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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Log-periodic quantum magneto-oscillations and discrete-scale invariance in topological material HfTe 5

Discrete-scale invariance (DSI) is a phenomenon featuring intriguing log-periodicity that can be rarely observed in quantum systems. Here, we report the log-periodic quantum oscillations in the longitudinal magnetoresistivity ( ρ xx ) and the Hall traces ( ρ yx ) of HfTe 5 crystals, which reveal the DSI in the transport-coefficients matrix. The oscillations in ρ xx and ρ yx show the consistent log B -periodicity with a phase shift. The finding of the log B oscillations in the Hall resistance supports the physical mechanism as a general quantum effect originating from the resonant scattering. Combined with theoretical simulations, we further clarify the origin of the log-periodic oscillations and the DSI in the topological materials. This work evidences the universality of the DSI in the Dirac materials and provides indispensable information for a full understanding of this novel phenomenon.

magnetoresistance↗
NASA NTRS2012

Requirements-Driven Log Analysis Extended Abstract

Imagine that you are tasked to help a project improve their testing effort. In a realistic scenario it will quickly become clear, that having an impact is diffcult. First of all, it will likely be a challenge to suggest an alternative approach which is significantly more automated and/or more effective than current practice. The reality is that an average software system has a complex input/output behavior. An automated testing approach will have to auto-generate test cases, each being a pair (i; o) consisting of a test input i and an oracle o. The test input i has to be somewhat meaningful, and the oracle o can be very complicated to compute. Second, even in case where some testing technology has been developed that might improve current practice, it is then likely difficult to completely change the current behavior of the testing team unless the technique is obviously superior and does everything already done by existing technology. So is there an easier way to incorporate formal methods-based approaches than the full edged test revolution? Fortunately the answer is affirmative. A relatively simple approach is to benefit from possibly already existing logging infrastructure, which after all is part of most systems put in production. A log is a sequence of events, generated by special log recording statements, most often manually inserted in the code by the programmers. An event can be considered as a data record: a mapping from field names to values. We can analyze such a log using formal methods, for example checking it against a formal specification. This separates running the system for analyzing its behavior. It is not meant as an alternative to testing since it does not address the important in- put generation problem. However, it offers a solution which testing teams might accept since it has low impact on the existing process. A single person might be assigned to perform such log analysis, compared to the entire testing team changing behavior.

log analysis↗
DOE OSTI2013

Borehole Calibration Facilities to Support Gamma Logging for Hanford Subsurface Investigation and Contaminant Monitoring - 13516

Repeated gamma logging in cased holes represents a cost-effective means to monitor gamma-emitting contamination in the deep vadose zone over time. Careful calibration and standardization of gamma log results are required to track changes and to compare results over time from different detectors and logging systems. This paper provides a summary description of Hanford facilities currently available for calibration of logging equipment. Ideally, all logging organizations conducting borehole gamma measurements at the Hanford Site will take advantage of these facilities to produce standardized and comparable results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Coulomb log for conductivity of dense plasmas

The Coulomb log (log Λ) approximation is widely used to approximate electron transport coefficients in dense plasmas. It is a classical approximation to the momentum transport cross section. Here, the accuracy of this approximation for electrical conductivity in dense plasmas is assessed by comparing to fully quantum mechanical calculations for realistic scattering potentials. It is found that the classical approximation is accurate to ±10% when log Λ > 3, irrespective of the plasma species. The thermodynamic regime (density and temperature) for which log Λ > 3 corresponds to does, however, strongly depend on the material. For increasing Z, log Λ is greater than 3 for increasingly high temperatures and lower densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

THE IMACS CLUSTER BUILDING SURVEY. IV. THE LOG-NORMAL STAR FORMATION HISTORY OF GALAXIES

We present here a simple model for the star formation history (SFH) of galaxies that is successful in describing both the star formation rate density (SFRD) over cosmic time, as well as the distribution of specific star formation rates (sSFRs) of galaxies at the current epoch, and the evolution of this quantity in galaxy populations to a redshift of z = 1. We show first that the cosmic SFRD is remarkably well described by a simple log-normal in time. We next postulate that this functional form for the ensemble is also a reasonable description for the SFHs of individual galaxies. Using the measured sSFRs for galaxies at z {approx} 0 from Paper III in this series, we then construct a realization of a universe populated by such galaxies in which the parameters of the log-normal SFH of each galaxy are adjusted to match the sSFRs at z {approx} 0 as well as fitting, in ensemble, the cosmic SFRD from z = 0 to z = 8. This model predicts, with striking fidelity, the distribution of sSFRs in mass-limited galaxy samples to z = 1; this match is not achieved by other models with a different functional form for the SFHs of individual galaxies, but with the same number of degrees of freedom, suggesting that the log-normal form is well matched to the likely actual histories of individual galaxies. We also impose the sSFR versus mass distributions at higher redshifts from Paper III as constraints on the model, and show that, as previously suggested, some galaxies in the field, particularly low mass galaxies, are quite young at intermediate redshifts. As emphasized in Paper III, starbursts are insufficient to explain the enhanced sSFRs in intermediate redshift galaxies; we show here that a model using only smoothly varying log-normal SFHs for galaxies, which allows for some fraction of the population to have peak star formation at late times, does however fully explain the observations. Finally, we show that this model, constrained in detail only at redshifts z < 1, also produces the main sequence of star-formation observed at 1.5 < z < 2.5, again suggesting that the log-normal SFHs are a close approximation to the actual histories of typical galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

SU-E-T-473: A Patient-Specific QC Paradigm Based On Trajectory Log Files and DICOM Plan Files

Purpose: To evaluate a remote QC tool for monitoring treatment machine parameters and treatment workflow. Methods: The Varian TrueBeamTM linear accelerator is a digital machine that records machine axis parameters and MLC leaf positions as a function of delivered monitor unit or control point. This information is saved to a binary trajectory log file for every treatment or imaging field in the patient treatment session. A MATLAB analysis routine was developed to parse the trajectory log files for a given patient, compare the expected versus actual machine and MLC positions as well as perform a cross-comparison with the DICOM-RT plan file exported from the treatment planning system. The parsing routine sorts the trajectory log files based on the time and date stamp and generates a sequential report file listing treatment parameters and provides a match relative to the DICOM-RT plan file. Results: The trajectory log parsing-routine was compared against a standard record and verify listing for patients undergoing initial IMRT dosimetry verification and weekly and final chart QC. The complete treatment course was independently verified for 10 patients of varying treatment site and a total of 1267 treatment fields were evaluated including pre-treatment imaging fields where applicable. In the context of IMRT plan verification, eight prostate SBRT plans with 4-arcs per plan were evaluated based on expected versus actual machine axis parameters. The average value for the maximum RMS MLC error was 0.067±0.001mm and 0.066±0.002mm for leaf bank A and B respectively. Conclusion: A real-time QC analysis program was tested using trajectory log files and DICOM-RT plan files. The parsing routine is efficient and able to evaluate all relevant machine axis parameters during a patient treatment course including MLC leaf positions and table positions at time of image acquisition and during treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO 2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO 2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO 2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO 2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Geochemistry & Geophysics↗
DOE OSTI2017

Log-normal Star Formation Histories in Simulated and Observed Galaxies

Gladders et al. have recently suggested that the star formation histories (SFHs) of individual galaxies are characterized by a log-normal function in time, implying a slow decline rather than rapid quenching. We test their conjecture on theoretical SFHs from the cosmological simulation Illustris and on observationally inferred SFHs. While the log-normal form necessarily ignores short-lived features such as starbursts, it fits the overall shape of the majority of SFHs very well. In particular, 85% of the cumulative SFHs are fitted to within a maximum error of 5% of the total stellar mass formed, and 99% to within 10%. The log-normal performs systematically better than the commonly used delayed-τ model, and is superseded only by functions with more than three free parameters. Poor fits are mostly found in galaxies that were rapidly quenched after becoming satellites. We explore the log-normal parameter space of normalization, peak time, and full width at half maximum, and find that the simulated and observed samples occupy similar regions, though Illustris predicts wider, later-forming SFHs on average. The ensemble of log-normal fits correctly reproduces complex metrics such as the evolution of Illustris galaxies across the star formation main sequence, but overpredicts their quenching timescales. SFHs in Illustris are a diverse population not determined by any one physical property of galaxies, but follow a tight relation, where width ∝ (peaktime){sup 3/2}. We show that such a relation can be explained qualitatively (though not quantitatively) by a close connection between the growth of dark matter halos and their galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Evaluation of Simple Causal Message Logging for Large-Scale Fault Tolerant HPC Systems

The era of petascale computing brought machines with hundreds of thousands of processors. The next generation of exascale supercomputers will make available clusters with millions of processors. In those machines, mean time between failures will range from a few minutes to few tens of minutes, making the crash of a processor the common case, instead of a rarity. Parallel applications running on those large machines will need to simultaneously survive crashes and maintain high productivity. To achieve that, fault tolerance techniques will have to go beyond checkpoint/restart, which requires all processors to roll back in case of a failure. Incorporating some form of message logging will provide a framework where only a subset of processors are rolled back after a crash. In this paper, we discuss why a simple causal message logging protocol seems a promising alternative to provide fault tolerance in large supercomputers. As opposed to pessimistic message logging, it has low latency overhead, especially in collective communication operations. Besides, it saves messages when more than one thread is running per processor. Finally, we demonstrate that a simple causal message logging protocol has a faster recovery and a low performance penalty when compared to checkpoint/restart. Running NAS Parallel Benchmarks (CG, MG and BT) on 1024 processors, simple causal message logging has a latency overhead below 5%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Deactivation and decommissioning web log analysis using big data technology - 15710

The D and D Knowledge Management Information Tool (D and D KM- IT) is a web-based knowledge management information tool built for the D and D user community. Big data is a massive volume of structured and unstructured datasets which is so large that it's difficult to process using traditional database techniques. Web logs are the repository of files which are generated automatically for any operation on the web site. Web log files generated from the D and D KM-IT will be processed using the Apache Hadoop Framework to extract meaningful data. Hadoop is an open-source software framework for storing and processing big data in a distributed environment on large clusters of commodity hardware. Hadoop framework consists of two main layers: Hadoop distributed file system (HDFS) and the Execution engine (MapReduce). Automatic parallelization and distribution; clean and simple programming abstraction. Implementation: Web log files from the D and D KM-IT server are fetched using Apache Flume and loaded into the Hadoop distributed file system. Using the Apache Hcatalog tool, the web log data is parsed and converted into a structured table format. A Pig program is developed to query the tables to retrieve and store the significant information onto the HDFS. The extracted data is fed to business visualization tools such as Microsoft Excel for analysis and reporting. Results The reports generated through the web log data processing will be analyzed to improve the usability and performance of the D and D KM-IT. Conclusion/Future Work: User keyword information can be explored to add new trending topics to the D and D KM-IT application. Statistics generated can be compared to analysis reports from existing analytical tools.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Virtual Log-Structured Storage for High-Performance Streaming

Over the past decade, given the higher number of data sources (e.g., Cloud applications, Internet of things) and critical business demands, Big Data transitioned from batch-oriented to real-time analytics. Stream storage systems, such as Apache Kafka, are well known for their increasing role in real-time Big Data analytics. For scalable stream data ingestion and processing, they logically split a data stream topic into multiple partitions. Stream storage systems keep multiple data stream copies to protect against data loss while implementing a stream partition as a replicated log. This architectural choice enables simplified development while trading cluster size with performance and the number of streams optimally managed. This paper introduces a shared virtual log-structured storage approach for improving the cluster throughput when multiple producers and consumers write and consume in parallel data streams. Stream partitions are associated with shared replicated virtual logs transparently to the user, effectively separating the implementation of stream partitioning (and data ordering) from data replication (and durability). We implement the virtual log technique in the KerA stream storage system. When comparing with Apache Kafka, KerA improves the cluster ingestion throughput by up to 4x when multiple producers write over hundreds of data streams.

consistent stream ordering↗
DOE OSTI2018

SECARB Cranfield Well Logs

Borehole logs from the SECARB project at the Cranfield oil site in Franklin, Mississippi . Well logs are a part of the geologic characterization phase of SECARB; CFU 31F-1, CFU 31F-2, CFU 31F-3 well logs from Detailed Area of Study (DAS) and other well logs. Associated Publications: Butsch, R., Brown, A. L., Bryans, B., Kolb, C., Hovorka, S. D., 2013, Integration of well-based subsurface monitoring technologies: lessons learned at SECARB study, Cranfield, MS: International Journal of Greenhouse Gas Control https://doi.org/10.1016/j.ijggc.2013.06.010.

Borehole log↗
DOE OSTI2016

Utah FORGE: Well 14-2 Logs and Data from Roosevelt Hot Spring Area

This is a compilation of logs and data from Well 14-2 in the Roosevelt Hot Springs area in Utah. This well is also in the Utah FORGE study area. Data includes: flowmeter survey (1989), geochemistry (1977-1978, 1977-1983), injection test data (1979, 1982), and spinner surveys (1989, 1985-1986). Logs include: borehole compensated sonic and gamma ray (600'-6112'), borehole geometry and gamma ray (50'-4829'), caliper (0'-1720'), compensated neutron formation density (600'-6121'), induction electric (650'-6118'), mud log (79'-6100'), steam injection survey (50'-1175'), subsurface pressure surveys (0'-6087'), and subsurface temperature surveys (0'-6106'). The file is in a compressed .zip format and there is a data inventory table (Excel spreadsheet) in the root folder that is a guide to the data that is accessible in subfolders.

14-2↗
DOE OSTI2016

Flowing fluid electrical conductivity logging of a deep borehole during and following drilling: estimation of transmissivity, water salinity and hydraulic head of conductive zones

Flowing fluid electrical conductivity (FFEC) logging is a hydrogeologic testing method that is usually conducted in an existing borehole. However, for the 2,500-m deep COSC-1 borehole, drilled at Åre, central Sweden, it was done within the drilling period during a scheduled 1-day break, thus having a negligible impact on the drilling schedule, yet providing important information on depths of hydraulically conductive zones and their transmissivities and salinities. In this paper, we present a reanalysis of this set of data together with a new FFEC logging data set obtained soon after drilling was completed, also over a period of 1 day, but with a different pumping rate and water-level drawdown. Their joint analysis not only results in better estimates of transmissivity and salinity in the conducting fractures intercepted by the borehole, but also yields the hydraulic head values of these fractures, an important piece of information for the understanding of hydraulic structure of the subsurface. Two additional FFEC logging tests were done about 1 year later, and are used to confirm and refine this analysis. Results show that from 250 to 2,000 m depths, there are seven distinct hydraulically conductive zones with different hydraulic heads and low transmissivity values. For the final test, conducted with a much smaller water-level drawdown, inflow ceased from some of the conductive zones, confirming that their hydraulic heads are below the hydraulic head measured in the wellbore under non-pumped conditions. Lastly, the challenges accompanying 1-day FFEC logging are summarized, along with lessons learned in addressing them.

58 GEOSCIENCES↗
DOE OSTI2014

Boron-coated straw neutron detector for well-logging-Final Technical Report

The Oil & Gas exploration industry makes extensive use of radiation detectors for well logging – a geologic formation map that reveals crucial information for the viability of oil/gas extraction in surveyed wells. Helium-3 gas filled neutron detectors have been the gold standard for neutron porosity logs, one of two components in nuclear logging (gamma ray logs being the other component). The finite supply of 3He gas has dwindled in recent years due to an increased use in Homeland Security applications; therefore the need for alternative technologies that would ensure oil & gas exploration for coming years.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Logging Crew Attributes by Region in the Southeast USA

Urbanization, shrinking markets, and reduced forestry investment may affect harvesting efficiency in regions of the US South. To monitor these conditions, logging businesses have been tracked by surveys conducted by universities and trade associations. This project used a sampling approach coordinated with FIA utilization studies to sample logging crews based on a harvesting location. The approach was used to develop relationships among firm attributes and site attributes in six southeastern states (AL, GA, FL, NC, SC, and VA) from 2011 to 2018. The data included harvest attributes (location, harvest size and stand type) and logging firm attributes (production, crew labor, crew number, the number of machines by type, and machine age). For crew capital value, an equation was developed for this study using machine number and average machine age. The data from logging crews on 419 harvests were analyzed by region, harvest size, and stand type. Mean values for crew labor ranged from 3.1 to 7.1 workers. The average capital value per crew ranged from $\$220,000$ to $\$524,000$ per crew in the Coastal Plain with a narrower range in the Piedmont. In the Coastal Plain, higher productivity was detected for larger harvests and pine versus hardwood and mixed stands; however, in the Piedmont those trends were less obvious. Ratio of feller-bunchers, skidders and loaders were mostly 1:1:1 or 1:2:1 with 41% and 24% of samples, respectively. There were notable trends among Coastal Plain loggers regarding capital value and productivity with evidence supported by a production function. The differences in Piedmont (e.g., ownership size, market access, terrain, population density, etc.) may combine to limit daily production and labor productivity.

54 ENVIRONMENTAL SCIENCES↗