Viscosity-adjustable grease matrices for serial nanocrystallography
Please check the README file for more information about the dataset.
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Please check the README file for more information about the dataset.
Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits. However, these control functions can become unstable, introducing oscillations in system voltages when not appropriately configured or maliciously altered during a cyberattack. In the event that Volt-VAR and Volt-Watt control functions in a portion of PV smart inverters in a distribution grid are unstable, the proposed adaptation scheme utilizes the remaining and stably-behaving PV smart inverters and other Distributed Energy Resources to mitigate the effect of the instability in real-time. The adaptation mechanism is entirely decentralized, model-free, communication-free, and requires virtually no external configuration. This repository provides code to simulate the algorithm in experiments on the IEEE 37 node test feeder.
An initial number of fissions on a fissile/fissionable material, defined by ENDF library availability, is used to produce an initial inventory of fission products (FPs). The FPs are decayed using full, analytical solutions to the Bateman equations to calculate the FP inventory at a user-specified time. The radiation detector, a high-purity germanium (HPGe) detector, has been modeled using every gamma-ray in the ENDF/TENDL libraries individually, and the modeled response can be multiplied by the activity of each fission product to determine a full, energy dependent detector response, including dead time. The detector response can be output as a PeakEasy (.spe) file, or as a text file. Since each radioisotope's inventory is known, the proportion to the total may be shown for each isotope, as well as many radionuclides in a user-specified energy window. Full execution and display of results takes 1-3 seconds, depending on the complexity of the input parameters.
This dataset and R script accompany the published paper Bruns et al. (2024) in Geophysical Research Letters. The data are from the first six years of a field manipulation of whole-ecosystem warming and elevated CO2 experiment (Salt Marsh Accretion Response to Temperature eXperiment, or SMARTX) in the Smithsonian's Global Change Research Wetland (GCReW), a brackish, microtidal wetland site on a subestuary of the Chesapeake Bay. These data were generated to understand how warming and elevated CO2 interact to structure ecosystem-level responses to global change, particularly in terms of carbon sequestration. The dataset covers 2017-2022 and includes peak annual above ground biomass, annual belowground fine root productivity, and porewater NH4 for each experimental plot. The overall experiment is replicated in two locations on the marsh, a lower elevation zone dominated the C3 sedge S. Americanus and a higher elevation plot dominated by the C4 species. This paper and its data release is only for the C3 plot. Variable descriptions for data file is available in variable_descriptions.pdf. The R script Bruns_et_al_2024_GRL_make_figures.Rmd contains model code and other scripts used to generate all paper figures.
For the Bayesian Monte Carlo methodology, a need to understand convergence of the posterior moments as a function of the number of parameter realizations is required. In high-dimensional systems, it can be very costly to sample entire parameter space and perform functional evaluation for every realization. Bayesian Monte Carlo allows one to relax the GLLS approximations of model linearity and prior/posterior PDF shape. The Bayesian Stochastic Collocation Method is a deterministic approach to “sample” the parameter space. It allows one to relax the GLLS approximations of model linearity and posterior PDF shape. Higher-order posterior moments (i.e., skewness, kurtosis, etc.) can be studied through polynomial expansion. Tensor product quadrature scales poorly and can use sparse grid quadrature methods.
Abstract not provided.
In the preceding project year two, we refined three DBC formulations from a selection of over 50 different chemistries. The optimization study primarily encompassed testing for (i) reversibility extent, (ii) performance in the presence of chemical additives, (iii) adhesion and friction behavior during displacement in wellbores and pipelines, (iv) corrosion protection performance, and (v) injection performance with model fracture systems at the laboratory scale. Highly promising results obtained from all these tests signify the significant potential of DBCs in enhancing hydrocarbon recovery from unconventional reservoirs. The primary activities in the third project year included publishing experimental findings across multiple articles and conducting outreach initiatives. Throughout the year, we undertook tasks such as replicating experimental results, further optimizing various formulations and their associated experimental sets, and conducting additional tests to address missing components based on reviewer feedback and suggestions. We also explored the surfactant and friction-reduction aspects of selected formulations through drag reduction tests. In addition, we constructed an improved fracturing performance setup and performed flow injection tests. The specific DBC formulations focused on during this project period were A8/B1, A12/B5, and A10/B12. We also obtained results for additional DBC formulations and a select few commercial fracturing fluids for the purpose of comparison. Within the project's scope, we aim to enhance the experimental findings with the development of various models. The first two years focused on two key aspects: (i) the creation of a high-fidelity hydraulic fracturing model for non-Newtonian fluids to gain insights into the implementation of DBC fluids in fracking environments, and (ii) the development of a multiphase flow simulator for estimating total production, fluid saturation in the reservoir, and the creation of a fracture propagation model and kinetic Monte Carlo (kMC) models for diverse applications. In the third year, we delved into the fundamental nanostructural properties of DBCs, exploring aspects such as material chemistry, pH tunability, and control of DBC formation and stability. Subsequently, in the extension year, we conducted a systematic investigation of various building blocks containing primary, secondary, and tertiary amine functional groups to understand their impact on rheological and viscoelastic properties. Furthermore, we explored a Dissipative Particle Dynamics (DPD) model to simulate self-assembly processes with precision, creating a high-fidelity representation of relevant nanostructures. The tasks performed this year with the significant results obtained have been discussed in Section 2.
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An exemplary method of displaying a data visualization includes retrieving a dataset that includes a plurality of fields and displaying a graphical user interface with a plurality of shelves. The method further includes receiving user actions to: (i) associate a first field with a first shelf, and (ii) associate a second field with a second shelf; displaying, in accordance with the received user actions, a data visualization, where each data mark has a first size defined according to a default position of a displayed slidable affordance; and receiving an additional user action that moves the slidable affordance from the default position to a user-defined position. In response to receiving the additional user action, the method further includes updating the data visualization, where each data mark included in the updated data visualization has a second size, different from the first size, defined according to the user-defined position of the slidable affordance.
The present invention provides for a method of fermenting or saccharifying a biomass comprising: (a) (i) contacting a biomass comprising a polysaccharide, and an ionic liquid (IL) to form a first solution, or (ii) providing the first solution comprising the biomass and the IL, (b) contacting the first solution and carbon dioxide such that the first solution results in a lower pH, (c) introducing (i) an enzyme capable of enzymatically to breakdown at least one bond in the polysaccharide or a breakdown product of the polysaccharide, and/or (ii) a microorganism that capable of producing the enzyme and/or fermenting the polysaccharide or a breakdown product of the polysaccharide, such that the polysaccharide is at least partially broken down and the first solution is transformed into a second solution.
A method of controlling a vehicle includes determining a current operating situation of the vehicle, and identifying a subset of a plurality of sensors of the vehicle needed to provide data to enable a vehicle control function for the current operating situation of the vehicle. A remainder of the plurality of sensors is disengaged to reduce electric energy usage by the vehicle while the vehicle is operating in the current operating situation of the vehicle. A sampling rate for the selected subset of sensors may be reduced to further reduce energy usage of the vehicle. Additionally, an energy reduction processing strategy may be implemented to reduce a processor frequency or a voltage of a computing device used to provide the vehicle control function to further reduce energy usage of the vehicle.
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