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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Conceptual Design for SuperCDMS SNOLAB

Beyond the present dark matter direct detection experiment at the Soudan underground laboratory, the SuperCDMS Collaboration is engaged in R and D activities for a 100-kg scale germanium dark matter experiment nominally sited at SNOLAB (2070 m overburden of rock). The expected sensitivity after 3 years of running is 3 x 10{sup -46} cm{sup 2} for the spin-independent cross section, an order of magnitude improvement over present exclusion limits for WIMP masses {approx}80 GeV/c{sup 2}. At this depth, and appropriate design of shielding and cryostat, neutron backgrounds will be negligible. The baseline design is an expanded version of CDMS II with Ge substrates (100 x 33 mm discs) instrumented with the iZIP phonon sensor layout to achieve the electron surface-event rejection power required.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2014

SU-E-J-91: Novel Epitaxial Silicon Array for Quality Assurance in Photon and Proton Therapy

Purpose: to demonstrate suitability of a novel silicon array for measuring the dose properties of highly conformal photon and proton beams. Methods: prototype under test is a 24cm long linear array prototype, although the underlying technology is suitable to construct 2D arrays as well. It is based on a 64pixels monolithic sensor with 1mm pixel pitch, made of epitaxial ptype silicon. Thanks to design modularity, more sensors can be placed side by side without breaking pixel pitch. Flattened and unflattened photon beams, as well as proton radiation from a cyclotron in pencil beam scanning mode, were considered. Measurements of beam characteristics as percentage depth doses, dose profiles, output factors and energy response, which are necessary to deliver radiation with high precision and reliability, were performed. Results: Dose rate independence with photons was verified in the dose per pulse range 0.03 to 2mGy. Results clearly indicate nondependence of the detector sensitivity both for flattened and unflattened beams, with a variation of at most 0.5percentage. OFs were obtained for field with a lateral size ranging from 0.8cm to 16cm and the results are in good agreement with ion chamber A1SL, max difference less than 1.5percentage. Field sizes and beam penumbra were measured and compared to EBT film results. Concerning proton beams, sensitivity independence on dose rate was verified by changing the beam current in the interval 2-130Gy/s. Field sizes and beam penumbra measurements are in agreement with data taken with a scintillating 2D array with 0.5mm resolution IBA Lynx, and a better penumbra definition than an array of ionization chambers IBA MatriXX is reached. Conclusion: The device is a novel and valuable tool for QA both for photon and proton dose delivery. All measurements demonstrated its capability to measure with high spatial resolution many crucial properties of the RT beam.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

A Study of a Monitoring Program for Intermediate Depth Disposal of Low- Level Radioactive Waste - 20167

This study investigated a monitoring program for intermediate depth disposal of low-level radioactive waste. At first, it extracted monitoring targets, such as the status of cracks in the low diffusion layer after closure of the facility. Secondly, it focused on distributed optical fiber sensors which can minimize the amount of cable, and measurement positions and methods were specifically determined to propose the monitoring method. Finally, conformity to regulatory requirements was verified to clarify key points and issues to be addressed in performing monitoring. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Computed Tomography Scanning and Geophysical Measurements of the Salina Formation from the #36 Brine Well

The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize core of the Salina Formation from a vertical well (Brine Well #36) from Marshall County, West Virginia at a depth of 6,555.6 to 6,719.5 ft. The primary impetus of this work is a collaboration between West Virginia Geological and Economic Survey (WVGES) and NETL to characterize core from multiple wells to better understand the key formations in West Virginia. As part of this effort, bulk scans of core were obtained from the Brine Well #36, provided by the WVGES. This report, and the associated scans generated, provide detailed datasets not typically available for researchers to analyze.

58 GEOSCIENCES↗
DOE OSTI2020

Using Strain in the Vadose Zone During Sinusoidal-Rate Well Tests to Characterize an Aquifer

Well tests with sinusoidal variation in pumping rate are appealing because of the ability to filter competing signals and they also can be set up with no net removal of water, which is an advantage at contaminated sites. Current methods of interpreting sinusoidal-rate well tests analyze the phase lag and amplitude of pressure signals in the aquifer. This approach is restricted by the availability of monitoring wells. An alternative approach could be to measure the stain in the vadose zone instead of pressure in the aquifer. The goal of this research is to evaluate the feasibility of using signals from in-situ sensors in the vadose zone to characterize sinusoidal-rate well tests. Well tests with a variety of periods and amplitudes were performed using a well completed in an unconfined saprolite aquifer with a water table at 9m depth at our test site near Clemson, South Carolina. Vertical strain was measured every meter between 1 to 7 m using a distributed optical fiber sensing technique called coherence length gated microwave photonics interferometry (CMPI). A novel packaging system was designed to couple the optical fiber to the saprolite. The results show that strain measured in the vadose zone during sinusoidal-rate pumping varies periodically with amplitudes from 0.1 to 3 microstrain, depending on the amplitude of the sinusoidal pumping rate. Hydraulic heads at similar radial distances were also periodic with changing amplitudes ranging up to ±0.2 meters. The strain in the vadose zone responds to pressure in the underlying aquifer—the strain is tensile when the pressure is falling, and compressive when the pressure is rising. The phase lag between the pumping rate and the strain was 3 min with a pumping period of 15 minutes, and this gives a hydraulic diffusivity of 0.06 m2/s. This is the same diffusivity obtained by analyzing a constant rate pumping test, and it is slightly larger than the diffusivity (0.044 m2/s) obtained by analyzing the phase lag of the pressure in a nearby monitoring well. Strain measurements in the vadose zone could be made with much less cost and effort than installing a deep monitoring well. These results suggest that strain measurements in the vadose zone could be used to supplement monitoring wells, thereby increasing the resolution, and reducing costs of analyzing well tests.

well test, sinusoidal, periodic pumping, strain↗
DOE OSTI2015

Characterization of 750 Large Area Photomultipliers for the KM3NeT-Italia towers

The KM3NeT European experiment aims to construct a large volume underwater neutrino telescope, in the depths of the Mediterranean Sea. Thanks to a dedicated funding by the Italian Ministry of Education, University and Research, in its first phase named KM3NeT-Italia, an 8-towers detector is under construction, equipped with 672 optical sensors. The detection element of the telescope, the 'optical module', is composed by a 13-inch high-pressure glass-vessel that contains a single 10-inch photomultiplier. As the key sensor, all the characteristics of the photomultiplier have a severe impact on the performance of the whole detector. The selected photomultiplier was the 10-inch R7081 PMT produced by Hamamatsu. In the frame of the optical modules mass production, performed in the INFN-LNS site of Catania, 750 photomultipliers have been tested by means of a dedicated test bench. The paper deals with the main results obtained from the massive photomultipliers measurements, which exhibited that such kind of devices comply with the general requirements imposed by the project. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Artificial intelligence models, photos, and data associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” (v2)

This data package is associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” published in Water Resources Research (Chen et al., 2024). This data package includes the training, validation, testing, and prediction data used by the artificial intelligence (AI) model for automated grain size and hydro-biogeochemistry quantification using streambed photos. The grain size data are extracted for each photo using You Look Only Once (YOLO), a pre-trained object detection model. This data package was originally published in October 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. Please see flmd.csv for a list of all files contained in this data package and descriptions for each. Please see dd.csv for a data dictionary that defines the column headers of .csv files in the data package. This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; and (4) six subfolders. Subfolders 1 to 4 include the training, validation, testing, and prediction data. Subfolder 5_Summary includes the summary results of different combinations of training, validation, testing, and prediction data. Subfolder 6_SupplementalData includes additional data downloaded from public sources (Kaufman et al., 2023a; Kaufman et al., 2023b; Garefalakis et al., 2023; Mair et al., 2024; https://github.com/river-corridors-sfa/Geospatial_variables). In total, the data package includes 110 folders and 44,283 files. These files include 9,047 .jpg photos, 1 .png photo, 3 .tif photos; 26,639 photo labels and individual grain sizes and probability from AI (.txt); 8,447 grain size distribution data (.dat); and 126 CSV files for results summary, and 14 required metadata files (.xlsx). The summary CSV files contain 68 columns and approximately 2,200 rows that represent photo names, site locations, recording time, GPS coordinates, grains sizes (D10, D50, D60, and D84), number of grains, and additional hydro-biogeochemical data such as water depth, flow velocity, Manning’s coefficient, friction factor, hydraulic conductivity, permeability, streambed interstitial velocity magnitude, mass transfer rate, and nitrate uptake velocity. The photos were obtained from 75 sites in the Yakima River Basin and the Columbia River shorelines, and other associated data from samples and sensors obtained when the photos were taken are publicly available (Fulton et al. 2022; Grieger et al. 2023). All files are .csv, .txt, .dat, .jpg, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of 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↗
DOE OSTI2015

The Second-phase Development of the China JinPing Underground Laboratory

During 2013-2015 an expansion of the China JinPing underground Laboratory (CJPL) will be undertaken along a main branch of a bypass tunnel in the JinPing tunnel complex. This second phase of CJPL will increase laboratory space to approximately 96,000 m 3 , which can be compared to the existing CJPL-I volume of ∼ 4,000 m 3 . One design configuration has eight additional hall spaces, each over 60 m long and approximately12 m in width, with overburdens of about 2.4 km of rock, oriented parallel to and away from the main water transport and auto traffic tunnels. There are additional possibilities for further expansions at a nearby second bypass tunnel and along the entrance and exit branches of both bypass tunnels, potentially leading to an expanded CJPL comparable in size to Gran Sasso. Concurrent with the excavation activities, planning is underway for dark matter and other rare- event detectors, as well as for geophysics/engineering and other coupled multi-disciplinary sensors. In the town meeting on 8 September, 2013 at Asilomar, CA, associated with the 13 th International Conference on Topics in Astroparticle and Underground Physics (TAUP), presentations and panel discussions addressed plans for one-ton expansions of the current CJPL germanium detector array of the China Darkmatter EXperiment (CDEX) collaboration and of the duel-phase xenon detector of the Panda-X collaboration, as well as possible new detector initiatives for dark matter studies, low-energy solar neutrino detection, neutrinoless double beta searches, and geoneutrinos. JinPing was also discussed as a site for a low-energy nuclear astrophysics accelerator. Geophysics/engineering opportunities include acoustic and micro-seismic monitoring of rock bursts during and after excavation, coupled-process in situ measurements, local, regional, and global monitoring of seismically induced radon emission, and electromagnetic signals. Additional ideas and projects will likely be developed in the next few years, driven by China’s domestic needs and by international experiments requiring access to very great depths.

58 GEOSCIENCES↗
DOE OSTI2013

Characterizing Aerosol Distributions and Optical Properties Using the NASA Langley High Spectral Resolution Lidar

The objective of this project was to provide vertically and horizontally resolved data on aerosol optical properties to assess and ultimately improve how models represent these aerosol properties and their impacts on atmospheric radiation. The approach was to deploy the NASA Langley Airborne High Spectral Resolution Lidar (HSRL) and other synergistic remote sensors on DOE Atmospheric Science Research (ASR) sponsored airborne field campaigns and synergistic field campaigns sponsored by other agencies to remotely measure aerosol backscattering, extinction, and optical thickness profiles. Synergistic sensors included a nadir-viewing digital camera for context imagery, and, later in the project, the NASA Goddard Institute for Space Studies (GISS) Research Scanning Polarimeter (RSP). The information from the remote sensing instruments was used to map the horizontal and vertical distribution of aerosol properties and type. The retrieved lidar parameters include profiles of aerosol extinction, backscatter, depolarization, and optical depth. Products produced in subsequent analyses included aerosol mixed layer height, aerosol type, and the partition of aerosol optical depth by type. The lidar products provided vertical context for in situ and remote sensing measurements from other airborne and ground-based platforms employed in the field campaigns and was used to assess the predictions of transport models. Also, the measurements provide a data base for future evaluation of techniques to combine active (lidar) and passive (polarimeter) measurements in advanced retrieval schemes to remotely characterize aerosol microphysical properties. The project was initiated as a 3-year project starting 1 January 2005. It was later awarded continuation funding for another 3 years (i.e., through 31 December 2010) followed by a 1-year no-cost extension (through 31 December 2011). This project supported logistical and flight costs of the NASA sensors on a dedicated aircraft, the subsequent analysis and archival of the data, and the presentation of results in conferences, workshops, and publications. DOE ASR field campaigns supported under this project included: • MAX-Mex /MILAGRO (2006), • TexAQS 2006/GoMACCS (2006), • CHAPS (2007), • RACORO (2009), and • CARE/CalNex (2010). In addition, data acquired on HSRL airborne field campaigns sponsored by other agencies were used extensively to fulfill the science objectives of this project and the data acquired have been made available to other DOE ASR investigators upon request.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2018

System for detecting objects in streaming 3D images formed from data acquired with a medium penetrating sensor

A detection system that detects subsurface objects within a medium and estimates various features of the objects is provided. The detection system receives a streaming sequence of image frames of the medium at various along-medium locations. An image frame contains voxel values (intensities) representing characteristics of the medium across the medium and in the depth (range) direction. The detection system depth-compensates the intensities for determining which voxels are part of an object using an unsupervised binary classifier. The detection system then connects object voxels into distinct objects and recursively estimates the features of those objects as the image frames stream based on the locations and intensities of the object voxels.

Paglieroni, David W.↗
DOE OSTI2022

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗
DOE OSTI2021

Porous Janus materials with unique asymmetries and functionality

Porous Janus materials have attracted widespread attention due to their asymmetry in wettability, charge, pore size or structure, thermal/electrical conductivity, and chemical activity, among other properties. Multifunction integration and unique directional manipulation of liquid, ions, or gas within porous Janus materials enable rapid progress in diverse applications such as water-oil separation, fog collection, personal moisture and healthcare management, energy conversion, desalination, sensor devices, and biomedical applications. Additionally, compared with conventional homogeneous materials, porous Janus materials not only showcase superior performance and energetic potential but also open up new applications by virtue of the synergistic or independent effects of asymmetry. This in-depth review systematically analyzes the research progress of porous Janus materials, highlighting fabrication strategies, synergistic/independent work mechanisms, and emerging advanced applications. Finally, ongoing challenges and outlook for the future research of porous Janus materials are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Rotation-Enabled 7-Degree of Freedom Seismometer for Geothermal Resource Development. Phase 1 Final Report

Under this Department of Energy (DOE) grant, A-Tech Corporation d.b.a. Applied Technology Associates (ATA), seeks to develop a seven-degree-of-freedom (7-DOF) seismic measurement tool for high-temperature geothermal applications. The Rotational-Enabled 7-DOF Seismometer includes a conventional tri-axial accelerometer, a conventional pressure sensor or hydrophone, and a tri-axial rotational sensor. The rotational sensing capability is novel, based upon ATA's innovative research in rotational sensing technologies. The geothermal industry requires tools for high-precision seismic monitoring of crack formation associated with Enhanced Geothermal System (EGS) stimulation activity. Currently, microseismic monitoring is conducted by deploying many seismic tools at different depth levels along a 'string' within drilled observation wells. Costs per string can be hundreds of thousands of dollars. Processing data from the spatial arrays of linear seismometers allows back-projection of seismic wave states. In contrast, a Rotational-Enabled 7-DOF Seismometer would simultaneously measure p-wave velocity, s-wave velocity, and incident seismic wave direction all from a single point measurement. In addition, the Rotational-Enabled 7-DOF Seismometer will, by its nature, separate p- and s-waves into different data streams, simplifying signal processing and facilitating analysis of seismic source signatures and geological characterization. By adding measurements of three additional degrees-of-freedom at each level and leveraging the information from this new seismic observable, it is likely that an equally accurate picture of subsurface seismic activity could be garnered with fewer levels per hole. The key cost savings would come from better siting of the well due to increased information content and a decrease in the number of confirmation wells drilled, also due to the increase in information per well. Improved seismic tools may also increase knowledge, understanding, and confidence, thus removing some current blocks to feasibility and significantly increasing access to potential geothermal sites. During the Phase 1 effort summarized in this final report, the ATA Team modeled and built two TRL 3 proof-of-concept test units for two competing rotational sensor technologies. The two competing technologies were based on ATA's angular rate and angular displacement measurement technologies; Angular rate: ATA's Magnetohydrodynamic Angular Rate Sensor (Seismic MHD); and Angular displacement: ATA's Low Frequency Improved Torsional Seismometer (LFITS). In order to down-select between these two technologies and formulate a go / no go decision, the ATA Team analyzed and traded scientific performance requirements and market constraints against sensor characteristics and components, acquiring field data where possible to validate the approach and publishing results from these studies of rotational technology capability. Based on the results of Phase 1, the ATA Team finds that the Seismic MHD (SMHD) technology is the best choice for enabling rotational seismometry and significant technical potential exists for micro-seismic monitoring using a downhole 7-DOF device based on the SMHD. Recent technical papers and field data confirm the potential of rotational sensing for seismic mapping, increasing confidence that cost-reduction benefits are achievable for EGS. However, the market for geothermal rotational sensing is small and undeveloped. As a result, this report recommends modifying the Phase 2 plan to focus on prototype development aimed at partnering with early adopters within the geothermal industry and the scientific research community. The highest public benefit will come from development and deployment of a science-grade SMHD rotational seismometer engineered for geothermal downhole conditions and an integrated test tool for downhole measurements at active geothermal test sites.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Site Selection for Field Testing of a Marine Hydrokinetic Turbine Platform to Serve as a Floating Unmanned Mobile Recharging Station for Aerial Drones

Abstract Expanding applications of unmanned aerial vehicles (UAVs) in coastal areas suggests a need for stations along or close to the coast where the UAV batteries can be easily recharged. A small-scale low flow marine current turbine in support of a floating autonomous recharge station for small UAVs has been designed and assembled at Florida Atlantic University and is being prepared for field testing. Surveys of identified test sites in the Intracoastal Waterways and at open ocean locations are being conducted to determine their suitability in meeting resource, system design and permitting requirements. A sensor platform has been developed for measuring local currents at the identified sites in support of resource characterization. Two out of the eight sites surveyed in the Intracoastal Waterways are found to meet the field-testing requirements for current resource, water depth, and sea bottom conditions. Planned field testing will include parametric characterization studies of the performance of the current turbine for a range of turbine blade configurations, current speeds, turbine wheel submergence levels, and test site location (channeled flow vs open ocean flow).

McKinney, Adriana Lynell↗
DOE OSTI2021

Numerical Analysis of Radiation Effects on Fiber Optic Sensors

Optical fiber sensors (OFS) are a potential candidate for monitoring physical parameters in nuclear environments. However, under an irradiation field the optical response of the OFS is modified via three primary mechanisms: (i) radiation-induced attenuation (RIA), (ii) radiation-induced emission (RIE), and (iii) radiation-induced compaction (RIC). For resonance-based sensors, RIC plays a significant role in modifying their performance characteristics. In this paper, we numerically investigate independently the effects of RIC and RIA on three types of OFS widely considered for radiation environments: fiber Bragg grating (FBG), long-period grating (LPG), and Fabry-Perot (F-P) sensors. In our RIC modeling, experimentally calculated refractive index (RI) changes due to low-dose radiation are extrapolated using a power law to calculate density changes at high doses. The changes in RI and length are subsequently calculated using the Lorentz–Lorenz relation and an established empirical equation, respectively. The effects of both the change in the RI and length contraction on OFS are modeled for both low and high doses using FIMMWAVE, a commercially available vectorial mode solver. An in-depth understanding of how radiation affects OFS may reveal various potential OFS applications in several types of radiation environments, such as nuclear reactors or in space.

42 ENGINEERING↗
DOE OSTI2025

Old Woman Creek Wetland Sediment and Electrochemical Sensor Microbial Community, 2023

We are developing a technique to monitor microbiological activities referred to as zero resistance ammetry, which entails the deployment of graphite electrodes in sediments. Measurement of current between electrodes of contrasting redox regimes and/or predominant terminal electron accepting processes can be used as an indicator of the extents of microbiological activity. We deployed an electrode array at depths of 2 mm, 4 mm, 76 mm, 78 mm, 152 mm, 154 mm, 227 mm, and 229 mm below the wetland sediment water interface in the Old Woman Creek National Estuarine Research Center, Huron, OH, USA (Lat. = 41.380833, Long. = -82.508889). A core was collected from adjacent sediment and subsamples were collected from depth intervals of 0 – 25 mm, 25 – 127 mm, 127 – 128 mm, and below 178 mm. To determine if the microbial communities attached to the electrodes were reflective of the adjacent sediment-associated microbial community, we conducted a 16S rRNA gene-based (V4 region) survey of these respective materials. This data package contains the results of these surveys, including metadata on the depths from which samples were collected (samples.csv), DNA extraction and sequencing information (OWC_DEPTH_AMPLICON_SEQUENCING_METADATA), sequence processing information (OWC_DEPTH_BIOINFORMATIC_METADATA.csv), an operational taxonomic unit (OTU) table (OWC_DEPTH_97OTUS_TABLE.csv), and nucleotide sequences of OTUs (OWC_DEPTH_97OTUS_SEQS.fasta). All files can be opened using a text-editing application. The fasta file is compatible with bioinformatics applications.

54 ENVIRONMENTAL SCIENCES↗