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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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This dataset contains the event log table that provides 10-minute wind statistics from the scanning lidars at AWAKEN's sites A1, A2, and H. This is a good dataset to start from for people unfamiliar with the AWAKEN project.
In this study we synthesize zigzag persistence from topological data analysis with autoencoder-based approaches to detect malicious cyber activity, and derive analytic insights. Cybersecurity aims to safeguard computers, networks, and servers from various forms of malicious attacks, including network damage, data theft, and activity monitoring. We focus on the cybersecurity domain and investigate the detection of malicious activity using log data. We consider the dynamics of the log data and explore the changing topology of a hypergraph representation of this data to gain insights into the underlying activity. These hypergraphs capture complex interactions between processes, together with their temporal information. To study the changing topology we use zigzag persistence, which captures how topological features persist at multiple dimensions over time. We observe that this detects malicious activity in a cyber data set. To automate this detection we implement an autoencoder trained on a vectorization of the resulting zigzag persistence barcodes. Our experimental results demonstrate the effectiveness of the autoencoder in detecting malicious activity. Overall, this study highlights the potential of zigzag persistence and its combination with temporal hypergraphs for analyzing cybersecurity log data and detecting malicious behavior.
Here, the ability to accurately predict the CO 2 storage resource in saline formations is important to make high-level, energy-related government policy and business decisions. CO 2 -SCREEN (Storage prospeCtive Resource Estimation Excel aNalysis) is a tool developed by the United States Department of Energy – National Energy Technology Laboratory (US-DOE-NETL) to screen saline formations for prospective CO 2 storage resources. CO 2 -SCREEN uses DOE methods and equations to serve as a consistent mechanism for calculating prospective CO 2 storage resources. CO 2 -SCREEN is comprised of two files: an Excel file used for inputs and outputs and a GoldSim Player file used to run Monte Carlo simulations. CO 2 -SCREEN requires input of physical geologic parameters (i.e. thickness, porosity) as well as efficiency factor ranges (i.e. net-to-gross thickness) to calculate a mass storage estimate. An application of CO 2 -SCREEN is demonstrated here using well log data from the Oriskany Sandstone portion in Pennsylvania. The Oriskany Sandstone is divided into 20 km x 20 km grid cells in which 151 cells contain well log data. CO 2 -SCREEN calculates prospective CO 2 storage resource for each grid cell based on the well log data and uses lithology and depositional environment information for efficiency factor ranges. The Oriskany Sandstone CO 2 storage resource estimate for Pennsylvania, calculated by CO 2 -SCREEN, ranges from 0.07 to 1.28 gigatons (Gt) with a P 50 value of 0.32 Gt. This resource assessment analysis is done to demonstrate the use of CO 2 -SCREEN and results are comparable to previous studies which encourages the application of CO 2 -SCREEN to other saline formations and warrants exploring the expansion of this tool to assess the CO 2 storage resource in other formations such as shale and depleted oil and gas reservoirs.
We detail previously undocumented but extensive gas hydrate accumulations in the mouth of Perdido Canyon in the northern Gulf of Mexico. The accumulations are located within central parts of structural domes (four-way closures) and are characterized by stacked, high-amplitude bottom simulating reflections (BSRs) that we call clustered BSRs. Seismic data from Perdido Canyon show two clustered BSRs associated with turbidite sequences within two dome folds formed from tectonic folding and salt diapir rise. The northwestern (NW) and southeastern (SE) clustered BSRs have aerial extents of ~25 km2 and 50 km2, respectively. Well log data confirm gas hydrate occurs above the NW clustered BSR, within a 225 m-thick consistently high-resistivity interval that we interpret as gas hydrate in near-vertical fractures and turbidite sands. The SE dome is only drilled at the edge of the BSR; nevertheless, the well log data indicate that a 30 m-thick gas hydrate accumulation is present. Gas chromatographic logs in both domes suggest a gradual transition from predominantly microbial gas below the BSR (500–1000 m below seafloor (mbsf)) to thermogenic gas at 1000–2000 mbsf. Based on the well log data and seismic stratigraphic analysis, we find gas hydrate is concentrated in fractures in marine mud, as well as in the pores of submarine fan turbidities, where saturations reach as high as 75%. An estimate of the total gas hydrate-bound gas volume at standard temperature and pressure is between 0.04 and 0.17 trillion cubic meters (TCM) assuming average hydrate saturation of 5-20% in a ~45 m thick turbidite sand unit above the Perdido Canyon BSR area. Measured BSR extent and gas volume estimates indicate that the NW and SE reservoirs are among the largest gas hydrate occurrences known in the Gulf of Mexico.
Analyzing the logs of even the smallest Information Technology (IT) system can be a challenge considering they can generate millions of lines of log data in a very short time. Splunk® Enterprise is an industry leading tool that allows analysis of log data, which can enhance troubleshooting capabilities, improve system performance, and improve the security posture of an IT system. Ivanti Connect Secure© (ICS) is a market-leading platform powered by the Ivanti Secure Socket Layer Virtual Private Network (SSL VPN) appliance, providing an architecture for secure access to and protection of network resources. This paper describes an approach for using Splunk Enterprise search capabilities to perform advanced data analysis of ICS logs.
The HPC system consists of a set of layers of software and hardware for I/O and networking. System logs are helpful resources to understand what is going on in the system. A challenge is that it is non-trivial to analyze the logs maintained in various levels of the stack. Independent analysis might lead to an incomplete conclusion due to the limited coverage of each log. This work takes a comprehensive approach to analysis that incorporates the logs in the multiple layers and components, in order to facilitate the detection of anomalous activities. This research aims to identify and predict potential performance bottlenecks in the HPC system, by capturing the temporal variation patterns from heterogeneous, high-dimensional, and non-linear log data. In this paper, we share our preliminary efforts for spatio-temporal analysis of HPC I/O and connection data, with our initial observations from the analysis of one-week HPC log data sets collected from one of NERSC systems.
An understanding of trap and fault seal quality is critical for assessing hydrocarbon prospectivity. To achieve this, modern analytical techniques leverage well data and conventional industry-standard 3D seismic data to evaluate the trap, and any faults displacing the reservoir and top seal intervals. Above all, geological interpretation provides the framework of trap and fault seal analyses, but can be hindered by the data resolution, quality and acquisition style of the conventional seismic data. Furthermore, limiting the analysis to only the petroleum system at depth may lead to erroneous perceptions because interpreting overburden features, such as shallow faults or gas chimneys, can provide valuable observations with respect to container performance, and can to help validate trap and fault seal predictions. Here, a supplement to conventional 3D data are high-resolution 3D seismic (HR3D) data, which provide detailed images of the overburden geology. This study utilizes an HR3D seismic volume in the San Luis Pass area of the Texas inner shelf, where shallow fault tips and a sizeable gas chimney are interpreted over an unsuccessful hydrocarbon prospect. Static post-drill fault seal and trap analyses suggest that the primary fault displacing the structural closure could have withheld columns of gas c. 100 m high, but disagree with our HR3D seismic interpretations and dry-well analyses. From our results, we hypothesize that tertiary gas migration through fault conduits reduced the hydrocarbon column in the prospective Early Miocene reservoir, and may have resulted from continued movement along the intersecting faults. Overall, this study reinforces the importance of understanding the overburden geology and geohistory of faulted prospects, and demonstrates the utility of pre-drill HR3D acquisition when conducting trap and fault seal analyses.
Purpose: To acquire correct information for inside the body in patient positioning of Real-time-image Gated spot scanning Proton Therapy (RGPT), utilization of tomographic image at exhale phase of patient respiration obtained from 4-dimensional Cone beam CT (4D-CBCT) has been desired. We developed software named “Image Analysis Platform” for 4D-CBCT researches which has technique to segment projection-images based on 3D marker position in the body. The 3D marker position can be obtained by using two axes CBCT system at Hokkaido University Hospital Proton Therapy Center. Performance verification of the software was implemented. Methods: The software calculates 3D marker position retrospectively by using matching positions on pair projection-images obtained by two axes fluoroscopy mode of CBCT system. Log data of 3D marker tracking are outputted after the tracking. By linking the Log data and gantry-angle file of projection-image, all projection-images are equally segmented to spatial five-phases according to marker 3D position of SI direction and saved to specified phase folder. Segmented projection-images are used for CBCT reconstruction of each phase. As performance verification of the software, test of segmented projection-images was implemented for sample CT phantom (Catphan) image acquired by two axes fluoroscopy mode of CBCT. Dummy marker was added on the images. Motion of the marker was modeled to move in 3D space. Motion type of marker is sin4 wave function has amplitude 10.0 mm/5.0 mm/0 mm, cycle 4 s/4 s/0 s for SI/AP/RL direction. Results: The marker was tracked within 0.58 mm accuracy in 3D for all images, and it was confirmed that all projection-images were segmented and saved to each phase folder correctly. Conclusion: We developed software for 4D-CBCT research which can segment projection-image based on 3D marker position. It will be helpful to create high quality of 4D-CBCT reconstruction image for RGPT.
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: `python -c "import appdirs; print(appdirs.user_data_dir())"`). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.
Processing parameters and machine log data for the MCPC LDRD Agile investment is collected material samples processed. This dataset captures the selected processing parameters, machine logs captured during material processing, and descriptions of how characterization samples were extracted from processed plates of material. The collect characterization data is captured in other datasets.
Canadian Nuclear Laboratories (CNL), formerly Atomic Energy of Canada Limited (AECL) is Canada's premier nuclear science and technology (S and T) organization in Canada. Over the next decade, CNL will be transforming its many different sites through the revitalization of essential site infrastructure, the decommissioning of aging infrastructure and a significant investment in new, world-class science facilities. In particular, the main site for CNL resides at the Chalk River Laboratories (CRL), where the majority of parallel activities of revitalization and decommissioning will take place. In 2016, CRL started an ambitious ten year plan to decommission more than 120 buildings. In order to ensure this project remains on track and the ALARA (as low as reasonably achievable) principle is maintained, a strategy is required. This strategy is to focus on integrated activities, which meet/exceed international best practices, to ensure the work is carried out as efficiently as possible. Given the complexity of CRL operations and the legacy issues accumulated over 60 years of operation, CNL needed to adopt a modern and integrated approach to handle competing project priorities. As a starting point, achieving complete radiological characterization of many building structures is required. CNL engaged various vendors to modernize radiological surveys from paper records to electronic documentation, deploying modern survey tools such as data-logging instruments, gamma cameras, robotic surveying tools, and introducing tablets to improve the flow of information while workers are in the field. Once fully implemented, these improvements will ensure that quality information is readily available at all stages of decommissioning, which greatly reduces project schedules and results in important company savings. These techniques will ensure that, during planning, historic radiological survey data is easily searchable from the electronic documentation system. Any additional data required can be quickly and safety collected using robotic surveying tools in areas of higher radiation to reduce possible exposure to individuals. As decommissioning progresses, individuals in the field can use tablets, gamma cameras and data-logging instruments to accurately retrieve, collect and share data. As a result of these efforts, CNL is progressing towards becoming a leader in the field of decommissioning within Canada, applying modern techniques and best practices to reduce decommissioning project risk. Access to high quality information results in improved decommissioning plans, which will reduce the amount of rework and scheduling delays required and improve worker safety as the project is executed. (authors)
We present a reservoir geophysics study, including rock physics modeling and seismic inversion, of a carbon dioxide sequestration site in Southwestern Wyoming, namely the Rock Springs Uplift, and build a petrophysical model for the potential injection reservoirs for carbon dioxide sequestration. Our objectives include the facies classification and the estimation of the spatial model of porosity and permeability for two sequestration targets of interest, the Madison Limestone and the Weber Sandstone. The available dataset includes a complete set of well logs at the location of the borehole available in the area, a set of 110 core samples, and a seismic survey acquired in the area around the well. The proposed study includes a formation evaluation analysis and facies classification at the well location, the calibration of a rock physics model to link petrophysical properties and elastic attributes using well log data and core samples, the elastic inversion of the pre-stack seismic data, and the estimation of the reservoir model of facies, porosity and permeability conditioned by seismic inverted elastic attributes and well log data. In particular, the rock physics relations are facies-dependent and include granular media equations for clean and shaley sandstone, and inclusion models for the dolomitized limestone. The permeability model has been computed by applying a facies-dependent porosity-permeability relation calibrated using core sample measurements. Finally, the study shows that both formations show good storage capabilities. The Madison Limestone includes a homogeneous layer of high-porosity high-permeability dolomite; the Weber Sandstone is characterized by a lower average porosity but the layer is thicker than the Madison Limestone.
This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.
A dynamic systems model of C4 photosynthesis was developed based on the previous NADP-ME metabolic model for maize (Wang et al., 2014 ab). The NADP-ME metabolic model is an ordinary differential equation model including all individual steps in C4 photosynthetic carbon metabolism. Here, the model is extended to include posttranslational regulation and temperature response of enzyme activities, dynamic stomata conductance, and leaf energy balance. This model is written in Matlab (R2019a) Steady-state and dynamic gas exchange data for maize (B73), sugarcane (CP88-1762) and sorghum (Tx430) were measured in a greenhouse in Urbana, IL from July 25, 2019 through August 8, 2019. Data include: CO2 response curves Light response curves Photosynthetic induction curves measured in the transition from darkness to high light (1800 μmol m-2 s-1), data logged every 1 min. Photosynthetic induction in the transition from darkness to high light (1800 μmol m-2 s-1) to determine the kinetics of rubisco activation in these C4 crops (τ_Rubisco), data logged every 10 s. Gas exchange under fluctuating light. After dark adaptation, the leaves undergo three light change steps, light intensity was set as 1800 µmol m-2 s-1, 200 µmol m-2 s-1 and 1800 µmol m-2 s-1 for each 1800 s step.
The project Characterization of Most Promising Sequestration Formations in the Rocky Mountain Region is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The Rocky Mountain Carbon Capture and Storage (RMCCS) project investigated multiple geologic formations and characterized a local site on the Colorado Plateau for future CCS opportunities. The RMCCS project focused on the Cretaceous Dakota, Jurassic Entrada, and Pennsylvanian Weber Sandstones, the three largest regional formations. All formations in this project are potential CO2 storage resources for future power plants, natural gas processing plants, cement plants, and oil shale development projects. The area adjacent to Craig, Colorado, (Sand Wash Basin) was the area selected for detailed geologic characterization on the RMCCS project. The basin was selected in part because the geology can be extrapolated to other sites on the Colorado Plateau. Field mapping and seismic surveys were conducted to identify and evaluate the basin's structural configuration. A 9,745-foot deep characterization well was drilled to collect 131 feet of core and a suite of geophysical well log data. Petrophysical tests on samples of core were used to calibrate geophysical log data, which can be used to obtain storage resource estimates and evaluate associated uncertainty as well as simulate the hydrologic behavior of injected CO2. A detailed analysis of the primary formations (Dakota, Entrada and Weber sandstones) yielded a more accurate CO2 storage resource assessment for these formations within the Colorado Plateau; RMCCS estimates indicate a total CO2 storage resource of more than 38,000 million metric tons. The characterization of the Sand Wash Basin (2-D seismic surveys, multiple well logs and lithological, petrophysical and geochemical analyses) allowed for a detailed 3-D model to be constructed. The model served as the framework for analyses ranging from CO2 storage resource, injectivity, and subsurface flow to uncertainty estimates to evaluation of risk.
In this paper, we describe the GUIDE framework to collect, federate, and analyze log data from the Oak Ridge Leadership Computing Facility's (OLCF), and how we use it derive insights into center operations. We collectsystem logs and extract monitoring data at every level of the various OLCF subsystems, and have developed a suite of pre-processing tools to make the raw data consumable. The cleansed logs are then ingested and federated into a central, scalable data warehouse, Splunk, that offers storage, indexing, querying, and visualization capabilities. We have further developed and deployed a set of analytics tools to analyze these multiple disparate log streams in concert, and derive operational insights. We describe our experience from developing and deploying the GUIDE infrastructure, and deriving valuable insights on the various subsystems, based on two years of operations in the production OLCF environment.