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Task 1 Report - Assessment of Data Availability to Inform Energy Planning Analyses: Energy Alternatives Study for the Lao People's Democratic Republic: Smart Infrastructure for the Mekong Program

In an effort to address concerns such as energy security, reliability, affordability, and other objectives, the Government of the Lao People's Democratic Republic (Lao PDR) is seeking to advance its expertise and experience in energy system analysis and planning to explore energy alternatives. Assessing the potential and alternatives for deploying energy technology options is often an early step - and, in most cases, an ongoing process - in planning for the development of the energy sector as a whole. Reliable and robust data are crucial to conducting these types of planning-related analyses in a transparent manner that builds confidence among power sector stakeholders and encourages investment in future energy project development and infrastructure opportunities. This report represents the first output of the Energy Alternatives Study for the Lao PDR (Energy Alternatives Study), a collaboration between Ministry of Energy and Mines and the United States Agency for International Development (USAID) under the auspices of the Smart Infrastructure for the Mekong (SIM) program. The Energy Alternatives Study includes five tasks that build upon each other to meet the goal of the project. The report summarizes the availability, quality, and accessibility of data that serve as key inputs to energy planning activities for the power sector. The purpose of this data assessment is two-fold: 1. To facilitate the informed use of existing data by highlighting applications for these data as they relate to priority energy planning analyses; and 2. To inform future investments in energy data collection and management by identifying significant data gaps and providing guidance on how to fill these gaps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Enhancing Energy Systems Cybersecurity using Blockchain (SBIR Final Phase I Report)

The goal of this SBIR research has been to investigate blockchain technology and its applications in the fossil fuel sector - with a focus on how this technology could be leveraged to improve the cybersecurity of these systems. The primary tasks that we carried out involved evaluating existing blockchain technology, identifying relevant and compelling use cases, researching the integration of blockchain technology into industrial systems, and the design and implementation of a proof of concept system. A key finding is that within industrial systems where many parties are involved, trust can be lacking. By providing technology that assures verifiable trust in the data, many processes may be able to be automated. We believe that verifiable trust in the data can only be provided if we trust the data from "cradle to grave." This means that we need to secure the systems that are creating the data, moving the data, and storing the data. The last of these is provided by blockchain. Blockchain technology will not only improve security and trust within these systems, in some cases it will also be able increase the operational tempo of some processes significantly. We are vendors of commercial communications software that is based upon the Open Management Group (OMG) data distribution service (DDS) standard; it secures data in motion. In addition to many defense, transportation, and medical systems, it is running in oil, gas, and coal systems. Over the course of this effort we have come to believe that blockchain technology could be an important enabler for this sector: we have engaged with a number of our customers and identified several compelling use cases beyond cryptocurrencies, which we document further within this report. Furthermore, as it turns out, we believe that many of these use cases are relevant across a wide swath of industries. This analysis provides necessary and important foundational work that could lead to new ways of securing oil and gas processing, and coal mining infrastructure against cyber threats.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

SU-F-P-03: Management of Time to Treatment Inititation: Case for An Electronic Whiteboard

Purpose: To determine if data mining of an electronic whiteboard improves the management of the Time to Treatment Initiation (TTI) in radiation oncology. Methods: An electronic whiteboard designed to help in managing the planning workflow and improves communication regarding patient planning progress was used to record the dates at which each phase of the planning process began or completed. These are CT Sim date, Plan Start, Physician Review, Physicist Review, Approval for Treatment Delivery, Setup or Verification of Simulation. Results: During clinical implementation, the electronic whiteboard was able to fulfill its primary objective of providing a transparent account of the planning progress of each patient. Peer pressure also meant that individual tasks, such as contouring, were easily brought to the attention of the responsible party and prioritized accordingly. Data mining to analyze the electronic whiteboard per patient (figure 1), per diagnosis (figure 2), per treatment modality (figure 3), per physician (figure 4), per planner (figure 5), etc., added another sophisticated tool in the management of Time to Treatment Initiation without compromising quality of the plans being generated. A longer than necessary time between CT Sim and Plan Start can be discussed among the members of the treatment team as an indication of inadequate/outdated CT Simulator, Contouring Tools, Image Fusion Tools, Other Imaging Studies (MRI, PET/CT) performed, etc. The same for the Plan Start to Physician Review where an extended time than expected may be due unrealistic planning goals, limited planning system features, etc. Conclusion: An Electronic Whiteboard in radiation oncology is not only helping with organizing planning workflow, it is also a potent tool that can be used to reduce the Time to Treatment Initiation by providing the clinic with hard data about the duration of each phase treatment planning as a function of different variable affecting the planning process. The work is supported by the Global Medical Physics Institute.

60 APPLIED LIFE SCIENCES↗

Discovering Blind Geothermal Systems in the Great Basin Region: An Integrated Geologic and Geophysical Approach for Establishing Geothermal Play Fairways: All Phases

Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada. These parameters were grouped into subsets and individually weighted to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis. After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley, deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.

15 GEOTHERMAL ENERGY↗

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗

Leaching Behavior of Rare Earth Elements in Fort Union Lignite Coals of North America

Rare earth elements are crucial materials in an incredible array of consumer goods, energy system components and military defense applications. However, the global production and entire value chain for rare earth elements is dominated by China, with the U.S. currently 100% import reliant for these critical materials. Traditional mineral ores including those previously mined in the U.S., however, have several challenges. Chief among these is that the content of the most critical and valuable of the rare earths are deficient, making mining uneconomical. Further, the supply of these most critical rare earths is nearly 100% produced in China from a single resource that is only projected to last another 10 to 20 years. The U.S. currently considers the rare earths market an issue of national security. It is imperative that alternative domestic sources of rare earths be identified and methods developed to produce them. Recently, coal and coal byproducts have been identified as one of these promising alternative resources. This paper details the results of a study on characterization of North Dakota lignite and lignite-related feedstocks as an assessment of their feasibility for rare earth element recovery. The abundance, distribution and modes of occurrence of the rare earth elements in the samples collected were determined in this initial study to inform the selection of appropriate extraction and concentration methods to recover the rare earth elements. Materials investigated include the lignite coals, clay-rich sediments associated with the coal seams, and materials associated with a lignite beneficiation system and power plant. The results show that high rare earth element levels exist both in lignite coals and associated sediments. The form of the rare earth elements in the clay materials is primarily as ultra-fine mineral grains. In the lignite coals, approximately 80-95% of the rare earths content is organically associated, primarily as coordination complexes.

Laudal, Daniel A.↗

Numerically approximating centrality for graph ranking guarantees

Many real-world datasets can be represented as graphs. Using iterative solvers to approximate graph centrality measures allows us to obtain a ranking vector on the nodes of the graph, consisting of a number for each vertex in the graph identifying its relative importance. In this study the centrality measures we use are Katz Centrality and PageRank. Given an approximate solution, we use the residual to accurately estimate how much of the ranking matches the ranking given by the exact solution. Using probabilistic matrix norms, we obtain bounds on the accuracy of the approximation compared to the exact solution with respect to the highly ranked nodes and apply numerical analysis to the computation of centrality with iterative methods. This relates the numerical accuracy of the linear solver to the data analysis accuracy of finding the correct ranking. In particular, we answer the question of which pairwise rankings are reliable given an approximate solution to the linear system. Experiments on many real-world undirected and directed networks up to several million vertices and several hundred million edges validate our theory and show that we are able to accurately estimate large portions of the approximation. We also analyze the difference between global centrality scores and personalized scores (w.r.t. specific seed vertices). By analyzing convergence error, we develop confidence in the ranking schemes of data mining. We show we are able to accurately guarantee ranking of vertices with an approximation to centrality metrics faster than current methods.

97 MATHEMATICS AND COMPUTING↗

Big Data Analysis and Technical Review of Regeneration for Carbon Capture Processes

Carbon capture remains an integral technology to mitigate pollution from one of the most prevalent greenhouse gases. CO 2 desorption/absorbent regeneration for both solid- and liquid-based systems is widely recognized as an energy-intensive and costly process operation. Consequently, tremendous work was devoted towards developing new absorbents and regeneration processes to promote their economic feasibility for extensive implementation. In this review, we broadly and deeply review more than 10,000 papers and extract the hidden trends of carbon capture and absorbents regeneration in the past few decades, using a novel data-mining analysis technique. We comprehensively analyzed an array of recent absorbent regeneration methods utilized in post-combustion, pre-combustion, carbon capture from industrial point sources, and direct air carbon capture, with an emphasis on sorbent and solvent-based techniques. In conclusion, advanced regeneration methods in these techniques were illustrated and discussed, followed by recommendations for further research efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NEWTS Coal Mine Drainage Dataset from Cravotta Brady (2015)

Data from Cravotta, Brady, "Priority pollutants and associated constituents in untreated and treated discharges from coal mining or processing facilities in Pennsylvania, USA". Applied Geochemistry, 2015. https://doi.org/10.1016/j.apgeochem.2015.03.001 Dataset includes information on water quality composition including inorganic compounds from untreated and treated streams of coal-mine discharge from coal mining and coal processing locations. Data is provided in the original version as well as in a summarized version for easy input into aqueous chemistry modeling software.

Aqueous Chemistry↗

Carbon Utilization and Storage Partnership of the Western United States

This technical report documents research conducted under DOE Award No. DE-FE0031837 focused on evaluating the feasibility of carbon capture, utilization, and storage (CCUS) systems in the central and western United States. The project integrated geologic characterization, reservoir simulation, infrastructure modeling, and economic analysis to assess CO₂ storage potential near industrial sources and develop strategies for transport and sequestration. The work included subsurface modeling, risk assessment, monitoring and verification (MRV) planning, and evaluation of regulatory pathways such as EPA Underground Injection Control (UIC) Class VI permitting and IRS 45Q tax credit eligibility. Results demonstrate the viability of multiple storage approaches, including saline formations, enhanced coalbed methane recovery, and basalt mineralization, supported by data-driven workflows and regional analyses. The project also produced permitting templates, technology transfer activities, and stakeholder engagement efforts to support deployment readiness. These findings contribute to the development of scalable, economically viable CCUS systems and provide a repeatable framework for future carbon management projects.

20 FOSSIL-FUELED POWER PLANTS↗

Theory of Carbon Dioxide Absorption by Hanford Waste - 18208

Hanford nuclear waste has a very high pH, and Hanford tanks are ventilated with air. Air has CO{sub 2(g)} in it, which is an acid, so it has some affinity for the waste. The absorption of CO{sub 2} by the waste is important because CO{sub 2} absorption consumes hydroxide at the liquid-air interface, and the hydroxide is important for inhibiting tank corrosion. NaOH is periodically added to some tanks in order to mitigate the surface acidification. The NaOH added then becomes part of the waste and must be treated as waste, increasing the cost of the mission. The Hobbs Equation, developed at the Savannah River site, is used at Hanford to predict CO{sub 2} absorption from the tank ventilation system. In this study, the Hobbs Equation functional form is theoretically evaluated from thermodynamic calculations of CO{sub 2} absorption, and diffusion rates in the waste surface. This study theoretically justifies the empirical equation reported by Hobbs. Thermodynamic calculations performed using the Environmental Simulation Program (ESP) software on a typical double-shell tank waste composition indicate that essentially 100 % of CO{sub 2} that contacts the waste will react with the waste whenever the pH is above 10. In the 1990's, a substantial amount of data was collected on the concentration of CO{sub 2} in the off-gas of ventilated double-shell tanks at Hanford. This historic data was mined in order to determine the quantity of CO{sub 2} absorbed by the waste. The results of this data mining activity determined that tanks below approximately pH 10 (i.e., tank 241-AN-107) will show limited CO{sub 2} absorption, consistent with both the Hobbs model and the theory developed here. Two other tank with higher pH showed contrasting CO{sub 2} absorption. One showed substantial CO{sub 2} absorption, and the other showed minimal CO{sub 2} absorption despite the high waste pH in one tank, presumably because there was minimal contact between the waste surface and most of the air flow. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Coupling Big Data Analytics and Reactive Transport Modeling for Cost-effective Groundwater Monitoring - 17163

This study presents an innovative approach for sustainable and cost-effective groundwater monitoring. This approach takes advantage of recent advances in various technologies: (1) in situ autonomous sensors, (2) big data analytics, and (3) parallel high-performance computing for flow and reactive transport modeling. In situ sensors are used to periodically measure the key variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control contaminant mobility and the plume spatial and temporal distribution. Based on a limited number of groundwater sampling, the data analytics methods-data mining and machine learning-allow us to identify and quantify the correlations between the in situ-measured variables and contaminant concentrations, and also to detect significant changes associated with the plume mobility. In addition, a state-of-the-art parallel numerical flow and reactive transport simulator Amanzi, and uncertainty quantification software Agni are used to provide an improved physical and mechanistic understanding of the contaminated groundwater system behavior, and to predict the long-term plume distribution for optimizing and adapting the monitoring strategy. Amanzi and Agni were developed as part of the Advanced Simulation Capability for Environmental Management (ASCEM) program of the DOE Office of Environmental Management. Such modeling is critical, particularly, for assessing the impact of climate change and associated hydrological shifts. The developed approach is expected to significantly reduce the groundwater sampling frequency and associated cost. In addition, the real-time information on plume mobility serves as an early warning system, improving the resiliency of contaminated or potentially contaminated sites. We demonstrate this approach using as an example the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗

Innovations in Sustainable Passive Wastewater Treatment - 20236

Sustainable water treatment systems, engineered to mimic natural processes and utilize natural materials, yield economic and environmental benefits through reductions in long term operational inputs, including: power, chemicals, and labor. Golder is an industry leader in the development and implementation of sustainable technologies at operating and closed mines. We continue to innovate these technologies and customize solutions to meet client needs by applying past experience and focused research. Sustainable (or passive) water treatment systems include ponds, wetlands, flow-through natural media beds, and limestone drains. These systems can blend in with a natural environment, are highly cost-effective for long-term water treatment, and can outcompete traditional active treatment technologies. Application of sustainable technologies relative to active technologies can be influenced by the influent water matrix, effluent limitations, flow rate of water requiring treatment, and available land area. In this presentation, four Golder-developed systems are reviewed as case studies, demonstrating our continued progressive approach to advancing sustainable water treatment technologies to meet industry needs. These case studies focus on design innovations and operational performance from data collected from the operational systems. Resources (power, chemicals, labor) required for the operation of each case study are compared with those that would be required for operation of their active treatment technology alternatives. - Case Study 1 presents an anaerobic biochemical reactor (BCR), a technology typically implemented for metals treatment and acid neutralization, for the treatment of sulfate to low concentrations (<250 mg/L). Active treatment via reverse osmosis (RO) membrane filtration is typically required for treatment of sulfate to similar concentrations, this innovation provides potential for sustainable treatment to replace the need for energy, chemical, labor, and waste (brine) intensive RO treatment for specific applications. Magnetite waste rock materials from the site were upcycled for use in this system to provide sequestration of the sulfide generated in the BCR. - Case Study 2 examines another novel application of the conventional BCR, modified for the removal of radionuclides and metals (uranium, radium, and selenium) at a closed uranium mine. - Case Study 3 presents implementation of sustainable treatment for mine drain water from a historic mine site, now a state park with around 100,000 visitors per year. The system includes a pond, wetland, and manganese removal bed surrounded by public trails designed to maintain the character of the park. The system treats for arsenic, iron, and manganese at relatively high flows (up to 1,200 gpm), with large seasonal fluctuations in flow and quality. - Case Study 4 is a unique project for which Golder is currently designing a sustainable treatment system, with no energy input, to treat nitrate in mine water post-closure at a site near the arctic circle. In this unprecedented application, climate, management of freshet, and space availability presented substantial technical challenges. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Data from Rossi-alpha and pulsed neutron prompt neutron time decay measurements at the Oak Ridge Critical Experiments Facility

This report briefly describes the Rossi-alpha and pulsed neutron prompt neutron time decay measurements performed at the Oak Ridge Critical Experiments Facility. This description includes the measurement equipment, type of data, and how to access the actual data from Records Management Services at Oak Ridge National Laboratory and the Nuclear Criticality Safety Benchmark Program (NCSBEP) of the Idaho National Laboratory. Most of the data were acquired between 1960 and 1975 on weapons-grade enriched uranium metal assemblies. Also included are measurements of the same type performed at Los Alamos National Laboratory by Oak Ridge National Laboratory with the JEZEBEL and FLATTOP criticality assemblies and other plutonium metal parts. The purpose of this report is to allow future mining of the data for publication of additional information that can be used to verify calculational methods and nuclear cross sections. These data can be used to create reactor physics benchmarks for the Nuclear Energy Agency’s International Handbook of Evaluated Reactor Physics Experiments. Present calculational methods can be used to directly calculate the measured data. The prompt neutron decay is more neutron spectrum sensitive than other measured quantities, particularly for interacting fissile metal systems where the flight time of neutrons between interacting sections is a large part of the neutron lifetime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Global- and Local-Scale High-Resolution Event Catalogs for Algorithm Testing

During the development of new seismic data processing methods, the verification of potential events and associated signals can present a nontrivial obstacle to the assessment of algorithm performance, especially as detection thresholds are lowered, resulting in the inclusion of significantly more anthropogenic signals. Here, we present two 14 day seismic event catalogs, a local–scale catalog developed using data from the University of Utah Seismograph Stations network, and a global–scale catalog developed using data from the International Monitoring System. Each catalog was built manually to comprehensively identify events from all sources that were locatable using phase arrival timing and directional information from seismic network stations, resulting in significant increases compared to existing catalogs. Here, the new catalogs additionally contain challenging event sequences (prolific aftershocks and small events at the detection and location threshold) and novel event types and sources (e.g., infrasound only events and long–wall mining events) that make them useful for algorithm testing and development, as well as valuable for the unique tectonic and anthropogenic event sequences they contain.

58 GEOSCIENCES↗

Automating Network Node Behavior Characterization by Mining Communication Patterns

Enterprise networks of scale are complex, dynamic computing environments that respond to evolv- ing business objectives and requirements. Characteriz- ing system behaviors in these environments is essential for network management and cyber security operations. Characterization of system’s communication is typical and is supported using network flow information (NetFlow). Related work has characterized behavior using theoretical graph metrics; results are often difficult to interpret by enterprise staff. We propose a different approach, where flow information is mapped to sets of tags that contextualize the data in terms of network principals and enterprise concepts. Frequent patterns are then extracted and are expressed as behaviors. Behaviors can be com- pared, identifying systems expressing similar behaviors. We evaluate the approach using flow information collected by a third party.

cyber system modeling↗

The EGS Collab Project – Stimulations at Two Depths

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.

EGS Collab, Enhanced Geothermal Systems, EGS, fiel↗