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

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Thin Film Physical Sensors for High Temperature Applications

For the advanced engines in the future, knowledge of the physical parameters of aerospace components is necessary on the test stand and in flight. NASA GRC is leveraging expertise in thin films and high temperature materials to measure hot section gas and surface temperature, heat flux and static and dynamic strain. The current challenges of instrumentation technology are to further develop systems packaging and component testing of specialized sensors, further develop instrumentation techniques on complex surfaces, improve sensor durability, and to address needs for extreme temperature applications. Applications of thin film ceramic sensors as replacement for noble metals are being investigated. Technologies demonstrated in aeronautics and space applications are outlined.

thin films↗
NASA NTRS1973

Fine pointing control, OAO-3

The fine error sensor located within the Princeton Experiment Package (PEP) is designed to aquire a star within its field of view to hold the spacecraft within plus or minus one-tenth of an arc second in pitch, and plus or minus three-arc seconds about the yaw axis. Pointing accuracy and stability tests of the fine error sensor exceeded the plus or minus 0.1 arc second requirement.

Moore, J. V.↗
DOE OSTI2019

Reduced Mode Sapphire Optical Fiber and Sensing System

This is the final technical report for the Virginia Tech Center for Photonics Technology (VT-CPT) research project entitled “Reduced Mode Sapphire Optical Fiber and Sensing System”. The objective of the five-year effort was to develop a low modal volume (LMV) single crystal sapphire fiber and demonstrate its advantages in current state-of-the-art sapphire-based temperature sensors and in novel distributed ultra-high temperature sensing systems. The project, sponsored by the Crosscutting Research Program, began in 2014 and had the original goal of developing the next generation of harsh environment sensing systems. After successfully completing the original scope of the award, the project was expanded and extended to allow for the development of harsh environment packaging and testing of a prototype sensor system. These novel fiber optic sensing technologies provide real-time, accurate, and reliable temperature measurements at distributed locations in existing coal-fueled electricity generating units to improve the overall performance, reliability, and flexibility of the nation’s coal-fired power plant fleet. In the final year (2018) of the project, prototype sensing systems were successfully deployed and operated in small commercial coal and natural gas fired boilers at the Virginia Tech Power Plant. The harsh environment quasi-distributed temperature sensing technology was advanced from a (Technology Readiness Level) TRL=1 to a TRL=7 by demonstrating the full validation of the sensing system prototype in a relevant industrial environment. The real-time, accurate and reliable monitoring of temperatures at distributed locations is expected to lower operating costs by allowing more accurate measurement of the harsh conditions inside a boiler system to better control its operation. The technologies developed by Virginia Tech’s Center for Photonics Technology will support the mission of the National Energy Technology Laboratory (NETL) to advance energy options to fuel our economy, strengthen our security, and improve the environment.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1999

Co-Operative Mission Concepts Using Biomorphic Explorers

Inspired by the immense variety of naturally curious explorers (insects, animals, and birds), their well integrated biological sensor-processor suites that are efficiently packaged in compact but highly dexterous forms, and their complex, intriguing, cooperative behaviors, this study focuses on Biomorphic Explorers and presents cooperative biomorphic planetary exploration scenarios.

cooperative behaviors↗
NASA NTRS2017

Atmospheric Correction for Hyperspectral Ocean Color Retrieval with Application to the Hyperspectral Imager for the Coastal Ocean (HICO)

The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/.

Ocean Color↗
DOE OSTI2024

Heterogeneous Integration Technologies for High-temperature, High-density, Low-profile Power Modules of Wide Bandgap Devices in Electric Drive Applications (Final Technical Report)

The goal of this project is to develop packaging technologies for making high-temperature, high-density, and low-profile wide-bandgap (WBG) power electronics modules for electric drives. These modules are aimed at enabling the DOE’s University Consortium to reach its 2025 inverter targets of ≥ 100 kW/L and ≤ 2.7 $/kW. The specific objectives are to: design and fabricate SiC half-bridge power modules with double-sided cooling and parasitic inductances < 5 nH, heat flux density > 400 W/cm 2 , and working junction temperature of 200 o C; design, fabricate, and deliver a gate driver with double-sided cooled modules for the construction of a 100 kW/L inverter at Oak Ridge National Lab; and design and prototype intelligent gate drivers with integrated current sensor and a low-profile DC-DC power supply with air-core transformer for testing power modules at 200 o C junction temperature. We followed an iterative technical approach of design, simulation, fabrication, and testing of various versions of modules, current sensors, and power supply. The state-of-the-art silicon carbide devices rated at 1.2 kV and 149 A were packaged by sintered-silver bonding on an aluminum nitride direct-bond-copper substrate for high thermal conductivity, high working temperature, and high joint reliability. Porous silver posts were used to interconnect the device’s source pads to the other direct-bond-copper substrate for low mechanical stresses, ease of manufacturing, and double-sided cooling. A current sensor based on package parasitic inductance was developed to measure switching current. A dynamic feedback scheme was developed to compensate the effect of parasitic resistance and temperature variation. A constant-current class-E dc-dc converter with air-core transformer was developed. Air-core transformer was used due to the unavailability of magnetic core at high temperatures. Gate driver and power supply were integrated with the double-side cooled, high temperature SiC power modules for testing the modules at 200 o C junction temperature. Double-pulse and continuous testing of the integrated technologies validated the design and fabrication of the three component technologies. Throughout the project, we overcame the challenge for design verification caused by low prototyping yield, which then helped train the graduate students, the future workforce, to learn the engineering know-how for low-cost manufacturing of reliable products. Below is a summary of the major accomplishments of this project: development of a prototyping process for fabricating double-side cooled (1200 V, 149 A) SiC phase-leg modules capable of working to 200 o C Tj; simulation and experimental verification of the improvement of thermo-mechanical reliability of the double-side cooled SiC phase-leg module by using rigid encapsulant; design and experimental validation of a current sensor based on package parasitic inductance and a compensation solution for eliminating the effect of parasitic resistance; design and experimental validation of a low-profile power supply with six-output air-core transformer for gate driver; functional demonstration of a SiC phase-leg module integrated with its gate driver, current sensor, and an air-core power supply at 200 o C Tj in a double-pulse switching test setup and Buck converter continuous test setup; successful completion of six PhD and two MS students who are or will work at Apple Inc., Tesla Inc., Wolfspeed Inc., Microchip Inc., Monolithic Power Systems Inc., and LG Magna Inc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

Piezoresistive stress sensors on (110) silicon wafers

Structural reliability of electronic packages has become an increasing concern for a variety of reasons including the advent of higher integrated circuit densities, power density levels, and operating temperatures. A powerful method for experimental evaluation of die stress distributions is the use of test chips incorporating integral piezoresistive sensors. In this paper, the basic equations needed for the design of stress sensors fabricated on the surface of (110) oriented silicon wafers have been presented. Several sensor rosette configurations have been explored, including the familiar three-element 0-45-90 rosette. Rosette designs have been found which minimize the necessary calibration procedures and permit more stress components to be measured. It has been established that stress sensors on the surface of (110) test chips are sensitive to four out of the six stress components at a point.

Kang, Y. L.↗
DOE OSTI2023

Soil water content, matric potential, carbon dioxide and oxygen concentrations, Oct 2018-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of soil sensor data (temperature, water content, bulk electrical conductivity, porewater dissolved oxygen and porewater dissolved carbon dioxide) in a vertical profile from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package includes: (1) soil temperature, volumetric water content and electrical conductivity at 40, 60 and 82.5 cm depth; (2) soil matric potential at 40, 60, 79 and 100 cm depth; (3) soil CO2 concentrations at 40, 60 and 82.5 cm depth; and (4) soil oxygen concentrations at 60, 82.5, 100, 135, 170 and 182 cm depth. Both the carbon dioxide and oxygen sensors are optical sensors that can measure the partial pressure of oxygen in both saturated and unsaturated conditions. Unfortunately, soil CO2 in the profile is unexpectedly high and above the sensor calibration range (0-25,000 ppm). In addition, soil CO2 sensors failed within a year of deployment, so we only report CO2 data from 2019-2020.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 4 "*data.csv" files, one for each calendar year in the dataset. Each "*data.csv" file has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding for each measured parameter. Because sensors have been added over time, not every sensor has data dating back to Oct 2018. Note that there is a data gap over winter 2019-2020 due to a power outage. While this repository currently only contains data through December 2021, the dataset will be updated as additional years are collected and processed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Secure Sensor Platform Software Utilities v.2.0

The SSP Software package allows a user to connect to a CoCIM via a Personality Programmer and: Reset the firmware of the CoCIM using the SSP Personality Programmer. The changes that can be made include things such as: Recovering from a tamper event; Resetting the initialization date and message counter; Change configuration values of the CoCIM using the SSP Seal Configuration or the RMSA Configuration File Editor programs. Configuration values that can be set will depend on what version of the CoCIM firmware is being used, but can include: The IP address of the translator with which this CoCIM (or RMSA) communicates; The number of attempts the CoCIM (or RMSA) will attempt to contact the translator; The primary CoCIM (or RMSA) channel; The secondary CoCIM (or RMSA) channel; Locations of files containing CoCIM (or RMSA) encryption keys SSPSerialDataDumper downloads a CoCIM’s stored messages to a computer connected to the CoCIM via a serial cable; SSPLogAnalyzer decrypts and authenticates messages that have been downloaded using the Serial Data Dumper program and then displays the messages values.

Hymel, Ross↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Seal the Deal

AvanTeco Corporation has developed a moisture/contaminant sensor chip. This newly developed chip rapidly responds to the presence of moisture, especially in condensed form and in presence of ionic impurities. It determines whether moisture has permeated a hermetically sealed package or a plastic encapsulated microcircuit. The sensor chip was designed as a "go/no go" chip, which is assembled with other chips in a multi-chip module to assess the risk of failure for the entire module. It has been commercialized and is sold by Revtek, Inc.

Source record↗
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↗
NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS2001

A Thin Film Multifunction Sensor for Harsh Environments

The status of work at NASA Glenn Research Center to develop a minimally intrusive integrated sensor to provide realtime measurement of strain, heat flux and flow in high temperature environments is presented in this paper. The sensor can be beneficial as a single package to characterize multiple stress and strain modes simultaneously on materials and components during engine development and validation. A major technical challenge is to take existing individual gauge designs and modify them into one integrated thin film sensor. Ultimately, the goal is to develop the ability to deposit the sensors directly onto internal engine parts or on a small thin substrate that can be attached to engine components. Several prototype sensors constructed of platinum, platinum-rhodium alloy, and alumina on constant-strain alumina beams have been built and bench-tested. The technical challenges of the design. construction, and testing are discussed. Data from the preliminary testing of the sensor array is presented. The future direction for the sensor development is discussed as well.

Wrbanek, John D.↗
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↗