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DOE OSTI2014

From Streaming Data to Streaming Insights: The Impact of Data Velocities on Mental Models

The rise of Big Data has influenced the design and technical implementation of visual analytic tools required to handle the increased volumes, velocities, and varieties of data. This has required a set of data management and computational advancements to allow us to store and compute on such datasets. However, as the ultimate goal of visual analytic technology is to enable the discovery and creation of insights from the users, an under-explored area is understanding how these datasets impact their mental models. That is, how have the analytic processes and strategies of users changed? How have users changed their perception of how to leverage, and ask questions of, these datasets?

analysis in motion↗

DOE OSTI2014

Utilizing rare earth elements as tracers in high TDS reservoir brines in CCS applications

In this paper we report the result of research associated with the testing of a procedures necessary for utilizing natural occurring trace elements, specifically the Rare Earth Elements (REE) as geochemical tracers in Carbon Capture and Storage (CCS) applications. Trace elements, particularly REE may be well suited to serve as in situ tracers for monitoring geochemical conditions and the migration of CO₂-charged waters within CCS storage systems. We have been conducting studies to determine the efficacy of using REE as a tracer and characterization tool in the laboratory, at a CCS analogue site in Soda Springs, Idaho, and at a proposed CCS reservoir at the Rock Springs Uplift, Wyoming. Results from field and laboratory studies have been encouraging and show that REE may be an effective tracer in CCS systems and overlying aquifers. In recent years, a series of studies using REE as a natural groundwater tracer have been conducted successfully at various locations around the globe. Additionally, REE and other trace elements have been successfully used as in situ tracers to describe the evolution of deep sedimentary Basins. Our goal has been to establish naturally occurring REE as a useful monitoring measuring and verification (MMV) tool in CCS research because formation brine chemistry will be particularly sensitive to changes in local equilibrium caused by the addition of large volumes of CO₂. Because brine within CCS target formations will have been in chemical equilibrium with the host rocks for millions of years, the addition of large volumes of CO₂ will cause reactions in the formation that will drive changes to the brine chemistry due to the pH change caused by the formation of carbonic acid. This CO₂ driven change in formation fluid chemistry will have a major impact on water rock reaction equilibrium in the formation, which will impart a change in the REE fingerprint of the brine that can measured and be used to monitor in situ reservoir conditions. Our research has shown that the REE signature imparted to the formation fluid by the introduction of CO₂ to the formation, can be measured and tracked as part of an MMV program. Additionally, this REE fingerprint may serve as an ideal tracer for fluid migration, both within the CCS target formation, and should formation fluids migrate into overlying aquifers. However application of REE and other trace elements to CCS system is complicated by the high salt content of the brines contained within the target formations. In the United States by regulation, in order for a geologic reservoir to be considered suitable for carbon storage, it must contain formation brine with total dissolved solids (TDS) > 10,000 ppm, and in most cases formation brines have TDS well in excess of that threshold. The high salinity of these brines creates analytical problems for elemental analysis, including element interference with trace metals in Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) (i.e. element mass overlap due to oxide or plasma phenomenon). Additionally, instruments like the ICP-MS that are sensitive enough to measure trace elements down to the parts per trillion level are quickly oversaturated when water TDS exceeds much more than 1,000 ppm. Normally this problem is dealt with through dilution of the sample, bringing the water chemistry into the instruments working range. However, dilution is not an option when analyzing these formation brines for trace metals, because trace elements, specifically the REE, which occur in aqueous solutions at the parts per trillion levels. Any dilution of the sample would make REE detection impossible. Therefore, the ability to use trace metals as in situ natural tracers in high TDS brines environments requires the development of methods for pre-concentrating trace elements, while reducing the salinity and associated elemental interference such that the brines can be routinely analyzed by standard ICP-MS methods. As part of the Big Sky Carbon Sequestration Project the INL-CAES has developed a rapid, easy to use process that pre-concentrates trace metals, including REE, up to 100x while eliminating interfering ions (e.g. Ba, Cl). The process is straightforward, inexpensive, and requires little infrastructure, using only a single chromatography column with inexpensive, reusable, commercially available resins and wash chemicals. The procedure has been tested with synthetic brines (215,000 ppm or less TDS) and field water samples (up to 5,000 ppm TDS). Testing has produced data of high quality with REE capture efficiency exceeding 95%, while reducing interfering elements by > 99%.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Advances in Cross-Cutting Ideas for Computational Climate Science

This report presents results from the DOE-sponsored workshop titled, ``Advancing X-Cutting Ideas for Computational Climate Science Workshop,'' known as AXICCS, held on September 12--13, 2016 in Rockville, MD. The workshop brought together experts in climate science, computational climate science, computer science, and mathematics to discuss interesting but unsolved science questions regarding climate modeling and simulation, promoted collaboration among the diverse scientists in attendance, and brainstormed about possible tools and capabilities that could be developed to help address them. Emerged from discussions at the workshop were several research opportunities that the group felt could advance climate science significantly. These include (1) process-resolving models to provide insight into important processes and features of interest and inform the development of advanced physical parameterizations, (2) a community effort to develop and provide integrated model credibility, (3) including, organizing, and managing increasingly connected model components that increase model fidelity yet complexity, and (4) treating Earth system models as one interconnected organism without numerical or data based boundaries that limit interactions. The group also identified several cross-cutting advances in mathematics, computer science, and computational science that would be needed to enable one or more of these big ideas. It is critical to address the need for organized, verified, and optimized software, which enables the models to grow and continue to provide solutions in which the community can have confidence. Effectively utilizing the newest computer hardware enables simulation efficiency and the ability to handle output from increasingly complex and detailed models. This will be accomplished through hierarchical multiscale algorithms in tandem with new strategies for data handling, analysis, and storage. These big ideas and cross-cutting technologies for enabling breakthrough climate simulation advancements also need the "glue" of outreach and learning across the scientific domains to be successful. The workshop identified several strategies to allow productive, continuous engagement across those who have a broad knowledge of the various angles of the problem. Specific ideas to foster education and tools to make material progress were discussed. Examples include follow-on cross-cutting meetings that enable unstructured discussions of the types this workshop fostered. A concerted effort to recruit undergraduate and graduate students from all relevant domains and provide them experience, training, and networking across their immediate expertise is needed. This will broaden and expand their exposure to the future needs and solutions, and provide a pipeline of scientists with a diversity of knowledge and know-how. Providing real-world experience with subject matter experts from multiple angles may also motivate the students to attack these problems and even come up with the missing solutions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Advances in Cross-Cutting Ideas for Computational Climate Science

This report presents results from the DOE-sponsored workshop titled, Advancing X-Cutting Ideas for Computational Climate Science Workshop,'' known as AXICCS, held on September 12--13, 2016 in Rockville, MD. The workshop brought together experts in climate science, computational climate science, computer science, and mathematics to discuss interesting but unsolved science questions regarding climate modeling and simulation, promoted collaboration among the diverse scientists in attendance, and brainstormed about possible tools and capabilities that could be developed to help address them. Emerged from discussions at the workshop were several research opportunities that the group felt could advance climate science significantly. These include (1) process-resolving models to provide insight into important processes and features of interest and inform the development of advanced physical parameterizations, (2) a community effort to develop and provide integrated model credibility, (3) including, organizing, and managing increasingly connected model components that increase model fidelity yet complexity, and (4) treating Earth system models as one interconnected organism without numerical or data based boundaries that limit interactions. The group also identified several cross-cutting advances in mathematics, computer science, and computational science that would be needed to enable one or more of these big ideas. It is critical to address the need for organized, verified, and optimized software, which enables the models to grow and continue to provide solutions in which the community can have confidence. Effectively utilizing the newest computer hardware enables simulation efficiency and the ability to handle output from increasingly complex and detailed models. This will be accomplished through hierarchical multiscale algorithms in tandem with new strategies for data handling, analysis, and storage. These big ideas and cross-cutting technologies for enabling breakthrough climate simulation advancements also need the "glue" of outreach and learning across the scientific domains to be successful. The workshop identified several strategies to allow productive, continuous engagement across those who have a broad knowledge of the various angles of the problem. Specific ideas to foster education and tools to make material progress were discussed. Examples include follow-on cross-cutting meetings that enable unstructured discussions of the types this workshop fostered. A concerted effort to recruit undergraduate and graduate students from all relevant domains and provide them experience, training, and networking across their immediate expertise is needed. This will broaden and expand their exposure to the future needs and solutions, and provide a pipeline of scientists with a diversity of knowledge and know-how. Providing real-world experience with subject matter experts from multiple angles may also motivate the students to attack these problems and even come up with the missing solutions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems

Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1973

Possible detection of interstellar deuterium

The abundance of deuterium relative to hydrogen is an important parameter in constructing models of the early expansion of the universe in Big-Bang cosmologies. Calculations by Wagoner et al. (1967) and by Wagoner (1973) show that the deuterium abundance would be very sensitive to the density of baryons in the fireball and hence to the present mean mass density of the universe. Reeves et al. (1973) use data on the abundance of deuterium in the solar system to deduce a ratio of n sub D/n sub H = 1.8 plus or minus 0.8 x 10 to the minus 5 power, and note that other authors have pointed out that processing of matter in stars slowly reduces the deuterium abundance in the galaxy.

Cesarsky, D. A.↗

DOE OSTI2018

Diagnostic Models for Wind Turbine Gearbox Components Using SCADA Time Series Data: Preprint

This paper presents a scalable, autonomous framework for diagnostics of wind turbine gearbox failures using a multi-feature supervisory control and data acquisition data set spanning multiple years. Because of the size of the data set studied, all algorithms were constructed to be scalable using a Spark-Hadoop data framework. An unsupervised approach was used to detect significant failures based on predetermined criteria. The initial criteria selected were an abnormal spike in turbine component temperature followed by a turbine power off, which helps reduce the number of potential false alarms. To detect abnormal spikes in component temperature, a model was introduced to adjust the temperature data for effects caused by ambient temperature and normal temperature increases when loaded. This study evaluates methods for normalizing temperature sensor data to identify anomalies. The models that performed the best were a linear regression and a multivariate polynomial regression. The proposed process for finding failures has tunable parameters that can be adjusted to be more or less sensitive. The combination of sensor data normalization and application of these criteria is successful in finding turbine failures resulting in downtime. The proposed methods can operate without failure or maintenance logs and can be utilized for offline analysis of large high-resolution data sets.

17 WIND ENERGY↗

DOE OSTI2017

Scaling up to address data science challenges

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithms and procedures for efficient processing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Rescuing Legacy Seismic Data FAIR’ly

The Earth’s interconnected and dynamical systems operate on a spectrum of time scales from millions of years to fractions of a second. While the evolution of the Earth and its deep interior are beyond the time span of human observations, understanding of many natural phenomena operating on human times scales have benefited from direct scientific observation. Continuous processes and those that are repeated over time shape the environment we live in. Furthermore, as we are faced with unprecedented changes to climate, understanding the deeper patterns and trends in natural systems through time have taken on new importance (Research Data Alliance, 2019). The call to reuse data is driven not only by economics but also by the recognition of their complete uniqueness (observations of natural systems are not repeatable) and scientific value in enhancing current understandings as well as potential new discoveries especially in the era of big data. These data are part of the historical record and our scientific heritage (American Geophysical Union, 2019) not only in explicitly recording earth observations but implicitly recording, and thus providing the evidence that addresses the manner in which science was conducted.

58 GEOSCIENCES↗

DOE OSTI2025

LandScan Mosaic

The LandScan program at Oak Ridge National Laboratory (ORNL), in collaboration with the National Geospatial-Intelligence Agency (NGA), continues to deliver the most accurate and up to date global, high resolution gridded population data. Additionally, the latest advancements in the LandScan HD methodology led to reduced latency in development of rapid updates for geopolitical events. With momentum towards reporting more up to date population estimates, feedback from the user community expressed interest in reporting population estimates in ranges - whether to express a level of uncertainty or confirm to leadership and stakeholders the modeled data are estimates. Building upon the need to understand uncertainty or confidence in the modeled data and report ranges at the global scale, LandScan Mosaic was developed. LandScan Mosaic represents the next generation of high-resolution population modeling, building upon the established success of previous LandScan HD iterations. While LandScan HD employed a deterministic big data fusion approach, LandScan Mosaic enhances this methodology by integrating advanced machine learning techniques to impute missing, yet crucial, population model parameters. This advancement allows for probabilistic modeling of building occupancy and population distribution, incorporating uncertainty quantification through Monte Carlo sampling methods. By combining big data fusion with machine learning-driven imputation and stochastic modeling, LandScan Mosaic provides a more comprehensive and robust representation of population dynamics. LandScan Mosaic will be following the in the footsteps of its longstanding counterpart LandScan Global and releasing a global gridded population raster, at the 3-arcsecond resolution. This technical report documents the current stage of development of LandScan Mosaic, detailing the methodologies and data sources behind the modeling. Stakeholders are encouraged to use this document as an authoritative reference for insight into Mosaic’s data development processes. However, readers should note that LandScan Mosaic remains in a late-stage research and development phase, and methodologies and data presented here are subject to refinements ahead of the anticipated global release in Summer 2025. Feedback and inquiries from users and stakeholders are welcomed as we continue to refine and enhance this important population resource.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Towards Automated Analytics of Research Publications

For readers of scientific publications it remains a big challenge to unambiguously relate the published research with the data used. To a substantial degree it is attributed to authors, journals, editors, and reviewers not prioritizing correct data citation, which impacts traceability, repeatability, and giving credits to published authors and their funding sources. Furthermore, uniform classification of the content of the published research is hampered by journals using journal specific topics and letting authors to assign free text keywords to their papers. We demonstrate automated analytics methods for extracting and relating datasets used and the research application areas by processing 1,300 research papers that referenced the NASA Giovanni service (but probably not the datasets in particular) as supporting their publication process. This presentation was given during the 2022 ESIP January meeting held virtually in January 2022.

Irina Gerasimov↗

DOE OSTI2012

Clinical evaluations of an amplitude-based binning algorithm for 4DCT reconstruction in radiation therapy

Purpose: Phase-binning algorithms are commonly utilized in 4DCT image reconstruction for characterization of tumor or organ shape and respiration motion, but breathing irregularities occurring during 4DCT acquisition can cause considerable image distortions. Recently, amplitude-binning algorithms have been evaluated as a potential improvement to phase-binning algorithms for 4DCT image reconstruction. The purpose of this study was to evaluate the performance of the first commercially available on-line retrospective amplitude-binning algorithm for comparison to the traditional phase-binning algorithm. Methods: Both phantom and clinical data were used for evaluation. A phantom of known geometry was mounted on a 4D motion platform programmed with seven respiratory waves (two computer generated and five patient trajectories) and scanned with a Philips Brilliance Big bore 16-slice CT simulator. 4DCT images were reconstructed using commercial amplitude- and phase-binning algorithms. Image quality of the amplitude- and phase-binned image sets was compared by evaluation of shape and volume distortions in reconstructed images. Clinical evaluations were performed on 64 4DCT patient image sets in a blinded review process. The amplitude- and phase-binned 4DCT maximum intensity projection (MIP) images were further evaluated for 28 stereotactic body radiation therapy (SBRT) cases of total 64 cases. A preliminary investigation of the effects of respiratory amplitude and pattern irregularities on motion artifact severity was conducted. Results: The phantom experiments illustrated that, as expected, maximum inhalation occurred at the 0% amplitude and maximum exhalation occurred at the 50% amplitude of the amplitude-binned 4DCT image sets. The phantom shape distortions were more severe in the images reconstructed from the phase-binning algorithm. In the clinical study, compared to the phase-binning algorithm, the amplitude-binning algorithm yielded fewer or less severe motion artifacts in 37.5% of the cases (24/64), comparable artifacts in 54.7% of the cases (35/64), and slightly greater artifacts in 7.8% of the cases (5/64). Evaluation of SBRT cases demonstrated that the reconstructed tumor sizes and locations were comparable in 96% (1/28) of the MIP image pairs generated from both amplitude- and phase-binning algorithms. In this case the amplitude-binned image set rendered a smaller tumor size, which was likely due to very shallow respiratory amplitudes occurring over several breathing cycles. Conclusions: Overall, the amplitude-binning algorithm for 4DCT reconstruction reduced the severity of tumor distortion and image artifacts compared to the phase-binning algorithm. However, the full range of motion may not be characterized using amplitude-binning algorithms. Despite superior performance, amplitude binning can still be susceptible to motion artifacts caused by large variations in amplitude of respiratory waves.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

FY2018 Discovery Through Situational Awareness: Anomalies, Oscillations, and Classification

This technical report is a summary of work performed under the DOE GMLC program under the task titled “Discovery through Situational Awareness”. The focus of this work was to apply big data, statistical techniques to PMU data to do the following: 1) baseline PMU data and find anomalies with a focus on phase angle pairs, 2) identify the frequencies of dominant modal oscillations between different parts of a power system, and 3) identify and classify frequency events in PMU data. This work was performed on PMU data from the Western Interconnect. Many of these algorithms will be applied to wide area phase angle pair data on the Eastern Interconnect in a tool called ESAMS (Eastern Interconnect Situational Awareness Monitoring System). ESAMS was built by EPG (Electric Power Group) and PNNL and is currently being installed on the Eastern Interconnect. The results discussed in this report were made from 14 PMUs from data ranging from Oct 2016 to May 2018. The PMU data were measured 60 times each second. Fourteen phase angle pairs were included, with reactive and active power being calculated at 14 sites. All results have been de-identified for this report. The methodologies applied in the ESAMS tool are described in Section 2. A baseline of PMU data is created using the last 120 days of data. This baseline describes typical behavior as understood from the data. The baseline is then applied to the next day’s data and anomalies from that baseline are identified. Plots are created to help describe what is anomalous during that specific moment in time. These algorithms are currently being installed on the Eastern Interconnect. FY19 work will include discussing the results and findings from the ESAMS tool. A new method of identifying the frequencies of dominant modal oscillations between different parts of a power system is described in Section 3. A list of 199 frequency events provided by Bonneville Power Administration were used to create algorithms to detect frequency events and to create classification rules that can be used to identify future frequency events. Section 4 discusses the results the performance of 7 different event detection, clustering-based methods. Accuracy percentages ranged from 93.59% to 99.16%, with most methods being very fast. GBM (Gradient Boosting Machine) performed well and quickly. These 7 different methods were also tested to determine how well each built classification rules on training data and then applied those rules to identify future frequency events. Accuracy percentages ranged from 89.09% to 99.9%, with all methods performing quickly. GLM (General Linear Model) was viewed as the top performer when considering accuracy and processing time. Because the list of events consisted of 80% of them as active power events and only 20% as faults, all methods struggled to identify events as faults. Small faults were especially difficult to identify. A separate algorithm was created to help with this. Additional work will be done in FY19 on event detection using more data and more events and refinements to the methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Monoenergetic photon-induced fission cross-section ratio measurements for U 235 , U 238 , and Pu 239 from 9.0 to 17.0 MeV

Background: High-accuracy data are needed to advance microscopic descriptions of the nuclear fission process, to improve the predictive precision of phenomenological models, and for applications in nuclear energy and homeland security. Purpose: In this work, the main goal is to provide high-accuracy cross-section data for photofission of 235 U, 238 U, and 239 Pu in the energy range from the fission threshold to the high-energy tail of the giant dipole resonance. These new data should contribute significantly to the reduction of the systematic uncertainty in evaluated photofission cross-section databases. Method: Cross-section ratios for photofission of the “big three” actinide nuclei were measured using a quasimonoenergetic photon beam and dual-fission chambers. The measurements were performed at the HIγS facility using a photon beam produced from Compton backscattering of free-electron laser light. The dual-fission chamber enabled simultaneous counting of fission events from two targets. This method allows for cross-section ratio measurements with very small systematic errors. The largest source of systematic error in the determination of the cross-section ratios is the uncertainty in the ratio of the target thicknesses. Results: We report photon-induced fission cross-section ratios for σ[ 235 U(γ,f) / 238 U(γ,f)] , σ[235U(γ,f) / 239 Pu(γ,f)] , and σ[ 238 U(γ,f) / 239 Pu(γ,f)] at photon energies from 9.0–17.0 MeV. More than 20 data points were measured for each ratio, and the systematic uncertainties are less than 2%. Conclusion: The present photofission cross-section ratio data sets are compared to ratios computed from previous measurements, to the corresponding neutron-induced fission cross-section ratios, and to ratios computed from evaluated databases. Finally, the data obtained in this work for σ[ 235 U(γ,f) / 238 U(γ,f)] are consistent with the existing data, while for σ[ 235 U(γ,f) / 239 Pu(γ,f)] and σ[ 238 U(γ,f) / 239 Pu(γ,f)] the present data are systematically lower.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

The Geothermal Data Repository: Five Years of Open Geothermal Data, Benefits to the Community: Preprint

In the five years since its inception, the Department of Energy's (DOE) Geothermal Data Repository (GDR) has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the DOE open data movement where it is providing a tangible benefit to the geothermal scientific community. Throughout this time, the GDR project team has been working closely with the community to refine the data submission process, improve the quality of submitted data, and embrace modern proper data management strategies to maximize the value and utility of submitted data. This paper explores some of the motivations behind various improvements to the GDR over the last 5 years, changes in data submission trends, and the ways in which these improvements have helped to drive research, fuel innovation, and accelerate the adoption of geothermal technologies.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

The Geothermal Data Repository: Five Years of Open Geothermal Data, Benefits to the Community

15 GEOTHERMAL ENERGY↗