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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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The generation of laser ablation depth data in the ultrafast (100ps) time regime is important for the validation of radiation hydrodynamic codes in that time regime. We present a technique using data from a velocimetry diagnostic to determine the hot electron penetration depth into a metal sample.
ABSTRACT In this work, we find empirical evidence that the scale-dependent statistical properties of solar wind and magnetohydrodynamic (MHD) turbulence can be described in terms of a family of parametric probability distribution functions (PDFs) known as Normal Inverse Gaussian (NIG). Understanding these PDFs is one of the most important goals in turbulence theory, as they are inherently connected to the intermittent properties of solar wind turbulence. We investigate the properties of PDFs of Elsasser increments based on a large statistical sample from solar wind observations and high-resolution numerical simulations of MHD turbulence. In order to measure the PDFs and their corresponding properties, three experiments are presented: fast and slow solar wind for experimental data and a simulation of reduced MHD (RMHD) turbulence. Conditional statistics on a 23-yr-long sample of WIND data near 1 au and high-resolution pseudo-spectral simulation of steadily driven RMHD turbulence on a $2048^3$ mesh are used to construct scale-dependent PDFs. The empirical PDFs are fitted to NIG distributions, which depend on four free parameters. Our analysis shows that NIG distributions accurately capture the evolution of the PDFs, with scale-dependent parameters, from large scales characterized by a Gaussian distribution, turning to exponential tails within the inertial range and stretched exponentials at dissipative scales. We also show that empirically-measured NIG parameters exhibit well-defined scaling properties that are similar across the three empirical data sets, which may be indicative of universal behaviour.
X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.
Estimating the structure of physical flow networks, such as power grids, is critical to secure delivery of energy. This article discusses statistical structure estimation in power grids in the “underexcited” regime, where a subset of internal nodes has zero injection fluctuations. Prior estimation algorithms based on nodal voltages fail for such grids as the voltage covariance matrix is not invertible. Here, we propose a novel topology learning algorithm for learning underexcited general networks. Our algorithm uses physics-informed conservation laws to first identify the zero-injection buses and their neighbors, and then estimates the remaining edges in the grid. We prove the asymptotic correctness of our algorithm for grids with nonadjacent internal zero-injection nodes. More important, we theoretically analyze our algorithm’s efficacy under noisy measurements, and determine bounds on maximum noise under which asymptotically correct recovery is guaranteed. Our approach is validated through simulations with voltage samples generated on test distribution grids with real injection data and nonlinear power flow models.
Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.
Rapid and standoff analysis of uranium is important for many applications. We present the recent advances in standoff detection of uranium using emission and laser-induced fluorescence spectroscopy in conjunction with ultrafast laser filament ablation-based solid sampling.
Requests for ultra-low-level 237 Np analyses highlight the importance of a robust chemical purification and measurement technique for Np. In FY2022, LANL is quantifying the instrumental 237 Np LoD. Need to incorporate a chemical purification procedure for TIMS-MICTE sample preparation. What is the recovery of the chemical purification procedure? What is the ultimate detection limit that can be achieved while incorporating a chemical purification procedure? We have adapted and refined the Pu TIMS-MICTE method for Np analyses. We have determined expected uncertainties for 237 Np TIMS-MICTE measurements. We have increased sensitivity through technique improvements. We have improved method reliability for Np analyses and increased throughput. Apply knowledge gained from progress made in FY2022 to further develop Np sample purification and analyses by TIMSMICTE.
Fractal coatings on various low-cost and high-temperature metallic substrates such as SS316, SS347, In800H, In740H, In625, Haynes 230, etc. were fabricated employing our patented novel electrodeposition process at various deposition potentials and deposition times, as well as chemical etching. The best combination of the materials and processing conditions were co-optimized for the reliable and repeatable fabrication of the multifunctional coatings. Surface morphologies were examined using SEM and AFM, and fractal characterization was done using a 3D optical surface profiler. Fractal dimension was used to identify successful fabrication and material parameters. The mechanical durability of the coatings was evaluated using several standardized tests, such as sand abrasion (ASTM D968) and adhesion (ASTM D3359), to fully characterize and demonstrate the mechanical durability of the fabricated surfaces. HTF immersion tests on the fractal multifunctional coatings were conducted based on a modification of the ASTM D870, which is established for static water immersion testing. To calculate the corrosion rates and descaled weight loss of the samples subjected to thermal exposure for different time durations (100 h, 300 h, 500 h, and optionally 750 h) to estimate the corrosion rate. While protecting the substrates from corrosion is an important goal, an allied goals is also to examine the post-corrosion temperature stability and properties of the molten salts themselves due to the dissolution of the constituents of the alloys and the corrosion products from the coatings that cause “fouling” of the molten salts. Fouling characteristics of the different molten salt HTFs exposed to coated and uncoated substrates were investigated. Finally, considering a prototype molten salt/sCO2 heat exchanger, cost/performance model is developed to demonstrate that the coatings on low-cost alloys such as stainless steel provide lower levelized cost of heat exchanger compared to high nickel content Ha230.
This study presents the validation of protocols for measuring ion exchange capacity (IEC) and alkaline stability of anion exchange membranes (AEMs) for low-temperature water electrolysis. While protocols are often tested within individual laboratories, their results across multiple laboratories with varying equipment, environmental conditions, and personnel qualification remain unverified. The validation involved Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), and University of Oregon (UO) using the same commercially available AEM to assess reproducibility and reliability of the protocols under diverse conditions. For the IEC protocol, results across laboratories were consistent within ±10% of the NMR-determined reference value. The alkaline stability protocol could pose greater challenges due to factors such as variations in sample collection timing, preservation methods, and analytical techniques, but consistent test results for percentage IEC loss were demonstrated across institutions. These results highlight the reliability and applicability of the protocols, emphasizing the importance of validation to ensure consistency in diverse research environments.
Bacteriophages have been widely investigated as a promising treatment of food, medical equipment, and humans colonized by antibiotic-resistant bacteria. Phages pose particular interest in combating those bacteria which form biofilms, such as the medically important human pathogen Pseudomonas aeruginosa and several plant pathogens, including P. syringae . In an undergraduate lab course, P. putida was used as the host to isolate novel anti-pseudomonal bacteriophages. Environmental samples of soil and water were collected, and purified phage isolates were obtained. After Illumina sequencing, genomes of these phages were assembled de novo and annotated. Assembled genomes were compared with known genomes in the literature and GenBank to identify taxonomic relations and to refine their functional annotations. The thirteen phages described are sipho-, myo-, and podoviruses in several families of Caudoviricetes , spanning several novel genera, with genomes ranging from 40,000 to 96,000 bp. One phage (DDSR119) is unique and is the first reported P. putida siphovirus. The remaining 12 can be clustered into four distinct groups. Six are highly related to each other and to previously described Autotranscriptaviridae phages: Waldo5, PlaquesPlease, and Laces98 all belong to the Waldovirus genus, whereas Stalingrad, Bosely, and Stamos belong to the Troedvirus genus. Zuri was previously classified as the founding member of a new genus Zurivirus within the family Schitoviridae . Ebordelon and Holyagarpour each represent different species within Zurivirus , whereas Meara is a more distantly related member of the Schitoviridae . Dolphis and Jeremy are similar enough to form a genus but have only a few distant relatives among sequenced phages and are notable for being temperate. We identified the lysis cassettes in all 13 phages, compared tail spike structures, and found auxiliary metabolic genes in several. Studies like these, which isolate and characterize infectious virions, enable the identification of novel proteins and molecular systems and also provide the raw materials for further study, evaluation, and manipulation of phage proteins and their hosts.
Statement of the problem or situation that is being addressed. Although thermal and cold neutron scattering is widely used and is critical for success in many areas of materials science and engineering, relatively low neutron fluxes severely limit applications of not only laboratory neutrons generators, but also large national neutron facilities. State-of-the-art thermal and cold neutron sources are large expensive national facilities, which serve diverse community of scientific and industrial users. The constant need to improve the instruments performance, stems from the fact that neutron methods are gaining in popularity, and becoming more and more powerful, while new neutron sources are not being constructed to keep pace with the developments and needs of the scientific community. Small research reactors at universities and National Labs, and laboratory-based neutron generators, are necessary not only for education and training, but also when samples cannot be transported to other facilities. However, the standard neutron techniques, which were developed for high-flux facilities, require much higher efficiencies to be used effectively with the low fluxes of small sources. Thus, the efficient use of neutron sources, such as with our proposed analyzer, is important for the progress and broader use of these neutron techniques. General statement of how this problem is being addressed. We propose to design and demonstrate novel diffractive optical device, which will enable very efficient residual stress neutron diffractometers. The proposed device will be a multi-foil analyzer, where each foil is constructed of focusing bent single crystals of Si. Such device will enable polychromatic residual stress neutron diffraction. At large national facilities, such as at Oak Ridge National Laboratory, these analyzers would enable very fast measurements for determining residual stress tensors, raster large samples or screen multiple samples. Commercial Applications and Other Benefits The outcome of this project would be the demonstration of commercial devices, novel neutron optical components, which could be utilized to improve the performance of existing instruments or build novel neutron scattering instruments at DOE neutron facilities and commercial laboratory neutron sources. These new devices will widen the scope of research conducted using neutrons and enable measurements not feasible at present. Summary for Members of Congress Thermal and cold neutron beams are a powerful materials science probe, which provide unique information about the structure of matter. The proposed innovations expand the reach of neutron-based investigations to new materials and industries by enabling new instrumentation capabilities, thereby greatly enhancing and expanding the role of small, laboratory-based neutron instrumentation, and improving education and training of neutron users.
Water-oil displacement is an important process that occurs in a shale matrix after hydraulic fracturing and in water-based enhanced oil recovery. Current understanding of this displacement process is limited because of the complicated pore structure and surface properties in shale. In this work, this process and its controlling factors are investigated through a comparative study of three shale samples that have different types of pore systems and wettability. Here, an integrated method of imbibition and multiscale imaging was applied, and a modified oleic tracer that can better represent oil flow was used in imbibition testing and micro-computed tomography (CT) imaging. Scanning electron microscope (SEM) pore characterization was then performed under high magnification with guidance from the micro-CT images showing the changes caused by oil or water imbibition. New insights were obtained on the importance of both wettability and pore size effect on oil recovery and the distribution of residual oil after water-oil displacement. Connectivity of pores with different wettability is also discussed based on 3D analysis and SEM pore characterization. Collectively, these new findings improve the understanding of the complicated process of water-oil displacement and the role of influencing factors. Important implications for improved oil recovery strategy in shale are discussed for different types of reservoir rocks. The integrated imaging and imbibition technique provides a new path for further investigation of improved oil recovery in shale.
Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.
Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.
Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO 2 ), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we (1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; (2) evaluate if sensors can distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and (3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO 2 (pH data that are presented throughout are reported on the total scale (pH T ) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35–55%, at both realistic and highly elevated alkalinity input levels. We also highlight the limitations of current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO 2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.
Abstract Thin‐ply woven composites exhibit exceptional mechanical performance by delaying the onset of damage, making them suitable for high‐performance structural applications. This study outlines the impact of manufacturing techniques (vacuum bagging and hot pressing), ply count, and fiber waviness on the mechanical properties of aromatic thermosetting copolyester/carbon‐fiber (ATSP/CF) composites. The analysis encompasses several different ply counts, evaluating their effects on the in‐plane elastic modulus (0°–90°) and tensile strength. Results demonstrate that reducing the waviness (or crimp) ratio significantly enhances the in‐plane elastic modulus, with improvements up to 188%, highlighting the importance of fiber alignment. Vacuum bagging produced ply thicknesses from 128 to 58 μm for 1–36 plies, with in‐plane elastic moduli from 30.3 to 87.2 GPa, and 4‐ and 8‐ply samples showed approximately 40% higher stiffness than those made by hot pressing. ATSP resin with high thermal stability (up to 400°C) and recyclability was used. Furthermore, this study highlights the microstructural enhancements achieved through vacuum bagging, as revealed by scanning electron and optical microscopy, and micro‐computed tomography imaging. This paper provides insights into damage mitigation and achieving enhanced mechanical properties by controlling fiber waviness using vacuum bagging and higher ply count in thin‐ply ATSP/CF composites. Highlights Crimp ratio reduction enhances elastic in‐plane elastic modulus up to 188%. Vacuum bagging increases modulus over the hot pressing manufacturing method by 40%. Lowering thickness‐per‐ply increases failure strain from 0.8% to 1.2%. Explored the performance of a novel ATSP matrix with high recyclability and thermal stability.
Microwave-zeolite interactions during direct non-oxidative reactions of methane were studied using three H-ZSM-5 zeolites with different concentrations of Bronsted acid sites. The formation of C 2 and aromatic hydrocarbons increased with increasing surface acidity. When compared to conventional heating, the microwave reaction produced less carbon with higher thermal stability. The increase in diffraction shifts and the decrease in its surface area of microwave-reacted zeolite suggested that the carbon started depositing inside the micropores rather than on the external surfaces. Inhomogeneous carbon deposits with filamentous carbon whiskers were observed under microwave compared to homogeneous spherical carbon deposits on the conventionally reacted sample. The dielectric properties and Raman analysis suggested that the graphitic nature and the thermal stability of deposited carbon increased with zeolite surface acidity under microwave. We believe that this is a very important study for designing microwave active zeolite-based catalysts with a low affinity towards carbon formation.
Here, we have investigated mechanochemical reactions with the calcium silicate wollastonite to probe potential mechanisms of sequestration and long-term storage of CO 2 as mineral carbonates in cement pastes. Wollastonite, CaSiO 3 , was milled under ambient and 13 C enriched CO 2 atmospheres. Milling induced a structure change from monoclinic wollastonite-2M to triclinic-1T, consistent with high pressure treatment. The results from solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy shows this process forms an extraordinarily stable amorphous calcium carbonate (ACC) phase that remains despite a high temperature dehydration treatment (130 °C) and persists in the same form after 3 yrs. of storage at ambient conditions. Thermogravimetric analysis (TGA) of these samples indicates that ACC is produced at the similar solid concentrations in both CO 2 -enriched and ambient atmospheres. The observation of meta-stable ACC due to wollastonite carbonation may play an important role in further exploration and optimization of mineral carbonation reactions for CO 2 capturing cement formulations.