Redundant screwjack
Device uses differential gears to drive either one of two nut-screw assemblies. In event that one assembly jams, second assembly is driven at twice its normal rate with no loss in overall performance.
Benjamin, R. W.↗
SEARCH · Search NASA
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.
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Legacy and Emergence of Spaceport Technology Development at the Kennedy Space Center
Kennedy Space Center (KSC) has a long and successful legacy in the checkout and launch of missiles and space vehicles. These operations have become significantly more complex, and their evolution has driven the need for many technology developments. Unanticipated events have also underscored the need for a local, highly responsive technology development and testing capability. This evolution is briefly described, as well as the increasing level of technology capability at KSC. The importance of these technologies in achieving past national space goals suggests that the accomplishment of low-cost and reliable access to space will depend critically upon KSC's future success in developing spaceport technologies. This paper concludes with a description KSC's current organizational approach and major thrust areas in technology development. The first phase of our historical review focuses on the development and testing of field- deployable short- and intermediate-range ballistic missiles (1953 to 1958). These vehicles are later pressed into service as space launchers. The second phase involves the development of large space lift vehicles culminating in the Saturn V launches (1959 to 1975). The third phase addresses the development and operations of the partially reusable launch vehicle, Space Shuttle (1976 to 2000). In the current era, KSC is teaming with the U.S. Air Force (AF), industry, academia, and other partners to identify and develop Spaceport and Range Technologies necessary to achieve national space goals of lower-cost and higher-reliability space flight.
Starr, Stanley↗
Web Monitoring of EOS Front-End Ground Operations, Science Downlinks and Level 0 Processing
This paper addresses the efforts undertaken and the technology deployed to aggregate and distribute the metadata characterizing the real-time operations associated with NASA Earth Observing Systems (EOS) high-rate front-end systems and the science data collected at multiple ground stations and forwarded to the Goddard Space Flight Center for level 0 processing. Station operators, mission project management personnel, spacecraft flight operations personnel and data end-users for various EOS missions can retrieve the information at any time from any location having access to the internet. The users are distributed and the EOS systems are distributed but the centralized metadata accessed via an external web server provide an effective global and detailed view of the enterprise-wide events as they are happening. The data-driven architecture and the implementation of applied middleware technology, open source database, open source monitoring tools, and external web server converge nicely to fulfill the various needs of the enterprise. The timeliness and content of the information provided are key to making timely and correct decisions which reduce project risk and enhance overall customer satisfaction. The authors discuss security measures employed to limit access of data to authorized users only.
Cordier, Guy R.↗
NASA GES DISC support of CO2 Data from OCO-2, ACOS, and AIRS
NASA Goddard Earth Sciences Data and Information Services Centers (GES DISC) is the data center assigned to archive and distribute current AIRS, ACOS data and data from the upcoming OCO-2 mission. The GES DISC archives and supports data containing information on CO2 as well as other atmospheric composition, atmospheric dynamics, modeling and precipitation. Along with the data stewardship, an important mission of GES DISC is to facilitate access to and enhance the usability of data as well as to broaden the user base. GES DISC strives to promote the awareness of science content and novelty of the data by working with Science Team members and releasing news articles as appropriate. Analysis of events that are of interest to the general public, and that help in understanding the goals of NASA Earth Observing missions, have been among most popular practices.Users have unrestricted access to a user-friendly search interface, Mirador, that allows temporal, spatial, keyword and event searches, as well as an ontology-driven drill down. Variable subsetting, format conversion, quality screening, and quick browse, are among the services available in Mirador. The majority of the GES DISC data are also accessible through OPeNDAP (Open-source Project for a Network Data Access Protocol) and WMS (Web Map Service). These services add more options for specialized subsetting, format conversion, image viewing and contributing to data interoperability.
Wei, Jennifer C↗
Mitigation of Finite Bandwidth Effects in Time-Division-Multiplexed SQUID Readout of TES Arrays
Time division multiplexing (TDM) is being developed as the readout technology of the X-ray integral field unit (X-IFU), a 3,168-pixel X-ray transition-edge sensor (TES) imaging spectrometer that is part of the European Space Agency's Athena satellite mission. Recent improvements in the low X-ray event count rate performance of TDM have been driven by increases in multiplexer bandwidth and the mitigation of settling transients. These methods and design changes have improved the 32-row multiplexed resolution of a NASA LPA 2.5a array from an initial (2.73 ± 0.03) eV to (1.97 ± 0.01) eV resolution at 5.9 keV. We discuss these recent advances in TDM readout, which have been implemented in an 8-column × 32-row spectrometer that will be deployed at the Lawrence Livermore National Laboratory electron beam ion trap (EBIT) facility, and present a model that will inform the design of future systems.
Transition-edge sensors↗
A Composite Analysis of Snowfall Modes from Four Winter Seasons in Marquette, Michigan
Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep)and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.
Claire Pettersen↗
Evaluating Faulty State Occurrence in Wildfire UAS Missions Using Markov Chains
As autonomous technology advances, unmanned aircraft systems are increasingly integrated into emergency response missions, such as wildfire response. These systems must be be safe with less risk than non-autonomous counter parts, yet quantifying the risk associated with present-day and future systems conventionally relies solely on expert opinion and little data. Instead, combining narrative mishap reports with probabilistic analysis can provide a method for evolutionary and timely risk analysis. In this paper, we present a framework for a data-driven probabilistic risk assessment style analysis, where hazard events and rates originate from documented UAS mishaps. The framework is applied to a UAS mapping mission in wildfire response, including a fault tree analysis, event tree analysis, and probabilistic analysis using Markov Chains. The analysis provides an enumeration of hazards in the system, hazard events that can lead to faults, the probability of a mission experiencing any fault, the probability of experiencing a specific fault, and the expected time spent until faulty states occur in present-day operations.
risk analysis↗
Evaluating Faulty State Occurrence in Wildfire UAS Missions Using Markov Chains
As autonomous technology advances, unmanned aircraft systems are increasingly integrated into emergency response missions, such as wildfire response. These systems must be be safe with less risk than non-autonomous counter parts, yet quantifying the risk associated with present-day and future systems conventionally relies solely on expert opinion and little data. Instead, combining narrative mishap reports with probabilistic analysis can provide a method for evolutionary and timely risk analysis. In this paper, we present a framework for a data-driven probabilistic risk assessment style analysis, where hazard events and rates originate from documented UAS mishaps. The framework is applied to a UAS mapping mission in wildfire response, including a fault tree analysis, event tree analysis, and probabilistic analysis using Markov Chains. The analysis provides an enumeration of hazards in the system, hazard events that can lead to faults, the probability of a mission experiencing any fault, the probability of experiencing a specific fault, and the expected time spent until faulty states occur in present-day operations.
risk analysis↗
Search for the chiral magnetic effect in Au+Au collisions at $\sqrt{s_{NN}}$ = 27 GeV with the STAR forward event plane detectors
A decisive experimental test of the Chiral Magnetic Effect (CME) is considered one of the major scientific goals at the Relativistic Heavy-Ion Collider (RHIC) towards understanding the nontrivial topological fluctuations of the Quantum Chromodynamics vacuum. In heavy-ion collisions, the CME is expected to result in a charge separation phenomenon across the reaction plane, whose strength could be strongly energy dependent. The previous CME searches have been focused on top RHIC energy collisions. In this Letter, we present a low energy search for the CME in Au+Au collisions at $\sqrt{s_{NN}}$ = 27 GeV. We measure elliptic flow scaled charge-dependent correlators relative to the event planes that are defined at both mid-rapidity |η| < 1.0 and at forward rapidity 2.1 < |η| < 5.1. We compare the results based on the directed flow plane ( $Ψ$ 1 ) at forward rapidity and the elliptic flow plane ( $Ψ$ 2 ) at both central and forward rapidity. The CME scenario is expected to result in a larger correlation relative to $Ψ$ 1 than to $Ψ$ 2 , while a flow driven background scenario would lead to a consistent result for both event planes. In 10-50% centrality, results using three different event planes are found to be consistent within experimental uncertainties, suggesting a flow driven background scenario dominating the measurement. We obtain an upper limit on the deviation from a flow driven background scenario at the 95% confidence level. This work opens up a possible road map towards future CME search with the high statistics data from the RHIC Beam Energy Scan Phase-II.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Characterization of Extreme Hydroclimate Events in Earth System Models using ML/AI
Focal Area(s): (1) We put forward the concepts of data assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization and unsupervised learning applied to downscale information within Earth System Models (ESMs). (2) We discuss predictive modeling through the use of AI techniques and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection) to improve the characterization of extreme hydroclimate events in ESMs. The AI-based models will run five-six order of magnitude times faster, yet will provide similar accuracy, allowing us to provide range bounds on uncertainty faster and thus enabling faster extreme event identification. (3) Further, we consider the insight gleaned from complex data (observed/simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI to improve the characterization of extreme hydroclimate events in ESMs.
54 ENVIRONMENTAL SCIENCES↗
WRF Modelling of Deep Convection and Hail for Wind Power Applications
Deep convection and the related occurrence of hail, intense precipitation and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the United States Southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modelling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates and precipitation totals show good agreement with observations. Here, the occurrence of precipitation during power producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modelled with a mean proportion correct of 0.77 and an odds ratio 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry and there is evidence for positive model bias in cloud reflectivity.
17 WIND ENERGY↗
A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics
Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.
Khan, Maira [Fermilab] (ORCID:0009000891602387)↗
Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources
As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.
data-driven forecasting↗
Accessing subnuclear fluctuations and saturation with multiplicity dependent J/ψ production in p+p and p+Pb collisions
We study the production of J/ψ vector mesons as a function of charged hadron multiplicity in p+p and p+Pb collisions at LHC energies. We employ the color glass condensate framework, using running coupling Balitsky-Kovchegov evolved dipole amplitudes, to compute gluon and cc¯-pair production. We use fragmentation functions to obtain charged hadrons, and explore two different hadronization schemes for the J/ψ: non-relativistic quantum chromodynamics and the improved color evaporation model. In our framework, event-by-event multiplicity fluctuations of both hadrons and J/ψ are driven by geometric and saturation scale normalization fluctuations. Studying the correlation between J/ψ and hadron multiplicity, we show that the characteristic difference between forward and backward rapidity in p+Pb collisions is a result of different degrees of saturation probed at different rapidities. We demonstrate that experimental data on heavy-flavor production as a function of event activity provide stringent constraints on the fluctuating proton structure
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Search for jet quenching effects in high-multiplicity pp collisions at $\sqrt{s}$ = 13 TeV via di-jet acoplanarity
The ALICE Collaboration reports a search for jet quenching effects in high-multiplicity (HM) proton-proton collisions at $\sqrt{s}$ = 13 TeV, using the semi-inclusive azimuthal-difference distribution Δφ of charged-particle jets recoiling from a high transverse momentum (high-p T,trig ) trigger hadron. Jet quenching may broaden the Δφ distribution measured in HM events compared to that in minimum bias (MB) events. The measurement employs a p T,trig -differential observable for data-driven suppression of the contribution of multiple partonic interactions, which is the dominant background. While azimuthal broadening is indeed observed in HM compared to MB events, similar broadening for HM events is observed for simulations based on the PYTHIA 8 Monte Carlo generator, which does not incorporate jet quenching. Detailed analysis of these data and simulations show that the azimuthal broadening is due to bias of the HM selection towards events with multiple jets in the final state. The identification of this bias has implications for all jet quenching searches where selection is made on the event activity.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Bridging Power System Protection Gaps with Data-driven Approaches
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 research, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A convolutional neural network (CNN) based fault detection approach is implemented to achieve data translation invariance 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–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 four kinds of protection gaps: high impedance faults, transformer/generator inter-turn faults, distribution system PV circuit faults, and the mis-operation situations of Zone 3 line protection relays operating under system stress. Finally, 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.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios
Management of the nation’s power grid over the coming decades will need to factor multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behavior caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We the used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.
electic grid, reinforcement learning, climate adap↗
CME Magnetic Structure and IMF Preconditioning Affecting SEP Transport
Coronal mass ejections (CMEs) and solar energetic particles (SEPs) are two manifestations of the various solar phenomena that are known to cause severe space weather effects throughout the heliosphere. The evolution of CMEs after eruption, especially in terms of their magnetic structure, and the configuration of the interplanetary magnetic field (IMF) that influences the transport of SEPs are currently areas of active research. These two aspects are not necessarily independent of each other, especially during solar maximum when multiple eruptive events can occur close in time. Accordingly, we present in this work theanalysis of a CME that erupted from the Sun on 2012 May 11 (SOL2012-05-11) and an SEP event following an eruption that took place on 2012 May 17 (SOL2012-05-17),∼20◦in both latitude and longitude away from the May 11 CME source region. After observing in detailthe eruption and early evolution of the May 11 CME using remote-sensing data from three viewpoints, we evaluate its propagation through interplanetary space using several models.Then, we analyse in-situ measurements from five predicted impact locations (Venus, Earth, the Spitzer Space Telescope, the Mars Science Laboratory en route to Mars, and Mars) in the inner heliosphere in order to search for CME signatures. We find that all five in-situlocations detect signatures of an impulsive SEP event, which we trace back to the May 17 eruption. These findings suggest that the May 11 CME, which was crossing the various locations around the time of the May 17 eruption, provided a direct magnetic connectivityfor the efficient transport of SEPs with an impulsive profile. We discuss the space weather implications of CME evolution, regarding in particular its magnetic structure, and CME-driven IMF preconditioning that facilitates SEP transport from a later event. Finally, this work remarks the importance of the availability of data from multiple spacecraft, even those that do not include space weather research as their primary objective.
Erika Palmerio↗