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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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At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRS1991

Solar-A reformatted data files and observing log

An overview is presented of the Solar-A telemetry data files which are to be created and the format and organization which the files are to use. The organization chosen is to be efficient in space, to facilitate access to the data, and to allow the data to be transportable to different machines. An observing log file is to be created automatically, using the reformatted data files as the input. It will be possible to perform searches with the observing log to list cases where instruments are in certain modes and/or seeing certain signal levels. A user will be able to search the observing log and obtain a list of all cases where a given set of conditions are satisfied. An event log will be created listing the times when the instrument or spacecraft modes change.

Morrison, M. D.↗

NASA NTRS1993

Optimal message log reclamation for independent checkpointing

Independent (uncoordinated) check pointing for parallel and distributed systems allows maximum process autonomy but suffers from possible domino effects and the associated storage space overhead for maintaining multiple checkpoints and message logs. In most research on check pointing and recovery, it was assumed that only the checkpoints and message logs older than the global recovery line can be discarded. It is shown how recovery line transformation and decomposition can be applied to the problem of efficiently identifying all discardable message logs, thereby achieving optimal garbage collection. Communication trace-driven simulation for several parallel programs is used to show the benefits of the proposed algorithm for message log reclamation.

Wang, Yi-Min↗

NASA NTRS1993

Optimal message log reclamation for independent checkpointing

Wang, Yi-Min↗

NASA NTRS1994

Optimal message log reclamation for uncoordinated checkpointing

Uncoordinated checkpointing for message-passing systems allows maximum process autonomy and general nondeterministic execution, but suffers from potential domino effect and the large space overhead for maintaining checkpoints and message logs. Traditionally, it has been assumed that only obsolete checkpoints and message logs before the global recovery line can be garbage-collected. Recently, an approach to identifying all garbage checkpoints based on recovery line transformation and decomposition has been developed. We show in this paper that the same approach can be applied to the problem of identifying all garbage message logs for systems requiring message logging to record in-transit messages. Communication trace-driven simulation for several parallel programs is used to evaluate the proposed algorithm.

Wang, Yi-Min↗

DOE OSTI2020

(NOICE) Neural Optical Image Categorizer for the E-log

The Fermilab Accelerator Division Electronic logbook (E-log) is a record of all the activities and events in the Division for the past 10 years and more. The E-log search function is a valuable resource and the institutional memory of the accelerator complex. About 300,000 files are stored in the E-log, of which the vast majority are images attached to entries and comments. The visual information contained in the images is not presently searchable. The goal of team NOICE (Neural Optical Image Categorizer for the E-log) was to design a neural network able to produce label categories for these images for use by searches. The group developed a dataset and trained a convolutional neural network (CNN) with optimized hyperparameter

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Archival, anonymization and presentation of HTCondor logs with GlideinMonitor

GlideinWMS is a pilot framework to provide uniform and reliable HTCondor clusters using heterogeneous and unreliable resources. The Glideins are pilot jobs that are sent to the selected nodes, test them, set them up as desired by the user jobs, and ultimately start an HTCondor schedd to join an elastic pool. These Glideins collect information that is very useful to evaluate the health and efficiency of the worker nodes and invaluable to troubleshoot when something goes wrong. This data, including local stats, the results of all the tests, and the HTCondor log files, is packed and sent to the GlideinWMS Factory. To access this information, developers and troubleshooters must exchange emails with Factory operators and dig manually into files. Furthermore, these files contain also information like email and IP addresses, and user IDs, that we want to protect and limit access to. GlideinMonitor is a Web application to make these logs more accessible and useful: it organizes the logs in an efficient compressed archive; it allows to search, unpack, and inspect them, all in a convenient and secure Web interface; via plugins like the log anonymizer, it can redact protected information preserving the parts useful for troubleshooting.

Mambelli, Marco↗

DOE OSTI2020

Gas Hydrate Saturation Estimation Form Acoustic Log Data in the 2018 Alaska North Slope Hydrate-01 Stratigraphic Test

Completed in December 2018, the Alaska North Slope Hydrate 01 stratigraphic test well provides a wealth of logging-while-drilling (LWD) data for strata to below the base of gas hydrate stability (BGHS). This well is intended to be the first of three wells drilled for a comprehensive long-term gas hydrate production test conducted by the National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey (USGS). The Hydrate 01 stratigraphic test well confirmed the presence of gas hydrate in two sand reservoirs within the hydrate stability zone, indicating the suitability of this location for a long-term gas hydrate production test.<p>The USGS, using an effective-medium-theory rock-physics approach, has estimated gas hydrate saturations from compressional (P) and shear (S) wave log data acquired in the Hydrate 01 well. We assume that gas hydrate occurs as pore-filling load-bearing material (i.e., part of the grain matrix). For Unit D, approximately 500 feet above the BGHS, both P-wave and S-wave acoustic logs indicate moderate gas hydrate saturations with S-wave results slightly lower than those for P-waves. For the Unit B, located just above the BGHS, we obtain moderate to high gas hydrate saturation estimates from both sonic logs. Our P-wave saturation estimates agree well with results from electrical-resistivity-based estimates, whereas estimates from nuclear magnetic resonance LWD data generally suggest 5 to 10 percent higher saturations; our S-wave results suggest lower saturations. These differences likely indicate complexities in the form of gas hydrate occurrence within the sediment pore space, potentially including differences between hydrate occurrence in Units D and B.</p>

Haines, Seth↗

DOE OSTI2015

New Mexico Play Fairway Analysis: Gamma Ray Logs and Heat Generation Calculations for SW New Mexico

For the New Mexico Play fairway Analysis project, gamma ray geophysical well logs from oil wells penetrating the Proterozoic basement in southwestern New Mexico were digitized. Only the portion of the log in the basement was digitized. The gamma ray logs are converted to heat production using the equation (Bucker and Rybach, 1996) : A[microW/m3] = 0.0158 (Gamma Ray [API] - 0.8).

15 GEOTHERMAL ENERGY↗

DOE OSTI2015

Fort Bliss Geothermal Area Data: Temperature profile, logs, schematic model and cross section

This dataset contains a variety of data about the Fort Bliss geothermal area, part of the southern portion of the Tularosa Basin, New Mexico. The dataset contains schematic models for the McGregor Geothermal System, a shallow temperature survey of the Fort Bliss geothermal area. The dataset also contains Century OH logs, a full temperature profile, and complete logs from well RMI 56-5, including resistivity and porosity data, drill logs with drill rate, depth, lithology, mineralogy, fractures, temperature, pit total, gases, and descriptions among other measurements as well as CDL, CNL, DIL, GR Caliper and Temperature files. A shallow (2 meter depth) temperature survey of the Fort Bliss geothermal area with 63 data points is also included. Two cross sections through the Fort Bliss area, also included, show well position and depth. The surface map included shows faults and well spatial distribution. Inferred and observed fault distributions from gravity surveys around the Fort Bliss geothermal area.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

EGS Collab Experiment 1: Core Logs

Core logs from the EGS Collab project Experiment 1 for the stimulation (Injection) well (E1-I), the Production well (E1-P), and monitoring wells (E1-OT, E1-OB, E1-PST, E1-PSB, E1-PDT, and E1-PDB) on the 4850 Level of SURF (the Sanford Underground Research Facility), single PDF file, 5-ft run intervals. In the monitoring well IDs, "O" indicates that the well is orthogonal to the anticipated fracture plane, "P" indicates that the well is parallel to the anticipated fracture plane, "S" indicates a shallow well, "D" indicates a deep well, "T" refers to top, and "B" refers to bottom. Logs include: experiment number; borehole ID; depth interval; run number; final packed core box number; scribe line (yes/no; red-on-right convention); logging dates; logger initials; as well as sketches of core foliation, folding, and fracturing with additional details and notes on other features of interest.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Utah FORGE: Updated FMI Fracture Log from Well 16A(78)-32

This dataset consists of an Excel spreadsheet detailing the fracture picks from a reinterpretation of the formation micro-imaging (FMI) log from Utah FORGE well 16A(78)-32. The provided information details fracture location, geometry, and type. Also included here is a link to the original raw and processed FMI logs, as well as other data from the 2021 well logging.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Utah FORGE: Well 56-32 Drilling Data and Logs

This dataset consists of drilling data (Pason data spreadsheets, daily reports, days v. depth, mud logs), Schlumberger logs (FMI, shear anisotropy analysis, memory, sonic, array induction/spectral density/dual spaced neutron/gamma ray/caliper, spectral GR/temperature, and Gardner density correlation), and an end of well report (EOWR) for Utah FORGE well 56-32. This is a vertical well that will be used for seismic monitoring. It was drilled between February 7th and February 21st 2021 to a depth of 9,145 feet. More information about this well can be found at: https://utahforge.com/2021/02/09/drilling-progress-of-well-56-32/ (linked below)

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

LogPath: Log data based energy consumption analysis enabling electric vehicle path optimization

Vehicle navigation and path optimization require a more meticulous approach when it deals with EVs (electric vehicles) and SDVs (software-defined vehicles), due to lengthy charging times and the lack of charging infrastructure. Long-distance freight EV trucking needs path guidance with accurate energy consumption estimates to prevent charging-related failures. We developed a novel energy consumption estimation approach that only uses battery log data to extract major vehicle parameters to increase EV navigation accuracy without additional sensors. This is enabled by extracting multiple drive modes from the log data for analysis. The system provides 1) routes, 2) charge locations, 3) charging times, and 4) optimal vehicle speeds that guarantee the shortest travel time. Here we successfully validated the system using log data collected from an EV and Tesla's Supercharging map in the US and compared it with the commercially available navigation system, Tesla's trip planner, whose capabilities solely include charging time and routing.

EV (Electric vehicles) navigation↗

DOE OSTI2022

Deep Learning-Assisted Real-Time Forward Modeling of Electromagnetic Logging in Complex Formations

Higher dimensional (i.e., 2-D and 3-D) modeling is indispensable to correctly evaluate the responses of electromagnetic logging tools in complex formation environments. However, limited by the high computational cost of rigorous modeling such as finite difference method and the finite element method, the real-time applications in the well logging industry primarily rely on the 1-D forward solver, which would result in erroneous formation evaluation for complex scenarios. As a result, aiming at realizing fast modeling for electromagnetic logging tools in complex formations, this paper proposes a general framework assisted by deep neural networks (DNNs). The framework consists of three modules: earth model classification, parameter extraction, and surrogate construction. Separate DNNs are trained and tested for different modules. The accuracy and efficiency of the DNN assisted fast modeling are validated by several experiments. Here, this study finds that the fast modeling assisted by DNNs is able to calculate the tool responses and reconstruct the subsurface formations in real-time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Remote Geophysical Logging at the Hanford Single-Shell Tank Farms: Maintaining Efficiency and Reducing Personnel Hazards - 16190

Vadose zone characterization around groups of underground waste storage tanks (known as tank farms) at the US DOE Hanford Site in Washington State is conducted to assess the presence and extent of manmade gamma-ray emitting radionuclides. Boreholes around the tanks are characterized using a high-resolution spectral gamma logging system (SGLS). In the past, logging operations required workers to remain inside the tank farm boundary during data acquisition. This approach was effective; however, personnel were at risk of exposure to potential radiological hazards, chemical hazards, and/or vapor releases that can be associated with the tank farm environment. Limitations associated with this work environment necessitated a new approach in order to decrease the amount of time personnel spent in the farm without affecting work productivity and efficiency. In order to accomplish this, a new remote logging cable support (RLCS) was developed and implemented which effectively removed workers from the hazards associated with the tank farm environment. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

Glass Buttes Well 52-33 Core Log

Core log from Glass Buttes Well 52-33, drilled by Geodrill Rig 6 in 2014 to a depth of 3000 feet. Logged at 500 ft intervals.

15 GEOTHERMAL ENERGY↗

DOE OSTI2018

Utah FORGE: Stress Logging Data

This spreadsheet consist of data and graphs from deep well 58-32 stress testing from 6900 - 7500 ft depth. Measured stress data were used to correct logging predictions of in situ stress. Stress plots shows pore pressure (measured during the injection testing), the total vertical in situ stress (determined from the density logging) and the total maximum and minimum horizontal stresses. The horizontal stresses were determined from the DSI (Dipole Sonic Imager) and corrected to match the direct measurements.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

Is the assumption of normality or log-normality for continuous response data critical for benchmark dose estimation?

Continuous responses (e.g. body weight) are widely used in risk assessment for determining the benchmark dose (BMD) which is used to derive a U.S. EPA reference dose. One critical question that is not often addressed in dose–response assessments is whether to model the continuous data as normally or log-normally distributed. Additionally, if lognormality is assumed, and only summarized response data (i.e., mean ± standard deviation) are available as is usual in the peer-reviewed literature, the BMD can only be approximated. In this study, using the “hybrid” method and relative deviation approach, we first evaluate six representative continuous dose–response datasets reporting individual animal responses to investigate the impact on BMD/BMDL estimates of (1) the distribution assumption and (2) the use of summarized versus individual animal data when a log-normal distribution is assumed. We also conduct simulation studies evaluating model fits to various known distributions to investigate whether the distribution assumption has influence on BMD/BMDL estimates. Our results indicate that BMDs estimated using the hybrid method are more sensitive to the distribution assumption than counterpart BMDs estimated using the relative deviation approach. The choice of distribution assumption has limited impact on the BMD/BMDL estimates when the within dose-group variance is small, while the lognormality assumption is a better choice for relative deviation method when data are more skewed because of its appropriateness in describing the relationship between mean and standard deviation. Additionally, the results suggest that the use of summarized data versus individual response data to characterize log-normal distributions has minimal impact on BMD estimates. - Highlights: • We investigate to what extent the distribution assumption can affect BMD estimates. • Both real data analysis and simulation study are conducted. • BMDs estimated using hybrid method are more sensitive to distribution assumption. • Summarized continuous data are adequate for BMD estimation.

60 APPLIED LIFE SCIENCES↗