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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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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Additive manufacturing of carbon fiber-reinforced thermoset composites via in-situ thermal curing

Fiber-reinforced polymer composites are lightweight structural materials widely used in the transportation and energy industries. Current approaches for the manufacture of composites require expensive tooling and long, energy-intensive processing, resulting in a high cost of manufacturing, limited design complexity, and low fabrication rates. Here, we report rapid, scalable, and energy-efficient additive manufacturing of fiber-reinforced thermoset composites, while eliminating the need for tooling or molds. Use of a thermoresponsive thermoset resin as the matrix of composites and localized, remote heating of carbon fiber reinforcements via photothermal conversion enables rapid, in-situ curing of composites without further post-processing. Rapid curing and phase transformation of the matrix thermoset, from a liquid or viscous resin to a rigid polymer, immediately upon deposition by a robotic platform, allows for the high-fidelity, freeform manufacturing of discontinuous and continuous fiber-reinforced composites without using sacrificial support materials. This method is applicable to a variety of industries and will enable rapid and scalable manufacture of composite parts and tooling as well as on-demand repair of composite structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A flexible test facility for liquid xenon detector development

As liquid xenon time projection chambers scale to ever-larger sizes, so too do the engineering challenges they pose. Here, we describe a large, flexible, multipurpose test facility capable of supporting the development of a number of key aspects of liquid xenon detector systems. Example applications of this facility include characterization of large-area light and charge sensor arrays, tests of xenon purification techniques and materials compatibility, and investigations into high-voltage phenomena. This facility uses an automated and remotely monitored cryo-cooling system based on immersion of the test chamber in a liquid bath rather than conductive coupling, leading to advantages in temperature and pressure stability, as well as increasing required response times in the case of cooling-power loss. Design advantages, operational procedures, and performance of the facility are described, as well as five examples of liquid xenon test chambers that use the facility.

Dark Matter detectors↗
DOE OSTI2022

Annual Summary Report for the Remote-Handled Low-Level Waste Disposal Facility—FY2021

This Fiscal Year (FY) 2021 annual summary report (ASR) documents the continued adequacy of the performance assessment (PA), the composite analysis (CA) and associated operating disposal-authorization statement (ODAS) technical basis documents for the Remote Handled (RH) Low-Level Waste (LLW) Disposal Facility at Idaho National Laboratory. Annual review of the adequacy of the PA and CA for the RHLLW Disposal Facility ensures that conclusions of the analyses remain valid in accordance with requirements of Department of Energy (DOE) Order 435.1, “Radioactive Waste Management.” In FY 2021, no significant operational changes or other activities occurred that would cause deviation from the assumptions in the PA and CA pertaining to disposal geometry, verification of waste characteristics, tracking disposal inventories against total limits, facility closure design, or institutional controls. Sixteen total waste-canister shipments were received at the RHLLW Disposal Facility, and 16 total waste canisters were emplaced.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA)

With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in-situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from June 21 to July 20, 2017 and January 15 to February 18, 2018 in the Azores. The flights were designed to maximize the synergy between in-situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Lastly, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bridging gaps in permafrost-shrub understanding

Permafrost, permanently frozen ground which underlies much of the Arctic and sub-Arctic, is influenced by deciduous shrubs, yet our understanding of permafrost-shrub interactions is limited. This is largely due to a lack of widespread, long-term, high-quality observations across Arctic and sub-Arctic systems, which are difficult to study due to their remote locations. Shrubs are rapidly expanding in many areas of Arctic tundra and can either amplify or inhibit permafrost thaw, making it crucial that we can understand and predict permafrost-shrub interactions. As we have limited time and resources to make observations, we must design our field campaigns to be as impactful as possible. Below we outline the current state of knowledge and give suggestions about how we can maximize our fieldwork’s impact. From field studies conducted around the Arctic, we know that shrubs generally have a cooling effect on permafrost in the summer, largely caused by the shrub canopy shading the soil. However, summertime cooling can be counteracted by a warming effect in winter, caused by the trapping of blowing snow by shrubs, which leads to increased snow depths. In addition to these commonly observed effects, both observations and models show that other local factors such as climate, soil, snow, and disturbances can interact to cause contrasting dominant effects on permafrost temperatures. For example, shrubs that protrude through the snowpack have been observed to cool permafrost in winter via thermal bridging between the air above the snow and the soil below. However, at other research sites, protruding shrubs lower the surface albedo and cause earlier snowmelt in spring, amplifying permafrost thaw by exposing the soil to thawing earlier. Protruding shrubs have also been observed to induce snow melt in autumn, creating ice layers at the snow surface that prevent further snow accumulation and eliminate the ability of shrubs to trap blowing snow, thereby reducing the winter warming effect. While we understand the physical processes that lead to these effects, we cannot explain why we observe different effects between sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Large Game Distribution Camera Study at Los Alamos National Laboratory

The unique habitats at the Laboratory support a diverse community of wildlife. The habitat types that include Pinon Juniper, Ponderosa Pine, and Mixed-conifer forests each provide different resources important for a variety of species. Wildlife monitoring for large game animals can be challenging because they avoid interactions with humans, and prefer undeveloped areas. This study was conducted to analyze the distribution of predators and other game animals seasonally across the Laboratory. This research informs decisions regarding wildlife management recommendations for conservation and protection. This study utilized game cameras to remotely monitor wildlife. As camera technology has advanced, game cameras have become adequate for documenting individuals, and are commonly used in wildlife studies on predation, abundance, occupancy, diversity, and endangered species detection. Twenty cameras were placed across the LANL landscape using a modified systematic sampling design. They were deployed in February 2018 and retrieved in January 2019, thus each camera sampled for one full year. All images were reviewed and the species, number, age, and sex of all animals in the photo were recorded. Here we present data for the most abundant species encountered: deer, elk, coyote, bear, bobcat, mountain lion, and fox respectively. The data were summarized by species and season. Future work will include occupancy modelling for each species. This will allow the development of heat maps showing species distributions across the Laboratory.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Typical Neutron Emission Spectra for Multi-Mission Radioisotope Thermoelectric Generator Fuel

The Dragonfly rotorcraft currently being designed by the Johns Hopkins Applied Physics Laboratory (APL) is a mission destined to explore, via autonomous flight, the Saturnian moon of Titan and currently scheduled to launch in 2027. This largest moon of Saturn contains a thick, dense atmosphere, that when coupled with the remote distance to the Sun, requires the use of a radioisotope power system (RPS). The multi-mission radioisotope thermoelectric generator (MMRTG) fueled at Idaho National Laboratory is currently the only flight-certified RPS still in production within the Department of Energy complex, thus, an MMRTG was chosen for the Dragonfly mission.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

ASNF Engineering Scale Drying Experiment Acceptance Criteria for Fabricated Items

This document specifies the acceptance criteria for items fabricated by Holtec International under the scope of INL Contract # 215725, consistent with the parameters described in INL/EXT-19-56017, Aluminum-clad Spent Nuclear Fuel Engineering Scale Drying Experiment Design. Verification is confirmed with initials (or the need for remediation may be identified) along with the date of inspection for each requirement. Verification may be performed by independent proxy (University of South Carolina participant, under INL Contract Number 207046) or by an INL employee, remotely by camera images.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Biconjugate Impedance Matching Control of the HERO WEC: Preprint

Effective control strategies are essential for maximizing the power output of wave energy converters (WECs). In this study, we explore the methodology of port-based modelling which can be used for bi-conjugate impedance matching, a type of reactive control that involves maximizing power transfer by setting the load (PTO) impedance to be the complex conjugate of the source impedance (WEC). Despite its foundational role in electrical engineering, this method has seen limited application in wave energy and has not yet been demonstrated in wave-powered desalination systems. We represent our complex multidisciplinary system as an equivalent circuit to explore frequency-based relations. The HERO WEC, a small-scale, modular point absorber (PA) designed for reverse osmosis desalination is used as a case study to demonstrate port-based modelling for control applications. Improving the performance of wave-powered water desalination systems could play a critical role in addressing freshwater scarcity for coastal communities, especially those in remote or disaster-prone regions.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Design of the Electromagnetic Particle Injector (EPI) for Tokamak Deployment

Both predicting and controlling disruptions are critical and urgent issues for ITER as some disruptions with a short warning time may be unavoidable. For these cases, a rapid response disruption mitigation system (DMS) referred to as the electromagnetic particle injector (EPI) is being developed. The primary advantages of the EPI are its fast response time and high velocity, which have been demonstrated in offline experiments (Raman et al., 2021). The EPI is capable of accelerating a metallic sabot electromagnetically using a rail gun to the required velocities ( > 2 km/s) within 2 ms. Two high-field racetrack magnets able to generate fields over 2 T are positioned above and below the rails to permit high velocity at low rail currents, a requirement to minimize electrode erosion. At the end of the acceleration phase, a sabot capture mechanism retains the spent sabot inside the vacuum chamber that houses the EPI. At this point, it releases well-defined microspheres, or a shell pellet, of a radiative payload into the disrupting plasma. A remotely operated sabot loading system positioned behind the injector contains several pre-equipped sabots that can be loaded by an operator from the tokamak control room. The injector is interfaced to the tokamak through a guide tube attached to the front of the EPI vacuum chamber. Finally, the advantages of the EPI system over other DMSs under consideration are described in conjunction with the details of an EPI system designed for near-term test on an existing large tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Nanosecond Gated CMOS Camera (NSGCC) ICD (Rev. 2.1)

The Ultra-Fast X-ray Imager (UXI) program is an ongoing effort at Sandia National Laboratories to create high speed, multi-frame, time-gated Read Out Integrated Circuits (ROICs), and a corresponding suite of photodetectors to image a wide variety of High Energy Density (HED) physics experiments on both Sandia’s Z-Machine and LLNL’s National Ignition Facility (NIF). Several cameras have been designed over the length of the program; one of the most recent is the Icarus, which is an improvement on past imagers (Furi and Hippogriff). A second sensor that can be connected is the Daedalus sensor. The Icarus is a 1024 × 512-pixel array with either 25 μm or 8 µm spatial resolution containing four frames of storage per pixel and has improved timing generation and distribution components while achieved 2 ns time gating. The Daedalus sensor is also a 1024 x 512-pixel array with 25 µm special resolution containing three frames of storage per pixel and has an increased set of features for a wider variety of applications from interlacing of rows in each frame to configurability of all shutters. See Section 14 for details regarding the Icarus implementation of the firmware and Section 15 for details regarding the Daedalus implementation. Due to the unique test environments UXI sensors are targeted for, full custom hardware was required to physically mount an Icarus or Daedalus sensor, manage its various functions, and read out pixel data for transfer to a host computer. Beyond experimental functionality, the hardware also needed to accommodate sensor characterization requirements. Lawrence Livermore National Laboratory’s ‘Version 4.0 Board’ was the result of these efforts. It mounts all the components required to fully utilize the Icarus and Daedalus sensors including analog to digital converters to convert pixel data and various system voltages to digital form for readout and analysis, DAC channels for remote configuration of critical bias voltages, static random-access memories to buffer pixel data, RS422 and Gigabit Ethernet communications for remote access, and an FPGA to tie these components together. This document describes the FPGA electrical interfaces in detail to allow the reader a greater understanding of the device, and to facilitate implementation of custom software to control and manage it. The Version 4.0 Board is a continuation of the Nano-second Gated CMOS hardware design that retains much of the functionality of the Version 1.0 Board while adding features including a DAC instead of digital potentiometers, as well as sensors for pressure and radiation. The Version 4.0 board is intended for applications requiring tight form-factor enclosures. It is composed of two stacking boards; one holds the FPGA and regulators to power the various components of the board while the other contains the mating connector to the sensor, image-readoff ADCs, the DAC, and other components.

42 ENGINEERING↗
DOE OSTI2023

Annual Summary Report for the Remote-Handled Low-Level Waste Disposal Facility—FY 2022

This Fiscal Year (FY) 2022 annual summary report (ASR) documents the continued adequacy of the performance assessment (PA), the composite analysis (CA) and associated operating disposal-authorization statement (ODAS) technical-basis documents for the Remote-Handled (RH) Low-Level Waste (LLW) Disposal Facility at Idaho National Laboratory. Annual review of the adequacy of the PA and CA for the RHLLW Disposal Facility ensures that conclusions of the analyses remain valid in accordance with requirements of Department of Energy (DOE) Order 435.1, “Radioactive Waste Management.” In FY 2022, no significant operational changes or other activities occurred that would cause deviation from the assumptions in the PA and CA pertaining to disposal geometry, verification of waste characteristics, tracking disposal inventories against total limits, facility-closure design, or institutional controls. Fifteen waste canister shipments were received at the RHLLW Disposal Facility, and 15 waste canisters were emplaced in disposal vaults.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station’s capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station’s use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station’s open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.

42 ENGINEERING↗
DOE OSTI2023

DAQs Quick Start Guide

DAQs is a Windows® computer app designed to communicate with the Advanced List Mode Module (ALMM), MC-15, and instrumentation based off these platforms. The ALMM communicates with DAQS over the Ethernet using TCP/IP. If you register the IPv4 address of the ALMM on the local network the ALMM can be operated remotely. However, if the ALMM is not connected to the local network then the computer running DAQs will need to be connected directly to the ALMM or an additional Ethernet adapter will need to be installed on the acquisition computer. The IPv4 address can be changed in the rei.ini file located on the µSD card. Please see the appendices for more information on the rei.ini file. This document is intended to be a quick start guide on how to get an ALMM connected to a computer and how to start a data acquisition. DAQs has much more capability than is presented in this document.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies

Because natural gas emits less carbon than other fossil fuels, it holds promise as a green energy transition fuel. However, the overall carbon footprint of natural gas is significantly elevated by methane emissions that occur during its production and transmission (Cusworth et al. 2022). Methane “super-emitters,” while comprising only about 1% of sites, are responsible for the majority of oil- and gas-sourced methane emissions, making their detection and mitigation critical in reducing the climate impact of natural gas and in meeting national and global sustainability goals (Sherwin et al. 2024). Yet, despite advancements in detection, significant uncertainties remain regarding the size, frequency, and duration distributions of methane emissions (e.g., Frankenberg et al. 2016, Cusworth et al. 2022, Chen, Sherwin et al. 2022, Conrad et al. 2023, Johnson et al. 2023, Sherwin et al. 2024) underscoring the need for comprehensive emissions inventories segmented by basin across the US. Airborne surveys are well-suited for collecting data to build these comprehensive, basin-level inventories because they allow for extensive spatial coverage, and have the spatial resolution, and the sensitivity to pinpoint individual methane sources. As remote sensing technologies enable rapid basin-scale surveys, it is imperative to establish scientifically and statistically robust standards to generate reliable and actionable emissions inventories. Recent work has shown that differences in airborne sampling strategies, detection technologies, and analysis can lead to large differences between survey conclusions if not correctly accounted for (Chen et al. 2024). This elevates the importance of incorporating proper sampling and analysis techniques when designing a methane emissions monitoring campaign to produce accurate results and facilitate cross-study comparisons. In this paper, we describe a survey strategy designed using the latest conclusions from the literature to align results from different aerial surveys. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions. We present results from a Department of Energy-funded project that deployed this survey strategy in two understudied oil and gas- producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma.

03 NATURAL GAS↗
DOE OSTI2024

Annual Summary Report for the Remote-Handled Low-Level Waste Disposal Facility—FY 2023

This Fiscal Year (FY) 2023 annual summary report (ASR) documents the continued adequacy of the performance assessment (PA), the composite analysis (CA) and associated operating disposal-authorization statement (ODAS) technical-basis documents for the Remote-Handled (RH) Low-Level Waste (LLW) Disposal Facility at Idaho National Laboratory. Annual review of the adequacy of the PA and CA for the RHLLW Disposal Facility ensures that conclusions of the analyses remain valid in accordance with requirements of Department of Energy (DOE) Order 435.1, “Radioactive Waste Management.” In FY 2023, no significant operational changes or other activities occurred that would cause deviation from the assumptions in the PA and CA pertaining to disposal geometry, verification of waste characteristics, tracking disposal inventories against total limits, facility-closure design, or institutional controls. Eleven waste canister shipments were received at the RHLLW Disposal Facility, and ten waste canisters were emplaced in disposal vaults.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗