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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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Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through November 2021.
Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through June 2022.
The CBETA energy recovery linac uses a single xed eld alternating gradient (FFAG) beam line to return the beam for electron beams with four energies, ranging from 42 MeV to 150 MeV. To keep the beam line compact, the ends of the return line have a small radius of curvature, but the central part of the return line is straight. These are connected by transition lines that adiabatically change from one to the other. We rst describe the design or the arc cell. We then describe how a straight cell is created to be a good match to this arc cell. We then describe the design of the transition line between them. The design process makes use of eld maps for the desired magnets. Because we switch magnet types as we move from the arc, through the transition, and into the straight, there are discrete jumps in the elds that degrade the adiabaticity of the transition, and we describe corrections to manage that.
Quantum annealers such as D-Wave machines are designed to propose solutions for quadratic unconstrained binary optimization (QUBO) problems by mapping them onto the quantum processing unit, which tries to find a solution by measuring the parameters of a minimum-energy state of the quantum system. While many NP-hard problems can be easily formulated as binary quadratic optimization problems, such formulations almost always contain one or more constraints, which are not allowed in a QUBO. Embedding such constraints as quadratic penalties is the standard approach for addressing this issue, but it has drawbacks such as the introduction of large coefficients and using too many additional qubits. In this paper, we propose an alternative approach for implementing constraints based on a combinatorial design and solving mixed-integer linear programming (MILP) problems in order to find better embeddings of constraints of the type Σ x i = k for binary variables x i . Our approach is scalable to any number of variables and uses a linear number of ancillary variables for a fixed k.
Stellar and black hole feedback heat and disperse surrounding cold gas clouds, launching gas flows off circumnuclear and galactic disks, producing a dynamic interstellar medium. On large scales bordering the cosmic web, feedback drives enriched gas out of galaxies and groups, seeding the intergalactic medium with heavy elements. In this way, feedback shapes galaxy evolution by shutting down star formation and ultimately curtailing the growth of structure after the peak at redshift 2–3. To understand the complex interplay between gravity and feedback, we must resolve both the key physics within galaxies and map the impact of these processes over large scales, out into the cosmic web. The Advanced X-ray Imaging Satellite (AXIS) is a proposed X-ray probe mission for the 2030s with arcsecond spatial resolution, large effective area, and low background. AXIS will untangle the interactions of winds, radiation, jets, and supernovae with the surrounding interstellar medium across the wide range of mass scales and large volumes driving galaxy evolution and trace the establishment of feedback back to the main event at cosmic noon. This white paper is part of a series commissioned for the AXIS Probe mission concept; additional AXIS white papers can be found at the AXIS website.
This dataset contains 40 years (1980-2019) of historical hourly meteorology and 80 years (2020-2099) of projected hourly meteorology for each county in the conterminous United States. Details about the scenarios and variables included in this dataset are in the readme.pdf file. This dataset is derived from the IM3/HyperFACETS Thermodynamic Global Warming (TGW) simulations (https://doi.org/10.57931/1885756). More details on the TGW approach can be found at: https://tgw-data.msdlive.org/. If you use this dataset please also cite the raw TGW dataset (Jones, A. D., Rastogi, D., Vahmani, P., Stansfield, A., Reed, K., Thurber, T., Ullrich, P., & Rice, J. S. (2022). IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets (v1.0.0) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/1885756). The four future climate scenarios in the TGW data are: rcp45cooler, rcp45hotter, rcp85cooler, and rcp85hotter. For the historical period and each of the four future scenarios the dataset has hourly estimates of the spatial-average of six meteorological variables for each county: Temperature, specific humidity, shortwave radiation, longwave radiation, and the U (east-west) and V (north-south) components of the wind speed. Times for each file are in the filename and all times are in Coordinated Universal Time (UTC). Counties are identified by their Federal Information Processing Standard (FIPS) code. The mapping between counties and FIPS codes is provided in the state_and_county_fips_codes.csv file. The code to go from the raw TGW data to county-level projections is available at: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.
Viruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs). The in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while k -mer- and blast-based tools were uniquely able to detect viruses from short (≤3 kbp) contigs. Notably, however, the performance increase of k -mer- and blast-based tools for short contigs was obtained at the cost of increased false positives (sometimes up to ~5% for virome and ~75% bulk samples), particularly when eukaryotic or mobile genetic element sequences were included in the test datasets. Furthermore, for viral classification, variously sized genome fragments were assessed using gene-sharing network analytics to quantify drop-offs in taxonomic assignments, which revealed correct assignations ranging from ~95% (whole genomes) down to ~80% (3 kbp sized genome fragments). A similar trend was also observed for other viral classification tools such as VPF-class, ViPTree and VIRIDIC, suggesting that caution is warranted when classifying short genome fragments and not full genomes. Finally, we highlight how fragmented assemblies can lead to erroneous identification of AMGs and outline a best-practices workflow to curate candidate AMGs in viral genomes assembled from metagenomes. Together, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses ‘hidden’ in diverse sequence datasets.
A method for precision snow removal analysis provides a melt rate in inches per day for roadways in a geometric region is provided. Elevation data is used to calculate upward looking hemispherical viewsheds at each roadway coordinate to determine the total solar radiation. The snowmelt capacity and slope are determined at each coordinate and the data is displayed on a geographical map of the roadway. The processing speed of the computer is improved since the snowmelt capacity is only determined for a roadway subset of the region coordinates.
Computational methods underpin advancing the science and engineering of energy efficiency, sustainable transportation, renewable power technologies, and developing a knowledge base to optimize energy systems. Researchers with access to enough computing, and the right type, can focus their ingenuity and creativity on addressing the energy challenges. NREL’s advanced computing influence spans several common themes across the Office of Energy Efficiency and Renewable Energy (EERE), including materials discovery, process modeling, fluid dynamics, resource mapping, and analysis of large-scale systems with real-time optimization.
Background/Objectives. Critical minerals (CM) are essential for numerous industrial and defense applications, including green technologies that will help to meet carbon emission reduction goals. Many CM are currently mined and processed in countries that lack stringent environmental and labor regulations. Unconventional sources such as existing industrial waste streams could play a part in building an ethical CM supply chain. Additionally, waste streams created in the extraction and processing of CM could be used for other industrial or commercial purposes, thereby reducing waste and advancing a circular economy. Approach/Activities. The Department of Energy funded Powder River Basin (PRB) CORE-CM project is exploring all aspects of the carbon ore, rare earth element (REE), and CM value chain, including the potential for extraction of CM from industrial waste streams in the basin. Waste streams that have potential as CM feedstocks are being inventoried to assess the concentration of CM in each waste stream and to estimate volume and accessibility. Although basin-specific technologies for extraction of CM are still in development, waste streams that may be produced during these processes are being cataloged and mapped to potential secondary uses. Results/Lessons Learned. Initial studies of the CM potential of PRB coal ash show that this waste stream contains concentrations of greater than 300 ppm total REE (Bagdonas et al., 2022, Renewable and Sustainable Energy Reviews). Moreover, calcium-rich PRB coal ash is amenable to REE extraction (Taggart et al., 2016, Environmental Science and Technology). Wyoming coal is shipped to 28 states, meaning that coal ash produced at power stations across the US represents a potential widespread resource for the extraction of REE. In addition to coal mining and coal fired electricity generation, the PRB is home to other energy industries including oil and gas production, in-situ uranium mining, and bentonite mining. Historically, precious and base metal mining has taken place on the perimeter of the PRB. Waste streams from these industries are currently being evaluated for their CM potential. Assessing the CM resource potential of waste streams could contribute to the development of an ethical CM supply chain. Results from these studies can be replicated for other waste streams, increasing the likelihood of successful creation of circular economies.
Fracture characterization is essential to hydrocarbon and geothermal exploration and production as well as carbon geo-sequestration. Active and passive measurements have been extensively applied to map fractures across various scales. Active and passive methods have inherent advantages and limitations that complement each other. Here, in this study, to improve the mapping of embedded fractures and the surrounding geomechanically altered regions, we integrate active shear-wave transmission measurement with passive acoustic-emission measurement collected during a lab-scale hydraulic-fracturing experiment. The proposed data fusion leverages the advantages of the two modalities of measurements, while minimizing their individual limitations. Polarized shear-wave transmission (active) measurement was collected before and after the hydraulic fracturing, and the acoustic emission (passive) measurement was collected during hydraulic fracturing. Two sets of two-dimensional maps of fracture and fracture-induced damage in axial, median and frontal planes were obtained by separately processing active and passive measurements. These 2D maps are then fed to wavelet-based image fusion technique to integrate the two sources of information for the reliable mapping/imaging of the embedded fractures and the surrounding geomechanically altered regions.
State and county mapping of U.S. census regions and divisions based on 2010 data. The raw and processed data sources are included as well as maps of the output.
Global sensitivity analysis (GSA) of the voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows us to resort to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with a different number and different probability distributions of uncertain sources demonstrate that the proposed method can achieve accurate GSA under various conditions.
Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues for building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. As the size of the simulated networks increases, however, sequential execution becomes time-consuming. Parallel execution presents a suitable method for scalable simulations of large-scale quantum networks, but the unique attributes of quantum information create unexpected challenges. In this work, we identify requirements for parallel simulation of quantum networks and develop the first parallel discrete-event quantum network simulator by modifying the existing serial simulator SeQUeNCe. Our contributions include the design and development of a quantum state manager (QSM) that maintains shared quantum information distributed across multiple processes. We also optimize our parallel code by minimizing the overhead of the QSM and decreasing the amount of synchronization needed among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network topology using 2 to 128 processes. We also observe an efficiency greater than 0.5 for up to 32 processes in a linear network topology of the same size and with the same workload. We repeat this evaluation with a randomized workload on a caveman network. We also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We have released the parallel SeQUeNCe simulator as an open source tool alongside the existing sequential version.
Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.
Since 2004, there has been extensive effort towards the development of a uranium molybdenum monolithic fuel system to convert high performance research and test reactors. The RERTR-6 experiment was the first to attempt a monolithic fuel instead of a dispersed fuel form. The fabrication methods evolved overtime and provided the basis for current fabrication methods. The various steps in the process inevitably tailor the fuel alloy microstructure which is known to influence irradiation behavior. This document aims to present and discuss the fabrication evolution that transpired through a recounting of historical data from the various fabrication campaigns. By overlaying this data with basic science studies on the U-Mo that explored transformation kinetics, it is possible to estimate a measure of the impact heat treatments have on the final as-fabricated microstructure.
Effective surface passivation of crystalline silicon (c-Si) surface by reducing the carrier recombination rate has led to modern c-Si solar cells with efficiencies > 25% in both laboratory and industrial settings. Typical mainstream surface passivation techniques include high-temperature silicon oxide (SiOx), amorphous silicon (a-Si:H), hydrogen-rich silicon nitride (SiNx), and aluminum oxide (Al2O3) [1]. They have demonstrated excellent surface recombination velocity of < 1 cm/s owing to both chemical passivation (via the hydrogen saturation of Si dangling bonds at the c-Si surface), and field-effect passivation (via the band bending from the fixed charge of the dielectric layers). Recently, several groups have studied solution-based organic materials for c-Si passivation, including bis(trifluoromethane)sulfonimide (TFSI), polystyrenesulfonate, and Nafion [2, 3] via spin or dip coating. All films were processed at ambient room temperature, and a high lifetime of 12 ms, as well as a low saturation current density (J0) of 16 fA/cm2, have been demonstrated using Nafion passivation [2]. However, despite the air instability, Nafion has several advantages when introduced to PV applications: (a) Wafer quality can be measured at high throughput after several process stages. (b) It is compatible with PL mapping, compared to HF liquid passivation which cannot be performed in room ambient. (c) Nafion process is fast and poses fewer constraints on process complexity, and cleanness, compared to Al2O3 and a-Si:H passivation. (d) Nafion is the ideal room temperature passivation, which can be applied onto a small fragment of a degraded module to investigate microscopic mechanisms when studying degradation mechanisms. Edge passivation is needed for advanced characterization tests such as Electron Paramagnetic Resonance (EPR), Electrically Detected Magnetic Resonance (EDMR), Deep Level Transient Spectroscopy (DLTS), local cell current-voltage (J-V), and Suns-Voc. In such cases, the laser-damaged minicell edges will obscure the true degradation mechanisms, and Nafion can effectively passivate them without causing further degradation through elevated-temperature processes. In this contribution, we show the passivation results of Nafion on bare nCz and compare it with a Al2O3 witness. We highlight the importance of edge pre-treatment before Nafion to achieve good passivation. We show that Nafion can reach decent passivation without undergoing high-temperature processes. We also explore the temperature dependency of Nafion through photoluminescence (PL) study and demonstrate the application of Nafion under cryogenic temperature (~6 K) through EPR. Our results reveal that Nafion can reduce surface and edge Si dangling bonds at low temperatures. Thus, it can be used as an effective room temperature passivation technique for advanced characterization methods.
When you hear standards mapping what do you think about? For the Secure Software Central (SSC) project this means comparing our process to the National Institute of Standards and Technology’s (NIST) security control catalog. So how is it done? The project completed this summer can be broken down into three simple phases. Phase one consists of mapping the SSC process to the NIST’s security control catalog. While phase two entails mapping mitigations from a threat profile to NIST. Lastly phase three requires the creation of a knowledge base allowing for the reuse of controls across numerous projects This project presented a few different challenges: locating the correct control within the NIST catalog while correctly matching the proposed mitigations from the SSC team, accurately leveling the mitigation to the security level of the system, creating a toolbox to showcase a dataset of mitigations and standards to be used in current and future projects. each challenge allowed for opportunities in growth and understanding of how security controls can map to a governing set of standards. These standards maps are a crucial element to support the insights provided by the SSC team and allow the client to have confidence in the work being completed. My internship has allowed me to set and achieve many goals such as coming to understand that the field of cybersecurity truly is the right place for me. I have also learned that it’s ok to be wrong if you learn something from it. For significant accomplishments I have helped integrate standards mapping into the fabric of SSC. The field experience that I have gained such as interacting with the SSC team along with other senior staff and building a good rapport are crucial skills to. This time at PNNL has been an irreplaceable experience.