically bright (mi ≤ 20.2), and contain no identifiable broad absorption lines in their optical/ultraviolet spectra. This sample includes quasars spanning z ≈ 0.1–4 and probes X-ray variability on time-scales of up to ≈12 rest-frame years. Variability amplitudes are computed between every epoch of observation for each quasar and are analysed as a function of time-scale and luminosity. The tail-heavy distributions of variability amplitudes at all time-scales indicate that extreme X-ray variations are driven by an additional physical mechanism and not just typical random fluctuations of the coronal emission. Similarly, extreme X-ray variations of low-luminosity quasars seem to be driven by an additional physical mechanism, whereas high-luminosity quasars seem more consistent with random fluctuations. The amplitude at which an X-ray variability event can be considered extreme is quantified for different time-scales and luminosities. Extreme X-ray variations occur more frequently at long time-scales (Δt ≳ 300 d) than at shorter time-scales and in low-luminosity quasars compared to high-luminosity quasars over a similar time-scale. A binomial analysis indicates that extreme intrinsic X-ray variations are rare, with a maximum occurrence rate of $\lt 2.4{{\ \rm per\ cent}}$ of observations. Finally, we present X-ray variability and basic optical emission-line properties of three archival quasars that have been newly discovered to exhibit extreme X-ray variability.

Timlin, III, John D.↗
DOE OSTI2021

Episodic Earthquake Swarms in the Mineral Mountains, Utah Driven by the Roosevelt Hydrothermal System

Over 1,000 earthquakes (-2.0 < M < 2.0), identified using a matched-filter method, occurred in the Mineral Mountains, Utah between 2016 and 2019. The enhanced catalog is complete down to M -0.9 and contains roughly 15 times more events than originally cataloged. Earthquake relocation of ~800 earthquakes shows that activity is concentrated in a <2 km long E-W striking narrow zone, ~4 km east of the Roosevelt hydrothermal system. Two fault orientations, both N-S and E-W parallel to the Opal Mound and Mag Lee faults, respectively, are observed after computing composite focal mechanisms of highly similar earthquakes. Looking solely at the temporal distribution of the seismicity, we identify 15 periods of swarm-like activity, with two major clusters occurring in December 2016, recorded by three stations, and in October 2019 recorded by eight stations. The October 2019 swarm, the best recorded sequence in the area, provides evidence for the underlying triggering mechanism. We show that a complex mechanism of fluid diffusion and aseismic slip is responsible for the swarm evolution with migration velocities reaching 10 km/day. We hypothesize that these episodic swarms in the Mineral Mountains are primarily driven by migrating fluids that originate within the Roosevelt hydrothermal system.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2025

Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large-cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Ripple-Type Control for Enhancing Resilience of Networked Physical Systems

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a “hotline” communication network. According to this protocol, agents act on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources and it is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests corroborate the efficacy of the novel scheme.

distributed control↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗
DOE OSTI2022

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Confinement of relativistic electrons in a magnetic mirror en route to a magnetized relativistic pair plasma

Creating a magnetized relativistic pair plasma in the laboratory would enable the exploration of unique plasma physics relevant to some of the most energetic events in the universe. As a step toward a laboratory pair plasma, we have demonstrated an effective confinement of multi-MeV electrons inside a pulsed-power-driven 13 T magnetic mirror field with a mirror ratio of 2.6. The confinement is diagnosed by measuring the axial and radial losses with magnetic spectrometers. The loss spectra are consistent with ≤2.5 MeV electrons confined in the mirror for ∼1 ns. With a source of 1012 electron-positron pairs at comparable energies, this magnetic mirror would confine a relativistic pair plasma with Lorentz factor γ∼6 and magnetization σ∼40.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

HL-LHC Computing Review: Common Tools and Community Software

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Ripple-Type Control for Enhancing Resilience of Networked Physical Systems: Preprint

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical in-frastructures under unexpected events. While purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physicalsystem and are allowed to communicate with peers over a 'hot-line' communication network. According to this protocol, agents act upon on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources, and is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests on both networks corroborate the efficacy of the novel scheme.

distributed control↗