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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cell specific photoswitchable agonist for reversible control of endogenous dopamine receptors

Dopamine controls diverse behaviors and their dysregulation contributes to many disorders. Our ability to understand and manipulate the function of dopamine is limited by the heterogenous nature of dopaminergic projections, the diversity of neurons that are regulated by dopamine, the varying distribution of the five dopamine receptors (DARs), and the complex dynamics of dopamine release. In order to improve our ability to specifically modulate distinct DARs, here we develop a photo-pharmacological strategy using a Membrane anchored Photoswitchable orthogonal remotely tethered agonist for the Dopamine receptor (MP-D). Our design selectively targets D1R/D5R receptor subtypes, most potently D1R (MP-D1 ago ), as shown in HEK293T cells. In vivo, we targeted dorsal striatal medium spiny neurons where the photo-activation of MP-D1 ago increased movement initiation, although further work is required to assess the effects of MP-D1 ago on neuronal function. Our method combines ligand and cell type-specificity with temporally precise and reversible activation of D1R to control specific aspects of movement. Our results provide a template for analyzing dopamine receptors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Extending Rucio with modern cloud storage support

Rucio is a software framework designed to facilitate scientific collaborations in efficiently organising, managing, and accessing extensive volumes of data through customizable policies. The framework enables data distribution across globally distributed locations and heterogeneous data centres, integrating various storage and network technologies into a unified federated entity. Rucio offers advanced features like distributed data recovery and adaptive replication, and it exhibits high scalability, modularity, and extensibility. Originally developed to meet the requirements of the high-energy physics experiment ATLAS, Rucio has been continuously expanded to support LHC experiments and diverse scientific communities. Recent R&D projects within these communities have evaluated the integration of both private and commercially-provided cloud storage systems, leading to the development of additional functionalities for seamless integration within Rucio. Furthermore, the underlying systems, FTS and GFAL/Davix, have been extended to cater to specific use cases. This contribution focuses on the technical aspects of this work, particularly the challenges encountered in building a generic interface for self-hosted cloud storage, such as MinIO or CEPH S3 Gateway, and established providers like Google Cloud Storage and Amazon Simple Storage Service. Additionally, the integration of decentralised clouds like SEAL is explored. Key aspects, including authentication and authorisation, direct and remote access, throughput and cost estimation, are highlighted, along with shared experiences in daily operations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Demonstration and Evaluation of an Advanced Integrated Operations Concept for Hybrid Control Rooms

The U.S. nuclear industry has an urgent need to reduce operations and maintenance costs to remain economically competitive in today’s energy market. Measures to improve efficiency in operations will need to leverage technology in a way that safely transforms how plants are operated. This work describes the Evaluation of an integrated operations concept that draws together data from existing Instrumentation and Control (I&C) infrastructure, upgraded I&C systems, new sensors, and field technologies such as computer-based procedures to provide operators with centralized, streamlined instructions. The concept was developed to allow for an operator to remotely supervise many plant activities and to dramatically streamline plant operations and maintenance. This describes the methods, results, and design recommendations for the concept based on an operator workshop to evaluate the concept.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

MULTI-MODAL global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques

This paper presents the work completed towards the development of a multi-modal global surveillance methodology using cube satellite (CubeSat) platforms and novel data analysis techniques. A CubeSat system equipped with adequate sensors and data analytics capabilities can autonomously characterize various phenomena of interest on the Earth’s surface. CubeSats are advantageous over conventional satellites in certain remote monitoring applications because of their reduced construction costs (due to the availability of commercially-off-the-shelf components) and are easier to launch. The CubeSat surveillance system developed in this paper focused on phenomena of interest surrounding the nuclear fuel cycle in support of nuclear non-proliferation and emergency response. To observe the phenomena, a constellation of 3U and 6U CubeSats deployed from the ISS with adequate components was chosen. Four different sensor configurations were identified for remote sensing: panchromatic/multispectral in the visible and near-infrared spectrum, multispectral in infrared spectrum, hyperspectral in infrared spectrum, and multispectral in ultraviolet spectrum. While a panchromatic/multispectral sensor configuration has CubeSat flight heritage at the required spatial resolutions, the other three sensor types need future 3 development to meet signature and system requirements. Once each sensor onboard the CubeSat system collects data on a target of interest, the onboard computers would then apply the deep learning-based characterization methodology developed in this paper to identify phenomena. Four surrogate datasets containing representative simplified “images” were created for each sensor type to train the characterization methodology. A convolutional neural network was applied to each dataset and produced recall rates for the phenomena between 89.7% - 99.3% and precision rates between 92.3% - 99.9%. Each phenomenon’s presence probability from each network is then combined into a final characterization solution for a target area. This paper covers multiple interdisciplinary areas to develop the foundation for a CubeSat surveillance system focused on phenomena surrounding the nuclear fuel cycle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Ageing Management for Extended Long-Term Dry Storage of Spent Nuclear Fuel and Transportation

The principal objectives of the Coordinated Research Project (CRP) and CRADA were to (1) investigate how ageing effects leading to degradation of materials used in the spent fuel dry storage systems could be managed by ageing management programs (AMPs) and (2) using existing AMPs as a basis, establish guidance on how to develop, generate, and maintain AMPs for dry storage systems of spent nuclear fuel (SNF) that can be accomplished in various ways. The PI (Dr. Liu) is an internationally recognized expert on ageing management for license renewal of nuclear power plants and independent spent fuel dry storage installations. Dr. Liu was invited by IAEA to participate in this CRP and served as Chair of the Working Group, supporting the IAEA CRP Lead, with other members from Argentina, the Czech Republic, France, Germany, Hungary, Japan, Pakistan, Spain, Switzerland, United Kingdom, and the United States of America. Argonne’s scope of work in the CRADA for the CRP included (1) ageing management guidance documents developed by Argonne for DOE and used by the Nuclear Regulatory Commission (NRC) and industry and (2) research leveraged from the DOE Office of Nuclear Energy on the mechanical properties of high-burnup fuel cladding and the ARG-US remote monitoring systems technology developed for the DOE Packaging Certification Program, Office of Packaging and Transportation, Office of Environmental Management.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Continued Evaluation of the Use of a Raman Spectrometer for H-Canyon Dissolver Monitoring

Remote monitoring of dissolver activities in H-Canyon can help operators avoid delays associated with excessive levels of fuel fragments remaining after a run. SRNL has proposed that effective monitoring can be achieved by using a Raman spectrometer to measure NO 2 concentrations in the offgas stream sampled from the facility stack. Prior work (SRNL-STI-2021-00451) measuring the offgas from one dissolution batch of High Flux Isotope Reactor (HFIR) fuel suggested a relationship between %NO 2 levels and fragment height. Herein, we report the results of monitoring and analysis of the dissolution of four batches of Material Test Reactor (MTR) fuel. A rigorous quantitative relationship between %NO 2 measurements and fragment heights could not be established, due to high uncertainties associated with both measurements. Uncertainties with gas measurements are associated with the %NO 2 levels in the offgas being close to the detection limit for the analyzer. Alternative gas measurement strategies are discussed which could improve sensitivity and reduce uncertainty. Limitations to the precision of the probe measurements are also discussed. It is also noted that the offgas is an average of the products from simultaneous dissolution of elements in multiple wells. Detection of a high fragment height level in an individual well may be hindered by low levels in other wells.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. In this work, we used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. Furthermore, we compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Isolation of a Cu–H Monomer Enabled by Remote Steric Substitution of a N-Heterocyclic Carbene Ligand: Stoichiometric Insertion and Catalytic Hydroboration of Internal Alkenes

Transient Cu–H monomers have long been invoked in the mechanisms of substrate insertion in Cu–H catalysis. Their role from Cu–H aggregates has been mostly inferred since ligands to stabilize these monomeric intermediates for systematic studies remain limited. Within the last decade, new sterically demanding N-heterocyclic carbene (NHC) ligands have led to isolable Cu–H dimers and, in some cases, spectroscopic characterization of Cu–H monomers in solution. In this work, we report an NHC ligand, IPr*R, containing para R groups of CHPh 2 and CPh 3 on the ligand periphery for the isolation of a Cu–H monomer for insertion of internal alkenes. This reactivity has not been reported for (NHC)CuH complexes despite their common application in Cu–H-catalyzed hydrofunctionalization. Changing from CHPh 2 to CPh 3 impacts the relative concentration of Cu–H monomers, rate of alkene insertion, and reaction of a trisubstituted internal alkene. Specifically, for R = CPh 3 , monomeric (IPr*CPh 3 )CuH was isolated and provided >95% monomer (10 mM in C 6 D 6 ). In contrast, for R = CHPh 2 , solutions of [(IPr*CHPh 2 )CuH] 2 are 80% dimer and 20% (IPr*CHPh 2 )CuH monomer at 25 °C based on 1 H, 13 C, and 1 H– 13 C HMBC NMR spectroscopy. Quantitative 1 H NMR kinetic studies on cyclopentene insertion into Cu–H complexes to form the corresponding Cu–cyclopentyl complexes demonstrate a strong dependence on the rate of insertion and concentration of the Cu–H monomer. Only (IPr*CPh 3 )CuH, which has a high monomer concentration, underwent regioselective insertion of a trisubstituted internal alkene, 1-methylcyclopentene, to give (IPr*CPh 3 )Cu(2-methylcyclopentyl), which has been crystallographically characterized. We also demonstrated that (IPr*CPh 3 )CuH catalyzes the hydroboration of cyclopentene and methylcyclopentene with pinacolborane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location. Performance and scalability of protein similarity search have proven to be a bottleneck in many bioinformatics pipelines due to increase in cheap and abundant sequencing data. This work presents a new distributed-memory software PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity search when coupled with a fully-distributed dictionary of sequences that allow remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in search without altering basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Advanced Design for the WIQ Magnet With Steering Corrector Function

The Facility for Rare Isotopes Beams (FRIB) delivers heavy-ion primary beams at energies of up to 300 MeV/u at 10 kW of beam power to generate rare isotope beams for experiments and will eventually operate at beam power of 400 kW. The preseprator of the Advanced Rare Isotope Separator (ARIS) is equipped with six warm-iron quadrupole (WIQ) singlets and two dipoles integrated right after the production target. They have a compact structure and operate in a high radiation vacuum environment within a hot cell having remote handling capabilities for installation and maintenance. Due to asymmetry with respect to the quadrupole poles, nested sextupole excitations in WIQs induce vertical dipoles that offset the centroid trajectory; Magnet misalignments also result in trajectory offsets. Such offsets degrade separator performance but can be minimized by changing the current distribution on sextupole and octupole coils. In this work, we show how modifications to the WIQ coil design can allow superimposed dipole fields to be included to the octupole and sextupole windings, as well as addition of dipole components by splitting coil currents over groups with separator power supplies. Adjusting the group currents can cancel the sextupole-induced vertical dipole component which can be as high as 0.012 Tm. Octupole coil changes may superimpose a horizontal dipole integrated strength as high as 0.0332 Tm. Unwanted higher harmonics induced as a side effect of the new design are kept to a minimum such that separator performance is preserved as much as possible.

Accelerator magnets↗
DOE OSTI2023

Power Supply Options for the Marpi Landfill, Saipan: Feasibility Study

The Marpi Landfill, located on the northern end of the island of Saipan in the Commonwealth of the Northern Mariana Islands (CNMI), is powered by an on-site diesel generator that only operates when the landfill is open and staffed. The CNMI Office of Planning and Development (OPD) aspires to provide the Marpi Landfill with 24-hour power availability despite its remote location and to increase the use of sustainable energy within the CNMI. CNMI has a 20% target for renewable energy consumption, as documented in Sustainable Development Goal #7 in the Comprehensive Sustainable Development Plan (OPD 2021) and the renewable portfolio standard (GPO 2014). To accomplish these goals, the U.S. Department of Energy, through its Interagency Reimbursable Work Agreement with the Federal Emergency Management Agency, funded this feasibility study to assess and prioritize power supply options for the landfill. The availability of solar and wind resources varies seasonally, as does the load. A BESS can help to balance mismatches between generation and load on short (hourly or daily) timescales, but not across seasons. The microgrid scenarios evaluated for Marpi consider options for technology combinations that will both meet the load and utilize available resources, despite the challenge presented by higher loads and lower solar and wind availability during the rainy season, depicted in Figure ES-2. The seven scenarios evaluated are summarized in Table ES-1. Each scenario’s configuration was optimized to include component capacities that reduce capital and operating costs, meet the load, and minimize carbon emissions, as feasible. The costs and levelized cost of energy (LCOE) shown do not assume the use of any grant funding or incentives, although these options were also evaluated. To assist with decision-making, a prioritization matrix (Table ES-3) was created to compare the microgrid scenarios evaluated in this feasibility study according to various stakeholder priorities. The prioritization metrics were chosen based on discussions with OPD and will be finalized through stakeholder feedback. The scenarios were given a score between 1 and 7 for each prioritization metric (the lower the score, the higher the priority), and total scores were calculated using assigned weights based on the relative priority of each metric. The total scores were then ranked to produce a prioritized list of microgrid scenarios based on the metrics most important to the project stakeholders. As shown, scenario 4 (100 kW of solar PV, a 75 kW/300 kWh BESS, and 160 kW of diesel generation) ranks highest.

14 SOLAR ENERGY↗
DOE OSTI2022

dlppi2 (b1)

The Doppler lidar (DL) is an active remote-sensing instrument that provides range- and time-resolved measurements of the line-of-sight component of air velocity (i.e., radial velocity) and attenuated aerosol backscatter. The DL operates in the near-infrared and is sensitive to backscatter from atmospheric aerosol, which are assumed to be ideal tracers of atmospheric wind fields. The DL works by transmitting short pulses of infrared laser light into the atmosphere. Atmospheric aerosols scatter a small fraction of that light energy back to the transceiver, where it is collected and recorded as a time-resolved signal. From the delay between the outgoing pulse and the backscattered signal, the instrument infers the distance to the scattering volume. Coherent detection is used to measure the Doppler frequency shift of the backscatter signal. This is accomplished by mixing the backscatter signal with a reference laser beam (i.e., local oscillator) of known frequency. The onboard signal processor then determines the Doppler frequency shift from the spectrum of the mixed signal. The Doppler frequency shift and thus the radial air velocity is determined from the peak of the Doppler spectrum. The attenuated backscatter is determined from the energy content of the Doppler spectra. The DL provides accurate measurements of radial velocity in regions of the atmosphere where aerosol concentrations are high enough to ensure good signal-to-noise ratio. Thus, valid data are usually limited to the atmospheric boundary layer where aerosol is ubiquitous. Valid measurements can also be obtained in elevated aerosol layers or in optically thin clouds above the boundary layer. Most of the ARM DLs have full upper-hemispheric scanning capability, enabling 3D mapping of turbulent flows in the atmospheric boundary layer. With the scanner pointed vertically, the DL provides height- and time-resolved measurements of vertical velocity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Panel Session 4 and 15: Japan Fukushima Daiichi D and D Update and Technological Challenges at Japan Fukushima Daiichi D and D and Update on Nuclear Overview and Development in Japan - Nuclear Fuel Cycle

Sessions 4 and 15 represent a two-part panel series that discusses progress and challenges associated with cleanup at Fukushima. Severe limitations on availability of original panelists from Japan due to strict restrictions put in place to alleviate the spread of coronavirus necessitated changes to both panels. The result was a significantly modified panel for Session 04 (shown above) and the elimination of all panelists for Session 15. The 4 and 15 Panel Sessions provide an overview of activities related to both the progress and challenges of cleanup and decommissioning of the Fukushima Daiichi Nuclear Power Station (NPS) in Japan. Five panelists discussed perspectives of the cleanup following a Tokyo Electric Power Company (TEPCO) video showing the progress on site since the devastating Great East Earthquake and tsunami that caused the explosions at three of the six reactors on the site. Three of the five panelists discussed on-going work being performed for the effort, while the other two provided expert perspectives of on strategic efforts at the site. The panel was attended by over 80 technologists and policy makers spanning the globe and was opened by Dr. Monica Regalbuto of Idaho National Laboratory and a short video provided by TEPCO. The video described changes at the site that spanned the cleanup efforts from stabilizing water intrusion into the contaminated reactor buildings to construction of new administrative facilities. The video explained the processes underway to retrieve spent fuel rods and challenges in retrieval of the compromised fuel debris. The video highlighted working condition improvements that included establishment of rest housing and a small convenience store on the site, and the rollback of protective equipment around the site due to decreases contamination. Panelists with presentations: Revision of 'the Mid-and-Long-Term Road-map towards the Decommissioning of TEPCO's Fukushima Daiichi Nuclear Power Station' (Paul Dickman); Sharing UK experience at Fukushima Daiichi (Adrian Simper); SRNL Japan (Andrew Fellinger); ABLE's Initiative to Dismantle the Exhaust Stack (Daniel Walter); JAEA R and D in Fukushima (Tokio Fukahori); TEPCO - Overview and Update of the Fukushima Decommissioning Process (Monica Regalbuto); Toshiba's Involvement in the Decommissioning of the Fukushima Daiichi Nuclear Power Plant (Yasuhiro Yuguchi); Remote Dismantling of the Exhaust Stack At Fukushima Dai-ichi NPS (Takashi Okutsu)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Drivers of Phytoplankton Variability in and Near the Pearl River Estuary, South China Sea During Typhoon Hato (2017): A Numerical Study

The Pearl River estuary-coastal system in the Northern South China Sea is frequently affected by tropical cyclones (TCs) every year. Previous in-situ and remote sensing studies have found typhoons can enhance phytoplankton biomass and induce blooms in this region. However, the mechanistic links between phytoplankton blooms and typhoons have not been well elucidated due to the interplay of multiple processes along the land-ocean-atmosphere interfaces. Unravelling these interactions will have to rely on an integrated modelling system. In this work, we constructed a realistic, 3-dimensional, land-ocean-atmosphere modelling system with the marine ecosystem and sediment components for the China Great Bay Area. By using the integrated modelling system, we quantitatively investigated phytoplankton response to hydrological conditions variations under Typhoon Hato (2017), a strong typhoon case. Passive tracer experiments showed that with high river discharge induced by heavy rainfall, the residence time of Lingding Bay is as short as 15-day, less than half of that under the climatological discharge. The increase in freshwater pulse washed out the phytoplankton biomass within Lingding Bay. While for the offshore region, the source and sink terms analysis showed that the increase of phytoplankton biomass in the first week was because of the uplift of nutrient-rich subsurface water, while in the second week was because of the seaward propagated nearshore high phytoplankton biomass water. While riverine nutrients support phytoplankton growth in the third week, a large part of phytoplankton biomass was lost to zooplankton grazing, showing the system shifted from the bottom-up control to the top-down control.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Irradiation Characterization of Pressure Transducers

The advancement of reactor technologies for space and remote applications requires further investigation into the long-term operation of instrumentation without the chance for standard scheduled maintenance. Specifically, the degradation of instrumentation due to exposure to nuclear radiation needs to be understood. Determining the methods of degradation can improve material selection for instrument construction and provide foundational data for the development of corrective algorithms. Initial work has been focused on the development of nuclear thermal rocket technology and the instrumentation needs that will led to its eventual deployment.

Floyd, Dan↗
DOE OSTI2023

Real-Time Monitoring of Fracture Dynamics with a Contrast Agent-Assisted Electromagnetic Method

In collaboration with the Advanced Energy Consortium, our team has previously demonstrated that the placement of electrically active proppants (EAPs) in a hydraulic fracture surveyed by electromagnetic (EM) methods can enhance the imaging of the stimulated reservoir volumes during hydraulic fracturing. That work culminated in constructing a well-characterized EAP-filled fracture anomaly at the Devine field pilot site (DFPS). In subsequent laboratory studies, we observed that the electrical conductivity of our EAP correlates with changes in pressure, salinity, and flow. Thus, we postulated that the EAP could be used as an in-situ sensor for the remote monitoring of these changes in previously EAP-filled fractures. This paper presents our latest field data from the DFPS to demonstrate such correlations at an intermediate pilot scale. We conducted surface-based EM surveys during freshwater (200 ppm) and saltwater (2,500 ppm) slug injections while running surfaced-based EM surveys. Simultaneously, we measured the following: 1) bottomhole pressure and salinity in five monitoring wells; 2) injection rate using high-precision data loggers; 3) distributed acoustic sensors in four monitoring wells; and 4) tiltmeter data on the survey area. We demonstrated that injections into an EAP-filled fracture could be successfully coupled with real-time electric field measurements on the surface, leading to remote monitoring of dynamic changes within the EAP-filled fracture. Furthermore, by comparing the electrical field traces with the bottomhole pressure, flow rate, and salinity, we concluded that the observed electric field in our study is influenced by fracture dilation and flow rate. Salinity effect was observed when saltwater was injected. EM simulations solely based on assumptions of fracture conductivity changes during injection did not reproduce all of the measured electric field magnitudes. Preliminary estimates showed that including streaming potential in our geophysical model may be needed to reduce the simulation mismatch. The methods developed and demonstrated during this study will lead to a better understanding of the extent of fracture networks, formation stress states, fluid leakoff and invasion, characterizations of engineered fracture systems, and other applications where monitoring subsurface flow tracking is deemed important.

02 PETROLEUM↗
DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗