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Results for “dynamic network”

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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Unraveling the Dynamic Network in the Reactions of an Alkyl Aryl Ether Catalyzed by Ni/γ-Al 2 O 3 in 2-Propanol

The reductive cleavage of aryl ether linkages is a key step in the disassembly of lignin to its monolignol components, where selectivity is determined by the kinetics of multiple parallel and consecutive liquid-phase reactions. Triphasic hydrogenolysis of 13 C-labeled benzyl phenyl ether (BPE, a model compound with the same C-O bond energy as the major β-O-4 linkage in lignin), catalyzed by Ni/γ-Al 2 O 3 , was observed directly at elevated temperatures (150 - 175 °C) and pressures (79-89 bar) using operando magic-angle spinning NMR spectroscopy. Liquid-vapor partitioning in the NMR rotor was quantified using the 13 C NMR resonances for the 2-propanol solvent, whose chemical shifts report on the internal reactor temperature. At 170 °C, BPE is converted to toluene and phenol with k 1 = 0.17 s -1 g cat -1 and an apparent activation barrier of (80 ± 8) kJ mol -1 . Subsequent phenol hydrogenation occurs much more slowly (k 2 = 0.0052 s -1 g cat -1 at 170-175 °C), such that cyclohexanol formation is significant only at higher temperatures. Toluene is stable under these reaction conditions, but its methyl group undergoes facile H/D exchange (k 3 = 0.046 s -1 g cat -1 at 175 °C). While the source of the reducing equivalents for both hydrogenolysis and hydrogenation is exclusively H 2 /D 2(g) rather than the alcohol solvent at these temperatures, the initial isotopic composition of adsorbed H/D on the catalyst surface is principally determined by the solvent isotopic composition (2-PrOH/D). All reactions are preceded by a pronounced induction period associated with catalyst activation. In air, Ni nanoparticles are passivated by a surface oxide monolayer, whose removal under H 2 proceeds with an apparent activation barrier of (72 ± 13) kJ mol -1 . Finally ,the operando NMR spectra provide molecularly-specific, time-resolved information about the multiple simultaneous and sequential processes as they occur at the solid-liquid interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Parameter Estimation and Model Validation of Nonlinear Dynamical Networks

In the performance period of this work under a DOE contract, the co-PIs, Philip Gill and Henry Abarbanel, developed new methods for statistical data assimilation for problems of DOE interest, including geophysical and biological problems. This included numerical optimization algorithms for variational principles, new parallel processing Monte Carlo routines for performing the path integrals of statistical data assimilation. These results have been summarized in the monograph: “Predicting the Future: Completing Models of Observed Complex Systems” by Henry Abarbanel, published by Spring-Verlag in June 2013. Additional results and details have appeared in the peer reviewed literature.

42 ENGINEERING↗
DOE OSTI2018

Dynamic Networks Experiment FY18 - Quality Control Focus Area Efforts

The availability of data whose quality is fittingly labeled is of tantamount importance to signal detection and all other downstream signal processing operations. Without identification, the goals of low yield nuclear monitoring cannot reach their full potential. A fundamental question that remains is the knowledge of the capability gaps that prevent us from using open network data. With these new channels, we will need better understanding of the frequency, type, severity, and impact of artifacts when compared to conventional networks. Moreover, beyond a simple quality label, thresholds for acceptable use of the data will need to be established, likely on a use case by use case basis. Finally, orders of magnitude more data with an at-best static analyst pool size means that quality artifacts must be identified with as little human interaction as possible. Automated methodologies must be developed to account for the scale of the problem when ad hoc networks are candidates for processing. At the same time that “data wealth” provides more opportunity for signal exploitation, the introduction of ad hoc networks introduces an increased risk quality issues in both sensor value and metadata issues. The artifacts shown below are examples of some sensor-based quality issues that impair downstream processing. The machine learning model used in this project was able to identify the artifacts that produce these spurious correlations, significantly improving analyst productivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamic Networks for On Demand Degradable Adhesives, Scaffolds and Templates (Abbreviated Final Report)

This feasibility study was motivated by a need for alternative mandrel materials to meet the next generation of inertial confinement fusion (ICF) ablator capsules. In response, a new polymer material based on reversible covalent bonds was proposed, with the objective to design thermosets to withstand elevated temperatures (140-200 degrees Celsius) while also being thermally degradable on-demand via controlled decomposition at temperatures above 200 degrees Celsius. A series of cyclopentadiene (Cp) containing thermosets were designed and synthesized to evaluate curing, reversibility and degradability controlled via retro-Diels Alder reactions using Cp-cycloaddition adducts as crosslinks. Ultimately, the current iteration of materials and strategy pursued in this study was assessed as impractical for meeting the needs and objectives of Lawrence Livermore National Laboratory’s (LLNL) target fabrication program.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamic Networks Experiment 2

Item Purpose: Presentation of LYNM DN DNE2 at the AFRL TIM. Internal Peer Review Status: This item has completed internal peer review. HQ review status: This item has been reviewed and approved by HQ.

Berg, Elizabeth Mary [Sandia National Laboratories↗