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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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The STS-4 Crew Activity Plan contains the on-orbit timeline, which is a flight data file article. Various time scales such as Mission Elapsed Time (MET), Greenwich Mean Time (GMT), and time until deorbit ignition as well as crew activities, day/night, orbit position, ground tracking, communication coverage, attitude, and maneuvers are presented in chart form.
The U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) is a publicly available tool used by researchers and policy makers to help understand motor vehicle emission sources at national, county, and project levels. However, estimates of heavy-duty activity (MOVES2014), have been identified as areas needing improvement. The start activities in MOVES2014 are calculated using a limited data set, prompting the concern that inventory values are not representative. In addition, MOVES2014 is believed to underestimate heavy-duty activity that is not captured in the current drive cycles used to represent on-network activity. For example, MOVES2014 does not account for work-day idling activity that takes place on off-network roads, such as at a distribution center while the truck is queuing or during loading and unloading. Under the guidance and expertise of the EPA, the National Renewable Energy Laboratory has leveraged its extensive Fleet DNA database of heavy-duty vehicles to reinforce the data set behind the next-generation MOVES model and enhance idle activity and start fractions using six heavy-duty vehicle classes. The data available in Fleet DNA from 420 conventional, diesel powered vehicles provided over 120,000 hours of operation. Start fraction, soak fraction, and idle fraction by hour of the day were derived for each source type, state, and vocation, and results were provided in the form of .CSV files representing MOVES table inputs. This midterm report details these results providing graphical analysis and context for the start, soak, and idle distributions.
Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.
Under AgRISTARS management, ground data were collected at 20 agricultural sites within Australia during the crop year 1981-82. The data collection activity is summarized. Specifically, the following information is provided: discussion of data procedures, methods, and products; crop production results; photographs of the Australia agriculture scene, map sheets of segments, LANDSAT full frames, and aerial photographs of data collection areas; and summarizations of district agronomist reports.
From its inception in 2000, one of the primary tasks of the Biomedical Data Reduction Analysis (BDRA) group has been translation of large amounts of data into information that is relevant to the audience receiving it. BDRA helps translate data into an integrated model that supports both operational and research activities. This data integrated model and subsequent visual data presentations have contributed to BDRA's success in delivering the message (i.e., the story) that its customers have needed to communicate. This success has led to additional collaborations among groups that had previously not felt they had much in common until they worked together to develop solutions in an integrated fashion. As more emphasis is placed on working with "big data" and on showing how NASA's efforts contribute to the greater good of the American people and of the world, it becomes imperative to visualize the story of our data to communicate the greater message we need to share. METHODS To create and expand its data integrated model, BDRA has incorporated data from many different collaborating partner labs and other sources. Data are compiled from the repositories of the Lifetime Surveillance of Astronaut Health and the Life Sciences Data Archive, and from the individual laboratories at Johnson Space Center that support collection of data from medical testing, environmental monitoring, and countermeasures, as designated in the Medical Requirements Integration Documents. Ongoing communication with the participating collaborators is maintained to ensure that the message and story of the data are retained as data are translated into information and visual data presentations are delivered in different venues and to different audiences. RESULTS We will describe the importance of storytelling through an integrated model and of subsequent data visualizations in today's scientific presentations and discuss the collaborative methods used. We will illustrate the discussion with examples of graphs from BDRA's past work supporting operations and/or research efforts.
NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.
To achieve hotspot ignition, an inertial confinement fusion implosion must achieve high hotspot pressure that is inertially confined by a dense shell of DT fuel. This requires a symmetric implosion having high in-flight shell velocity and high areal density at stagnation. The size of the driver and scale of the capsule required can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Significant 3D low mode asymmetries, however, are commonly observed in indirect-drive implosions and reduce the coupling of shell kinetic energy to the hotspot. To better quantify the magnitudes and impacts of shell density asymmetries, we have developed new analysis techniques and analytic models [Hurricane et al., Phys. Plasmas 27(6), 062704 (2020)]. To build confidence in the underlying data, we have also developed an analytic neutron transport model to cross-compare two independent measurements of asymmetry, which shows excellent agreement across shots for mode-1 (ℓ = 1). This work also demonstrates that asymmetry can introduce potential sampling bias into down-scattered ratio measurements causing the solid-angle-average and uncertainty-weighted-average down-scattered ratios to differ significantly. Diagnosing asymmetries beyond mode-1 (ℓ > 1) presents significant challenges. Using new diagnostic instruments and analysis techniques, however, evidence of significant Legendre mode P2 (ℓ = 2, m = 0) and additional 3D asymmetries (ℓ > 1, m ≠ 0) are beginning to emerge from the high precision activation diagnostic data (real-time nuclear activation detectors) and down-scattered neutron imaging data.
Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs) can extend their reach. However, the sensitivity of multireference calculations to the choice of the active space complicates the consistent evaluation of energies and gradients across structurally diverse nuclear configurations. To overcome this limitation, we introduce the weighted active space protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrated the approach on the TiC + -catalyzed C–H activation of methane, a reaction that poses challenges for Kohn–Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.
This report presents an application of activity tracking for pilot error detection from flight data, and describes issues surrounding such an application. It first describes the Crew Activity Tracking System (CATS), in-flight data collected from the NASA Langley Boeing 757 Airborne Research Integrated Experiment System aircraft, and a model of B757 flight crew activities. It then presents an example of CATS detecting actual in-flight crew errors.
Materials International Space Station Experiment 7B (MISSE 7B) is the most recent in a series of experiments flown on the exterior of International Space Station for the purpose of determining the durability of materials and components in the space environment. A collaborative effort among the Department of Defense, the National Aeronautics and Space Administration, industry, and academia, MISSE 7B will be flying a number of NASA experiments designed to gain knowledge in the area of space environmental effects to mitigate risk for exploration missions. Consisting of trays called Passive Experiment Containers, the suitcase sized payload opens on hinges and allows active and passive experiments contained within to be exposed to the ram and wake or zenith and nadir directions in low Earth orbit, in essence, providing a test bed for atomic oxygen exposure, ultraviolet radiation exposure, charged particle radiation exposure, and thermal cycling. New for MISSE 7B is the ability to monitor experiments actively, with data sent back to Earth via International Space Station communications. NASA?s active and passive experiments cover a range of interest for the Agency. Materials relevant to the Constellation Program include: solar array materials, seal materials, and thermal protection system materials. Materials relevant to the Exploration Technology Development Program include: fabrics for spacesuits, materials for lunar dust mitigation, and new thermal control coatings. Sensors and components on MISSE 7B include: atomic oxygen fluence monitors, ultraviolet radiation sensors, and electro-optical components. In addition, fundamental space environmental durability science experiments are being flown to gather atomic oxygen erosion data and thin film polymer mechanical and optical property data relevant to lunar lander insulation and the James Web Space Telescope. This paper will present an overview of the NASA experiments to be flown on MISSE 7B, along with a summary of the thermal environment to be expected during the 1 yr mission scheduled for launch in 2009.
The formylglycine-generating enzyme (FGE) is required for the posttranslational activation of type I sulfatases by oxidation of an active-site cysteine to C α -formylglycine. FGE has emerged as an enabling biotechnology tool due to the robust utility of the aldehyde product as a bioconjugation handle in recombinant proteins. Here, we show that Cu(I)–FGE is functional in O 2 activation and reveal a high-resolution X-ray crystal structure of FGE in complex with its catalytic copper cofactor. We establish that the copper atom is coordinated by two active-site cysteine residues in a nearly linear geometry, supporting and extending prior biochemical and structural data. The active cuprous FGE complex was interrogated directly by X-ray absorption spectroscopy. These data unambiguously establish the configuration of the resting enzyme metal center and, importantly, reveal the formation of a three-coordinate tris(thiolate) trigonal planar complex upon substrate binding as furthermore supported by density functional theory (DFT) calculations. Critically, inner-sphere substrate coordination turns on O 2 activation at the copper center. These collective results provide a detailed mechanistic framework for understanding why nature chose this structurally unique monocopper active site to catalyze oxidase chemistry for sulfatase activation.
Recent tests using the Common Research Model (CRM) at the Langley National Transonic Facility (NTF) and the Ames 11-foot Transonic Wind Tunnel (11' TWT) produced large sets of data that have been used to examine the effects of active damping on transonic tunnel aerodynamic data quality. In particular, large statistically significant sets of repeat data demonstrate that the active damping system had no apparent effect on drag, lift and pitching moment repeatability during warm testing conditions, while simultaneously enabling aerodynamic data to be obtained post stall. A small set of cryogenic (high Reynolds number) repeat data was obtained at the NTF and again showed a negligible effect on data repeatability. However, due to a degradation of control power in the active damping system cryogenically, the ability to obtain test data post-stall was not achieved during cryogenic testing. Additionally, comparisons of data repeatability between NTF and 11-ft TWT CRM data led to further (warm) testing at the NTF which demonstrated that for a modest increase in data sampling time, a 2-3 factor improvement in drag, and pitching moment repeatability was readily achieved not related with the active damping system.
This record provides the data reported in Schwartz et al. "Incorporating Variable Sediment Microbial Activity into Methylmercury Production Kinetics Modeling".
The ERTS Data Collection System makes it feasible for the first time to monitor the level of activity at widely separated volcanoes and to relay these data rapidly to one central office for analysis. While prediction of specific eruptions is still an evasive goal, early warning of a reawakening of quiescent volcanoes is now a distinct possibility. A prototypical global volcano surveillance system was established under the ERTS program. Instruments were installed in cooperation with local scientists on 15 volcanoes in Alaska, Hawaii, Washington, California, Iceland, Guatemala, El Salvador and Nicaragua. The sensors include 19 seismic event counters that count four different sizes of earthquakes and six biaxial borehole tiltmeters that measure ground tilt with a resolution of 1 microradian. Only seismic and tilt data are collected because these have been shown in the past to indicate most reliably the level of volcano activity at many different volcanoes. Furthermore, these parameters can be measured relatively easily with new instrumentation.
Promyelocytic leukaemia (PML) is a tumor suppressor protein covalently conjugated with SUMO family proteins, leading to the formation of PML nuclear bodies (NBs). PML-NBs provide a platform for efficient posttranslational modification of targets and protein-protein interaction, contributing to the adjustment of gene expression and chromatin integrity. Although PML SUMOylation is thought to play important roles in diverse cellular functions, the control mechanisms of adequate modification levels have remained unsolved. Here, we report that Cullin-related protein CACUL1/CAC1 (CACUL1) inhibits PML posttranslational modification. CACUL1 interacts with PML and suppresses PML SUMOylation, leading to the regulation of PML-NB size in the nucleus. We also found that Ubc9, a SUMO-conjugating enzyme, binds to CACUL1 and antagonizes the interaction between CACUL1 and PML. Furthermore, CACUL1 attenuates p53 transcriptional activity. These data suggest that CACUL1 is a novel regulator that negatively controls p53 activity through the regulation of PML SUMOylation. - Highlights: • CACUL1 suppresses PML SUMOylation. • CACUL1 regulates PML-NBs size. • CACUL1 attenuates p53 transcriptional activity.
The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.
In this study, we reported that the optical and electrical analysis of amorphous Se{sub 90-x}S{sub x}Zn{sub 5} (x=0.2, 2, 5, 10) thin films. Bulk samples of the investigated material were prepared by melt quenching technique. Thin films of ~ 300nm thickness were deposited on cleaned glass substrates by thermal evaporation technique. The morphological study of the investigated material in powder form carried out by scanning electron microscopy (SEM) confirms the disorder of the material increases at lower sulfur doping (up to 5%) whereas at higher (S) doping (10%) the defects of the material decreases. The optical parameters were estimated from optical absorption spectra data measured from UV-Vis-spectrophotometer in the wavelength range 200-900 nm. It was found that the value of optical band gap (E{sub g}) of the investigated thin films decreases up to lower S doping and increases at higher (S) doping. The other optical parameters such as absorption coefficient (α) and extinction coefficient (K) increases up to lower S doping and decreases at higher S doping. This remarkable change in the values of optical parameters was interpreted on the basis of model proposed by Davis and Mott. Electrical parameters of the investigated thin films were carried out in the temperature range 309-370 K. Analysis of data shows activation energy decreases with the increase of concentration of (S) increases up to 5% and at 10% it increases again.
Temporal and spatial variation of sea ice type in the Arctic is an indicator of regional and global change. Arctic sea ice can be classified into two major categories: multiyear ice (MYI) and first-year ice. In this paper, classification method based on machine learning is established and applied to produce daily sea ice classification data set during the winter (November–April) from 2002 to 2017 using active microwave data from QuikSCAT and Advanced Scatterometer as well as passive microwave data from Advanced Microwave Scanning Radiometer for EOS, Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2 radiometer. First, the open water area is flagged out using brightness temperature (Tb) from the passive microwave sensor. Then, K-means algorithm is applied to identify the clusters of the two ice types in the Tb/backscatter parameter space and finally assign pixels to each class. Two optimization methods based on the movement of MYI and marginal ice zone are used to correct the misclassification of MYI. The results have shown a decrease of MYI in winter from 2002 to 2017, especially in 2008 and 2013 with a remarkable recovery in 2014. The classifications are consistent with results by visual interpretation from synthetic aperture radar images in the Canadian Arctic Archipelago with overall classification accuracy over 93%. Comparison with classifications from previous studies and products shows that our method could reflect more differences in MYI declining trend interannually and less anomalous fluctuations in certain years.