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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Generating and Analyzing Program Call Graphs using Ontology

Call graph or caller-callee relationships have been used for various kinds of static program analysis, performance analysis and profiling, and for program safety or security analysis such as detecting anomalies of program execution or code injection attacks. However, different tools generate call graphs in different formats, which prevents efficient reuse of call graph results. In this paper, we present an approach of using ontology and resource description framework (RDF) to create knowledge graphs for specifying call graphs to facilitate the construction of full-fledged and complex call graphs of computer programs, realizing more interoperable and scalable program analyses than conventional approaches. We create a formal ontology-based specification of call graph information to capture concepts and properties of both static and dynamic call graphs so different tools can collaboratively contribute to more comprehensive analysis results. Our experiments show that ontology enables merging of call graphs generated from different tools and flexible queries using a standard query interface. Index Terms—Callgraph, ontology, knowl

Dorta, E.↗
DOE OSTI2025

Analyzing the Effects of the Interference between One- and Two-Body Currents on Neutrino-Nucleus Scattering in ACHILLES

Modeling improvements of neutrino–nucleus scattering are an essential aspect of reducing systematic uncertainties for current and future neutrino oscillation experiments. In particular, retaining quantum mechanical effects such as the interference between one-body (1p1h) and two-body (2p2h) currents in our cross-section models is an essential consideration. This work investigates the impact of this interference term on neutrino–argon scattering using the ACHILLES event generator, which directly implements the interference model developed by Lovato, Rocco, and Steinberg. Using fluxes relevant to SBND, the DUNE near detector, and MiniBooNE, charged- and neutral-current event samples are generated and predicted event rates are plotted against a variety of kinematic variables. For events simulated using the SBND flux, it is found that interference contributes approximately 10 \% to the total charged-current event rate, and serves to enhance the quasi-elastic peak. When considering events generated using the DUNE flux, resonance interactions contribute more to the predicted event rate, as the neutrino flux extends out to higher energies. The relative interference contribution subsequently decreases to roughly 7 \%. Neutral-current simulations show a higher rate of neutron knockout. Finally, a comparison of argon-to-carbon cross-section ratios is performed utilizing fluxes from SBND and MiniBooNE.

Pando, Nathan [Fermilab]↗
DOE OSTI2020

Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

Engineering↗
DOE OSTI2023

Data Summary Report for Soil and Slab Sampling at Former Building 175

Lawrence Livermore National Security (LLNS) and the U.S. Department of Energy (DOE) are in the process of returning the area where Building 175 existed to beneficial use for future site development. The completed assessment will help LLNS and DOE to outline project scope and costs associated with the removal, characterization, and disposal of materials generated when the concrete slab for the former building and any associated remaining subsurface structures are demolished. Slab removal under the Transition & Disposition process requires full screening for potential site contamination to determine appropriate future land use. Borehole locations and depths were designed to correctly assess DOE’s future liability for characterizing residual vadose zone contamination. If a residual source area is identified at depth, DOE will need to evaluate the additional cost of future subsurface cleanup (with limited access) due to planned development. This is necessary to leave the location in a “ready-to-build” status. As a result, Phase II sampling requirements may in some cases extend beyond the construction zone required for slab removal to ensure the necessary due diligence. To avoid the potential spread of contamination and/or the creation of an environmental release by impacting the integrity of known contamination areas (e.g., pit within room 102 and the east to west boundary seam), these locations were not sampled for this project. During demolition of the slab, these locations and their underlying soils should be further evaluated. The assessment was conducted in accord with the May 2022 Lawrence Livermore National Laboratory Former Building 175 Assessment Soil Sampling and Analysis Plan / Quality Assurance Plan. Thirty-six, direct-push borings were advanced, along with collecting concrete (where present) and soil samples for laboratory analyses. Thirty-two of the borings were advanced to a depth of 25 feet below ground (bgs), and four borings were advanced to a depth of 55 feet bgs. The concrete slab for former Building 175 ranged from approximately 8 inches to over two feet in thickness. Soils encountered during the investigation consisted primarily of clayey silt, with interbeds of sandy gravel and silty sand to the total depth explored of 55 feet bgs. Field photoionization detector readings – checking for volatile organic compound (VOC) vapors in soils, ranged from zero (0) to a peak of 33 parts per million at 25 feet bgs in boring PC-B175- 028. The cause for the peak reading is unknown, however, no visually discolored or odorous soils were encountered during the assessment and all VOC results were below Lawrence Livermore National Laboratory’s (LLNL’s) Soil Screening and Management Plan (SSMP) soil screening levels (SSL). Groundwater was not encountered during the assessment and is expected to occur at roughly 65 feet bgs in the project area. No metals were detected at concentrations of concern in the concrete core samples collected and analyzed. Low gross alpha and beta activity concentrations were detected in the concrete core samples collected and analyzed. The detected activity concentrations would appear to be from naturally occurring radioactive isotopes, i.e., potassium 40, present in the raw materials used to make concrete and not artificially added. Tritium was not detected above the testing laboratory's Method Detection Limit (MDL) in any of the concrete core samples collected and analyzed. The radioactive isotopes - actinium 228, bismuth 214, lead 212, lead 214, potassium 40, radium 226, radium 228, and thallium 208, were detected at low activity concentrations in the concrete core samples analyzed. Based on the detected activity concentrations, the isotopes would appear to be naturally occurring in the raw materials used to make concrete and not artificially added. Acetone – a common laboratory contaminant, was detected in nine of the soil samples collected and analyzed. Concentrations of two other volatile organic compounds (benzene and tetrachloroethene) were detected in five of the soil samples analyzed. All concentrations were below SSLs. Total petroleum hydrocarbons as diesel range organics were detected at 43.9 milligrams per kilogram (mg/kg) in the 20-foot bgs duplicate sample from boring PC-B175-007. Total petroleum hydrocarbons as motor oil were detected at 7.20 mg/kg in the 15-foot bgs routine sample from boring PC-B175-001, and at 20.7 mg/kg in the 20-foot bgs duplicate sample from boring PC-B175-007. The SSL for diesel-range TPHs is 260 mg/kg. LLNL does not have an SSL for motor oil range TPHs. No indications of a release, e.g., visibly stained, or odorous soil, were present at the boring locations. No samples collected contained polychlorinated biphenyls (PCBs) in concentrations above the laboratory reporting limit. Arsenic was detected above its SSL of 8.51 mg/kg in the 25-foot bgs routine sample collected from boring PC-B175-023. Nickel was detected above its SSL of 86.0 mg/kg in the 10-foot bgs routine sample from boing PC-B175-005, the 20-foot bgs routine sample from boring PC-B175-025, and the 25-foot bgs routine sample from boring PC-B175-034. The detected concentrations, however, were well below ten times (10x) their respective STLCs. Gross alpha and/or gross beta were detected above their respective SSL activity concentrations in three routine soil samples. Retesting (two per sample) of the samples showed that the initial reported activity concentrations were anomalous. Tritium was not detected in any of the routine or duplicate soil samples collected and analyzed during the investigation. The radioactive isotopes - actinium 228, bismuth 212 and 214, lead 212 and 214, potassium 40, radium 224, 226 and 228, thallium 208, thorium 234 and uranium, were detected at low activity concentrations in the soil samples analyzed for radioactive constituents. The detected isotopes and their associated activity concentrations are typical of those naturally occurring in the marine-type sedimentary deposits underlying the Livermore Valley. No radiological controls are necessary for the soil evaluated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Excerpt from Oxygen Monitor Clean Testing Report Performed in 2010

The Rosemount X-STREAM Series X2GP is a paramagnetic oxygen analyzer with a 0-25% oxygen measurement range. It claims a drift less than 2% of span (0.10% volume oxygen) per week, and a response time of less than 5 seconds (for the analyzer to report 90% of a step change). Our test equipment was set-up such that the same gas was running through each oxygen analyzer at the same time. This allowed Calibration Error, Calibration Drift, and Loss of Power Testing to be run on all monitors at the same time, eliminating any variability between the analyzers from tolerances in the gas cylinder, ambient temperature, ambient pressure, test duration, etc. An APTech pressure regulator was placed on the gas cylinder manifold and a 23 psia Paroscientific pressure transducer measured the regulator’s outlet pressure. This did not give the inlet pressure on any one analyzer but outlet pressure from the manifold before the stream was split. The APTech pressure regulator was used to keep operating conditions consistent. A DWYER rotameter was installed on each oxygen monitor’s outlet piping. With the exception of Variation of Flow Rate Testing, the rotameter was left fully open for all testing. The flow rate through each oxygen monitor was controlled by a Swagelok needle valve that was installed on each analyzer’s inlet piping. Additionally, a filter was installed in the each monitor’s inlet piping to keep particles from the measurement cells and a Swagelok ball valve was installed on each monitor’s outlet piping to isolate each monitor between tests. No pumps were installed. Each of the cylinders used was at a pressure greater than ambient room barometric pressure. This pressure differential was used to move gas through the testing equipment. The flow rate through each analyzer was controlled by the Swagelok needle valve installed on each analyzer’s inlet piping. Finally, for all tests, a Paroscientific Digiquartz Barometer was used to measure ambient room barometric pressure and a Fluke Thermo-hygrometer was used to measure ambient room temperature.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Ice nucleation measurements from DRUM impactors during TRACER campaign in the Houston TX region from July to September 2022

During TRACER, three Davis Rotating Uniform size-cut Monitors (DRUM; DRUMAir 4-DRUM) were used to collect aerosols for ice nucleation measurements in the Brooks laboratory at Texas A&M University. The three instruments were located at AMF1 in La Porte, Ancillary site in Guy (ANC), and onboard the Texas A&M Rapid Onsite Atmospheric Measurements Van (ROAM-V). ROAM-V was deployed to capture airmasses behind (maritime) and ahead (continental) of the passage of the sea-breeze front through Houston. On select sampling days, ROAM-V sampled in the morning/mid-day on the coast and then transited to a second inland site for the afternoon/evening. The suite of instruments deployed on ROAM-V included a Condensation Particle Counter (CPC; GRIMM Model 5.403 CPC), Scanning Mobility Particle Sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A Differential Mobility Analyzer), Cloud Condensation Nuclei counter (Droplet Measurement Technologies CCN Counter), micro pulse lidar (Droplet Measurement Technologies Micro Pulse LiDAR (miniMPL)), and one of the Davis Rotating Uniform size-cut Monitors (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the GPS on the phone application “My Altitude”. Each DRUM sampler was operated at a flow rate of 23 LPM. Each DRUM has four stages where the aerodynamic diameter size cuts are as follows: stage 1 larger than 3 μm, stage 2 from 3 to 1.2 μm, stage 3 from 1.2 to 0.34 μm, and stage 4 from 0.34 to 0.15 μm. Pretreated aluminum foil was used as a substrate on all stages. At AMF1 and ANC sites, the DRUMs were operated on the shared aerosol inlet, with generous support of the DOE ARM site staff. These instruments rotated 24 mm per day and only contained aluminum foil substrates. Substrates were changed weekly and transported to Texas A&M for storage in -80C freezer until analysis. The DRUM onboard ROAM-V was operated at a faster rotation rate of 150 mm per day to clearly separate the multiple deployment locations for ROAM-V. For the ROAM-V DRUM, substrates were changed every deployment and transported to Texas A&M for storage in -80C freezer until analysis. At Texas A&M, ice nucleation experiments were conducted to measure the ice nucleation temperature of the ambient aerosol samples collected from the three DRUMs using our previously established procedures (Alsante et al., 2023; Fornea et al., 2009; Matthews et al., 2023). For ice nucleation, only samples collected on stage 3 were analyzed, given that these are the most relevant size (1.2 to 0.34 μm diameter) for potential ice nucleating particles. For the AMF1 and ANC sites, we cut and analyzed 2 mm (2-hour) samples. We analyzed the time periods of the AMF1 site instrument when the ROAM-V was deployed. A 72-hour period from July 11th at 23:49 through July 15th at 1:49 was analyzed from the ANC site instrument. For the ROAM-V instrument, we separated the daily samples by site location. Between 1- and 6-hour independent samples were analyzed at each location. We also cut the ROAM-V samples in half to allow for compositional analysis of the aerosol on the other half of the substrate. All viable samples from the ROAM-V were analyzed. Analysis was done using a custom-built ice nucleation apparatus, recently updated to include an array of 16 individual samples (Matthews et al., 2023). On our experimental setup, we used 100 μL of Ultra-High-Performance Liquid Chromatography (UHPLC) water (Sigma Aldrich, >99.9% purity) to wash off the aerosol from the DRUM substrate. Then, we micropipetted 2 μL droplet samples into each of the 16 wells of the array, using hydrophobically coated microscope slides. Experiments would cycle 28 times from 10 C to -40C over a 20-hour period. During analysis of the TRACER samples, nine experiments were conducted with UHPLC water process blanks that followed an identical preparation procedure. The average freezing temperature and standard deviation for these process blanks is -27.7±2.4 C. This data was collected for ARM Field Campaign AFC07023 and supported by DOE ASR grant DE-SC0021047. For any further questions, please feel free to contact the instrument PI, Sarah D. Brooks, sbrooks@tamu.edu. Alsante, A. N., Thornton, D. C., & Brooks, S. D. (2023). Ice nucleation catalyzed by the photosynthesis enzyme RuBisCO and other abundant biomolecules. Communications Earth & Environment, 4(1), 51. Fornea, A. P., Brooks, S. D., Dooley, J. B., & Saha, A. (2009). Heterogeneous freezing of ice on atmospheric aerosols containing ash, soot, and soil. Journal of Geophysical Research: Atmospheres, 114(D13). DOI:10.1029/2009JD011958 Matthews, B. H., Alsante, A. N., & Brooks, S. D. (2023). Pollen Emissions of Subpollen Particles and Ice Nucleating Particles. ACS Earth and Space Chemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Reversed Impedance-Based Stability Criterion for IBR Grids

The existing impedance-based stability criterion is effective for analyzing local control interactions; however, it is difficult to scale the existing criterion to analyze wide-area control interactions among numerous IBRs through a complex power system network. The scaled version of the existing criterion requires the impedance response of each IBR in the system as well as of the network looking from all the IBRs. It is quite challenging to obtain all these impedance responses because of the computational effort and the requirement of separately scanning the impedance of the network and the IBRs. We propose a reversed criterion for the impedance-based stability analysis to address these problems. In contrast to the existing criterion, the reversed criterion analyzes the stability of a power system when an IBR is disconnected from the system. The reversed criterion estimates the impact of an IBR on the frequency and damping of power system oscillation modes using the impedance scans of only the IBR and the grid at its terminal. It can be sequentially applied at different IBRs to evaluate their impact on the power system stability. In addition to scalability, the reversed criterion gives flexibility to focus only on a few selected IBRs, depending on their rating, the magnitude of oscillations observed at their terminals, and the vendor support available for implementing stabilizing control system updates. The reversed criterion is demonstrated on a 14-bus power system with 100% IBRs.

control interactions↗
DOE OSTI2021

Graded Buffer Bragg Reflectors with High Reflectivity and Transparency for Metamorphic Optoelectronics

A graded buffer Bragg reflector (GBBR) is a bifunctional device component that provides the reflection of a distributed Bragg reflector and the adjustable lattice constant of a compositionally graded buffer. Prior work showed that these properties can be combined with low threading dislocation density and high reflectivity. Here, we design and demonstrate complex GBBRs for specific metamorphic solar cell applications. We design buffers that provide (1) high reflectivity over a narrow bandwidth, for quantum well solar cells, (2) reflectivity over a wide bandwidth, for optically thin solar cells, and (3) low sidelobe reflection, for multijunction devices that demand low out-of-band reflection. Apodized and triple GBBRs are demonstrated, and transparency is always considered, requiring designs with carefully engineered material combinations. A GBBR with a reflection of 99% is demonstrated, as well as a triple GBBR that has over 80% reflection for 100?nm of the spectrum around 800?nm. We also analyze potential deviations in a baseline GBBR from that of a perfect DBR using transmission electron microscopy to analyze imperfections in the material and modeling to analyze the impact of imperfect refractive index data for lattice-mismatched AlGaInAs. Minimal crosshatch roughness and unintentional thickness variation occur throughout the buffer, which likely influences reflection slightly. Small deviations between the calculated AlGaInAs and utilized AlGaAs refractive index exist, giving methods for future GBBR improvement. While the GBBR designs are intended for metamorphic solar cells, the broad and high reflection may also be useful for other optoelectronic devices such as light-emitting diodes or lasers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

SAIL-Isotopologue (SAIL-iso) Field Campaign Report

The SAIL-iso project took place from November 19, 2022 to June 3, 2023 as part of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Surface Atmosphere Integrated Field Laboratory (SAIL) project at the main project site in Gothic, Colorado. The project sought to use measurements of the stable isotopic composition of water vapor to better understand cloud-aerosol interactions in a remote mountainous watershed. Water vapor isotopologue and humidity measurements were determined using a Los Gatos Research Triple Water Vapor Isotope Analyzer (TWVIA). The instrument was deployed in the guest van and consists of three main components: a water vapor isotope analyzer, a Los Gatos Research Water Vapor Isotope Standard Source (WVISS), and a Los Gatos Research Dry Air Source. Ambient air samples were collected through an inlet located about one meter above roof of the shipping container. Samples were then delivered to the analyzer through Teflon tubing by use of an external pump to minimize the transport time between the inlet and analyzer. The inlet tubing was surrounded by a Briskheat heat trace and insulating material to ensure ambient air samples did not fall below the dew point and result in condensation. The analyzer uses cavity ringdown spectroscopy to report isotopic ratios of ambient air samples at a frequency of 1 Hz. The calibration unit of the instrument was used in conjunction with the dry air source to periodically measure the δ values of standard waters. It uses a nebulizer to push small water droplets into a hot chamber that vaporizes the water without fractionation. This vapor was then transported to the analyzer using a built-in compressor and the dry air source, which allows each standard with known δ values to be measured at a wide range of humidity values for post-measurement calibration of ambient air samples. Multiple sources of uncertainty are introduced at different stages during the data collection and processing. These include (1) instrument precision, (2) uncertainty in the secondary standards, (3) humidity-correction uncertainty, and (4) Vienna Standard Mean Ocean Water – Standard Light Antarctic Precipitation (VSMOW-SLAP) calibration uncertainty. Uncertainty from each step is propagated in quadrature to calculate a total uncertainty of each isotopologue. Uncertainty was determined to be 2.3‰ for δD and 0.4‰ for δ 18 O.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Water vapor isotopic composition during TRACER IOP

The TRACER-Iso project took place from June 1 to September 30, 2022 as part of the intensive observation period (IOP) of the Tracking Aerosol Convection Interactions (TRACER) project at the main project site at the La Porte, Texas, municipal airport. The project sought to use measurements of the stable isotopic composition of water vapor to better understand cloud-aerosol interactions in deep convection in a polluted urban setting. Water vapor isotopologue and humidity measurements were determined using a Picarro L2130 Water Vapor Isotope Analyzer. The instrument was deployed in the guest van and consists of three main components: a water vapor isotope analyzer, and a Los Gatos Research Water Vapor Isotope Standard Source (WVISS), and a Los Gatos Research Dry Air Source. Ambient air samples were collected through an inlet located about one meter above roof of the shipping container. Samples were then delivered to the analyzer through teflon tubing by use of an external pump to minimize the transport time between the inlet and analyzer. The inlet tubing was surrounded by a Briskheat heat trace and insulating material to ensure ambient air samples did not fall below the dew point and result in condensation. The analyzer uses cavity ringdown spectroscopy to report isotopic ratios of ambient air samples at a frequency of 1 Hz. The calibration unit of the instrument was used in conjunction with the dry air source to periodically measure the δ values of standard waters. It uses a nebulizer to push small water droplets into a hot chamber that vaporizes the water without fractionation. This vapor was then transported to the analyzer using a built-in compressor and the dry air source, which allows each standard with known δ values to be measured at a wide range of humidity values for post-measurement calibration of ambient air samples. There are multiple sources of uncertainty introduced at different stages during the data collection and processing. These include (1) instrument precision, (2) uncertainty in the secondary standards, (3) humidity-correction uncertainty, and (4) VSMOW-SLAP calibration uncertainty. Uncertainty from each step is propagated in quadrature to calculate a total uncertainty of each isotopologue. Uncertainty was determined to be 2.3‰ for δD and 0.4‰ for δ18O.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of Zero-Net-Rate Pumping Tests

Accurately estimating the distribution of aquifer properties is key to understanding contaminant movement in the subsurface. The distribution of aquifer properties is typically addressed using slug or constant-rate well tests, and the pros and cons of these tests are well known. Slug tests are appealing because they avoid removing contaminated water, but their results are affected by well skin and the small volume of displaced water limits the volume of aquifer that can be evaluated. Constant-rate well tests have the disadvantage of requiring disposal of potentially contaminated water, but they can generate properties that are more representative than slug tests, and they can be used to estimate well efficiency and storativity, which are difficult to characterize using slug tests. Periodic pumping tests are appealing because they have many of the advantages and few of the disadvantages of slug and constant-rate well tests. Periodic pumping tests involve cycling the pumping rate with a regular period and measuring the resulting response in monitoring wells. Some of these tests involve imposing a periodic rate on a constant mean rate. However, other tests involve moving water out and back into the well at rates that are balanced so the net rate after each period is zero. Some zero-net-rate (ZNR) well tests use rates that follow a sinusoidal pattern, whereas others use a square wave pattern that switches between a constant rate of pumping and a constant rate of injection to achieve a zero-net rate. ZNR well tests appear to be a useful compromise between slug and constant rate tests, but methods for conducting and analyzing the results from these tests have received limited evaluation. Periodic pumping tests are typically evaluated through recorded pressure responses to a disturbance source. Recent work has demonstrated that the strain field in the vadose zone shows a response to disturbances in the underlying aquifer. Various instrument designs are available that can record vertical and horizontal strain to very precise resolutions (10-9 ε). Measuring the strain in the vadose zone can present lower costs than measuring pressure in a monitoring well, so using strain could improve the resolution of ZNR tests, as well as make these tests potentially cheaper. A ZNR periodic pumping system was constructed and used to perform tests in the Clemson, South Carolina area. The system was designed to pump and inject with a periodic square-wave, so pumping occurs at a constant rate for half the period and is followed by injection at the same rate for half the period resulting in no net gain or loss of water from the aquifer. The system is designed to generate flow rates from 1 to 3.5 gallons per minute (gpm) with a capacity of 875-gallon per half period. Pressure and strain monitoring points are located in the vicinity of the pumping well. A series of 10 ZNR periodic pumping tests were conducted at the field site, with periods ranging from 2 to 540 minutes. Pressure data were collected and recorded in four monitoring wells in various locations around the pumping well. In addition, vertical and horizontal strain and tilt data were recorded in various locations around the pumping well. Traditional aquifer tests, including slug tests and constant-rate pumping tests were conducted at the field site to provide a baseline of aquifer parameter estimates that are compared the results of ZNR tests. Pressure data measured at monitoring wells during ZNR tests were analyzed utilizing an analytical solution (Streltsova, 1988) that assumes a confined aquifer and estimates hydraulic diffusivity and transmissivity using the time lag and amplitude of the pressure signal. The storativity can be separated out from the hydraulic diffusivity using the transmissivity estimate. The time-lag and amplitude of the pressure signals were estimated using a Fourier transform. The time-lag of the primary period of the pressure increases as a linear function of distance from the pumping well, and it increases as the square root of the period of the ZNR test over a range of periods spanning two orders of magnitudes. The first few harmonics follow similar trends, but the higher frequency harmonics diverge from this trend. Strain and tilt data from various instruments in the vadose and saturated zones are periodic during ZNR tests. The time lags of the strain components increase roughly linearly with distance from the well. Many of the time lags of the strain data from the vadose zone are similar to the time lags observed in the pressure data from a similar distance and pumping period. Theoretical experiments were performed to understand how pressure and strain responds to ZNR periodic pumping. The time-lags of the simulated pressure responses match the field data trends. The results were used to validate the Streltsova (1988) solution for periodic pumping to estimate hydraulic diffusivity. Hydraulic diffusivity was estimated to be 1.8 x 10-2 m2s-1 < Dh < 9.0 x 10-2 m2s-1 using pressure data from all the wells during ZNR tests. Transmissivity was estimated to be 0.8 x 10-4 m2s-1 < T < 4.3 x 10-4 m2s-1. Assuming Storativity = T/Dh gives 1.1 x 10-3 < S < 19 x10-3. Transmissivity is used to estimate the hydraulic conductivity, K, by dividing by the assumed aquifer thickness. Two constant-rate pumping tests were conducted and analyzed using two conceptual models: The Hantush (1961) solution was used to analyze data assuming confined conditions and the Neuman (1974) solution was used to analyze data assuming unconfined conditions. The Hantush (1961) solution estimated transmissivity to be 1.6 x 10-4 m2s-1 < T < 1.8 x 10-4 m2s-1 when using data from all the wells. Storativity was estimated to be 4.4 x 10-3 < S < 8.2 x 10-3. Assuming Dh = T/S gives 0.22 x 10-2 m2s-1 < Dh < 3.6 x 10-2 m2s-1. The Neuman (1974) solution estimated transmissivity to be 0.80 x 10-4 m2s-1 < T < 0.84 x 10-4 m2s-1. Total storativity (S + Sy) was estimated to be 22 x 10-3 < S < 110 x 10-3. Storativity excluding the Sy term was estimated to be 2.2 x 10-3 < S < 2.5 x 10-3. Assuming Dh = T/S gives 0.073 x 10-2 m2s-1 < S < 0.36 x 10-2 m2s-1. Two slug tests were performed on the pumping well, PW-2. The Bouwer and Rice (1976) solution for slug tests was used to analyze the data. This solution works for both confined and unconfined conditions, so both conceptual models were covered through the analysis. Hydraulic conductivity was estimated to be 1.95 x 10-6 ms-1 < K < 2.49 x 10-6 ms-1. Statistical comparison show generally no significant difference in the estimates of Dh, K, or S made using ZNR and conventional pumping tests.. However, the total storage estimated by the Neuman (1974) solution for unconfined settings is larger than that estimate using the Streltsove analysis applied to the ZNR tests. This is likely because the Neuman solution considers delayed yield from storage that the water table, whereas the Streltsova (1988) solution assumes confined conditions. The analysis also indicates that there is a statistically significant difference between parameters estimated with conventional slug tests and those measured with either constant-rate pumping tests or ZNR tests. Hydraulic diffusivity is estimated from the time-lag and the distance from the well. The time-lags of the strain data from the vadose zone are similar to the lags of the pressure data from a similar distance. This indicates that hydraulic diffusivities estimated from the strain data measured in the vadose zone would be similar to the estimates from the pressure data measured in the aquifer. There appear to be sign reversals in some of the strain data that were corrected to estimate the time lag. The dissolved oxygen (CO) concentration was measured during several ZNR tests to evaluate the feasibility of using the procedure to increase contaminant degradation kinetics that are related to DO. The results indicate that DO can be increased during the cycling of ZNR tests in some cases.

Smith-Jones, Austin↗