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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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DOE OSTI2026

Dataset for Cruz-O'Byrne et al (2026): "Divergent biogeochemical responses in upland coastal forest soils to repeated flooding and shifts in water chemistry"

Hydrologic disturbances from accelerated sea-level rise and the increasing frequency and intensity of storms and tidal flooding are altering biogeochemical processes in upland coastal forests, transforming these ecosystems into wetlands. However, the initial effects of flooding on belowground biogeochemistry and the mechanisms driving greenhouse gas dynamics and soil organic matter stability during the early stages of this transition remain poorly understood. This dataset presents the results of a mesocosm experiment conducted in a controlled, highly instrumented laboratory environment, in which freshwater and brackish water pulses were applied to intact soil monoliths from a temperate upland coastal forest to examine how floodwater chemistry influences soil biogeochemistry and organo-mineral interactions. All data files are plain-text CSV (comma-separated value), and no special software is required to read them. Details about the content of each file are available in the document “Dataset_readme”. The dataset consists of the following data: • rcruzobyrne_moisture: Soil volumetric water content (VWC) • rcruzobyrne_GHG: Headspace greenhouse gas (GHG) concentration and fluxes • rcruzobyrne_methane_isotopes: Headspace methane isotope signature • rcruzobyrne_porewater: Porewater chemistry • rcruzobyrne_CDOM: Porewater colored dissolved organic matter (CDOM) • rcruzobyrne_FTIR: Soil Fourier-transform infrared (FTIR) spectroscopy Details of the experimental setup, data collection, and data analysis are provided in the manuscript by Cruz-O’Byrne et al (2026) Divergent biogeochemical responses in upland coastal forest soils to repeated flooding and shifts in water chemistry. Biogeochemistry. https://doi.org/10.1007/s10533-026-01340-0

EARTH SCIENCE > ATMOSPHERE > GREENHOUSE GAS↗
DOE OSTI2026

CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation

This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). Field observations of LAI were collected within 72 hours of airborne data collection by the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. LAI measurements were collected using the LICOR LAI-2200C Plant Canopy Analyzer following protocols outlined in the instrument manual (LI-COR 2019). Sampling targeted four distinct vegetation types: meadows, shrubs, trees, and aspen forest understory. We have archived data separately by site type because different field methods were used for each. At meadow sites, measurements were made at the four corners of 1m x 1m plots, with the instrument moving inward toward the center of the plot. At shrub sites, we measured the canopies of individual shrubs. At tree sites, we made measurements within a 10m x 10m subplot centered around a focal tree, with 30 observations taken on a regular grid. At aspen understory sites, we measured overstory trees following the tree protocol and understory herbaceous vegetation following the meadow protocol. All measurements included above-canopy (A) and below-canopy (B) readings, with specific protocols for scattering correction measurements in direct-sun conditions. Data were processed using the R package `rlai` (Worsham 2025). This package includes functions to calculate LAI, gap fraction, apparent clumping factor (Ω), scattering correction, and other canopy metrics. Package contents: Full file descriptions appear in ‘flmd.csv’. Files named according to the convention ‘lai_*_summary_data_cleaned.csv’ contain summary values of LAI, apparent clumping factor (Ωapp), and scattering correction factors for each site. These are the analysis-ready products that most data users will work with. Files named ‘lai_*_metadata_cleaned.csv’ contain additional site-level observations made during field collection. We have also archived intermediate and supplementary data for users who wish to check our processing approach or apply alternative methods. ‘raw_lai_2200C.zip’ contains the raw files as read from the LI-COR instrument, with no processing applied, in TXT format. The zip archive contains subdirectories by site type, which are further subdivided by sampling area. Filenames correspond to the sampling site number. ‘intermediate_results.zip’ contains detailed output from the processing routines, in JSON format. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘scattering_correction_logs.zip’ contains logfiles from the implementation of Kobayashi et al.'s (2013) scattering correction algorithm. The logfiles report values of several parameters at each iteration of the algorithm, as the model converges toward a stable solution. They are intended for users who want to verify scattering correction performance. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘spot_checks.csv’ reports LAI and other values for a small number of files processed with LI-COR FV2200 software (LI-COR 2013) using the same control parameters as in our R-based approach. Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). All zip files can be expanded with common archive utilities. TXT, CSV, and JSON files can be ingested into R or Python computing environments or read in common text editor utilities. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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. * Todorov and Worsham are co–first authors.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2020

5G Enabled Energy Innovation: Advanced Wireless Networks for Science (Workshop Report)

Rapidly expanding, new telecommunications infrastructure based on 5G technologies will disrupt and transform how we design, build, operate, and optimize scientific infrastructure and the experiments and services enabled by that infrastructure, from continental-scale sensor networks to centralized scientific user facilities, from intelligent Internet of Things devices to supercomputers. Concurrently, 5G will introduce, or exacerbate, challenges related to protecting infrastructure and associated scientific data as well as to fully leveraging opportunities related to expanded infrastructure scale and complexity. The U.S. Department of Energy (DOE) Office of Science operates scientific infrastructure, supporting some of the nation’s most advanced intellectual discoveries, spanning the country and including 30 world-class user facilities from supercomputers to accelerators. Along with field experiments and remote observatories, every aspect of DOE’s scientific enterprise will be affected by 5G, which amounts to a complete renovation of the underpinnings of the nation’s information infrastructure. In this report we explore the scientific opportunities and new research challenges associated with 5G, ranging from scalability to heterogeneity to cybersecurity. The rapid commercial deployment of 5G opens the opportunity to rethink and reinvent DOE’s scientific infrastructure and experimentation, from intelligent sensor networks at unprecedented scales to a digital continuum of cyberinfrastructure spanning low-power sensors, high-performance computing embedded within and at the edge of the network, and DOE’s large-scale user instrument and computing facilities. New programming paradigms, workflow and data frameworks, and AI-based system design, operation, and autonomous adaptation and optimization will be necessary in order to exploit these new opportunities. Field deployments and centralized scientific instruments can also be revolutionized, moving (without traditional performance penalties) from wired to wireless connectivity for data and control systems, improving flexibility, and opening new sensing modalities, including the use of the 5G electromagnetic spectrum itself as an environmental probe. For DOE science, in contrast to commercial 5G applications and settings, devices will be deployed in extreme environments such as cryogenically cooled instrument control systems and in remote settings with harsh conditions, requiring the design of new materials for RF communication and edge processing to operate in these regimes. Concurrently, 5G infrastructure comprises both hardware and sophisticated software systems - currently closed and proprietary. The cybersecurity challenges to 5G-empowered reinvention mirror the complexity and variety of new 5G features, from virtualization to private network slices to ubiquitous access. Research is also needed in order to accelerate the development of secure and open 5G software infrastructure, reducing reliance on hardware and software produced outside the United States and providing the transparency and rigorous evaluation and testing afforded through open software. Twelve broad research thrusts are laid out in four chapters, with a companion fifth chapter (and three additional research thrusts) underscoring the needs and opportunities for an aggressive testbed program co-designed by networking experts and scientists involved in the 15 research thrusts. The urgency of undertaking this research is fueled by a global, accelerating deployment of new telecommunications infrastructure that is designed for entertainment and commercial applications - barely scratching the surface of what 5G can do to extend U.S. leadership in scientific discovery.

42 ENGINEERING↗
NASA NTRS1976

Mission Control Center (MCC) System Specification for the Shuttle Orbital Flight Test (OFT) Timeframe

System specifications to be used by the mission control center (MCC) for the shuttle orbital flight test (OFT) time frame were described. The three support systems discussed are the communication interface system (CIS), the data computation complex (DCC), and the display and control system (DCS), all of which may interfere with, and share processing facilities with other applications processing supporting current MCC programs. The MCC shall provide centralized control of the space shuttle OFT from launch through orbital flight, entry, and landing until the Orbiter comes to a stop on the runway. This control shall include the functions of vehicle management in the area of hardware configuration (verification), flight planning, communication and instrumentation configuration management, trajectory, software and consumables, payloads management, flight safety, and verification of test conditions/environment.

Source record↗
NASA NTRS2003

NASA Tech Briefs, February 2003

opics covered include: Integrated Electrode Arrays for Neuro-Prosthetic Implants; Eroding Potentiometers; Common/Dependent-Pressure-Vessel Nickel-Hydrogen Batteries; 120-GHz HEMT Oscillator With Surface-Wave-Assisted Antenna; 80-GHz MMIC HEMT Voltage-Controlled Oscillator; High-Energy-Density Capacitors; Microscale Thermal-Transpiration Gas Pump; Instrument for Measuring Temperature of Water; Improved Measurement of Coherence in Presence of Instrument Noise; Compact Instruments Measure Helium-Leak Rates; Irreversible Entropy Production in Two-Phase Mixing Layers; Subsonic and Supersonic Effects in Bose-Einstein Condensate; Nanolaminate Mirrors With "Piston" Figure-Control Actuators; Mixed Conducting Electrodes for Better AMTEC Cells; Process for Encapsulating Protein Crystals; Lightweight, Self-Deployable Wheels; Grease-Resistant O Rings for Joints in Solid Rocket Motors; LabVIEW Serial Driver Software for an Electronic Load; Software Computes Tape-Casting Parameters; Software for Tracking Costs of Mars Projects; Software for Replicating Data Between X.500 and LDAP Directories; The Technical Work Plan Tracking Tool; Improved Multiple-DOF SAW Piezoelectric Motors; Propulsion Flight-Test Fixture; Mechanical Amplifier for a Piezoelectric Transducer; Swell Sleeves for Testing Explosive Devices; Linear Back-Drive Differentials; Miniature Inchworm Actuators Fabricated by Use of LIGA; Using ERF Devices to Control Deployments of Space Structures; High-Temperature Switched-Reluctance Electric Motor; System for Centering a Turbofan in a Nacelle During Tests; Fabricating Composite-Material Structures Containing SMA Ribbons; Optimal Feedback Control of Thermal Networks; Artifacts for Calibration of Submicron Width Measurements; Navigating a Mobile Robot Across Terrain Using Fuzzy Logic; Designing Facilities for Collaborative Operations; and Quantitating Iron in Serum Ferritin by Use of ICP-MS.

Source record↗
NASA NTRS2014

Technology Estimating 2: A Process to Determine the Cost and Schedule of Space Technology Research and Development

As a leader in space technology research and development, NASA is continuing in the development of the Technology Estimating process, initiated in 2012, for estimating the cost and schedule of low maturity technology research and development, where the Technology Readiness Level is less than TRL 6. NASA' s Technology Roadmap areas consist of 14 technology areas. The focus of this continuing Technology Estimating effort included four Technology Areas (TA): TA3 Space Power and Energy Storage, TA4 Robotics, TA8 Instruments, and TA12 Materials, to confine the research to the most abundant data pool. This research report continues the development of technology estimating efforts completed during 2013-2014, and addresses the refinement of parameters selected and recommended for use in the estimating process, where the parameters developed are applicable to Cost Estimating Relationships (CERs) used in the parametric cost estimating analysis. This research addresses the architecture for administration of the Technology Cost and Scheduling Estimating tool, the parameters suggested for computer software adjunct to any technology area, and the identification of gaps in the Technology Estimating process.

Cole, Stuart K.↗
NASA NTRS2010

Automation of Cassini Support Imaging Uplink Command Development

"Support imaging" is imagery requested by other Cassini science teams to aid in the interpretation of their data. The generation of the spacecraft command sequences for these images is performed by the Cassini Instrument Operations Team. The process initially established for doing this was very labor-intensive, tedious and prone to human error. Team management recognized this process as one that could easily benefit from automation. Team members were tasked to document the existing manual process, develop a plan and strategy to automate the process, implement the plan and strategy, test and validate the new automated process, and deliver the new software tools and documentation to Flight Operations for use during the Cassini extended mission. In addition to the goals of higher efficiency and lower risk in the processing of support imaging requests, an effort was made to maximize adaptability of the process to accommodate uplink procedure changes and the potential addition of new capabilities outside the scope of the initial effort.

Ly-Hollins, Lisa↗
NASA NTRS2020

AMPR CAMP2Ex Calibrated and Quality-Controlled Dataset Level 2B, Revision B

Data were acquired by the Advanced Microwave Precipitation Radiometer (AMPR) during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign in August-October of 2019. These files include the Level 2B calibrated, corrected, and geo-referenced brightness temperature for the four AMPR-observed frequencies (10, 19, 37, 85 GHz). These data are archived in a self-describing, Climate and Forecasting (CF) 1.6-compliant Version 4 Network Common Data Format (netCDF4) format.Python software has been developed for reading, plotting, and providing some additional analysis capabilities. This software is available from: https://github.com/nasa/pyampr.The AMPR instrument is explained in more detail here: https://weather.msfc.nasa.gov/ampr/.These data have been determined to be viable for publishable scientific research, and alsoshould be useful for generating quicklooks or understanding what happened during a flight. Note: AMPRis not expected to provide useful data during significant aircraft maneuvers

Timothy Lang↗
NASA NTRSDate not supplied

Development of an Inertial Sensor-based Methodology for Spacesuited Geology Task Assessments during Simulated Lunar Extravehicular Activities

Lunar surface exploration during Artemis missions will require the specific skill set of geology sampling. Apollo astronauts had extensive training and used specialized tools to collect lunar rocks, core samples, pebbles, sand, and dust. The inflexibility of the pressurized Apollo spacesuits forced sampling to be taken at a standstill posture. However, new exploration spacesuits are expected to incorporate advanced materials and joint bearings, allowing for greater mobility and a wider range of functional postures. Thus, science and exploration during Artemis missions will likely involve a variety of standing, squatting, and kneeling postures. In preparation for future lunar exploration missions, NASA provides geologic training to astronauts and other mission personnel. This professional training with a spacesuit in simulated lunar environments will enhance performance and reduce risk of injury to astronauts on the lunar surface. However, anecdotally, untrained or newly trained people wearing prototype planetary spacesuits have been observed to performing motions differently than a trained geologist would when conducting the same geology sampling tasks. Therefore, a tool for evaluating geology postures at extravehicular activity (EVA) training facilities becomes required. In this paper, we introduce a novel inertial measurement unit (IMU)-based method of geology task assessments in spacesuited conditions during simulated lunar EVAs. As a case study, two subjects (one geologist and one non-geologist) participated and donned the Mark III prototype planetary spacesuit during offloading with the spreader bar gimbal in NASA’s Active Response Gravity Offload System (ARGOS). For automated geology task assessments, the spacesuit was instrumented with three wireless IMUs (APDM Opal, OR, USA): one on the chest and one each on the left and right ankle bearings. Then subjects performed geology tasks using various tools (rake, trench, hammer chisel, scoop, and drive tube) for 45 minutes each. The chest IMU measured the torso tilt angle in the sagittal plane. We used an ensemble learning method with the ankle IMUs to discriminate between standing and kneeling activities. IMU data were processed using custom MATLAB (Mathworks, MA, USA) software. In our case study, the developed method was able to discriminate differences in standing and kneeling activity levels between subjects who were all highly experienced with spacesuited testing. Our preliminary data showed one subject maintained the constant and lower range of the upper body tilt angle while both standing and kneeling, while the other subject showed more variation of the upper body tilt angle and preferred bending the upper body rather than changing from standing to kneeling posture and vice versa. While geology experience may be a factor, these results need further investigation as suit sizing and ARGOS offloading configurations have been proven to have a significant influence on suited ARGOS tasks. Also, more subjects will be needed to complete these tasks for validation. IMU-based geology task assessments can provide useful information for geology training programs. Additionally, our IMU-based posture analysis can provide new insights into how to evaluate spacesuited geology task characteristics of astronauts during simulated lunar EVAs.

Kyoung Jae Kim↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Optimization of Microelectronic Devices for Sensor Applications

The NASA/JPL goal to reduce payload in future space missions while increasing mission capability demands miniaturization of active and passive sensors, analytical instruments and communication systems among others. Currently, typical system requirements include the detection of particular spectral lines, associated data processing, and communication of the acquired data to other systems. Advances in lithography and deposition methods result in more advanced devices for space application, while the sub-micron resolution currently available opens a vast design space. Though an experimental exploration of this widening design space-searching for optimized performance by repeated fabrication efforts-is unfeasible, it does motivate the development of reliable software design tools. These tools necessitate models based on fundamental physics and mathematics of the device to accurately model effects such as diffraction and scattering in opto-electronic devices, or bandstructure and scattering in heterostructure devices. The software tools must have convenient turn-around times and interfaces that allow effective usage. The first issue is addressed by the application of high-performance computers and the second by the development of graphical user interfaces driven by properly developed data structures. These tools can then be integrated into an optimization environment, and with the available memory capacity and computational speed of high performance parallel platforms, simulation of optimized components can proceed. In this paper, specific applications of the electromagnetic modeling of infrared filtering, as well as heterostructure device design will be presented using genetic algorithm global optimization methods.

Cwik, Tom↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

ATMOS data processing and science analysis methods

The atmospheric trace molecule spectroscopy (ATMOS) instrument, a high-speed Fourier transform spectrometer operating in the middle IR (2.2-16 microns), recorded more than 1500 solar spectra at about 0.0105/cm resolution during its first mission onboard the shuttle Challenger in the spring of 1985. These spectra were acquired during high-sun conditions for studies of the solar atmosphere and during low-sun conditions for studies of the earth's upper atmosphere. This paper describes the steps by which the telemetry data were converted into spectra suitable for analysis, the analysis software and methods developed for the atmospheric and solar studies, and the ATMOS data analysis facility.

Norton, Robert H.↗
DOE OSTI2023

Local Power Impact Experiment Design for a New Fuel Type for use in the Advanced Test Reactor

The Advanced Test Reactor (ATR), and complimentary zero-power ATR Critical (ATRC) reactor, located at Idaho National Labs (INL), are undergoing conversion from Highly Enriched Uranium (HEU) to Low Enriched Uranium (LEU). Both have a variety of testing locations that can receive large variations in flux due to its unique serpentine design, consisting of five lobes (see Figure 1). Initial criticality and power distribution throughout the core are controlled by core-external outer shim control cylinders (OSCCs). Distinct test loops allow for testing at specific temperatures, pressures, and irradiation conditions. The ATR is one of the key nuclear engineering research and testing facilities within the DOE National Laboratory Complex, and the ATRC supports its operation [1]. Currently, the Office of Material Management and Minimization (M3) within the National Nuclear Security Administration of the DOE is working to convert the remaining research reactors, including the ATR, from 93% HEU fuel to 19.75% LEU fuel (LEU) to support non-proliferation [2]. Extensive materials testing at INL and internationally has demonstrated that a high-density uranium molybdenum (U 10Mo) alloy can meet the performance requirements of the remaining high powered research reactors. However, there are many technical challenges to address before the conversion to LEU can be successful, including the accurate characterization of the reactor core physics with LEU fuel. Reactor physics safety evaluations currently use Monte Carlo for the 21st Century (MC21), a continuous-energy Monte Carlo radiation transport code [3]. Existing MC21 models of the ATR and ATRC cores have a validation basis for use in neutronics analyses with HEU fuel. The models are used to support safety analyses that include comparisons to the safety requirements for the reactors. However, the use of the LOWE element in the ATR and ATRC is not currently covered by the current model validation basis. To deploy the new fuel type, extensive computational reactor physics support is necessary to support the use of LOWE in the ATR and ATRC. Therefore, LOWE requires a rigorous validation basis, aligned with that of HEU fuel, that takes advantage of the existing software tools and processes currently used for the ATR and ATRC. The experiment to validate of the MC21 models for determining power, the Power Impact Validation Experiment, will consist of two flux runs in the ATRC, one with fully HEU loading and one with a single LOWE element. Both flux runs will be instrumented with 20 sets of azimuthal fission wires and 3 sets of axial fission wires, as shown in Figure 4. Standard flux run methodology will be used [4]. Power Impact Validation Experiment data will be compared against MC21 calculated data, both for absolute fission rate accuracy and to determine the relative change in fission rates between the two runs. The results of the Power Impact Validation Experiment and subsequent evaluations will provide the validation basis for MC21 for use with LOWE elements. Key features of the Power Impact Validation Experiment include: (1) Two flux runs to allow for LOWE perturbed measurements to be compared to already validated measurements taken from a full core of HEU fuel, (2) Optimization of instrumentation to balance analytical needs with practical considerations (e.g., limited time window to count beta particles from fission products), and (3) Standard ATRC core loading, including both driver positions and flux traps, to minimize cost while remaining representative of typical ATR core loading.

42 ENGINEERING↗
DOE OSTI2020

From smart homes to smart laboratories: connected instruments for materials science

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Doucet, Mathieu↗
NASA NTRS1975

Man-machine interactive imaging and data processing using high-speed digital mass storage

The role of vision in teleoperation has been recognized as an important element in the man-machine control loop. In most applications of remote manipulation, direct vision cannot be used. To overcome this handicap, the human operator's control capabilities are augmented by a television system. This medium provides a practical and useful link between workspace and the control station from which the operator perform his tasks. Human performance deteriorates when the images are degraded as a result of instrumental and transmission limitations. Image enhancement is used to bring out selected qualities in a picture to increase the perception of the observer. A general purpose digital computer, an extensive special purpose software system is used to perform an almost unlimited repertoire of processing operations.

Alsberg, H.↗
NASA NTRS2006

Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challenge

The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), manages the project and is responsible for flight operation; Orbital Sciences Corporation (OSC), is the spacecraft builder and is responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), is responsible for science planning and operations. As a cost-capped mission, one of Dawn's implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL's ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL's GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project's commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to fundamental systems engineering practices: decomposition of the project request into manageable requirements; integration of multiple ground disciplines and experts into a focused team effort; definition of a structured yet flexible development process; definition of an in-process risk reduction plan; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.

Ground Data System (GDS)↗
NASA NTRS1994

Galileo mission planning for Low Gain Antenna based operations

The Galileo mission operations concept is undergoing substantial redesign, necessitated by the deployment failure of the High Gain Antenna, while the spacecraft is on its way to Jupiter. The new design applies state-of-the-art technology and processes to increase the telemetry rate available through the Low Gain Antenna and to increase the information density of the telemetry. This paper describes the mission planning process being developed as part of this redesign. Principal topics include a brief description of the new mission concept and anticipated science return (these have been covered more extensively in earlier papers), identification of key drivers on the mission planning process, a description of the process and its implementation schedule, a discussion of the application of automated mission planning tool to the process, and a status report on mission planning work to date. Galileo enhancements include extensive reprogramming of on-board computers and substantial hard ware and software upgrades for the Deep Space Network (DSN). The principal mode of operation will be onboard recording of science data followed by extended playback periods. A variety of techniques will be used to compress and edit the data both before recording and during playback. A highly-compressed real-time science data stream will also be important. The telemetry rate will be increased using advanced coding techniques and advanced receivers. Galileo mission planning for orbital operations now involves partitioning of several scarce resources. Particularly difficult are division of the telemetry among the many users (eleven instruments, radio science, engineering monitoring, and navigation) and allocation of space on the tape recorder at each of the ten satellite encounters. The planning process is complicated by uncertainty in forecast performance of the DSN modifications and the non-deterministic nature of the new data compression schemes. Key mission planning steps include quantifying resource or capabilities to be allocated, prioritizing science observations and estimating resource needs for each, working inter-and intra-orbit trades of these resources among the Project elements, and planning real-time science activity. The first major mission planning activity, a high level, orbit-by-orbit allocation of resources among science objectives, has already been completed; and results are illustrated in the paper. To make efficient use of limited resources, Galileo mission planning will rely on automated mission planning tools capable of dealing with interactions among time-varying downlink capability, real-time science and engineering data transmission, and playback of recorded data. A new generic mission planning tool is being adapted for this purpose.

Gershman, R.↗