Data-driven evaluations of Euclidean windows to scrutinize hadronic vacuum polarization
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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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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Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability
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Brewer, Justin↗
X-ray diffraction under grazing incidence conditions
Material properties frequently relate to structures at or near surfaces, particularly in thin films. As a result, it is essential to understand these structures at the molecular and atomistic levels. The most accurate and widely used techniques for characterizing crystallographic order are based on X-ray diffraction. When dealing with thin films or interfaces, standard approaches, such as single crystal or powder diffraction, are not suitable. However, X-ray diffraction under grazing incidence conditions can provide the required information. Here, in this Primer, grazing incidence X-ray diffraction (GIXD) is comprehensively introduced, starting from basic considerations on X-ray diffraction at crystals with reduced dimensionality and the optical properties of X-rays, followed by a more in-depth description of an experimental performance, including X-ray sources, goniometers and detectors. Experimental errors, limitations and reproducibility are discussed. Various applications, from highly ordered inorganic single crystal surfaces to weakly ordered polymer thin films, are presented to illustrate the potential of GIXD. Data visualizations, representations and evaluation strategies are summarized, based on the example of anthracene thin films. The Primer compiles information relevant to perform high-quality GIXD experiments, evaluate data and interpret results, to extend knowledge about X-ray diffraction from surfaces, interfaces and thin films.
36 MATERIALS SCIENCE↗
Summation Calculations for Reactor Antineutrino Spectra, Decay Heat and Delayed Neutron Fractions Involving New TAGS Data and Evaluated Databases
Fallot, M.; Giot, L.; Guadilla-Gomez, V.; Le Meur, L.; Porta, A.; Algora, A.; Tain, J. -L.; Briz, J. A.; Agramunt, J.; Aysto, J.; Cormon, S.; Cucoanes, A.; Eronen, T.; Fraile, L. M.; Ganogliu, E.; Gelletly, W.; Gorelov, D.; Hakala, J.; Jokinen, A.; Jordan, M. D.; Kankainen, A.; Kolhinen, V. S.; Koponen, J.; Lebois, M.; Martinez, T.; Monserrate, M.; Montaner-Piza, A.; Moore, I.; Nacher, E.; Orrigo, S. E. A.; Penttila, H.; Pohjalainen, I.; Reinikainen, J.; Reponen, M.; Rinta-Antila, S.; Rytkonen, K.; Rubio, B.; Shiba, T.; Sonnenschein, V.; Valencia, E.; Vedia, V.; Voss, A.; Weber, C.; Wilson, J. N.; Zakari-Issoufou, A. -A.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Evaluating Nuclear Data and their Uncertainties
Explore the source record for details and available documents.
Talou, Patrick↗
Neutronics Calculations to Support the Fusion Evaluated Nuclear Data Library (FENDL)
Not provided.
Nuclear Science & Technology↗
The Need for a New LLNL Pulsed Sphere Neutron Leakage Spectra Series
Here, it is shown that spectra measured as part of the Lawrence Livermore National Laboratory Pulsed Sphere (LPS) program offer decisive information to locate formatting or physics issues in nuclear data of key interest for fusion reactor simulations. However, experiments from this measurement series are not benchmarks. For instance, their uncertainties are incomplete. There are also many open questions—e.g., on the setup, the detector response, and whether LPS are accurately modeled—that cannot be answered anymore given the limited documentation and that many of the experimenters are no longer actively working. This limited knowledge has implications when one tries to adjust nuclear data to LPS spectra. Usually, one adjusts to benchmarks representing an application with the hope to get more precise nuclear data for the application of interest where differential data might be scarce and/or to reduce nuclear data uncertainties in the application simulations. However, it is demonstrated that adjustment with LPS spectra without accounting for missing uncertainties and modeling potential biases in the experimental data leads to adjusted data that are highly unphysical. That means adjusted data differ significantly from evaluated data based on information from differential experiments; also, application quantities predicted with the adjusted data deviate distinctly from experimental ones. While we can approximate our limited knowledge on these experiments with Gaussian processes in the adjustment process, this modeling of bias is arbitrary rather than based on a physics explanation, calling into doubt the validity of resulting adjusted data. Thus, we discuss here the need for a new measurement series, learning from the strengths and weaknesses of the LPS program, to yield decisive and well-benchmarked integral experiments to support fusion reactor research.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DataRaceBench: A Benchmark Suite for Systematic Evaluation of Data Race Detection Tools
Abstract not provided
97 MATHEMATICS AND COMPUTING↗
Data for Evaluation of 1,2-Diacyl-3-Acetyl Triacylglycerol Production in Yarrowia lipolytica
Plants produce many high-value oleochemical molecules. While oil-crop agriculture is performed at industrial scales, suitable land is not available to meet global oleochemical demand. Worse, establishing new oil-crop farms often comes with the environmental cost of tropical deforestation. The field of metabolic engineering offers tools to transplant oleochemical metabolism into tractable hosts while simultaneously providing access to molecules produced by non-agricultural plants. Here, we evaluate strategies for rewiring metabolism in the oleaginous yeast Yarrowia lipolytica to synthesize a foreign lipid, 3-acetyl-1,2-diacyl-sn-glycerol (acTAG). Oils made up of acTAG have a reduced viscosity and melting point relative to traditional triacylglycerol oils making them attractive as low-grade diesels, lubricants, and emulsifiers. This manuscript describes a metabolic engineering study that established acTAG production at g/L scale, exploration of the impact of lipid bodies on acTAG titer, and a techno-economic analysis that establishes the performance benchmarks required for microbial acTAG production to be economically feasible.
Biomass Analytics↗
Data for "Evaluating the industrial potential of emerging biomass pretreatment technologies in bioethanol production and lipid recovery from transgenic sugarcane"
The selection of pretreatment methods is critical to achieving high product yields during bioconversion of lignocellulosic biomass. Hydrothermal, soaking-in-aqueous ammonia, and ionic liquid pretreatment methods are viable candidates for minimizing sugar decomposition, permitting the effective hydrolysis of structural carbohydrates, and producing a fermentable substrate suitable for achieving industrial ethanol titers and yields. In this study, the effect of these three pretreatment methods on non-modified sugarcane cultivar CP88-1762 and two transgenic lipid-accumulating sugarcane lines, oilcane 1565 and oilcane 1566, were investigated and compared in terms of lipid recovery, sugar yield, and ethanol yields within the lignocellulosic biomass conversion pipeline. Fed-batch enzymatic hydrolysis at high solid loading yielded hydrolysates capable of supporting industrial bioethanol titers across all conditions. The highest sugar yields were obtained on ammonia-pretreated biomass hydrolysate (253.73 g L−1), followed by hydrothermally pretreated hydrolysate (213.10 g L−1) and ionic liquid-pretreated hydrolysate (154.20 g L−1). Commercially viable ethanol titers of 100.62 g L−1, 64.47 g L−1, and 52.95 g L−1 were achieved from ammonia, hydrothermal, and ionic liquid pretreated hydrolysate with the corresponding ethanol productivities of 2.08 g L−1 h−1, 0.53 g L−1 h−1, and 0.36 g L−1 h−1. The lower acetic acid concentration in ammonia-pretreated hydrolysate may have enhanced its fermentability relative to the hydrothermal pretreatment condition, as indicated by the differences in ethanol titer and productivity. Lower sugar yields and ethanol productivities under the ionic liquid conditions likely resulted from the inhibitory effect of cholinium lysinate. Oilcane 1565 and oilcane 1566 bagasse accumulated over 16- and 3 times higher lipids than the non-modified sugarcane CP88-1762. The total fatty acid content in the oilcane samples was reduced in ammonia and ionic liquid-pretreated bagasse relative to the hydrothermal pretreatment condition. While all pretreatment techniques tested are industrially viable, the observed differences in titer, productivity, and lipid content indicate that careful selection and validation of upstream processing methods can contribute to improved economic and environmental outcomes.
biomass analytics↗
Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation
Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.
97 MATHEMATICS AND COMPUTING↗
Utah FORGE Well 16A(78)-32 Stimulation DFN Fracture Plane Evaluation and Data
This dataset includes files used to fit planar fractures through the preliminary earthquake catalogs of the three stages of the April 2022 well 16A(78)-32 stimulation which is linked bellow. These planar features have been used to update the FORGE reference Discrete Fracture Network (DFN) model. The files are provided to encourage other modelers to use additional workflows to find additional/alternative features. To this end, the dataset includes the cleaned earthquake catalog data translated to the FORGE reference model global reference frame, the well trajectory of 16A(78)-32 in those same coordinates, the fit 15 planar features in csv format, and a pdf file with slides illustrating the process used to fit the features. A recorded presentation of this material is available from the October 2022 FORGE Modeling and Simulation Forum which is also linked below.
15 GEOTHERMAL ENERGY↗
Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.
The Bello et al. study evaluates the impact of contrasting pressure drawdown on gas productivity and the resulting economics of a well in the Marcellus Shale of the Appalachian Basin. This research applies a techno-economic analysis approach to help identify potential ways pressure management strategies can be used to improve cumulative recovery of hydraulically fractured horizontal wells while maintaining project profitability. Gas production forecast outlook scenarios of the Marcellus Shale Energy and Environment Laboratory Laboratory's MIP-3H well were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning (PIML) workflow and 2) via traditional reservoir simulation in Computer Modeling Group’s (CMG) GEM Compositional & Unconventional Simulator. Cash flow and other economic metrics of interest were compiled on the production outlook using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Unconventional Shale Well Economic Model (UShWEM).The sheets within this Microsoft ExcelTM workbook provide the economic metric outputs for the baseline condition and the one-at-a-time (OAT) sensitivity analysis of UShWEM's input parameters for each of the production scenarios evaluated.
Fracture Network Model↗
Evaluating Named Data Networking for Industrial Control System [Slides]
Current proposed work is: See if the inherent security that comes with Named Networking (NDN) can be applied to Industrial Control Systems; and, Every packet is required to be cryptographically signed which makes every single piece of data communicated in the system secure and authenticated.
42 ENGINEERING↗
Evaluated Nuclear Data Requirements in Support of Fission and Fusion Applications [Slides]
This presentation touches on thermal scattering cross section and scattering lengths including criticality benchmarks (fission). Secondly, development and completeness of charged particle libraries: criticality benchmarks (fission), neutron source (fission/fusion), simulation benchmarks and light elements generation (fusion). Additionally, this presentation details on the covariances (methodology and completeness) with adjustment and optimization of nuclear data libraries (fission/fusion). Finally nuclear data reproducibility and conclusions are discussed.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Nuclear Data: What is the big deal? [Slides]
This presentation begins with a look at the practical application: differential and integral data. It also includes a nuclear data evaluation: cross section formalisms, data processing and cross section library generation, computer tools, and criticality safety assessment: nuclear data. The presentation ends with some concluding remarks.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Evaluation of Data Lake Design for the Accelerator Control System
In this modern world, the user expects faster processing and real-time response to operate accelerator control devices. The existing framework with its infrastructure does not have the ability to satisfy these future needs. Therefore, modernization is required to reach industry standards and develop a modular framework that can provide flexibility and dynamic scalability. The data lake architecture, comprising three layers, ingestion, processing, and data consumption, provides flexibility and scalability to meet current and future demands for the accelerator control system.
Jaikar, Amol [Fermilab]↗
Evaluating lossy data compression on climate simulation data within a large ensemble
High-resolution Earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives, for example, by forcing reductions in data output frequency, simulation length, or ensemble size. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists with access to publicly available climate data that have undergone lossy data compression. In particular, we report on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which we challenge climate scientists to examine features of the data relevant to their interests, and attempt to identify which of the ensemble members have been compressed and reconstructed. We find that while detecting distinguishing features is certainly possible, the compression effects noticeable in these features are often unimportant or disappear in post-processing analyses. In addition, we perform several analyses that directly compare the original data to the reconstructed data to investigate the preservation, or lack thereof, of specific features critical to climate science. Overall, we conclude that applying lossy data compression to climate simulation data is both advantageous in terms of data reduction and generally acceptable in terms of effects on scientific results.
54 ENVIRONMENTAL SCIENCES↗