SEARCH · Engineering Papers

Results for “event driven”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 901 records · Page 50

All sources NASA NTRS DOE OSTI

NASA NTRS2016

Solar Coronal Jets: Observations, Theory, and Modeling

Coronal jets represent important manifestations of ubiquitous solar transients, which may be the source of significant mass and energy input to the upper solar atmosphere and the solar wind. While the energy involved in a jet-like event is smaller than that of "nominal" solar flares and coronal mass ejections (CMEs), jets share many common properties with these phenomena, in particular, the explosive magnetically driven dynamics. Studies of jets could, therefore, provide critical insight for understanding the larger, more complex drivers of the solar activity. On the other side of the size-spectrum, the study of jets could also supply important clues on the physics of transients close or at the limit of the current spatial resolution such as spicules. Furthermore, jet phenomena may hint to basic process for heating the corona and accelerating the solar wind; consequently their study gives us the opportunity to attack a broad range of solar-heliospheric problems.

Raouafi, N. E.↗

NASA NTRS1979

Transversely accelerated ions - An ionospheric source of hot magnetospheric ions

The paper considers observational data on transversely accelerated ion (TAI) events observed from the Isis 1 and 2 satellites. A source of hot magnetospheric ions was discovered in the auroral topside ionosphere, and a part of the cold ionospheric ion distribution is transversely accelerated within a source region as low as 1000 km. Subsequent to transverse acceleration, the ions are driven upward into the magnetosphere by the gradient B mirror force; they are observed by the soft particle spectrometers on the Isis 1 and 2 satellites, with sharply defined pitch angle distributions centered between 90 and 120 deg, and with energy distributions up to several hundred electron volts. Observations at 1400 km indicate that the transversely accelerated ion (TAI) events are predominantly a winter nightside auroral zone phenomenon, but in the summertime TAI events were observed at altitudes above 2750 km on the day-side within the cleft. Typical intensities exceeding 10 to the 8th power ions/sq cm s sr during TAI events suggest that they may be a significant source of hot ions for the magnetosphere.

Klumpar, D. M.↗

NASA NTRS2024

Data-Driven Method for Nonlinear Modal Interaction Identification in Fighter Aircraft

The purpose of this paper is to report a new data-driven analysis methodology for identifying nonlinear modal interactions and its application to aeroelastic wind-tunnel data from a generic fighter configuration, which reveals global modal interactions in this system caused by multiple resonance capture events initiated by local nonlinearities. It is shown that during supersonic testing at the Transonic Dynamics Tunnel, vibro-impacts at the embedded external fuel tank shakers on the inboard underwing stations of the generic fighter introduce strong nonlinearity that gives rise to nonlinear modal interactions. Specifically, time–frequency analysis of the acceleration data demonstrates that 6∶5 and 7∶6 transient resonance captures occur across the aircraft exclusively during this vibro-impact forcing window. Further analysis suggests that these resonance captures are dependent on Mach number. These results show experimentally that local vibro-impacts can cause global nonlinear effects in an aeroelastic system and that these effects can be identified using the new methodology herein. The data-driven analysis scheme employed shows applicability to full-aircraft flight test data for identifying nonlinear modal interactions without knowledge of the precise nonlinear contributors.

nonlinear dynamics↗

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↗

NASA NTRS2022

Limitations in Representation of Physical Processes Prevents Successful Simulation of PM2.5 During KORUS-AQ

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea United States-Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by -64% but overestimates nitrate by +36%. The largest underestimate in sulfate occurs during the pollution event, where models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100% against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of five increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model’s inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66% to 54%. Locally-produced sulfate increased from 1% to 25% of locally-produced PM2.5, implying that local emissions controls would have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction which affects the aerosol liquid water abundance and chemistry driving sulfate-nitrate-ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30% less sulfate due to 40% less nitrate and aerosol water, and results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate+nitrate+ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements.

Katherine R. Travis↗

NASA NTRS2016

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Agricultural↗

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↗

NASA NTRS1986

Injection of dust into the Martian atmosphere - Evidence from the Viking Gas Exchange experiment

The hypothesis that predawn midlatitude storms are triggered by a soil humidification process is examined. A freeze/thaw model of the process is evaluated in the Viking Gas Exchange experiments conducted on Mars. The humidification-driven desorption and desiccation state of Martian soil samples are analyzed. The periodic humidification of equatorial regolith soil is studied in terms of pore space pressure during desorption events and soil diffusivity; the thermal properties of the regolith surface layer are modeled using the program of Clifford (1984). Consideration is given to the diurnal and seasonal cycles of the humidification process, the permanent, low-albedo features in the midlatitudes, and the production of H2SO4 and HCl aerosols.

Huguenin, R. L.↗

NASA NTRS2003

Solar Coronal Heating and the Magnetic Flux Content of the Network

We investigate the heating of the quiet corona by measuring the increase of coronal luminosity with the amount of magnetic flux in the underlying network at solar minimum when there were no active regions on the face of the Sun. The coronal luminosity is measured from Fe IX/X-Fe XII pairs of coronal images from SOHO/EIT. The network magnetic flux content is measured from SOHO/MDI magnetograms. We find that the luminosity of the corona in our quiet regions increases roughly in proportion to the square root of the magnetic flux content of the network and roughly in proportion to the length of the perimeter of the network magnetic flux clumps. From (1) this result, (2) other observations of many fine-scale explosive events at the edges of network flux clumps, and (3) a demonstration that it is energetically feasible for the heating of the corona in quiet regions to be driven by explosions of granule-sized sheared-core magnetic bipoles embedded in the edges of network flux clumps, we infer that in quiet regions that are not influenced by active regions the corona is mainly heated by such magnetic activity in the edges of the network flux clumps. Our observational results together with our feasibility analysis allow us to predict that (1) at the edges of the network flux clumps there are many transient sheared-core bipoles of the size and lifetime of granules and having transverse field strengths greater than approximately - 100 G, (2) approximately 30 of these bipoles are present per supergranule, and (3) most spicules are produced by explosions of these bipoles.

Moore, R. L.↗

NASA NTRS2003

Solar Coronal Heating and the Magnetic Flux Content of the Network

We investigate the heating of the quiet corona by measuring the increase of coronal luminosity with the amount of magnetic flux in the underlying network at solar minimum when there were no active regions on the face of the Sun. The coronal luminosity is measured from Fe IX/X-Fe XII pairs of coronal images from SOHO/EIT. The network magnetic flux content is measured from SOHO/MDI magnetograms. We find that the luminosity of the corona in our quiet regions increases roughly in proportion to the square root of the magnetic flux content of the network and roughly in proportion to the length of the perimeter of the network magnetic flux clumps. From (1) this result, (2) other observations of many fine-scale explosive events at the edges of network flux clumps, and (3) a demonstration that it is energetically feasible for the heating of the corona in quiet regions to be driven by explosions of granule-sized sheared-core magnetic bipoles embedded in the edges of network flux clumps, we infer that in quiet regions that are not influenced by active regions the corona is mainly heated by such magnetic activity in the edges of the network flux clumps. Our observational results together with our feasibility analysis allow us to predict that (1) at the edges of the network flux clumps there are many transient sheared-core bipoles of the size and lifetime of granules and having transverse field strengths > approx. 100 G, (2) approx. 30 of these bipoles are present per supergranule, and (3) most spicules are produced by explosions of these bipoles.

Falconer, D. A.↗

NASA NTRS2019

A Two-sided-Loop X-Ray Solar Coronal Jet and a Sudden Photospheric Magnetic-field Change, Both Driven by a Minifilament Eruption

Most of the commonly discussed solar coronal jets are of the type consisting of a single spire extending approximately vertically from near the solar surface into the corona. Recent research of a substantial number of events shows that eruption of a miniature filament (minifilament) drives at least many such single-spire jets, and concurrently generates a miniflare at the eruption site. A different type of coronal jet, identified in X-ray images during the Yohkoh era, are two-sided-loop jets, which extend from a central excitation location in opposite directions, along two opposite low-lying coronal loops that are more-or-less horizontal to the surface. We observe such a two-sided-loop jet from the edge of active region (AR) 12473, using data from Hinode XRT and EIS, and SDO AIA and HMI. Similar to single-spire jets, this two-sided-loop jet results from eruption of a minifilament, which accelerates to over 140 km/s before abruptly stopping upon striking overlying nearlyhorizontal magnetic field at ∼30,000 km altitude and producing the two-sided-loop jet via interchange reconnection. Analysis of EIS raster scans show that a hot brightening, consistent with a small flare, develops in the aftermath of the eruption, and that Doppler motions (∼40 km/s) occur near the jet-formation region. As with many single-spire jets, the trigger of the eruption here is apparently magnetic flux cancelation, which occurs at a rate of ∼4×10^18 Mx/hr, comparable to the rate observed in some single-spire AR jets. This example of a two-sided jet, along with numerous examples of single-spire jets, supports that essentially all coronal jets result from eruptions of minifiaments, and frequently the eruption of the minifilment is triggered by magnetic flux cancelation. (Details are in Sterling et al. 2019, ApJ, 871, 220.)

Sterling, Alphonse C.↗

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↗

DOE OSTI2025

Energy storage planning for enhanced resilience of power systems against wildfires and heatwaves

Extreme weather events pose significant risks to power grid stability due to their severe consequences and potential for widespread failures. Energy storage systems hold great potential for enhancing grid resilience against such events by providing reliable power during peak demand periods. However, accurately quantifying the size, location, and investment costs of new energy storage assets is a complex task, as energy storage planning decisions depend on the investment choices of other generation technologies and the integration of new transmission projects. Here, this paper presents a novel capacity expansion planning framework that simultaneously optimizes investments in energy storage, generation, and transmission, determining their optimal size, location, and type, while incorporating extreme weather events into long-term planning. More specifically, our stress-event-informed planning framework integrates the impact of heatwaves and wildfires into the planning process, identifying least-cost investment solutions that comply with policy goals and enhance grid resilience. The proposed framework employs machine-learning-based modeling to project heatwave-induced loads and performance-based risk assessment to evaluate wildfire-driven transmission line derates. Using industry-standard datasets to accurately represent the transmission topology of the Western Interconnection (WI) system, the proposed framework is applied to the WI 40-zone system, with investment decisions reported for the years 2030, 2035, and 2040. Simulation results reveal that with just a 10% increase in investment costs, resilience against extreme events can be significantly improved, with investment decisions heavily favoring energy storage, particularly 4-hour energy storage systems.

25 ENERGY STORAGE↗