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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A critical review and meta-analysis of energy demand, carbon footprint, and other environmental impacts from carbon fiber manufacturing

The demand for carbon fibers and carbon fiber-reinforced polymers (CFRPs) is rapidly growing due to their outstanding mechanical properties and potential to enhance sustainability, particularly for lightweighting applications. However, carbon fibers are typically produced from fossil-based feedstocks, involve energy-intensive processes, and have limited options for sustainable end-of-life management or circularity. Despite these challenges, the energy demand and lifecycle environmental implications of their production remain poorly understood. Here, we conduct a critical literature review and meta-analysis of carbon fiber manufacturing, revealing significant variations in reported energy demand, carbon footprint, and lifecycle inventory data. Our analysis makes two novel contributions. First, we identify key underlying factors driving these variations. Second, we highlight that carbon fiber, far from being a homogeneous product, has grades varying substantially in mechanical properties, end-use markets, energy intensity of manufacturing processes, and therefore environmental impacts—an aspect often underrepresented in life cycle assessments. We assert that current data are insufficient for reliably evaluating environmental impacts, posing a risk of misleading decision-making. Addressing this gap requires new lifecycle inventory datasets clearly incorporating carbon fiber heterogeneity and key influencing factors identified in this study. Additionally, we propose actionable recommendations, including a checklist, to advance sustainability in the carbon fiber sector.

CED↗
DOE OSTI2020

The influence of environmental microseismicity on detection and interpretation of small-magnitude events in a polar glacier setting

Glacial environments exhibit temporally variable microseismicity. To investigate how microseismicity influences event detection, we implement two noise-adaptive digital power detectors to process seismic data from Taylor Glacier, Antarctica. We add scaled icequake waveforms to the original data stream, run detectors on the hybrid data stream to estimate reliable detection magnitudes and compare analytical magnitudes predicted from an ice crack source model. We find that detection capability is influenced by environmental microseismicity for seismic events with source size comparable to thermal penetration depths. When event counts and minimum detectable event sizes change in the same direction (i.e. increase in event counts and minimum detectable event size), we interpret measured seismicity changes as ‘true’ seismicity changes rather than as changes in detection. Generally, one detector (two degree of freedom (2dof)) outperforms the other: it identifies more events, a more prominent summertime diurnal signal and maintains a higher detection capability. We conclude that real physical processes are responsible for the summertime diurnal inter-detector difference. One detector (3dof) identifies this process as environmental microseismicity; the other detector (2dof) identifies it as elevated waveform activity. Our analysis provides an example for minimizing detection biases and estimating source sizes when interpreting temporal seismicity patterns to better infer glacial seismogenic processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bulk Glassy GeTe 2 : A Missing Member of the Tetrahedral GeX 2 Family and a Precursor for the Next Generation of Phase-Change Materials

Vitreous germanium disulfide GeS 2 and diselenide GeSe 2 belong to canonical chalcogenide glasses extensively studied over the past half century. Their high-temperature orthorhombic polymorphs are congruently melting compounds, and the tetrahedral crystal and glass structure is largely preserved in the melt. In contrast, the ditelluride counterpart is absent in the Ge-Te phase diagram, which shows only a single compound, monotelluride GeTe. Phase-change materials based on GeTe have become a technologically important class of solids, and their structure and properties are also widely studied. Surprisingly, very scarce information is available for alloys having GeTe 2 stoichiometry. Using a fast quenching procedure in silica capillaries, high-energy X-ray diffraction, and Raman spectroscopy supported by first-principles simulations, we show that bulk glassy GeTe 2 differs substantially from the lighter GeX 2 members, revealing 46% of trigonal germanium, 31% of three-fold coordinated tellurium, and only 20% of edge-sharing tetrahedra or pyramids. The fraction of homopolar Ge-Ge bonds is low; however, the population of dominant Te-Te dimers and Te n oligomers, n <= 10, appears to be significant. The complex structural and chemical topology of g-GeTe 2 is directly related to the thermodynamic metastability of germanium ditelluride, schematically represented by the following reaction: GeTe 2 $\rightleftarrows$ GeTe + Te. Disproportionation is complete above liquidus in the temperature range of semiconductor-metal transition, and the dense metallic GeTe 2 liquid, mostly consisting of five-fold coordinated Ge species, exhibits high fluidity, strong fragility (m = 99 ± 5), and presumably a fast structural transformation rate combined with low atomic mobility in the vicinity of the glass transition temperature, favorable for reliable long-term data retention in nonvolatile memories. The observed and predicted characteristic features make GeTe 2 a promising precursor for the next generation of phase-change materials, especially coupled with additional metal doping, depolymerizing the tetrahedral interconnected glass network and accelerating (sub)nanosecond crystallization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

CHUWD-H v1.0: a comprehensive historical hourly weather database for U.S. urban energy system modeling

Reliable and continuous meteorological data are crucial for modeling the responses of energy systems and their components to weather and climate conditions, particularly in densely populated urban areas. However, existing long-term datasets often suffer from spatial and temporal gaps and inconsistencies, posing great challenges for detailed urban energy system modeling and cross-city comparison under realistic weather conditions. Here we introduce the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H) v1.0, a 23-year (1998-2020) gap-free and quality-controlled hourly weather dataset covering 550 weather station locations across all urban areas in the contiguous United States. CHUWD-H v1.0 synthesizes hourly weather observations from stations with outputs from a physics-based solar radiation model and a reanalysis dataset through a multi-step gap filling approach. A 10-fold Monte Carlo cross-validation suggests that the accuracy of this gap filling approach surpasses that of conventional gap filling methods. Designed primarily for urban energy system modeling, CHUWD-H v1.0 should also support historical urban meteorological and climate studies, including the validation and evaluation of urban climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Spin wave theory for the triaxial magnetic anisotropy 2D van der Waals antiferromagnet CrSBr

The magnetic properties of two-dimensional (2D) materials have been attracting increasing attention in recent years due to their unique behavior and possible applications in new devices. One material of great interest is the 2D van der Waals (vdW) crystal CrSBr, which exhibits antiferromagnetic (AF) order at low temperatures due to an interlayer AF exchange interaction. Here, we present a full quantum spin-wave theory for vdW crystals considering one interlayer and three intralayer exchange interactions, and triaxial magnetic anisotropy. The fits of the theoretical results to antiferromagnetic resonance measurements and inelastic neutron scattering data in CrSBr yield reliable values for the seven interaction parameters that can be used to calculate other properties of this interesting material.

2D materials↗
DOE OSTI2023

Comprehensive Analysis of Streaming and Shutdown Dose Rate Experiments at JET with ORNL Fusion Neutronics Workflows

Current experimental fusion systems and conceptual designs of fusion pilot plants (FPPs) are growing in complexity and size. Several radiation metrics are crucial to the safe operation of fusion machines, including neutron flux streaming through openings and the shutdown dose rate (SDDR). Most current designs of advanced experimental fusion systems—and the most probable candidates for FPPs—are based on the tokamak concept, which is prone to neutron streaming through the myriad openings needed for diagnostic and support systems. SDDR is caused by decay gamma rays from radionuclides that become activated by neutrons during the operation of a fusion system that use deuterium-deuterium (DD), tritium-tritium, or deuterium-tritium plasma. Because computational tools have become essential for determining these radiation metrics, they must be validated against reliable and applicable experimental data. Experiments at the Joint European Torus (JET) provide a unique source of experimental data for validating computational tools and nuclear data used to determine SDDR and neutron fluxes in streaming-dominated geometries. Here, this paper presents the comprehensive analysis of the high-performance DD JET SDDR, and streaming experiments performed using Oak Ridge National Laboratory (ORNL) fusion workflows. The computational results were compared with experimental results that consist of online SDDR measurements with ionization chambers and neutron fluence streaming measurements using thermoluminescent detectors. The ratio of calculated-to-experimental SDDR values ranges from 0.6 to 2.5, and the streaming results range from 0.5 to 8.0. Future work will include analyzing the JET 2021 DTE2 campaign alongside the integration of the Shift Monte Carlo transport code into all ORNL fusion neutronics workflows.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

SDSS-IV MaNGA: calibration of astrophysical line-widths in the Hα region using HexPak observations

ABSTRACT We have re-observed $\rm \sim$40 low-inclination, star-forming galaxies from the MaNGA survey (σ ∼ 65 km s−1) at ∼6.5 times higher spectral resolution (σ ∼ 10 km s−1) using the HexPak integral field unit on the WIYN 3.5-m telescope. The aim of these observations is to calibrate MaNGA’s instrumental resolution and to characterize turbulence in the warm interstellar medium and ionized galactic outflows. Here we report the results for the Hα region observations as they pertain to the calibration of MaNGA’s spectral resolution. Remarkably, we find that the previously reported MaNGA line-spread-function (LSF) Gaussian width is systematically underestimated by only 1 per cent. The LSF increase modestly reduces the characteristic dispersion of H ii regions-dominated spectra sampled at 1–2 kpc spatial scales from 23 to 20 km s−1 in our sample, or a 25 per cent decrease in the random-motion kinetic energy. This commensurately lowers the dispersion zeropoint in the relation between line-width and star-formation rate surface-density in galaxies sampled on the same spatial scale. This modest zero-point shift does not appear to alter the power-law slope in the relation between line-width and star-formation rate surface-density. We also show that adopting a scheme whereby corrected line-widths are computed as the square root of the median of the difference in the squared measured line width and the squared LSF Gaussian avoids biases and allows for lower signal-to-noise data to be used reliably.

Chattopadhyay, Sabyasachi↗
DOE OSTI2026

Photonuclear cross sections for the 197 Au ⁢(𝛾, 𝑝⁢𝑛)⁢ 195⁢𝑚 Pt reaction near threshold

Platinum radioisotopes are of growing interest for targeted cancer therapy and diagnostic imaging because their decay delivers highly localized radiation doses in tissue, herewith enabling precise DNA damage through Auger-electron emission. Developing production technologies that provide platinum isotopes with high specific activity is essential in radioisotope therapy. Photonuclear reactions on stable nuclei offer a viable accelerator-based route for isotope production when supported by reliable cross-section data. We report photonuclear cross-section measurements for the 197 Au(γ, pn) 195m Pt reaction at incident γ-ray energies of 27, 29, and 31 MeV using the activation method. The measurements were performed by irradiating a stack of concentric-ring gold targets with a quasi-monoenergetic γ-ray beam provided by the High Intensity Gamma-ray Source (HI γS). The induced 195m Pt activity was quantified using off-line γ-ray spectroscopy. These data provide the first experimental constraints on the 197 Au(γ, pn) 195m Pt cross section in the near-threshold region. Furthermore, the comparison of the measured excitation function to PHITS and TALYS calculations indicates that the reaction becomes measurable only near 30 MeV and that substantially higher bremsstrahlung end-point energies are required for practically meaningful production.

190 ≤ A ≤ 219↗
DOE OSTI2020

Algorithm-Based Fault Tolerance for Convolutional Neural Networks

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus on how to protect the CNN inference process against soft errors as efficiently as possible, with the following three contributions. (1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations. (2) We design a novel workflow integrating all the proposed schemes to obtain a high detection/correction ability with limited total runtime overhead. (3) We perform our evaluation using ImageNet with well-known CNN models including AlexNet, VGG-19, ResNet-18, and YOLOv2. Here, experimental results demonstrate that our implementation can handle soft errors with very limited runtime overhead (4%~8% in both error-free and error-injected situations).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Building water resilience in the face of cascading wildfire risks

Severe wildfire is altering the natural and the built environment and posing risks to environmental and societal health and well-being, including cascading impacts to water systems and built water infrastructure. Research on wildfire-resilient water systems is growing but not keeping pace with the scale and severity of wildfire impacts, despite their intensifying threat. In this study, we evaluate the state of knowledge regarding wildfire-related hazards to water systems. We propose a holistic framework to assess interactions and feedback loops between water quality, quantity, and infrastructure hazards as determinants of post-fire water availability and access. Efforts to address the evolving threat of wildfires to water systems will require more interdisciplinary research on the complex relationships shaping wildfire’s threat to water availability and access. To support this, we need reliable long-term data availability, consistent metrics, greater research in shared contexts, more extensive research beyond the burn area, and multistakeholder collaboration on wildfire risks to water systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Methods—Analyzing Electrochemical Kinetic Parameters in Deep Eutectic Solvents Using an Extended Butler-Volmer Equation

Deep eutectic solvents (DESs) are promising electrolytes for electrochemical redox reactions, which can be used in redox flow batteries (RFBs). However, in some systems like the Fe 2+/3+ redox reaction in ethaline, traditional Tafel-based kinetic analysis generates unreasonable kinetic parameters (i.e., large anodic/cathodic charge transfer coefficients ( α a , α c ) along with low exchange current densities ( i 0 )). This hinders a comprehensive kinetic and kinetic mechanism study. Here, we perform a detailed investigation of the Tafel analysis using a series of synthetic rotating disk electrode (RDE) data. We find the Tafel analysis only works well when i 0 < 0.57 i lim (limiting current density) in our scenario and leads to abnormal kinetic values once i 0 exceeds this limiting value. Thus, we propose an extended Butler-Volmer (ex-BV) analysis based on modern non-linear fitting techniques to obtain the actual kinetic parameters for such systems. The results show that this method fits the RDE data closely and generates reliable α a , α c and i 0 values, demonstrating that it is a good replacement for traditional Tafel analysis for kinetic studies in high-viscosity electrolytes such as DES systems.

Electrochemistry↗
DOE OSTI2021

Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems (Final Report)

This is the end of project report for the DOE Project DE-FE-0027844 “Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems”. The objectives of the project were to develop several technologies required for the mass adoption of Solid Oxide Fuel Cell systems for medium to large power generation applications. The key achievements of this project were: 1) Development of fuel cells with complete internal fuel reforming capability. 2) Fuel cell power scale up with production of cells with larger active areas. 3) Assembly of an integrated 5 kW modular stack platform capable of installation into medium to large power systems. 4) Demonstration of a 10 kW Fuel Cell Power System utilizing the integrated 5 kW modular stack platform. 5) Cost modelling to show system cost of $1,500/kW are achievable at production volumes. 6) Development of cathode air poison getters for reduced degradation and extended fuel cell life. 7) Assessment of fuel poison on anode degradation, particularly poisons frequently produced from the process of gasification of coal. The Cummins led Voice of Customer (VOC) work identified several significant markets for natural gas powered fuel cell systems, where the end customer is interested in reducing energy consumption and emissions, or increasing reliability of electrical supply. Data centers and truck Auxiliary Power Units (APU) were highlighted as 2 key future markets for fuel cell systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Design of the New DARHT X-ray and Neutron Shielding and Measured Results

The DARHT Weather Enclosure (WE) project provided an excellent opportunity to redesign and rebuild some of DARHT systems without introducing interruptions to normal operations. Multiple improvements were made to the facility to improve data quality and operational reliability.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Solar, Wind, and Load Forecasting Dataset for MISO, NYISO, and SPP Balancing Areas

The Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) program is an initiative intended to foster "a fundamental shift in grid management rooted in an understanding of asset risk and system risk" (ARPA-E 2020). Launched by the Advanced Research Projects Agency-Energy (ARPA-E), the program supports efforts to incorporate uncertainty in electric power decision making. In support of PERFORM, the National Renewable Energy Laboratory (NREL) has produced a set of time-coincident forecasts of solar, wind, and load profiles. As part of Phase I of the PERFORM effort, NREL created a dataset that consists of one year of time-coincident load, wind, and solar actuals and probabilistic forecasts based on data from the Electric Reliability Council of Texas (ERCOT) (Bryce et al. 2023). In Phase II, NREL developed similar datasets for three other U.S. Independent System Operators (ISO): the Midcontinent Independent System Operator (MISO), the New York Independent System Operator (NYISO), and the Southwest Power Pool (SPP).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

National Electric Vehicle Infrastructure Formula Program (NEVI) Brief for State Public Utility Commissions

The National Electric Vehicle Infrastructure (NEVI) Formula Program (NEVI program) is a funding opportunity for all 50 U.S. states, the District of Columbia, and Puerto Rico, established by the Bipartisan Infrastructure Law (BIL). The NEVI program instructs states to “strategically deploy electric vehicle (EV) charging infrastructure and to establish an interconnected network to facilitate data collection, access, and reliability.” The NEVI program allocates more than $\$$5 billion to states from Fiscal Year (FY) 2022 to 2026 on a formula basis. NEVI funds are available to private entities, including utilities, to build EV Supply Equipment (EVSE)1 and associated EV grid infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Consistent $\overline{ν}$ evaluation for minor U isotopes with $\tt{CGMF}$

Following several successful prompt $\overline{ν}$ evaluations using $\tt{CGMF}$, including consistent evaluations for minor Pu isotopes, we detail in this report our efforts to perform a consistent $\overline{ν}$ evaluation for minor U isotopes during FY25. Although we have not yet produced a finalized evaluation, we present the progress that we have made towards such an evaluation for 232,233,234,236,237,239 U prompt $\overline{ν}$. Our milestone explicitly calls out evaluations for 233 U, 234 U, and 236 U, however, to better constrain the model with reliable experimental $\overline{ν}$ data, we also include 235 U and 238 U in the evaluation procedure. Then, we additionally produce evaluations for 232 U, 237 U and 239 U $\overline{ν}$ as a byproduct. Elsewhere, we will report our efforts on a stand-alone 233 U $\overline{ν}$ evaluation. This report is organized in the following manner. In Sec. 2, we briefly outline the updates to CGMF that were needed to be able to calculate all of these minor U fission reactions. The experimental data overview is given in Sec. 3. The evaluation methodology and results are presented in Secs. 4 and 5, respectively. Finally, we conclude and outline work for FY26 in Sec. 6.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗