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Results for “DATA EVALUATION”
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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Data-driven evaluation of building materials using Ground Penetrating Radar
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Data-driven evaluations of Euclidean windows to scrutinize hadronic vacuum polarization
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Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability
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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.
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.
Evaluating Nuclear Data and their Uncertainties
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Neutronics Calculations to Support the Fusion Evaluated Nuclear Data Library (FENDL)
Not provided.
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.
DataRaceBench: A Benchmark Suite for Systematic Evaluation of Data Race Detection Tools
Abstract not provided
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.
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.
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.
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.
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.
Characterization and Monitoring Data for Evaluating Constructed Emergent Sandbar Habitat in the Missouri River Mainstem 2004-2009
The U.S. Army Corps of Engineers (Corps) provides the primary operational management of the Missouri River Main Stem Reservoir System. Management of the Missouri River has generally reduced peak river flows that form and maintain emergent sandbar habitat. Emergent sandbars provide non-vegetated nesting habitat for the endangered interior least tern (Sternula antillarum athalassos) and the threatened Northern Great Plains piping plover (Charadrius melodus). Since 2000, piping plover nesting habitat within the Gavins Point Reach, Garrison Reach, Lake Oahe, and Lake Sakakawea has fledged the majority of piping plovers produced along the Missouri River system. Habitats within Lewis and Clark Lake have also recently become important plover production areas. Mechanical construction of emergent sandbar habitat (ESH) within some of these reaches within the Missouri River began in 2004. Through 2009, 11 sandbar complexes had been constructed (10 in Gavins Point Reach, 1 in Lewis and Clarke Lake) totaling about 543 ac of piping plover and interior least tern nesting habitat. ESH Construction has resulted in a net gain of tern and plover nesting habitat. Both terns and plovers successfully nest and fledge young on constructed sandbars, and constructed habitats were preferred over natural habitats. Natural processes may limit the viability of constructed sandbars as nesting habitat. Continued research is needed to identify if changes in constructed sandbar engineering and management increase the length of time constructed habitats effectively function as nesting habitat. However, the transfer of information from researchers to planners through technical research reports may not be timely enough to effectively foster the feedback mechanisms of an adaptive management strategy.
Using NJOY to Calculate "Damage" with Modern Evaluated Nuclear Data Files [Slides]
Abstract not provided.
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.