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DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Principle Findings from Development of a Recirculated Exhaust Gas Intake Sensor (REGIS) Enabling Cost-Effective Fuel Efficiency Improvement

Kick-off of the Bosch scope of work for the REGIS project started in October 2012. The primary work-packages included in the Bosch scope of work were the following: overall project management, development of the EGR sensor (design of sensor element, design of protection tube, and design of mounting orientation), development of EGR system control strategy, build-up of prototype sensors, evaluation of system performance with the new sensor and the new control strategy, long-term durability testing, and development of a 2nd generation sensor concept for continued technology development after the REGIS project. The University of Clemson was a partner with Bosch in the REGIS project. The Clemson scope of work for the REGIS project started in June 2013. The primary work-packages included in the Clemson scope of work were the following: development of EGR system control strategy, and evaluation of system performance with the new sensor and new control strategy. This project was split into phase I, phase II and phase III. Phase I work was completed by the end of June 2014 and included the following primary work packages: development of sensor technical requirements, assembly of engine testbench at Clemson, design concept for sensor housing, connector, and mounting orientation, build-up of EGR flow test benches at Bosch, and build-up of first sensor prototypes. Phase II work was completed by the end of June 2015 and included the following primary work pack ages: development of an optimizing function and demonstration of robustness of sensor, system control strategy implementation and initial validation, completion of engine in the loop testing of developed control algorithm, completion of sensor testing including characteristic line, synthetic gas test stand, and pressure dependency characterization, demonstration of benefits of control w/o sensing via simulation, development of 2nd generation sensor concept. Notable technical achievements from phase II were the following: publication of two new technical papers by Clemson detailing the control strategies used for the EGR system control. The two papers was published in the 2016 SAE World Congress in April 2016. The titles of each paper are, “Physics-Based Exhaust Pressure and Temperature Estimation for Low Pressure EGR Control in Turbocharged Gasoline Engines,” by K. Siokos, and “A Control Algorithm for Low Pressure – EGR Systems using a Smith Predictor with Intake Oxygen Sensor Feedback”, by R. Koli. All phase III work packages have been completed. The primary work packages in phase III were the following: completion of long-term sensor durability testing, final demonstration of benefits of EGR control w/o sensing, final decision of the second generation sensor development path.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Sap Velocity, Leaf-level Measurements, and Meteorological Data in Manaus and Santarem, Plant Science, Brazil: 2015-2017

These datasets were used in the paper Gimenez et al., 2019 (Species-Specific Shifts in Diurnal Sap Velocity Dynamics and Hysteretic Behavior of Ecophysiological Variables During the 2015–2016 El Niño Event in the Amazon Forest) published in Frontiers in Plant Science, special issue: Water-Use Efficiency: Advances and Challenges in a Changing Climate. This data package contains data from ICT sap flow sensors, leaf temperature (Infrared Sensors), leaf gas exchange, leaf water potential and leaf thermocouple data used for some experiments of the NGEE-Tropics project in the Amazon Forest (sites: BR-Ma2 and BR-Sa1). The data package also includes environmental data (Relative humidity, air temperature, and VPD) collected using thermo-hygrometers installed in K-34 tower vertical profile (Manaus - BR-Ma2).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra: Supporting Data

High-resolution classification maps derived from occupied aerial systems (UASs). The UAS data were collected in August 2021 using a Skydio 2+ drone equipped with a 4K resolution red-green-blue (RGB) camera (2024 Skydio Inc) and a 3DR SOLO Quadcopter carried a Parrot Sequoia+ Multispectral Sensor (2023 Parrot Drone SAS). This package includes vegetation classification maps at four locations around Next Generation Ecosystem Experiment Arctic (NGEE Arctic) Council watershed study site on the Seward Peninsula, Alaska. The classification maps were generated using a combination of RGB and canopy height information. The map data and metadata are provided as ENVI image (.dat) and text (.txt, *hdr) formats. Additional map quicklooks are provided as GIS *.kml files. These datasets are provided in support of Yang et al., (In review), “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) 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↗

DOE OSTI2021

Geophysical and Environmental Monitoring Data, Lower Watershed, Teller Road Mile Marker 27, Seward Peninsula, Alaska, 2017-2019

This data set contains geophysical and environmental monitoring data acquired between September 2017 and 2019 at the lower Teller watershed, Seward Peninsula, Alaska. Geophysical data comprises processed resistivity data, acquired daily between Spring and Fall of 2018 and 2019, and a baseline measurement of Fall 2017. The environmental monitoring data comprises depth resolved, distributed soil moisture and soil temperature data. These measurements were obtained used Decagon 5TE sensors placed at 0.1 m, 0.2 m , 0.3 m, and 0.4 m depth. In addition, temperature data at 0.5 m, 1.0 m, and 1.5 m were acquired using Hobo temperature sensors. Data were collected to improve our understanding of the hydrological response of discontinuous permafrost systems, in particular focusing on multi-annual dynamics and short term disturbances, such as snowmelt or precipitation events. The electrical resistivity tomography (ERT) monitoring data are included as processed resistivity models, with model cells x dimension equal to the distance along the profile, and model z dimension being elevation. The start and end point of the transect are (UTM Zone 3N): E 454881.66 m, N 7178949.45 m, and E 454772.60, N 7178885.81. Locations of the soil moisture and temperature sensors are provided in the data package. This dataset is discussed in detail in the Uhlemann, S. et al 2021 paper listed in the references. This dataset includes one *.pdf user guide and 315 *.csv data files included within four zipped files.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↗

DOE OSTI2012

Acoustics- Version 1.0

This package contains modules that model acoustic sensors and acoustic sources (hearable) in Umbra. It is typically used to represent hearing in characters within Umbra. Typically, the acoustic sensors detect acoustic sources at a given point; however, it also contains the capability to detect bullet cracks by detecting the sound along the bullet path that is closest to the sensor. A memory module, acoustic memory, represents remembered sounds within a given character. Over time, the sounds are removed, as a character forgets what it has heard.

Source record↗

DOE OSTI2018

In-situ packaging ultra-uniform 3D hematite nanotubes by polyaniline and their improved gas sensing properties

Highlights: • Ultra-uniform 3D α-Fe{sub 2}O{sub 3} nanotubes were packaged by PANI through the two-stage solution processes. • PANI/α-Fe{sub 2}O{sub 3}-based sensor could detect low concentration of acetone (1 ppm) at lower temperature of 180 °C. • The unique structure of α-Fe{sub 2}O{sub 3} support and its synergetic effect with PANI layers improved the sensor performances. - Abstract: This paper reports the PANI (polyaniline)-packaged three-dimensional (3D) α-Fe{sub 2}O{sub 3} nanotubes P/N heterojunction, which was constructed successfully though a facile hydrothermal reaction without adding any surfactant followed by in-situ chemical polymerization in the liquid phase. The structure and morphology of the products were characterized by means of FESEM, XRD, TEM & HRTEM, TGA and FT-IR. Gas sensing test results found that, the PANI/α-Fe{sub 2}O{sub 3} nanocomposite sensor exhibited obviously higher response, lower working temperature (180 °C), fast response and recovery speeds, good selectivity and excellent stability to acetone gas, indicating its promising application for high-performance acetone sensors. The enhanced gas sensing performances can be attributed to the factors like ultra-uniform 3D nanotube-like structure for α-Fe{sub 2}O{sub 3}, the noticeable advantages of PANI, as well as the construction of heterojunction barrier at their interface. The as-prepared PANI/α-Fe{sub 2}O{sub 3} nanocomposite may be also applied in other areas like photocatalysis, water purification, ion batteries, optical devices, and so on.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

UAS remote sensing (Autel EVO II platform): Red-green-blue (RGB) imagery and derived products (Level 0-2 data), Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using a optical red-green-blue (RGB) sensor installed on an Autel EVO II unoccupied aerial system (UAS). This package includes data from 15 flights flown over the NGEE-Arctic Council Mile Marker (MM) 71, Kougarok MM64, and Teller MM27 sites on the Seward Peninsula, Alaska USA, in July 2022. This package provides the Level 0 (raw, unprocessed) data collected by the platform and sensors, and Level 1-2 processed products including digital surface models (DSM), photo orthomosaics, canopy height models (CHM) and digital terrain models (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as image (*.jpg, *.tif), point cloud (*.laz), tabular (*.csv) and *.pdf formats. Raw images are compressed as tar.gz. The metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the data and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) 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↗

DOE OSTI2024

UAS remote sensing (Autel EVO II platform): Red-green-blue (RGB) imagery and derived products (Level 0-2 data), Seward Peninsula, Alaska, 2023

Airborne remote sensing data collected using a optical red-green-blue (RGB) sensor installed on an Autel EVO II unoccupied aerial system (UAS). This package includes data from 4 flights flown over the NGEE-Arctic Council Mile Marker (MM) 71 and Teller MM27 sites on the Seward Peninsula, Alaska USA, in July 2023. This package provides the Level 0 (raw, unprocessed) data collected by the platform and sensors, and Level 1-2 processed products including digital surface models (DSM), photo orthomosaics, canopy height models (CHM) and digital terrain models (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as image (*.jpg, *.tif), point cloud (*.laz), tabular (*.csv) and *.pdf formats. Raw images are compressed as tar.gz. The metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the data and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) 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↗

DOE OSTI2024

Optimizing Transmission of Acoustic Signals to Monitor Internal Conditions of Canisters for Dry Storage of Commercial Spent Nuclear Fuel

Safe storage of spent nuclear fuel (SNF) is critical to the nuclear fuel cycle and the future of nuclear energy. In the United States, SNF is stored primarily via two methods regulated by the U.S. Nuclear Regulatory Commission: wet storage in SNF pools and dry storage in dry cask storage systems (DCSSs). After about five years of cooling in spent fuel pools, the fuel assemblies are transferred into DCSSs, and the systems are filled with helium and sealed by welding. Deterioration of conditions inside of a DCSS is reflected in changes in the internal gas properties; this motivates the development of acoustic techniques to monitor internal gas properties, over extended storage periods, using sensors mounted on the exterior of the storage packages. However, a major challenge in collecting acoustic signals is the impedance mismatch between the steel canister shell and the gas. Only a small fraction of the ultrasonic signal can be transmitted through the gas medium. This paper documents experimental studies conducted on a full-scale canister mock-up to capture the gas-borne signals. Damping materials were pasted on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The results show that the excitation frequency plays an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight. A high signal-to-noise ratio was achieved in the measurements. Next, the acoustic impedance matching layers were introduced, and the gas signal was drastically improved compared with that using no AIM layers.

Spent nuclear fuel (SNF), Canisters, Internal cond↗

DOE OSTI2013

Pressure Sensor and Telemetry Methods for Measurement While Drilling in Geothermal Wells (Final Technical Report)

The development and demonstration in this digital telemetry project has brought SiC-based high temperature electronics to a new level of complexity and integration with the active electronic devices and the packaging materials operating at 300°C for greater than 2000 hours. Our highest level of integration is a 6x6mm die with 474 transistors with the most complex functionality to date. Advances were made in the area of device modeling and fabrication, circuit simulation and design, device testing, and packaging. The technologies developed here would help enable sensor systems in enhanced geothermal systems, as well as other applications with high temperature requirements.

15 GEOTHERMAL ENERGY↗

DOE OSTI2012

Field testing the Raman gas composition sensor for gas turbine operation

A gas composition sensor based on Raman spectroscopy using reflective metal lined capillary waveguides is tested under field conditions for feed-forward applications in gas turbine control. The capillary waveguide enables effective use of low powered lasers and rapid composition determination, for computation of required parameters to pre-adjust burner control based on incoming fuel. Tests on high pressure fuel streams show sub-second time response and better than one percent accuracy on natural gas fuel mixtures. Fuel composition and Wobbe constant values are provided at one second intervals or faster. The sensor, designed and constructed at NETL, is packaged for Class I Division 2 operations typical of gas turbine environments, and samples gas at up to 800 psig. Simultaneous determination of the hydrocarbons methane, ethane, and propane plus CO, CO2, H2O, H2, N2, and O2 are realized. The capillary waveguide permits use of miniature spectrometers and laser power of less than 100 mW. The capillary dimensions of 1 m length and 300 μm ID also enable a full sample exchange in 0.4 s or less at 5 psig pressure differential, which allows a fast response to changes in sample composition. Sensor operation under field operation conditions will be reported.

42 ENGINEERING↗

DOE OSTI2012

A modular architecture for multi-channel external cavity quantum cascade laser-based chemical sensors: a systems approach

A multi-channel laser-based chemical sensor platform is presented, in which a modular architecture allows the exchange of complete sensor channels without disruption to overall operation. Each sensor channel contains custom optical and electronics packages, which can be selected to access laser wavelengths, interaction path lengths and modulation techniques optimal for a given application or mission. Although intended primarily to accommodate mid-infrared (MIR) external cavity quantum cascade lasers (ECQCLs)and astigmatic Herriott cells, channels using visible or near infrared (NIR) lasers or other gas cell architectures can also be used, making this a truly versatile platform. Analog and digital resources have been carefully chosen to facilitate small footprint, rapid spectral scanning, ow-noise signal recovery, failsafe autonomous operation, and in-situ chemometric data analysis, storage and transmission. Results from the demonstration of a two-channel version of this platform are also presented.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

Sensor Placement Optimization using Chama

Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama is currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Large Area Picosecond Photodetector (LAPPD TM ) - Pilot production and development status

Here, we discuss performance results achieved for fully functional sealed Large Area Picosecond Photodetectors (LAPPD (TM)) in tests performed at Incom Inc., as well as independent test results reported by our early adopters. The LAPPD is a microchannel plate (MCP) based large area picosecond photodetector, capable of imaging with single-photon sensitivity at high spatial and temporal resolutions in a hermetic package. The LAPPD has an active area of 350 square centimeters in an all-glass hermetic package with a fused silica window and bottom plate and sidewalls made of borosilicate float glass. Signals are generated by a bi-alkali Na 2 KSb photocathode and amplified with a stacked chevron pair of MCPs produced by applying resistive and emissive atomic layer deposition coatings to glass capillary array (GCA) substrates. Signals are collected on RF stripline anodes applied to the bottom plates which exit the detector via pin-free hermetic seals under the side walls. LAPPD test and performance results for product produced and delivered to early adopter customers during the first half of 2018 are reviewed. These results include electron gains ≥ 7.5 x 10 6 @ 850/950 V (entry/exit MCP), low dark noise rates (22 Cts/s/cm 2 ), single photoelectron (PE) timing resolution of 64 picoseconds RMS, and single photoelectron spatial resolution along and across strips of 2.8 mm and 1.3 mm RMS respectively. Many of these devices also had very high QE photocathodes that were uniform over the full 195 mm x 195 mm window active area (LAPPD #15 QE% @ 365 nm Max/Avg/Min = 25.8/22.3 ± 3/15.7). An update is additionally provided of developments that enable capacitive signal coupling from the detector to application specific pads or stripline readout patterns deployed on printed circuit boards positioned beneath the file, outside of the vacuum package. We conclude with examples of how sensors offering picosecond timing, in diverse applications can bring transformative change to detector technology and applications in future experiments.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

National Center for Airborne Laser Mapping (NCALM) LiDAR, Imagery, and DEM data from five NGEE Arctic Sites, Seward Peninsula, Alaska, August 2021

From August 8 through August 16 of 2021, airborne remote sensing data was collected by the National Center for Airborne Laser Mapping (NCALM) in collaboration with NGEE Arctic scientists. Data was collected around five NGEE Arctic study sites on the Seward Peninsula of Alaska: Teller mm 27, Teller mm 47, Kougarok mm 64, Kougarok mm 86, and Council mm 71. A Robinson R44 II helicopter with a RIEGL VQ-580 II airborne laser scanner was used to collect the LiDAR point cloud data for each study site. A Phase One iXM-RS100F camera was integrated with the Riegl sensor to collect RGB imagery. This data package contains LiDAR point clouds (.las), RGB imagery (tif), 1 m or 50 cm Digital Elevation Models (.tif) generated from the LiDAR data, and shapefiles of the .las tiling system for each site (.shp). Two supplemental documents are also included in the package: 1) a report describing data collection details, GNSS corrections, and processing steps and 2) a document describing the LiDAR Classification used (.pdf). This survey was conducted towards the end of the summer on the Seward Peninsula, and can be paired with data collected in April of 2022 during the snow-on campaign "National Center for Airborne Laser Mapping (NCALM) LiDAR and DEM data from two NGEE Arctic Sites, Seward Peninsula, Alaska, Winter 2022" (Singhania et.al, 2023) (NGA314). 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↗

DOE OSTI2017

Vision - Vision 1.0

This package contains modules that model a visual sensor in Umbra. It is typically used to represent eyesight of characters in Umbra. This library also includes the sensor property, seeable, and an Active Denial sensor.

Hart, Brian E.↗

DOE OSTI2021

Continuous sensor suite measurements of weather, energy, soil and flux at four PumpHouse sites at the East River, Colorado

The purpose of this package is to monitor energy and water fluxes using a comprehensive set of sensors integrated on a tripod set up at 4 locations at the East River watershed in Colorado in the Upper Colorado River Basin as part of the Scientific Focus Area (SFA) research led by the Lawrence Berkeley National Lab (LBNL). This is a continuous sensor suite measurements of weather, energy, soil and flux at four East River pumphouse sites (ER-PHS1, ER-PHS2, ER-PHS3 and ER-PHS4) include: (1) soil CO2 concentration, CO2 flux and heat flux from soil surface; (2) photosynthetic active radiation flux at 1 m; (3) incoming/outcoming short and long wave radiation at 1 m, and user calculated net short and long wave radiation; (4) oxygen concentration and surface infrared temperature; (5) soil volumetric water content, temperature, and electrical conductivity at 10, 30, 60, 115 cm depth 115 cm depth; (6) soil matric potential and temperature at 30 cm depth; (7) incoming short wave radiation at 3 m height; (8) rainfall precipitation; (9) lightning strikes and strike distances; (10) wind direction and speed, maximum wind gust at 3 m; (11) air temperature, vapor pressure and barometric pressure at 3 m; (12) X _axis_level and Y_axis_level_(degree), tilt of the ATMOS 41 sensor; (13) maximum rainfall rate; (14) relative humidity sensor temperature at 3 m; (15) internally calculated vapor pressure deficit at 3 m with vapor pressure, relative humidity and sensor temperature measurements; (16) user calculated vapor pressure.These datasets contain data from Oct 11 2019 to March 20, 2020, and new datasets will be updated on annual basis. Files uploaded in Excel and Word, with copies in csv and pdf format.

54 ENVIRONMENTAL SCIENCES↗