Rapid Imaging of CO2 Storage using Deep Learning with 4D Electromagnetic Data, Phase I Final Report
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58 GEOSCIENCES↗
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INTER-DIGITAL CAPACITIVE SENSOR FOR EVALUATING CABLE INSULATION THROUGH JACKET
Interdigital capacitive (IDC) sensors have previously been used to measure cable-age/damage indicative dielectric properties of cable insulation when placed in direct contact with the insulation. Often cable insulation is covered by a polymer jacket precluding direct contact with the insulation. As part of a continuing program to develop and evaluate nondestructive examination methods that may be applied to cable condition assessment, an IDC to measure the effect of cable polymer dielectric change through a covering jacket has been developed. The IDC consists of two fork-like electrodes facing each other with the fork tines interspersed and separated by a small gap. On one electrode, the gap is 5 mm while on the other, the gap is 2 mm. Electric field depth-of-field penetrations are similar to the electrode gaps. The electrodes are printed on a flexible substrate that can be conformed to the outside jacketed surface of a cylindrical cable, with tines parallel to the cable axis. The electrodes are connected to a broad frequency-spectral impedance meter that senses the capacitance between the narrowly gapped electrode tines. This capacitance is known to vary as a function of the permittivity of any material in close proximity to the electrodes. For jacketed cable systems, the IDC response is dominated by the jacket but, by analyzing measurements from IDC sensors with different depths-of-field penetration into the cable under test, the influence of the cable jacket material (primarily influenced by the shallow-depth-of field IDC) can be separated from the deeper-depth-of-field IDC sensing the combined effects of both jacket and underlying insulation. No other NDE method is known that is capable of sensing insulation dielectric properties through an overlying jacket.
Cable NDE, Interdigital Capacitance, Insulation Pe↗
Spatial Study 2022: Surface Water Samples, Cotton Strip Degradation, and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v3)
This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides data and photos generated from sample collection during the same one-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. The contents include surface water geochemistry data; river substrate grain size photos; stream depth data; manual chamber open channel respiration data; and field metadata (including qualitative information on instream and river corridor characteristics). Grain size photos can be used to improve estimates of channel substrate D50 data. The dataset also includes tensile strength and photos from cotton strip field degradation experiments; five-week sensor time series temperature, dissolved oxygen, pressure, pH, specific conductance, chlorophyll A, and turbidity data; plots of the sensor data; and R scripts used to generate the plots. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. SSS data published in the CM data package were not included in this data package. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Additionally, sensor data from a similar 2021 spatial study can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052 and 2021 sample data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1898914. The 2021 spatial study had some sites in common with this 2022 spatial study. This dataset is comprised of three photo folders and one main data folder with six subfolders. The photo folders contain photographs and videos of cotton strip retrieval and sediment quadrats. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) total suspended solids (TSS) data and cotton strip tensile strength data and averages; (5) field protocol; (6) readme; (7) methods codes; (8) international generic sample number (IGSN) mapping file; (9) sensor installation methods summary; (10) stream depth and averages; and (11) Ultrameter data and averages. The Sonar subfolder consists of Sonar time-series depth data and a processing script. The BarotrollAtm, DepthHOBO, MantaRiver, miniDOT, and miniDOTManualChamber subfolders contain time-series data, plots, and summary files. All files are .csv, .pdf, .txt, .R, .Rmd, .jpg, .jpeg, .AVI, .mp4, or .mov. The data package was originally published in April 2023. It was updated in August 2023 (v2; modified files) and September 2024 (v3; modified files). See the change history section in the readme for details. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.
54 ENVIRONMENTAL SCIENCES↗
NDT System for Metal Components in Concrete Structures (Final Report)
Steel reinforcements embedded within concrete can corrode and crack, leading to weakening of the structures, and existing NDT (non-destructive testing) techniques do not provide the full picture. Condition assessment is critical in evaluating these structures in order to ensure safe, long-term operation of the nuclear power plants. Improved NDT techniques are needed as the nation’s generating stations age and require action based on increasingly critical inspection data. The power industry uses eddy current test (ECT) during in-service inspections to detect and monitor defects such as cracks and corrosion in metals, particularly metal pipes. However, the detection of defects in the reinforcing steel embedded in concrete is far more challenging for numerous reasons. Primary among them is the concrete layer’s thickness, which prevents the ability to have a close proximity between sensor and metallic sample. One solution to this problem is to create a higher sensitivity technique which can inspect at depth, with good spatial resolution and excellent directional sensitivity, to isolate the metal buried deep in the concrete and characterize defects in the metal. The goal of the phase II research program was to study and develop a new reinforced concrete NDT technology based on anisotropic magnetoresistive (AMR) sensors designed specifically for ECT of steel reinforcements in concrete. The first technical objective was to demonstrate the capability of inspecting steel reinforcement structures, including metal rebar and tensioning cable at or below 6” of depth embedded in concrete. Another objective was to be able to confidently identify multiple layers of reinforcements at varying depths. In addition, the technique needed to be able to provide condition assessment of the metal, providing data on the structure and positioning of the steel members, material loss, and detection of the presence of breaks. An important part of the research and development was aimed at achieving superior performance compared to existing technology. The ability to detect corrosion breaks in unbonded multi-wire and multi-strand tendons was another important objective. The final objective that led to a successful program was to build a complete prototype portable scanning system capable of demonstrating the probe performance in the field. All of these objectives were met, which provided data for the system development work going forward. RMD worked with the engineering firm Simpson Gumpertz & Heger to develop a detailed problem definition for concrete structures and the necessary sensor specifications. In addition, we developed finite element simulation models for the eddy current based NDT system through the analysis of the electromagnetic (EM) properties of sample materials and the orientation of the transmitter, sensor array (receivers), metal, and intervening materials like concrete and air. Based on the modeling and specifications, we designed and fabricated prototype sensor modules. We then acquired and fabricated test samples to characterize and optimize the sensors. Finally, we built a complete prototype for testing on actual concrete structures. This new NDT technology has high promise for greatly improving capabilities in the inspection of steel reinforcements within concrete structures, such as rebar, liners, anchors, and prestressing cables. This method could be valuable in the maintenance of nuclear power plants, and many other structures critical to our nation’s infrastructure.
42 ENGINEERING↗
Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska
The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Recovery Act: High-Temperature Circuit Boards for use in Geothermal Well Monitoring Applications
The U.S. Department of Energy is leading the development of alternative energy sources that will ensure the long-term energy independence of our nation. One of the key renewable resources currently being advanced is geothermal energy. To tap into the large potential offered by generating power from the heat of the earth, and for geothermal energy to be more widely used, it will be necessary to drill deeper wells to reach the hot, dry rock located up to 10 km beneath the earth’s surface. In this instance, water will be introduced into the well to create a geothermal reservoir. A geothermal well produced in this manner is referred to as an enhanced geothermal system (EGS). EGS reservoirs are typically at depths of 3 to 10 km, and the temperatures at these depths have become a limiting factor in the application of existing downhole technologies. These high temperatures are especially problematic for electronic systems such as downhole data-logging tools, which are used to map and characterize the fractures and high-permeability regions in underground formations. Information provided by these tools is assessed so that underground formations capable of providing geothermal energy can be identified, and the subsequent drilling operations can be accurately directed to those locations. The mapping of geothermal resources involves the design and fabrication of sensor packages, including the electronic control modules, to quantify downhole conditions (300°C temperature, high pressure, seismic activity, etc.). Because of the extreme depths at which these measurements are performed, it is most desirable to perform the sensor signal processing downhole and then transmit the information to the surface. This approach necessitates the use of high-temperature electronics that can operate in the downhole environment. Downhole signal processing in EGS wells will require the development and demonstration of circuit boards that can withstand the elevated temperatures found at these depths. At present, the highest-temperature commercially available circuit boards are based on polyimide materials, and those have maximum use temperatures of 200 to 250°C. In addition to thermal stability, downhole electronics must also be fabricated into high-aspect-ratio packages. For example, the multilayer assemblies produced at SNL were approximately 2.5 cm wide and 50 cm long. Because of this very high form factor, glass-fiber-reinforced polymers are much more desirable than multilayer ceramic modules (MCM). MCMs have many advantages for some applications, but are susceptible to damage induced by the mechanical and vibrational loads commonly experienced by data-logging tools. Thus, as EGS technology continues to advance, there is a strong need for multilayer electronics that can provide the necessary thermal performance while also being compatible with high-form-factor circuit designs. This project involved the design and development of high-temperature circuit materials, as well as the fabrication and testing of circuit components. The material development included the evaluation of various polymer/fiberglass composites, whereas the circuit components were tested using conventional microelectronic evaluation techniques. This effort targeted development of a new class of high-temperature multilayer circuit boards for use in downhole data-logging applications where temperatures are on the order of 300°C. This is consistent with DOE’s multiyear plan for advancing technologies for use in enhanced geothermal systems. Organic and inorganic polymer systems, both with glass reinforcements, were considered to provide the following performance at elevated temperatures: • Mechanical strength and durability • High dielectric strength and electrical resistivity • Thermal stability • Strong adhesion to copper to ensure the reliability of the multilayer assemblies • Processing characteristics that are consistent with state-of-the-art multilayer circuit board manufacturing practices
15 GEOTHERMAL ENERGY↗
Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions
Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF
Li, Bonan↗
Silicon-based three-dimensional microstructures for radiation dosimetry in hadrontherapy
In this work, we propose a solid-state-detector for use in radiation microdosimetry. This device improves the performance of existing dosimeters using customized 3D-cylindrical microstructures etched inside silicon. The microdosimeter consists of an array of micro-sensors that have 3D-cylindrical electrodes of 15 μm diameter and a depth of 5 μm within a silicon membrane, resulting in a well-defined micrometric radiation sensitive volume. These microdetectors have been characterized using an {sup 241}Am source to assess their performance as radiation detectors in a high-LET environment. This letter demonstrates the capability of this microdetector to be used to measure dose and LET in hadrontherapy centers for treatment plan verification as part of their patient-specific quality control program.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Design, upgrade and characterization of the silicon photomultiplier front-end for the AMIGA detector at the Pierre Auger Observatory
AMIGA (Auger Muons and Infill for the Ground Array) is an upgrade of the Pierre Auger Observatory to complement the study of ultra-high-energy cosmic rays (UHECR) by measuring the muon content of extensive air showers (EAS). It consists of an array of 61 water Cherenkov detectors on a denser spacing in combination with underground scintillation detectors used for muon density measurement. Each detector is composed of three scintillation modules, with 10 m 2 detection area per module, buried at 2.3 m depth, resulting in a total detection area of 30 m 2 . Silicon photomultiplier sensors (SiPM) measure the amount of scintillation light generated by charged particles traversing the modules. In this paper, the design of the front-end electronics to process the signals of those SiPMs and test results from the laboratory and from the Pierre Auger Observatory are described. Compared to our previous prototype, the new electronics shows a higher performance, higher efficiency and lower power consumption, and it has a new acquisition system with increased dynamic range that allows measurements closer to the shower core. The new acquisition system is based on the measurement of the total charge signal that the muonic component of the cosmic ray shower generates in the detector.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Defect analysis in low temperature atomic layer deposited Al{sub 2}O{sub 3} and physical vapor deposited SiO barrier films and combination of both to achieve high quality moisture barriers
Al{sub 2}O{sub 3} [20 nm, atomic layer deposition (ALD)] and SiO films' [25 nm, physical vacuum deposition (PVD)] single barriers as well as hybrid barriers of the Al{sub 2}O{sub 3}/SiO or SiO/Al{sub 2}O{sub 3} have been deposited onto single 100 nm thick tris-(8-hydroxyquinoline) aluminum (AlQ{sub 3}) organic films made onto silicon wafers. The defects in the different barrier layers could be easily observed as nonfluorescent AlQ{sub 3} black spots, under ultraviolet light on the different systems stored into accelerated aging conditions (85 °C/85% RH, ∼2000 h). It has been observed that all devices containing an Al{sub 2}O{sub 3} layer present a lag time τ from which defect densities of the different systems start to increase significantly. This is coherent with the supposed pinhole-free nature of fresh, ALD-deposited, Al{sub 2}O{sub 3} films. For t > τ, the number of defect grows linearly with storage time. For devices with the single Al{sub 2}O{sub 3} barrier layer, τ has been estimated to be 64 h. For t > τ, the defect occurrence rate has been calculated to be 0.268/cm{sup 2}/h. Then, a total failure of fluorescence of the AlQ{sub 3} film appears between 520 and 670 h, indicating that the Al{sub 2}O{sub 3} barrier has been totally degraded by the hot moisture. Interestingly, the device with the hybrid barrier SiO/Al{sub 2}O{sub 3} shows the same characteristics as the device with the single Al{sub 2}O{sub 3} barrier (τ = 59 h; 0.246/cm{sup 2}/h for t > τ), indicating that Al{sub 2}O{sub 3} ALD is the factor that limits the performance of the barrier system when it is directly exposed to moisture condensation. At the end of the storage period (1410 h), the defect density for the system with the hybrid SiO/Al{sub 2}O{sub 3} barrier is 120/cm{sup 2}. The best sequence has been obtained when Al{sub 2}O{sub 3} is passivated by the SiO layer (Al{sub 2}O{sub 3}/SiO). In that case, a large lag time of 795 h and a very low defect growth rate of 0.032/cm{sup 2}/h (t > τ) have been measured. At the end of the storage test (2003 h), the defect density remains very low, i.e., only 50/cm{sup 2}. On the other hand, the device with the single PVD-deposited SiO barrier layer shows no significant lag time (τ ∼ 0), and the number of defects grows linearly from initial time with a high occurrence rate of 0.517/cm{sup 2}/h. This is coherent with the pinhole-full nature of fresh, PVD-deposited, SiO films. At intermediate times, a second regime shows a lower defect occurrence rate of 0.062/cm{sup 2}/h. At a longer time span (t > 1200 h), the SiO barrier begins to degrade, and a localized crystallization onto the oxide surface, giving rise to new defects (occurrence rate 0.461/cm{sup 2}/h), could be observed. At the end of the test (2003 h), single SiO films show a very high defect density of 600/cm{sup 2}. Interestingly, the SiO surface in the Al{sub 2}O{sub 3}/SiO device does not appeared crystallized at a high time span, suggesting that the crystallization observed on the SiO surface in the AlQ{sub 3}/SiO device rather originates into the AlQ{sub 3} layer, due to high humidity ingress on the organic layer through SiO pinholes. This has been confirmed by atomic force microscopy surface imaging of the AlQ{sub 3}/SiO surface showing a central hole in the crystallization zone with a 60 nm depth, deeper than SiO thickness (25 nm). Using the organic AlQ{sub 3} sensor, the different observations made in this work give a quantitative comparison of defects' occurrence and growth in ALD-deposited versus PVD-deposited oxide films, as well as in their combination PVD/ALD and ALD/PVD.
42 ENGINEERING↗
Raw tensiometer soil matric potential data, from Oct 2017-Jun 2020 in Manaus, Brazil
This data package contains raw output from two Campbell Scientific CR1000 data loggers each connected to three UMS T4e tensiometer sensors in Manaus, Brazil. Tensiometers at each data logger location were installed to depths of 10 cm, 20 cm and 50 cm below the ground surface for the purpose of measuring soil matric potential at each depth. “Station 3” tensiometers were installed at approximately 2°36'31.68"S, 60°12'34.70"W. “Station 4” tensiometers were installed at approximately 2°36'32.46"S, 60°12'34.92"W. The files “tensio3.edit.202109.csv” and “tensio4.edit.202109.csv” contain raw data (with invalid data removed). A text editor will be required to view the raw files in .csv (comma-separated values) format. Data processing was limited to: 1) Editing column headers for clarity; 2) Applying a correction that converts voltage read by the data loggers to pressure units (kPa); 3) removing all data greater than 50 kPa and less than -115 kPa; 4) Adding columns entitled “Caution.10cm”, “Caution.20cm” and “Caution.50cm” that contains values of either “NA” or “Caution”, the latter of which is applied when data exceeds the manufacturer reported data validity range of less than -85 kPa to suggest use of these data with caution; 5) Adding columns entitled “Invalid.10cm”, “Invalid.20cm”, and “Invalid.50cm” that contains values of either “NA” or “Invalid”, the latter of which is applied when data accuracy is deemed compromised due to the introduction of air bubbles into the instrument. Data marked “Invalid” may still indicate general trends of wet/high matric potential and dry/low matric potential, but values are inaccurate and dry/low matric potential values are likely not low enough. Wet/high matric potential values are likely to be more accurate than dry/low matric potential values; 6) Data format changed from .dat to .csv. This research was performed as a part of the NGEE Tropics project, which aims to advance model predictions of tropical forest carbon cycle responses to a changing climate over the 21st Century.
54 ENVIRONMENTAL SCIENCES↗
Performance Evaluation of Comparative Vacuum Monitoring and Piezoelectric Sensors for Structural Health Monitoring of Rotorcraft Components
The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
42 ENGINEERING↗
An electrically tunable plenoptic camera using a liquid crystal microlens array
Plenoptic cameras generally employ a microlens array positioned between the main lens and the image sensor to capture the three-dimensional target radiation in the visible range. Because the focal length of common refractive or diffractive microlenses is fixed, the depth of field (DOF) is limited so as to restrict their imaging capability. In this paper, we propose a new plenoptic camera using a liquid crystal microlens array (LCMLA) with electrically tunable focal length. The developed LCMLA is fabricated by traditional photolithography and standard microelectronic techniques, and then, its focusing performance is experimentally presented. The fabricated LCMLA is directly integrated with an image sensor to construct a prototyped LCMLA-based plenoptic camera for acquiring raw radiation of targets. Our experiments demonstrate that the focused region of the LCMLA-based plenoptic camera can be shifted efficiently through electrically tuning the LCMLA used, which is equivalent to the extension of the DOF.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Managing systematic errors in the NBSR thermal power calorimetric measurements - 214
There has been a calorimetric misbalance between the primary and secondary loops of the National Bureau of Standards Reactor (NBSR) since the installation of a set of new heat exchangers in the early 90's. The NBSR's primary and secondary process instrumentation were investigated to resolve the underlying causes. The main issue was found to be the immersion length of the thermowells, resulting in non-exemplary process measurements. Furthermore, thermal insulation on various temperature sensors was found to be degraded. Hence, the following were found to be inconsistent: the heat exchanger output temperatures, previously-used differential temperature sensors, reactor inlet sensor, and the reactor outlet temperature indications. Therefore, thermodynamic analyses using these sensor measurements were inconclusive. Secondly, the discrepancy between primary and secondary loops and the gradual inconsistency between primary side sensors went largely unnoticed. We implemented sustainable, state-of-the-art upgrades to resolve systematic errors in the NBSR reactor thermal process instrumentation. Several digital upgrades were completed, along with detailed 50.59 reviews. Redundancy, defense-in-depth, reliability, diversity and accuracy of the thermal monitoring system was established by implementing an inclusive engineering approach by analyzing the sensors as a whole system instead of individual assessments. The upgrades produced two important outcomes. First, the long-term existing calorimetric discrepancy between the primary and secondary loops was resolved. Secondly, excellent agreement was achieved within the primary process measurement instrumentation, resulting in reliable and stable thermal power assessment. We will present lessons learned performing instrumentation upgrades and our future enhancement plans in process monitoring for the NBSR reactor. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico
Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collected at the Teller 27 Watershed and Kougarok 64 Hillslope during the 2021 - 2022 water year on the Seward Peninsula, Alaska using distributed temperature profiling (DTP) systems. We then applied this model to other sites where ground surface or shallow soil temperature data was available for at least one water year (see Related Datasets). Many of these temperature measurements were collocated with snow depth observations. Ground surface temperature (i.e. snow-ground interface temperature) is easy to measure using small, cheap and easy-to-deploy temperature sensors such as iButtons and TinyTags, and such measurements have previously been used to calculate a variety of snow metrics (e.g. snow onset date). However, this is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sites with snow depth predictions. No training data predictions are included in the *.csv file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
SABRE-SHEATH hyperpolarized 15 N 2 -imidazole for Zn 2+ sensing
Zinc ions are essential for numerous biological functions and activities. Accordingly, Zn 2+ sensors are crucial in biomedical research to understand the role of Zn 2+ in health and disease. Here, we demonstrated the viability of SABRE-SHEATH hyperpolarized 15 N 2 -imidazole, providing an NMR signal enhancement of 45 700 fold (p = 2.15%), as a probe for Zn 2+ sensing by monitoring the Zn-imidazole interaction using NMR and extracted a LOD of 1.3 mM. This study is one of the first demonstrations of SABRE-SHEATH hyperpolarized 15 N as a sensor of other non-hyperpolarized species, which promises chemical sensing without penetration-depth limitations.
Hyperpolarization↗
Pollution inequality 50 years after the Clean Air Act: the need for hyperlocal data and action
Fifty years ago the Clean Air Act amendments of 1970 were the first major US legislation that authorized regulation of air pollutants, creating National Ambient Air Quality Standards (NAAQSs) to protect public health and the environment. While US air quality has improved, with average PM 2.5 concentrations in 2016 a third of 1981 levels, air pollution remains a major health risk in the US and globally. Moreover, air pollution impacts are still uneven, with the most polluted US communities of 50 years ago still so today. Air pollution “hot spots” result in disproportionate exposure at neighborhood scales within cities, particularly in disadvantaged communities, but an in-depth understanding at these scales is lacking. This policy perspective discusses how trends in sensor technology, spatial data collection, analytics and retrieval are converging to enable the production of hyperlocal air pollution data, and that this needs to be done in a manner that enables marginalized communities to shape decision making.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels
Abstract. The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.
54 ENVIRONMENTAL SCIENCES↗