SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This dataset contains simulations generated by Sunny.jl of the magnetic excitations on the NiPS3 lattice. The exchange parameters are varied over the system so these simulations can be used for AI model training.
Previous work done by Galloway, et. al. on EBR-II ternary (U-Pu-Zr) fuel constituent redistribution yielded accurate simulation data for the limited data sets of Zr redistribution. The data sets included EPMA scans of two different irradiated rods. First, T179, which was irradiated to 1.9 at% burnup, was analyzed. Second, DP16, which was irradiated to 11 at% burnup, was analyzed. One set of parameters that most accurately represented the zirconium profiles for both experiments was determined. Since the binary fuel (U-Zr) has previously been used as the driver fuel for sodium fast reactors (SFR) as well as being the likely driver fuel if a new SFR is constructed, this same process has been initiated on the binary fuel form. From limited binary EPMA scans as well as other fuel characterization techniques, it has been observed that zirconium redistribution also occurs in the binary fuel, albeit at a reduced rate compared to observation in the ternary fuel, as noted by Kim et. al. While the rate of redistribution has been observed to be slower, numerous metallographs of U-Zr fuel show distinct zone formations.
Wind Integration National Dataset Toolkit The Wind Integration National Dataset (WIND) Toolkit is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.
We consider turbulent synchrotron-emitting media that also exhibit Faraday rotation and provide a statistical description of synchrotron polarization fluctuations. In particular, we consider these fluctuations as a function of the spatial separation of the direction of the measurements and as a function of wavelength for the same line of sight. On the basis of our general analytical approach, we introduce several measures that can be used to obtain the spectral slopes and correlation scales of both the underlying magnetic turbulence responsible for emission and the spectrum of the Faraday rotation fluctuations. We show the synergetic nature of these measures and discuss how the study can be performed using sparsely sampled interferometric data. We also discuss how additional characteristics of turbulence can be obtained, including the turbulence anisotropy and the three-dimensional direction of the mean magnetic field. In addition, we consider the cases when the synchrotron emission and Faraday rotation regions are spatially separated. Appealing to our earlier study, we explain that our new results are applicable to a wide range of spectral indexes of relativistic electrons responsible for synchrotron emission. We expect wide application of our techniques, both with existing synchrotron data sets and with big forthcoming data sets from LOFAR and SKA.
Abstract Several hundred simulated radioxenon beta-gamma data files were developed to assist in evaluating the performance and results from radioxenon concentration calculation analysis at the International Data Center (IDC) and other National Data Centers (NDC). PNNL developed a Beta-Gamma Simulator (BGSim) that incorporated GEANT-modeled data sets from radioxenon decay chains, as well as functionality to use nuclear detector-acquired data sets to create new beta-gamma spectra with varying amounts of background, 133Xe, 131mXe, 133mXe, 135Xe, and 222Rn and its decay products. The program has been implemented on a web-based applications platform and allows the user to create very specific data sets that incorporate most of the operational parameters for the current beta-gamma systems deployed in the International Monitoring System (IMS) and the On-site Inspection (OSI) equipment. After an initial beta-gamma simulations program was developed, additional uses began to be identified for the program output: training sets of two-dimensional spectra for data analysts at the IDC and other NDC, spectra for exercises such as the Integrated Field Exercise 2014 (IFE14) held in Jordan at the Dead Sea, and testing new analysis methods and algorithms
The crystal structure of a -lactamase-like protein from Brucella melitensis was initially solved by SAD phasing from an in-house data set collected on a crystal soaked with iodide. A high-resolution data set was collected at a synchroton at the Se edge wavelength, which also provided an independent source of phasing using a small anomalous signal from metal ions in the active site. Comparisons of anomalous peak heights at various wavelengths allowed the identification of the active-site metal ions as manganese. In the native data set a partially occupied GMP could be identified. When co-crystallized with AMPPNP or GMPPNP, clear density for the hydrolyzed analogs was observed, providing hints to the function of the protein.
The global forest carbon stocks represent the amount of carbon stored in woody vegetation and are important for quantifying the ability of the global forests to sequester atmospheric CO 2 and to provide ecosystem services (e.g., timber) under climate change. The forest ecosystem carbon pool estimates are highly variable and poorly quantified in areas lacking forest inventory estimates. Here, we compare and analyze aboveground biomass (AGB) estimates from five satellite-based global data sets and nine dynamic global vegetation models (DVGMs). We find that across the data sets, mean AGB exhibits the largest variability around the tropical area. In addition, AGB shows a similar latitudinal trend but large variability among the data sets. Satellite-based AGB estimates are lower than those simulated by DVGMs. The divergence among the satellite-based AGB estimates can be driven by the methodology, input satellite products, and the forested areas used to estimate AGB. The modeled NPP, autotrophic respiration, and carbon allocation mostly drive the variability of AGB simulated by DGVMs. The future availability of a high-quality global forest area map is anticipated to improve AGB estimate accuracy and to reduce the discrepancies among different satellite- and model-based AGB estimates. Furthermore, we suggest the carbon-modeling community reexamine the methodology used to estimate AGB and forested areas for a more robust global forest carbon stock estimation.
The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this research made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. Finally, these results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.
The 2B-CLDCLASS-LIDAR R05 (2BCL5) and the raDAR/liDAR (DARDAR) satellite retrievals of cloud occurrence are compared as a function of altitude and latitude. The largest disparities are observed at low altitudes over high southern latitudes. These data sets are cross referenced to ground-based measurements from the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) campaign at McMurdo Station, Antarctica. Compared with AWARE observations, both 2BCL5 and DARDAR underestimate cloud occurrence below 1.5 km, with 2BCL5 and DARDAR distinguishing roughly one third of cloud occurrences observed by AWARE at 0.5 km. While DARDAR identifies greater cloud occurrences than 2BCL5 below 1.5 km, cloud occurrence values for the two data sets have similar differences relative to ground-based measurements. Therefore, the DARDAR retrievals of greater cloud occurrence at low altitudes are likely due to a larger quantity of false positives associated with radar ground clutter or attenuated lidar retrievals. DARDAR cloud occurrences match better with AWARE than 2BCL5 above 5 km. However, the likely underestimation of ground-based measurements at higher altitudes suggests DARDAR may underestimate high level cloud occurrence. Finally, both data sets indicate the presence of liquid containing clouds at temperatures within the homogeneous freezing regime, despite the fact that the ECMWF-AUX data set implemented in their processing clearly indicates temperatures below -38°C. Finally, using AWARE radiosonde (ECMWF-AUX) temperature data, we find that 2BCL5 detects 13.3% (13.8%) of mixed phase clouds below -38°C, while DARDAR detects 5.7% (6.6%) of mixed phase and 1.1% (1.3%) of liquid phase clouds below -38°C.
The study of the atmospheres of transiting exoplanets requires a photometric precision, and repeatability, of one part in ∼10{sup 4}. This is beyond the original calibration plans of current observatories, hence the necessity to disentangle the instrumental systematics from the astrophysical signals in raw data sets. Most methods used in the literature are based on an approximate instrument model. The choice of parameters of the model and their functional forms can sometimes be subjective, causing controversies in the literature. Recently, Morello et al. (2014, 2015) have developed a non-parametric detrending method that gave coherent and repeatable results when applied to Spitzer/IRAC data sets that were debated in the literature. Said method is based on independent component analysis (ICA) of individual pixel time-series, hereafter “pixel-ICA”. The main purpose of this paper is to investigate the limits and advantages of pixel-ICA on a series of simulated data sets with different instrument properties, and a range of jitter timescales and shapes, non-stationarity, sudden change points, etc. The performances of pixel-ICA are compared against the ones of other methods, in particular polynomial centroid division, and pixel-level decorrelation method. We find that in simulated cases pixel-ICA performs as well or better than other methods, and it also guarantees a higher degree of objectivity, because of its purely statistical foundation with no prior information on the instrument systematics. The results of this paper, together with previous analyses of Spitzer/IRAC data sets, suggest that photometric precision and repeatability of one part in 10{sup 4} can be achieved with current infrared space instruments.
Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.
The optimization of WbdD crystals using a novel dehydration protocol and experimental phasing at 3.5 Å resolution by cross-crystal averaging followed by molecular replacement of electron density into a non-isomorphous 3.0 Å resolution native data set are reported. WbdD is a bifunctional kinase/methyltransferase that is responsible for regulation of lipopolysaccharide O antigen polysaccharide chain length in Escherichia coli serotype O9a. Solving the crystal structure of this protein proved to be a challenge because the available crystals belonging to space group I23 only diffracted to low resolution (>95% of the crystals diffracted to resolution lower than 4 Å and most only to 8 Å) and were non-isomorphous, with changes in unit-cell dimensions of greater than 10%. Data from a serendipitously found single native crystal that diffracted to 3.0 Å resolution were non-isomorphous with a lower (3.5 Å) resolution selenomethionine data set. Here, a strategy for improving poor (3.5 Å resolution) initial phases by density modification and cross-crystal averaging with an additional 4.2 Å resolution data set to build a crude model of WbdD is desribed. Using this crude model as a mask to cut out the 3.5 Å resolution electron density yielded a successful molecular-replacement solution of the 3.0 Å resolution data set. The resulting map was used to build a complete model of WbdD. The hydration status of individual crystals appears to underpin the variable diffraction quality of WbdD crystals. After the initial structure had been solved, methods to control the hydration status of WbdD were developed and it was thus possible to routinely obtain high-resolution diffraction (to better than 2.5 Å resolution). This novel and facile crystal-dehydration protocol may be useful for similar challenging situations.
This data set contains measurements of anaerobic oxidation of methane (AOM), net methane (CH4) production, carbon dioxide (CO2) production, and gross CH4 production measurements from the SPRUCE experiment (Hanson et al. 2017). CH4 is greenhouse gas with 45 times the sustained global warming potential of CO2 over 100 years, which may have significant implications on global climate change. The process of AOM plays a large role in the extent of CH4 emissions from wetland environments like those at the SPRUCE experiment site. This study conducted two on different dates. In experiment 1, net CH4 and CO2 production were measured with a gas chromatograph through the peat profile at in situ temperatures of the SPRUCE plots four times from May to October, 2016 (2016-05 to 2016-10). Two treatments were examined, a Porewater Treatment that was a 1:1 volumetric peat:porewater slurry and a No Porewater Treatment that was field-moist peat with no porewater added. Additionally, net CH4 production were measured at frequent intervals (from 4 hr to 9 d) in peat from selected SPRUCE plots from soil collected on October 16, 2016 (2016-10-16). Samples were collected from the SPRUCE plots, immediately made anaerobic, and transported to the University of Oregon for rate measurements within several days of collection. In experiment 2, AOM, net CH4 production, CO2 production, and gross CH4 production were measured through the peat profile at in situ field temperatures once from samples collected in July, 2021 (2021-07). The same two porewater treatments were examined. AOM was measured with a 13CH4 tracer method using a Picarro G2201-i analyzer for isotopic CO2/CH4 quantification with a small stable isotope module attached. This data set contains three data files in comma separate (*.csv) format. This data set is associated with the following publication: Barney, M, AM Hopple, LL Gregory, JK Keller, and SD Bridgham. 2024. Anaerobic oxidation of methane mitigates net methane production and responds to long-term experimental warming in a northern bog. Soil Biology and Biochemistry, 190, 109316. https://doi.org/10.1016/j.soilbio.2024.109316.
The Pacific Northwest National Laboratory (PNNL) will produce a non-cooperative (i.e. not posing for the camera) facial recognition video data set for research purposes to evaluate and enhance facial recognition systems technology. The aggregate data set consists of 1) videos capturing PNNL role players and public volunteers in three key operational settings, 2) photographs of the role players for enrolling in an evaluation database, and 3) ground truth data that documents when the role player is within various camera fields of view. PNNL will deliver the aggregate data set to DHS who may then choose to make it available to other government agencies interested in evaluating and enhancing facial recognition systems. The three operational settings that will be the focus of the video collection effort include: 1) unidirectional crowd flow 2) bi-directional crowd flow, and 3) linear and/or serpentine queues.
This report documents a new large public data set for researchers studying the behavior of large commodity high-performance computing systems. Such large data sets are typically confined within institutions and access to them is limited to institutional partners. We provide it to promote HPC research more widely. The data set provides a two week time series of performance data collected once per minute using the Lightweight Distributed Metric Service from the system Skybridge at Sandia National Laboratories and the corresponding job-level accounting information. General system log information is not provided.
Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback–Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback–Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.
This report describes our evaluation of the T-Plan Integrator software application as it was used to transfer a real data set from the Teamcenter for Systems Engineering (TcSE) software application to the DOORS software application. The T-Plan Integrator was evaluated to determine if it would meet the needs of Sandia National Laboratories to migrate our existing data sets from TcSE to DOORS. This report presents the struggles of migrating data and focuses on how the Integrator can be used to map a data set and its data architecture from TcSE to DOORS. Finally, this report describes how the bulk of the migration can take place using the Integrator; however, about 20-30% of the data would need to be transferred from TcSE to DOORS manually. This report does not evaluate the transfer of data from DOORS to TcSE.
In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.