A Speckle Patterning Study For Laboratory-Scale DIC Experiments.
Abstract not provided.
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Abstract not provided.
A search for evidence of physics beyond the Standard Model in final states with multiple high-transverse-momentum jets is performed using 20.3 fb -1 of proton-proton collision data at √s = 8 TeV recorded by the ATLAS detector at the LHC. No significant excess of events beyond Standard Model expectations is observed, and upper limits on the visible cross sections for non-Standard Model production of multi-jet final states are set. A wide variety of models for black hole and string ball production and decay are considered, and the upper limit on the cross section times acceptance is as low as 0.16 fb at the 95% confidence level. For these models, excluded regions are also given as function of the main model parameters.
Not Available
In this paper, pole placement control techniques are utilized to compensate for variations in the torsional dynamics resulting from the different boundary conditions experienced by a wind turbine nacelle when mounted on a test bench. Simulation case studies are used to understand the baseline torsional characteristics of the nacelle coupled to the test bench versus that coupled to the rotor. It was found that the frequency shifts by up to 200% and 400% for the first and second torsional modes, respectively. The feasibility of utilizing pole placement technique to match the torsional characteristics of the turbine on the test bench is investigated using PI and PID controllers. Here, the performance of the tuned controller is then verified under two test scenarios: Low Voltage Ride Through (LVRT), and a highly dynamic turbulent wind input. It is demonstrated that tuning the test bench speed controller can effectively shift the poles of the closed-loop system to match the desired first and second modes of the full turbine and replicate transient field events on the test bench.
Water is essential for the world’s food supply, for energy production, including bioenergy and hydroelectric power, and for power system cooling. Water is already scarce in many regions of the world and could present a critical constraint as society attempts simultaneously to mitigate climate forcing and adapt to climate change, and to provide for a larger and more prosperous human population. Numerous studies have pointed to growing pressures on the world’s scarce fresh water resources from population and economic growth, and climate change. This study goes further. We use the Global Change Assessment Model to analyze interactions between population, economic growth, energy, land and water resources simultaneously in a dynamically evolving system where competing claims on water resources from all claimants—energy, land, and economy—are reconciled with water resource availability—from renewable water, non-renewable groundwater sources and desalinated water—across 14 geopolitical regions, 151 agriculture-ecological zones, and 235 major river basins. We find that previous estimates of global water withdrawal projections are overestimated. Model simulations show that it is more economical in some basins to alter agricultural and energy activities rather than utilize non-renewable groundwater or desalinated water. This study highlights the importance of accounting for water as a binding factor in agriculture, energy and land use decisions in IAMs and implications for global responses to water scarcity, particularly in the trade of agricultural commodities and land-use decisions.
Shock compression of iron microstructures above a threshold stress results in a α (BCC) → ε (HCP) transformation and the propagation of the release wave brings the metal back to the α phase following the ε→α transformation. Predicting failure behavior under shock loading conditions (spallation) relies on understanding the evolution of defects in the microstructure as it undergoes the α→ε→α phase transformation. This study uses molecular dynamics (MD) simulations to investigate the role of defect evolution during α→ε→α phase transformation on the spall strength values of single-crystal (sc) Fe microstructures. The MD simulations aim to characterize the ε phase fraction formed during shock compression and the defects during shock release for variations in loading orientations and shock stresses. The simulations are carried out for loading along the [100], [110], [111], and [112] orientations and for impact velocities ranging from 600 m/s to 1 km/s. The ε phase fractions during compression and defects (dislocations, twins) characterized during spall failure show an orientation dependence that affects the spall strength values. Here, the lowest value for spall strength is observed for the <110> loading orientation that shows a high density of twinning at the spall plane, whereas the highest value is observed for the <100> orientation and is associated with a α (BCC) → γ (FCC) transformation at the spall plane. The correlations of the spall strength values with the strain rates and with the ε phase fractions are discussed.
Here, this paper discusses the development of a separation method for isolation of Tm-171 from a half-gram irradiated erbium target in support of stockpile stewardship and astrophysics research. The developed procedure is based on cation exchange separation using alpha-hydroxyisobutyric acid (α-HIBA) as chelating agent. It is able to achieve either a decontamination factor of 1.4(4) × 10 5 with 68.9(3) % recovery or 95.4(3) % recovery with a decontamination factor of 5.82(7) × 10 3 for a mock 500-mg target containing 17.9 mg thulium in a single pass-through at room temperature.
Not provided.
Not provided.
Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. Here, we present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.
Not Available
Not provided.
Not Available
Not provided.