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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Heterogeneous multiphase flow properties of volcanic rocks and implications for noble gas transport from underground nuclear explosions

Of interest to the Underground Nuclear Explosion Signatures Experiment are patterns and timing of explosion-generated noble gases that reach the land surface. The impact of potentially simultaneous flow of water and gas on noble gas transport in heterogeneous fractured rock is a current scientific knowledge gap. This article presents field and laboratory data to constrain and justify a triple continua conceptual model with multimodal multiphase fluid flow constitutive equations that represents host rock matrix, natural fractures, and induced fractures from past underground nuclear explosions (UNEs) at Aqueduct and Pahute Mesas, Nevada National Security Site, Nevada, USA. Capillary pressure from mercury intrusion and direct air–water measurements on volcanic tuff core samples exhibit extreme spatial heterogeneity (i.e., variation over multiple orders of magnitude). Petrographic observations indicate that heterogeneity derives from multimodal pore structures in ash-flow tuff components and post-depositional alteration processes. Comparisons of pre- and post-UNE samples reveal different pore size distributions that are due in part to microfractures. Capillary pressure relationships require a multimodal van Genuchten (VG) constitutive model to best fit the data. Relative permeability estimations based on unimodal VG fits to capillary pressure can be different from those based on bimodal VG fits, implying the choice of unimodal vs. bimodal fits may greatly affect flow and transport predictions of noble gas signatures. The range in measured capillary pressure and predicted relative permeability curves for a given lithology and between lithologies highlights the need for future modeling to consider spatially distributed properties.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗
DOE OSTI2023

River Geomorphology Affects Biogeochemical Responses to Hydrologic Events in a Large River Ecosystem

Shifts in the frequency and intensity of high discharge events due to climate change may have important consequences for the hydrology and biogeochemistry of rivers. However, our understanding of event-scale biogeochemical dynamics in large rivers lags that of small streams. To fill this gap, we used high-frequency sensor data collected during four consecutive summers from a main channel and backwater site of the Upper Mississippi River. We identified high discharge events and calculated event concentration-discharge responses for both physical-chemical (nitrate, turbidity, and fluorescent dissolved organic matter) and biological (chlorophyll-a and cyanobacteria) constituents using metrics of hysteresis and slope. We found a range of responses across events, particularly for nitrate. Although fluorescent dissolved organic matter (FDOM) and turbidity exhibited more consistent responses across events, contrasting hysteresis metrics indicated that FDOM was flushed to the river from more distant sources than turbidity. Biological responses (chlorophyll a and cyanobacteria) differed more between sites than physical and chemical constituents. Lastly, we found that the event characteristics best explaining concentration responses differed between sites, with event magnitude more frequently related to responses in the main channel, and antecedent wetness conditions associated with response variation in the backwater. Furthermore, our results indicate that event responses in large rivers are distinct across the diverse habitats and biogeochemical components of a large floodplain river, which has implications for local and downstream ecosystems as the climate shifts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hayward Fault Creep From Coda Wave Interferometry

Understanding creep is essential for assessing seismic hazards along the creeping Hayward Fault, which threatens the San Francisco area. Current 1-D monitoring methods, like creepmeters and theodolites, provide limited measurements with low spatial resolution. InSAR offers higher 2-D spatial sampling but lower temporal resolution. We introduce a novel method using coda wave interferometry (CWI) to monitor creep in 2-D with high resolution. By calculating changes in ambient seismic noise travel times, we relate these measurements to creep offsets, bridging the gap between existing methods. Applied to seismic data from 2021 around the Hayward Fault, our CWI-derived offsets aligned with creepmeter measurements, revealing continuous and episodic creep. A 2-D regionalization identified a spatial boundary between these regimes, corroborating previous findings. This approach could enhance seismic monitoring and improve understanding of fault mechanics and earthquake risks.

Luckie, Thomas William [Sandia National Laboratori↗
DOE OSTI2023

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States.

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MW dc ) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.

14 SOLAR ENERGY↗
DOE OSTI2021

Resolving buried optoelectronic features in metal halide perovskites via modulation spectroscopy studies

As research on both bulk and low dimensional metal halide perovskites (MHPs) continues to grow, the tools necessary to gain insights into their exotic and highly convoluted optoelectronic features must also expand. Electroabsorption (EA) is a modulation spectroscopy technique that is exceptionally valuable at deconvoluting overlapping spectral features as well as discerning between different electronic environments in semiconductors. This review outlines the critical shortcomings of traditional optical spectroscopy in determining essential properties such as the bandgap and exciton binding energy. In this report we provide a brief overview of the working principles of EA, focusing on the differences between bound and unbound charge carriers that result in unique behavior when interacting with an applied electric field. We discuss key studies highlighting the unique characterization available when coupling EA spectroscopy with traditional steady-state optical tools to investigate interesting and complex optical features in MHPs. We conclude that EA is a crucial tool for pushing the boundaries of our understanding of the optoelectronic properties of MHP thin films. When other optical characterization methods fall short in the richness of data provided, EA can bridge the knowledge gap to provide a complete optoelectronic characterization of novel MHPs and deepen the understanding of current structures and compositions as a function of their structural and morphological dimensionality.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Spurious solar-wind effects on acceleration noise in LISA Pathfinder

Spurious solar-wind effects are a potential noise source in future Laser Interferometer Space Antenna (LISA) measurements. One noise coupling mechanism is constrained by estimating solar-wind effects on acceleration noise in LISA Pathfinder (LPF). While LISA is designed for drag-free differential measurement, predicting the realistic impact both bounds the operational environment and assesses whether LISA could provide serendipitous space-weather observations. Data from NASA's Advanced Composition Explorer (ACE), situated at the L1 Lagrange point, serves as a reliable source of solar-wind data. The data sets are compared over the 114 d time period from 1 March 2016 to 23 June 2016. This period gives the longest readily-available open data set, without interference from other commissioning activities. To evaluate space weather effects, the data from both satellites are formatted, gap-filled/interpolated, and fast-Fourier transformed for amplitude spectral density and coherence comparisons. Solar wind effects are not seen in a coherence plot between LPF and ACE; modest coherence in the planned LISA observational frequency band can be attributed to chance. This result indicates that measurable correlation due to solar-wind acceleration noise over 3 month timescales will be a negligible noise source. LISA is unlikely to inform solar wind measurements routinely. Another source of noise from the Sun, solar radiation pressure, is estimated to impart greater acceleration noise, but has yet to be analyzed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our models’ performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model’s performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

Kapoor, Gunnika [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

Data-driven analysis to understand GPU hardware resource usage of optimizations

With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money in expensive accelerators. Although related work develops optimizations to improve application performance, none studies how these optimizations impact hardware resource usage or average GPU utilization. Here, this paper takes a data-driven analysis approach in addressing this gap by (1) characterizing how hardware resource usage affects device utilization, execution time, or both, (2) presenting a multiobjective metric to identify important application-device interactions that can be optimized to improve device utilization and application performance jointly, (3) studying hardware resource usage behaviors of several optimizations for a benchmark application, and finally (4) identifying optimization opportunities for several scientific proxy applications based on their hardware resource usage behaviors. Furthermore, we demonstrate the applicability of our methodology by applying the identified optimizations to a proxy application, which improves the execution time, device utilization, and power consumption by up to 29.6%, 5.3% and 26.5% respectively.

Computer science↗
DOE OSTI2024

Deep learning enhanced super-resolution x-ray fluorescence microscopy by a dual-branch network

X-ray fluorescence (XRF) microscopy is a powerful technique for quantifying the distribution of elements in complex materials, which makes it a crucial imaging technique across a wide range of disciplines in physical and biological sciences, including chemistry, materials science, microbiology, and geosciences. However, as a scanning microscopy technique, the spatial resolution of XRF imaging is inherently constrained by the x-ray probe profile and scanning step size. Here we propose a dual-branch machine learning (ML) model, which can extract scale-variant features and bypass abundant low-frequency information separately, to enhance the spatial resolution of the XRF images by mitigating the effects of blurring from the probe profile. The model is trained by simulated natural images, and a two-stage training strategy is used to overcome the domain gap between the natural images and experimental data. The tomography reconstruction from enhanced XRF projections shows an improvement in resolution by a scale factor of four and reveals distinct internal features invisible in low-resolution XRF within a battery sample. This study offers a promising approach for obtaining high-resolution XRF imaging from its low-resolution version, paving the way for future investigations in a broader range of disciplines and materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

System Integration of Rationally Designed Dilute Alloy Catalysts for Energy-Efficient Electrochemical CO 2 -To-Fuel Conversion

Energy effiecient electrochemical conversion of CO 2 to high-demand chemicals and transportation fuels using renewable solar and wind energy is a key technology needed for a high-productivity, low-carbon future. However, the development of scalable, low-cost, active, selective, and stable electrocatalysts remains a key challenge that needs to be overcome to enable high-volume conversion of CO 2 to feedstock chemicals for the chemical industry. In previous work, Lawrence Livermore National Laboratory (LLNL) has developed a rational design platform for dilute alloy transition metal electrocatalysts that promise to make electrochemical CO 2 conversion more energy efficient and selective. In this project, we worked with our industrial partners, Twelve and TotalEnergies, to improve scale up, integration, and stability of LLNL’s dilute alloy catalyst technology into an industry-relevant zero gap electrolyzer platform. Through virtual experiments and data analysis, we designed efficient and cost-effective copper-based catalysts. The catalyst was specifically designed to streamline the slowest and most energy-intensive step of the electrochemical chemical transformation of CO 2 to multi-carbon products – that is making the carbon-carbon bond by dimerization of the reaction intermediate carbon monoxide - resulting in up to 10% improvement in energy efficiency for C 2 products while simultaneously increasing the selectivity towards C 2 products. We tested two different scalable catalyst coating technologies and down-selected magnetron sputtering as the technique that provided the best control over catalyst loading, composition, and morphology. Using this technology, we successfully demonstrated integration of our dilute alloy catalysts into a 100 cm 2 electrolyzer platform with Faradaic efficiencies for ethylene production reaching 40% at a current density of 200 mA/cm 2 . We also developed the technology to integrate a well-defined nanoscale porosity by depositing alloy compositions that were compatible with dealloying, that is, selective leaching of an alloy component to generate nanoscale porosity. We observed that integration of the dealloying-derived nanoporosity improved catalyst performance and stability by leading to a more hydrophobic catalyst/anion exchange membrane interface. Unsolved problems that still need to be addressed are corrosion of the Cu catalyst -specifically if the used catalyst is exposed to air - as well as long term stability do to salt formation/deposition and flooding of the catalyst/electrolyzer flow channels, especially at higher current densities. As we only worked on optimization of catalyst composition, coating thickness, and morphology, further performance optimization will require a system level approach that includes optimization of electrolyzer design and membrane technology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Miscanthus carbon flux data for SABR and UIEF

This eddy covariance (EC) gap-filled and partitioned flux dataset for Miscanthus from the SABR and UIEF farms was collected to evaluate carbon fluxes during the establishment phase of Miscanthus. The goal of this research is to quantify net carbon exchange using the EC technique at two sites and assess how carbon uptake varies with different planting densities. The dataset is contains two .csv files.

Aslan Sungur, Guler (Rojda) [Department of Agronom↗
DOE OSTI2021

FLUXNET-CH 4 : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Methane (CH 4 ) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH 4 emissions due to quasi-continuous and high-temporal-resolution CH 4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH 4 Version 1.0, the first open-source global dataset of CH 4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH 4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH 4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH 4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20°S to 20°N) the spring onset of elevated CH 4 emissions starts 3 d earlier, and the CH 4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH 4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH 4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH 4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH 4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH 4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH 4 peak). FLUXNET-CH 4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH 4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH 4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

High temperature - low mass solar blanket

Interconnect materials and designs for use with ultrathin silicon solar cells are discussed, as well as the results of an investigation of the applicability of parallel-gap resistance welding for interconnecting these cells. Data relating contact pull strength and cell electrical degradation to variations in welding parameters such as time, voltage and pressure are presented. Methods for bonding ultrathin cells to flexible substances and for bonding thin (75 micrometers) covers to these cells are described. Also, factors influencing fabrication yield and approaches for increasing yield are discussed. The results of vacuum thermal cycling and thermal soak tests on prototype ultrathin cell test coupons and one solar module blanket are presented.

Mesch, H. G.↗
NASA NTRS1981

Synthetic battery cycling

The use of interactive computer graphics is suggested as an aid in battery system development. Mathematical representations of simplistic but fully representative functions of many electrochemical concepts of current practical interest will permit battery level charge and discharge phenomena to be analyzed in a qualitative manner prior to the assembly and testing of actual hardware. This technique is a useful addition to the variety of tools available to the battery system designer as he bridges the gap between interesting single cell life test data and reliable energy storage subsystems.

Thaller, L. H.↗