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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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Nonlinear Finite Element Model Updating Part I: Experimental Techniques and Nonlinear Modal Model Parameter Extraction.
Abstract not provided.
Goal-oriented adaptive modeling of random heterogeneous media and model-based multilevel Monte Carlo methods
Not Available
Modeling molecular interactions of sodium montmorillonite clay with 3D kerogen models
Not provided.
Modeling aqueous multivalent polyelectrolytes systems with polyelectrolyte NRTL model
Not provided.
Hydrogen Tunneling in Enzymes and Biomimetic Models
Hydrogen transfer reactions play an important role throughout chemistry and biology. In general, hydrogen transfer reactions encompass proton and hydride transfer, which are associated with the transfer of a positively or negatively charged species, respectively, and proton-coupled electron transfer (PCET), which corresponds to the net transfer of one electron and one proton in the simplest case. Such PCET reactions can occur by either a sequential mechanism, in which the proton or electron transfers first, or a concerted mechanism, in which the electron and proton transfer in a single kinetic step with no stable intermediate. Furthermore, concerted PCET reactions can be subdivided into hydrogen atom transfer (HAT), which corresponds to the transfer of an electron and proton between the same donor and acceptor (i.e., the transfer of a predominantly neutral species), and electron-proton transfer (EPT), which corresponds to the transfer of an electron and proton between different donors and acceptors, possibly even in different directions. In all of these types of hydrogen transfer reactions, hydrogen tunneling could potentially play a significant role. The biomimetic portion was supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences.
First-Principles Modeling of the Initial Stages of Organic Solvent Decomposition on LixMn2O4(100) Surfaces
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Growth and modelling of spherical crystalline morphologies of molecular materials
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Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models
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Evaluation of Solar Optical Modeling Tools for Modeling Complex Receiver Geometries.
Abstract not provided.
Modeling Electrochemical Decomposition of Fluoroethylene Carbonate on Silicon Anode Surfaces in Lithium Ion Batteries
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Non-Equilibrium Law-of-the-wall Modeling for Improved Heat Transfer Predictions: Model Development and Validation.
Abstract not provided.
Kamodo: A Model-Agnostic Tool for Model-Model and Model-Data Comparisons
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What’s Driving Uncertainty? The Model or the Model Parameters (What’s Driving Uncertainty? The influences of model and model parameters in data analysis)
Here, one of the substantial improvements to the practice of data analysis in recent decades is the change from reporting just a point estimate for a parameter or characteristic, to now including a summary of uncertainty for that estimate. Understanding the precision of the estimate for the quantity of interest provides better understanding of what to expect and how well we are able to predict future behavior from the process. For example, when we report a sample average as an estimate of the population mean, it is good practice to also provide a confidence interval (or credible interval, if you are doing a Bayesian analysis) to accompany that summary. This helps to calibrate what ranges of values are reasonable given the variability observed in the sample and the amount of data that were included in producing the summary.
Capturing the two-way hydromechanical coupling effect on fluid-driven fracture in a dual-graph lattice beam model
Fluid-driven fractures of brittle rock is simulated via a dual-graph lattice model. The new discrete hydromechanical model incorporates a two-way coupling mechanism between the discrete element model and the flow network. Through adopting an operator-split algorithm, the coupling model is able to replicate the transient poroelasticity coupling mechanism and the resultant Mandel-Cryer hydromechanical coupling effect in a discrete mechanics framework. As crack propagation, coalescence and branching are all path-dependent and irreversible processes, capturing this transient coupling effect is important for capturing the essence of the fluid-driven fracture in simulations. Injection simulations suggest that the onset and propagation of fractures is highly sensitive to the ratio between the injection rate and the effective permeability. Moreover, we show that in a permeable rock, the borehole breakdown pressure, the pressure at which fractures start to grow from the borehole, depends on both the given ratio between injection rate and permeability and the Biot coefficient.