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DOE OSTI2024

Performance data for an axial-flow turbine with passive adaptive blades

To support the development of simulation tools for passive adaptive turbine rotors, an experimental data set from a laboratory-scale axial-flow turbine with passive adaptive blades is provided. The 0.45-meter diameter turbine was tested in the Alice C. Tyler Flume at the University of Washington. Blade and rotor loads were measured at 1 kHz using six-axis force/torque sensors while deflection and twist at the blade tip were tracked using a high-speed camera. This data submission includes a technical report, the raw and processed experimental data, and a README file explaining the file/folder structure and where to find the processing/plotting scripts used to produce the figures in the technical report.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Real-time kinematic (RTK) Drone-collected Data and Processed Models of Port Arthur Coastal Neighborhood and Pleasure Island Golf Course, June 2024

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset will support researchers' decision-making processes under uncertainties.

2D mapping↗

NASA NTRS1988

Progress in utilization of a mobile laboratory for making storm electricity measurements

A mobile atmospheric science laboratory has been used to intercept and track storms on the Great Plains region of the U.S., with the intention of combining the data obtained with those from Doppler and conventional radars, NASA U-2 aircraft overflights, balloon soundings, and fixed-base storm electricity measurements. The mobile lab has proven to be valuable in the gathering of ground truth verifications for the two commercially operated lightning ground-strike locating systems. Data acquisition has recently been expanded by means of mobile ballooning before and during storms.

Rust, W. David↗

DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.

McSpadden, Helen↗

DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

McSpadden, Helen↗

NASA NTRS2000

BOREAS TGB-3 CH4 and CO2 Chamber Flux Data over NSA Upland Sites

The BOReal Ecosystem-Atmosphere Study Trace Gas Biogeochemistry (BOREAS TGB-3) team collected methane and carbon dioxide (CH4, CO2) chamber flux measurements at the Northern Study Area (NSA) Fen, Old Black Spruce (OBS), Young Jack Pine (YJP), and auxiliary sites along Gillam Road and the 1989 burn site. Gas samples were extracted from chambers and analyzed at the NSA lab facility approximately every 7 days during May to September 1994 and June to October 1996. The data are provided in tabular ASCII files.

Savage, Kathleen↗

NASA NTRS2016

A Multi-Center Space Data System Prototype Based on CCSDS Standards

Deep space missions beyond earth orbit will require new methods of data communications in order to compensate for increasing Radio Frequency (RF) propagation delay. The Consultative Committee for Space Data Systems (CCSDS) standard protocols Spacecraft Monitor & Control (SM&C), Asynchronous Message Service (AMS), and Delay/Disruption Tolerant Networking (DTN) provide such a method. However, the maturity level of this protocol stack is insufficient for mission inclusion at this time. This Space Data System prototype is intended to provide experience which will raise the Technical Readiness Level (TRL) of this protocol set. In order to reduce costs, future missions can take advantage of these standard protocols, which will result in increased interoperability between control centers. This prototype demonstrates these capabilities by implementing a realistic space data system in which telemetry is published to control center applications at the Jet Propulsion Lab (JPL), the Marshall Space Flight Center (MSFC), and the Johnson Space Center (JSC). Reverse publishing paths for commanding from each control center are also implemented. The target vehicle consists of realistic flight computer hardware running Core Flight Software (CFS) in the integrated Power, Avionics, and Power (iPAS) Pathfinder Lab at JSC. This prototype demonstrates a potential upgrade path for future Deep Space Network (DSN) modification, in which the automatic error recovery and communication gap compensation capabilities of DTN would be exploited. In addition, SM&C provides architectural flexibility by allowing new service providers and consumers to be added efficiently anywhere in the network using the common interface provided by SM&C's Message Abstraction Layer (MAL). In FY 2015, this space data system was enhanced by adding telerobotic operations capability provided by the Robot API Delegate (RAPID) family of protocols developed at NASA. RAPID is one of several candidates for consideration and inclusion in a new international standard being developed by the CCSDS Telerobotic Operations Working Group. Software gateways for the purpose of interfacing RAPID messages with the existing SM&C based infrastructure were developed. Telerobotic monitor, control, and bridge applications were written in the RAPID framework, which were then tailored to the NAO telerobotic test article hardware, a product of Aldebaran Robotics.

Rich, Thomas M.↗

DOE OSTI2022

MARMOK-Oscillating Water Column: WEC Tank Testing Campaign Data (November 2022)

The main objective of this project is advancing towards the commercial viability of IDOM's floating Oscillating Water Column (OWC) technology by performing a detailed WEC design to fit PacWave-South site conditions. This includes tank testing campaign data conducted by IDOM at TEXAS A&M OTRC wave basin. This submission has the tank wave test data, summary of the project, and details on specifics of the project.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Cooperative Education

M-3 Focused Experiments is a group at Los Alamos National Lab (LANL) and is under the Dynamic Experiments Division. Their work surrounds the diagnostics and study of explosives and their interaction with materials. The data collected is used to better understand the erratic nature of explosions and high velocity projectiles. Through better comprehension of this science, the overall national security will be improved while innovations are made along the way. Despite the strong drive for research and development, the lab holds safety above all else and constantly updates procedures to protect LANL employees and their environment. At any time, there are multiple projects going on at once within the team and I am tasked with assisting the Firing Site Leaders and various research diagnosticians in any way possible with the setup, testing, and execution of data collection for each experiment. Such data includes velocity, fragmentation characteristics, and the spread of energy across the material to name a few. During my summer employment with the lab, my main goal is to maintain a questioning attitude and learn as much possible about the research and development side of science and engineering. Throughout the duration of the Co-op, many experiments will take place at specially designed firing sites which is where I will be spending most of my time.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Low global warming potential (GWP) refrigerant supermarket refrigeration system modeling and its application

As an environmentally friendly low global warming potential (GWP) refrigerant, Carbon dioxide (CO 2 ) has continuously gained popularity and research attention as alternative refrigerant for supermarket refrigeration system. In this paper, to fulfill the increasing need of accurate Low-GWP supermarket refrigeration models for development of supervisory level control and optimization strategies, a high fidelity model is developed for CO 2 transcritical supermarket refrigeration system which includes compressor rack of low temperature (LT) compressors and medium temperature (MT) compressors, air-cooled gas cooler, evaporator, expansion valves and other auxiliary equipment. A resistance-capacity model structure is proposed to simulate the display cases. Semi-thermodynamic models are proposed to estimate reciprocating compressors volumetric efficiency and power consumption. Furthermore the zone modeling approach is used for evaporator simulation, and air-cooled gas cooler is modeled with distributed modeling method. The expansion valve simulation is based on orifice flow model. To calibrate these models, both manufacture performance data and experimental data are used. The experiments are conducted with a full instrumental CO 2 supermarket refrigeration system installed in Oak Ridge National Lab Flexible Research Platform (FRP). The simulation model can predict the system performance, including power consumption, cooling capacity, mass flow rate, temperature, and pressure, with high accuracy (within ±4%) compared to experimental data. In addition, this developed model has been used to create the system optimum high side pressure for high side expansion valve control, and to generate wide operating range simulation data for developing the batter-equivalent commercial refrigeration system model which can be used in grid interactive control development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1990

Quantitative assessment of human motion using video motion analysis

In the study of the dynamics and kinematics of the human body, a wide variety of technologies was developed. Photogrammetric techniques are well documented and are known to provide reliable positional data from recorded images. Often these techniques are used in conjunction with cinematography and videography for analysis of planar motion, and to a lesser degree three-dimensional motion. Cinematography has been the most widely used medium for movement analysis. Excessive operating costs and the lag time required for film development coupled with recent advances in video technology have allowed video based motion analysis systems to emerge as a cost effective method of collecting and analyzing human movement. The Anthropometric and Biomechanics Lab at Johnson Space Center utilizes the video based Ariel Performance Analysis System to develop data on shirt-sleeved and space-suited human performance in order to plan efficient on orbit intravehicular and extravehicular activities. The system is described.

Probe, John D.↗

DOE OSTI2024

Machine Learning and Data Science to Advance Laboratory Earthquake Prediction and Illuminate the Mechanics of Precursors to Failure

Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work has provided a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. The work conducted under our project has dramatically expanded these efforts. We have developed an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. Our work has accelerated the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. Our work has leveraged data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that is available to everyone, our work has advanced the fundamental understanding of seismic forecasting, earthquake physics, and fault rheology

58 GEOSCIENCES↗

NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗

NASA NTRS1973

Development of flight experiment work performance and workstation interface requirements, part 1. Technical report and appendices A through G

A skill requirement definition method was applied to the problem of determining, at an early stage in system/mission definition, the skills required of on-orbit crew personnel whose activities will be related to the conduct or support of earth-orbital research. The experiment data base was selected from proposed experiments in NASA's earth orbital research and application investigation program as related to space shuttle missions, specifically those being considered for Sortie Lab. Concepts for two integrated workstation consoles for Sortie Lab experiment operations were developed, one each for earth observations and materials sciences payloads, utilizing a common supporting subsystems core console. A comprehensive data base of crew functions, operating environments, task dependencies, task-skills and occupational skills applicable to a representative cross section of earth orbital research experiments is presented. All data has been coded alphanumerically to permit efficient, low cost exercise and application of the data through automatic data processing in the future.

Hatterick, R. G.↗

DOE OSTI2025

Bridging the Gap for Powering Data Centers

The rapid expansion of data centers, primarily driven by artificial intelligence, is outpacing the adaptability of the U.S. electric grid. This report, developed by Idaho National Laboratory (INL) , presents a gap analysis of some of the infrastructure challenges associated with large-scale data center deployment. Drawing from a national workshop hosted by INL in October of 2025, the report synthesizes stakeholder insights, survey data, and technical discussions to identify critical barriers and research needs. Key findings highlight the growing preference for behind-the-meter generation, the perceived inadequacy of legacy interconnection processes, and the urgent need for improved coordination between utilities, regulators, and data center developers. Environmental concerns such as water use and noise pollution, as well as economic constraints like equipment lead times and cost allocation, are also explored. The report outlines national lab capabilities in modeling, simulation, and technical assistance, and proposes targeted R&D priorities to support resilient, scalable, and efficient integration of data centers into the grid.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Performance and Commissioning of the BigBite Timing Hodoscope for Nucleon Form Factor Measurements at Jefferson Lab

The BigBite Timing Hodoscope detector is the primary subject of this thesis. The Super BigBite Spectrometer is a Jefferson Lab Hall A Collaboration project that has and will continue to measure nucleon electromagnetic form factors. This spectrometer includes the Timing Hodoscope which provides high resolution particle timing data for scattered electrons in the electron arm of BigBite. The Timing Hodoscope utilizes 90, 25 × 25 × 600 mm3 scintillator bars stacked on top of each other to form a single detector plane, and these bars are connected to 180 photo-multiplier tubes via light guides. Particles collide with the scintillating material creating a shower of optical photons and these particle events in the bars are collected to generate signals that are readout by the data acquisition (DAQ) electronics. NINO ASIC amplifier-discriminator cards output signals from the photo-multiplier tubes into analogue and logic signals, which are sent to analogue-to-digital (ADC) and time-to-digital (TDC) converter data acquisition readout modules. This data is then used for analysis of the detector. The focus of this thesis is the construction, commissioning, calibration, and performance of the BigBite Timing Hodoscope before and during the first of five nucleon electromagnetic form factor experiments at Jefferson Lab Hall A. Before the neutron magnetic form factor, G n M, experiment, cosmic ray data was collected during commissioning to confirm proper operation of the Timing Hodoscope electronics by observing the ADC and TDC data. Commissioning studies for charge normalization, gain matching, and other ADC and TDC detector data variables were performed before moving the detector into Hall A. Following installation in Hall A, several calibration studies were implemented to fine-tune the detector in preparation for use in the experiment. The calibration studies included analysis of timing cuts, TDC alignment, the time-walk effect, time difference offsets, and scintillator velocity corrections. Once the Timing Hodoscope was well-calibrated, data-taking during the experiment commenced and the beam-on-target data was used to characterize the Timing Hodoscope performance during the G n M experiment run-time. The performance analysis included studies observing energy deposit, cluster size, rates, accidentals, pile-up, tracking efficiency, position resolution, and time resolution. After application of physics cuts to ensure a data set comprised of particle tracks corresponding to elastic electrons, which is the main data of interest for measurement of G n M, the Timing Hodoscope is shown on average across all kinematic settings to have a >98% tracking efficiency, a position resolution of 4-6 cm in the non-dispersive plane and 1.5-2 cm in the dispersive plane, and a time resolution of 500-750 ps. These performance results are compared to a GEANT4 based performance simulation of the BigBite Timing Hodoscope for reference, showing to what degree the measured performance values match those taken from the simulation.

Marinaro, Ralph↗

DOE OSTI2022

Results from Laboratory and Field Study of Thin Triple Pane Windows

Heat transfer through windows accounts for a significant percentage of a building’s energy use and adds substantially to the peak cooling load of a home. In recent years, improvements in glass manufacturing have enabled the use of a very thin central pane of glass similar to a cell phone screen to produce a thin triple-pane window, for finished insulated glass units (IGUs) with an overall thickness similar to standard double-pane windows. Because this highly insulating “thin triple” glass product can be incorporated into almost any existing window frame and can be fabricated at a modest added cost, the U.S. Department of Energy sponsored laboratory and field demonstration testing of thin triple-pane windows to validate thermal performance and installation requirements in real-life field settings. Thin triple pane windows were evaluated at the PNNL Lab Homes, a matched pair of manufactured houses located on PNNL’s campus in Richland, Washington and also at 16 different field study sites around the country. The experimental results include a comparison of heating, ventilation, and air-conditioning (HVAC) energy usage, condensation potential, occupant comfort, sound infiltration, and thermal performance. Field study data will be gathered through June of 2022; preliminary results are being shared in this paper. The lab houses are identical except that the reference house had standard double pane windows with assembly U-0.66 and the test house had thin triple pane windows with assembly U-0.19. Across the experimental test days, the daily HVAC savings ranged from 0.2 to 18.7 kWh (3%–18%) for the heating season and from 2.5 to 8.0 kWh (23%–41%) for the cooling season. The higher thermal performance of the thin triple-pane windows also reduced the condensation potential on the interior surface during winter months and provided more even distribution of temperatures throughout the home in comparison to the baseline. In addition to the added thermal performance, the thin triple-pane windows demonstrated significant acoustic benefits, reducing sound infiltration by 8 dB to 10 dB. For the field test portion of the project thin triple pane insulated glass units were produced by two different manufacturers, and then installed without modification into the ½” IGU pockets of the standard double pane frames of four other manufacturers. Field tests performed on existing homes in Washington, Montana, Colorado, and New York compared thin triple pane retrofits to original window conditions (before and after). Field tests at new construction sites in Minnesota, Michigan, and New York compared thin triple pane windows to commercially available solutions such as double pane or traditional triple pane (with a standard-thickness center pane). Field test work is ongoing, but preliminary results appear to follow the sound, surface temperature, and energy improvement results from the Lab Homes comparison. Additionally, reports from builders and installers indicate that thin triples require almost no added time or effort to install and look nearly identical to other windows, indicating the possibility of offering next-level performance with a product that requires very little modification to current production or installation practices, long considered a major barrier to technology uptake in the construction market.

energy efficiency, home retrofit, Windows, window ↗

DOE OSTI2020

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗