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Electronic display unit provides operator of longwall shearer with information needed to control machine. Unit samples sensors on shearer. Using sensor signals, executes mathematical calculations, performs decisionmaking routines and displays results to operator. Unit also logs data for later analysis.
Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.
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In the coming decade, it is anticipated that deep-sea mining activities will commence throughout the deep ocean, at depths around 4000-6000m where vast deposits of baseball-sized rocks called polymetallic nodules lay on the seabed. These nodules contain resources such as cobalt, copper and nickel which are needed to produce batteries for electric vehicles. Collector machines driving on the seabed and gathering these nodules will stir up clouds of sediment, and there is concern about how the resulting concentration of sediment in the near-bottom ocean water will affect deep and mid-ocean biology. There is currently no technology capable of comprehensively monitoring the concentrations and properties of stirred up sediment in the deep. This project addressed this critical gap by developing a deep-sea particle measurement system (Real-Time Size and Settling Velocity, RTSSV) that uses a multi-camera high-resolution video imaging system, with accompanying image processing, to measure the concentration, size distribution and settling speed of sediment. Importantly, these measurements are made in-situ, so that delicate sediment aggregates will not be disturbed, as is the case when collecting samples at depth to be measured in the lab. The instrument can be mounted on a variety of established deep-sea platforms such as moorings, AUVs and ROVs, providing data in near real time, where the key measurements need to be made—from the source of the sediment disturbance and throughout the resulting plume—vital for determining if deep-sea mining can be done in an environmentally responsible manner. The developed system (RTSSV) will possess the only such capability worldwide, giving a US-based group a substantial competitive advantage as this newly evolving global industry develops. As added value, the technology will also have widespread application to the offshore oil and gas industry, dredging industry, and to sediment science research.
The Verifiable Fuel Cycle Simulation (VISION) is a system-dynamics model of the entire nuclear fuel cycle, from mining to disposal, using user-defined deployment scenarios, fuel recipes, and recycling parameters. It is written in the PowerSim simulation environment and utilized MS Excel to manage data inputs and outputs.
Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.
CO2 soil flux data collected from Farnsworth Unit field, Ochiltree County, Texas between 11/2013 and 06/2024.
The 2018 Industry Energy Data Book summarizes the status of, and it identifies the key trends in energy use and its underlying economic drivers across the four industrial subsectors: agriculture, construction, manufacturing, and mining.
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.
This paper describes a general purpose imaging technology developed by the U.S. Bureau of Mines (USBM) that, when fully implemented, will solve the general problem of 'seeing into the earth.' A first-generation radar coal thickness sensor, the RCTS-1, has been developed and field-tested in both underground and highwall mines. The noncontacting electromagnetic technique uses spatial modulation created by moving a simple sensor antenna in a direction along each axis to be measured while the complex reflection coefficient is measured at multiple frequencies over a two-to-one bandwidth. The antenna motion imparts spatial modulation to the data that enables signal processing to solve the problems of media, target, and antenna dispersion. Knowledge of the dielectric constant of the media is not necessary because the electrical properties of the media are determined automatically along with the distance to the target and thickness of each layer of the target. The sensor was developed as a navigation guidance sensor to accurately detect the coal/noncoal interface required for the USBM computer-assisted mining machine program. Other mining applications include the location of rock fractures, water-filled voids, and abandoned gas wells. These hazards can be detected in advance of the mining operation. This initiating technology is being expanded into a full three-dimensional (3-D) imaging system that will have applications in both the underground and surface environment.
Aqueous speciation of rare earth elements (REE) controls their mobilization, fractionation, and enrichment in the natural waters. Geochemical modeling of their speciation is key to improve our understanding of the formation of economic mineral deposits, for developing mineral separation and mine tailing recovery technologies, and for characterizing the geochemistry of thermal water. However, our ability to predict the fate of REE in a wide pH and temperature range is limited by the scarcity of thermodynamic data for the REE hydroxyl complexes. In part I of this study (H. J. Han and A. P. Gysi, Dalton Trans., 2024, 53, 13129–13141), the optical properties of m-cresol purple (mCP) were determined using UV-Vis spectrophotometry between 25 and 75 °C in order to develop a method for deriving the hydrolysis constants of erbium (Er). Here, UV-Vis spectrophotometry experiments were conducted as a function of temperature between 35 and 75 °C to determine the hydrolysis of Er in near-neutral to alkaline solutions using mCP as an in situ pH color indicator. Here, the experiments were conducted with Er concentrations from 0 to ~0.253 mmol kg –1 in low ionic strength solutions (≤0.001 mol kg –1 ).
The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.
Concentrating solar power (CSP) plants depend on thousands of heliostats whose reflectance declines as dust accumulates. Operators routinely measure reflectance to estimate soiling and, in turn, inform cleaning schedules, but the value of collecting more frequent or more accurate data has not been formally quantified. This study introduces a Monte Carlo discrete event simulation framework that integrates stochastic models of soiling, weather, and measurement error with a dynamic cleaning dispatch policy to estimate annual energy production and operations costs. Applied to two representative central-receiver field configurations, the results show that both the frequency and accuracy of reflectance measurements can meaningfully impact plant performance. In both case studies, reducing measurement intervals yields significant returns, with the energy gains greatly exceeding the cost of more frequent data collection. The simulation framework serves as a decision-support tool for CSP operators, allowing them to input site-specific soiling conditions, measurement accuracy, and survey frequency to evaluate the tradeoffs between data collection cost and energy recovery, and to identify measurement strategies that maximize plant profit.
The Sandia Solution Mining Code (SANSMIC) has been used for many years to examine the development of salt cavern geometry, both in a confirmatory manner with comparisons made to real-world sonar data and in a predictive manner when updated sonar data are not available. SANSMIC models require some modeling choices in order to incorporate real-world data. Key modeling choices include the vertical resolution of cavern geometry to implement, as well as how to incorporate daily raw water injection data into the SANSMIC model. This report documents five studies that address the impact of the modeling choices on the predicted cavern geometries and calculated leaching efficiencies. In most cases, hypothetical cylindrical initial cavern geometries are used to provide a common baseline against which to test the systematic variation of input variables including cavern radius, oil-brine-interface (OBI) depth, vertical cell size, raw water injection rate, raw water injection duration, workover time, and number of leaching stages. The use of smaller cell sizes is recommended moving forward to provide a better one-to-one relationship between sonar data and the modeled cavern. A new methodology for incorporating raw water injection data is also recommended, in order to more closely model real-world injection and workover times. Overall, the systematic studies performed here have increased our confidence in previous SANSMIC model results, as well future use of the code for predicting leaching effects on cavern geometries. Some minor changes to modeling choices are recommended, which can easily be applied with the version of SANSMIC currently under development.
The first two prototype lunar regolith simulants were to replicate characteristics of the lunar highlands. A major change from initial plans was to use an estimate of typical Apollo 16 highland material rather than a specific core. This change was compatible with project objectives and necessitated by the lack of adequate data from a single core. To make the initial simulant, a crystalline component and a glass component were deemed necessary. Lithic feedstocks were obtained with the assistance of the Stillwater Mining Company. The mixing of the rock constituents was done based on normative mineralogy rather than modal mineralogy. This was done to simplify development. A major design decision was not to attempt simulation of the range of glass chemistries observed in Apollo samples. A single glass was assumed to be adequate for engineering purposes for which the simulant would be used. Glass was made in a process developed at Zybek Advanced Products of Boulder, Colorado. Mill sand was used as the feedstock for this process. A second generation of the simulant was made that incorporated the additional minerals apatite, synthetic whitlockite, and pyrite. The olivine source was changed to the commercially produced Twin Sisters Dunite, and a pseudo-agglutinate product was invented and added to the -2M product. The pseudo-agglutinate captures all of the lunar agglutinate features but does not attempt to incorporate nanophase Fe.
We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.
At the forefront of developments in synchrotron x-ray microscopy, we report nanoscale-resolution high-dimensional spectrotomography under controlled sample environments has been demonstrated. Such cutting-edge experimental capability has been broadly applied to scientific studies in the field of energy materials science, where the dynamically evolving structural and chemical defects play a vital role in the functionality. In this article, we review novel developments of this technique from both experimental and data/information mining perspectives. Using studies on lithium-ion battery electrode materials as examples, we highlight the rich information in the high-dimensional and high-resolution x-ray tomographic data, which can be used to interpret the complicated thermal-electro-chemo-mechanical interplay that occurs under the operating conditions and collectively determines battery performance. We also discuss the frontier challenges in this field and our perspectives of the future directions in the context of projected major developments in the landscape of largescale x-ray facilities across the globe.
Seismologists distinguish underground nuclear explosions from more commonly occurring earthquakes using moment tensor inversion, high-frequency P/S amplitude ratios, m b :M s comparisons, and P–pP differential travel times. These methods are generally successful for large seismic events (M >3-4) well recorded at regional-to-teleseismic distances (>150 km); however, it is unclear whether they can be modified to work for small events (M <3) well recorded only at local distances (<150 km). Here, we evaluate a recently proposed, local-distance seismic source discriminant—the difference between local magnitude (M L ) and coda duration magnitude (M C )—using seismograms of earthquakes and buried, single-fired chemical explosions recorded in three regions of the western United States. The quantity M L –M C was previously found to be sensitive to source depth, effectively discriminating mine blasts, induced earthquakes, and very shallow tectonic earthquakes from deeper crustal earthquakes. In this study, we report the first evaluation of M L –M C as a depth discriminant using data from buried, single-fired explosions that, unlike the seismic sources studied earlier, are good analogs for underground nuclear explosions. We find that even when using generic, uncalibrated methods of assigning magnitudes, M L –M C separates single-fired explosions and earthquakes. The area under the receiver operating characteristic curve is 0.92 for 19 explosions and 14 earthquakes in Washington, 0.90 for 22 explosions and 90 earthquakes in Wyoming, and 0.99 for three explosions and 149 earthquakes in Nevada. Finally, M L :M C comparisons have the potential to enhance discrimination based on high-frequency P/S amplitudes ratios—which perform less well at local than regional distances—because the two metrics have complementary sensitivities.