Publications

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To further understand the combustion characteristics and the reaction pathways of acyclic ethers, the oxidation of di-n-propyl ether (DPE) was investigated in a jet-stirred reactor (JSR) combined with a photoionization molecular-beam mass spectrometer. The experiments were carried out at near-atmospheric pressure (700 Torr) and over a temperature range of 425–850 K. Based on the experimental data and previous studies on ether oxidation, a new kinetic model was constructed and used to interpret the oxidation chemistry of DPE. In DPE oxidation, a high reactivity at low temperatures and two negative temperature coefficient (NTC) zones were observed. These behaviors are explained in this work by taking advantage of the obtained species information and the modeling analyses: the two NTC zones are caused by the competition of chain branching and termination reactions of the fuel itself and specific oxidation intermediates, respectively. Furthermore, the general requirements to have double-NTC behavior are discussed. A variety of crucial fuel-specific C6 species, such as ketohydroperoxides and diones, were detected in the species pool of DPE oxidation. Their formation pathways are illuminated based on rate-of-production (ROP) analyses. Propanal was identified as the most abundant small molecule intermediate, and its related reactions have an important impact on the oxidation process of DPE. Both acetic acid and propionic acid were detected in high concentrations. A new formation pathway of propionic acid is proposed and incorporated into the kinetic model to achieve a more accurate prediction for propionic acid mole fractions.

Multi-objective optimization methods can be criticized for lacking a statistically valid measure of the quality and representativeness of a solution. This stance is especially relevant to metaheuristic optimization approaches but can also apply to other methods that typically might only report a small representative subset of a Pareto frontier. Here we present a method to address this deficiency based on random sampling of a solution space to determine, with a specified level of confidence, the fraction of the solution space that is surpassed by an optimization. The Superiority of Multi-Objective Optimization to Random Sampling, or SMORS method, can evaluate quality and representativeness using dominance or other measures, e.g., a spacing measure for high-dimensional spaces. SMORS has been tested in a combinatorial optimization context using a genetic algorithm but could be useful for other optimization methods.

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This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

This report provides basic background data on the Manipulate-2020 code. This code is used for processing and "manipulation" of nuclear data in support of radiation metrology applications. The code is made available on the open GitHub repository and is available to the general nuclear data community.

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This report presents an analysis of the performance of deployable energy systems comprised of wind energy systems integrated with diesel generators, photovoltaic systems, and battery storage to meet the load requirements of a representative U.S. Army forward operating base. The analysis is conducted using HOMER, a microgrid analysis software that can search through a wide range of parameters to design and optimize microgrid power systems. The search parameters include the system architecture, the wind and solar resources, and the availability of diesel fuel. The results of the analysis measure the relative performance of the different systems and environments in terms of the overall transportation cost to deploy the system and the ability to provide resilience in terms of meeting mission critical loads.

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Calcium-silicate-hydrate (C–S–H) represents a key microstructural phase that governs the mechanical properties of concrete at a large scale. Defects in the C–S–H phase are also responsible for the poor ductility and low tensile strength of concrete. Manipulating the microstructure of C–S–H can lead to new cementitious materials with improved structural performance. This paper presents an experimental investigation aiming to characterize a new synthetic polymer-modified synthetic calcium-silicate-hydrate (C–S–H)/styrene-butadiene rubber (SBR) binder. The new C–S–H/SBR binder is produced by calcining calcium carbonate and mixing this with fumed silica (SiO2), deionized water and SBR. Mechanical, physical, chemical and microstructural characterization was conducted to measure the properties of new hardened C–S–H binder. Results from the experimental investigation demonstrate the ability to engineer a new C–S–H binder with low elastic modulus and improved toughness and bond strength by controlling the SBR content and method of C–S–H synthesis. The new binder suggests the possible development of a new family of low-modulus silica-polymer binders that might fit many engineering applications such as cementing oil and gas wells.

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The need to reduce the carbon footprint from medium- and heavy-duty diesel engines is clear; low-carbon biofuels are a powerful means to achieve this. Liquid fuels are rapidly deployed because existing infrastructure can be utilized for their production, transport, and distribution. Their impact is unique as they can decrease the greenhouse gas (GHG) emissions of existing vehicles and in applications resistant to electrification. However, introducing new diesel-like bio-blends into the market is very challenging. At a minimum, it requires a comprehensive understanding of the life-cycle GHG emissions of the fuels, the implications for refinery optimization and economics, the fuel’s impact on the infrastructure, the effect on the combustion performance of current and future vehicle fleets, and finally the implications for exhaust aftertreatment systems and compliance with emissions regulations. Such understanding is sought within the Co-Optima project.

Shear-based deformation processing by hybrid cutting-extrusion and free machining are used to make continuous strip, of thickness up to one millimeter, from low-workability AA6013-T6 in a single deformation step. The intense shear can impose effective strains as large as 2 in the strip without pre-heating of the workpiece. The creation of strip in a single step is facilitated by three factors inherent to the cutting deformation zone: highly confined shear deformation, in situ plastic deformation-induced heating and high hydrostatic pressure. The hybrid cutting-extrusion, which employs a second die located across from the primary cutting tool to constrain the chip geometry, is found to produce strip with smooth surfaces (S_a < 0.4 μm) that is similar to cold-rolled strip. The strips show an elongated grain microstructure that is inclined to the strip surfaces – a shear texture – that is quite different from rolled sheet. Furthermore, this shear texture (inclination) angle is determined by the deformation path. Through control of the deformation parameters such as strain and temperature, a range of microstructures and strengths could be achieved in the strip. When the cutting-based deformation was done at room temperature, without workpiece pre-heating, the starting T6 material was further strengthened by as much as 30% in a single step. In elevated-temperature cutting-extrusion, dynamic recrystallization was observed, resulting in a refined grain size in the strip. Implications for deformation processing of age-hardenable Al alloys into sheet form, and microstructure control therein, are discussed.

Two-color infrared multiphoton dissociation (2C-IRMPD) spectroscopy is a technique that mitigates spectral distortions due to nonlinear absorption that is inherent to one-color IRMPD. We use a 2C-IRMPD scheme that incorporates two independently tunable IR sources, providing considerable control over the internal energy content and type of spectrum obtained by varying the trap temperature, the time delays and fluences of the two infrared lasers, and whether the first or second laser wavelength is scanned. In this work, we describe the application of this variant of 2C-IRMPD to conformationally complex peptide ions. The 2C-IRMPD technique is used to record near-linear action spectra of both cations and anions with temperatures ranging from 10 to 300 K. We also determine the conditions under which it is possible to record IR spectra of single conformers in a conformational mixture. Furthermore, we demonstrate the capability of the technique to explore conformational unfolding by recording IR spectra with widely varying internal energy in the ion. The protonated peptide ions YGGFL (NH3+-Tyr-Gly-Gly-Phe-Leu, Leu-enkephalin) and YGPAA (NH3+-Tyr-Gly-Pro-Ala-Ala) are used as model systems for exploring the advantages and disadvantages of the method when applied to conformationally complex ions.

The Big Adaptive Rotor (BAR) project was initiated by the U.S. Department of Energy (DOE) in 2018 with the goal of identifying novel technologies that can enable large (>100 meter [m]) blades for low-specific-power wind turbines. Five distinct tasks were completed to achieve this goal: 1. Assessed the trends, impacts, and value of low-specific-power wind turbines; 2. Developed a wind turbine blade cost-reduction road map study; 3. Completed research-and-development opportunity screening; 4. Performed detailed design and analysis; and, 5. Assessed low-cost carbon fiber. These tasks were completed by the national laboratory team consisting of Sandia National Laboratories (Sandia), the National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory.

We study the structure of the threading edge dislocations, or “elbows,” which are an essential component of the well-known herringbone reconstruction of the (111) surface of Au. Previous work had shown that these dislocations can be stabilized by long-range elastic relaxations into the bulk. However, the validity of the harmonic spring model that had been used to estimate the energies of the dislocations is uncertain. To enable a more refined model of the dislocation energetics, we have imaged the atomic structure of these dislocations using scanning tunneling microscopy. We find that the harmonic spring model does not adequately reproduce the observed structure. We are able to reproduce the structure, however, with a two-dimensional Frenkel-Kontorova (FK) model that uses a pairwise Morse potential to describe the interactions between the top layer Au atoms on a rigid substrate. The parameters of the potential were obtained by fitting the energy of uniaxially compressed phases, or “stripes”, computed with density functional theory, as a function of surface Au density. Within this model, the formation of the threading dislocations remains unfavorable. However, the large forces on the substrate atoms near the threading-dislocation cores, render the assumption of a completely rigid substrate questionable. Indeed, if the FK parameters are modified to account for the relaxation of just one more atomic layer, threading dislocations can, in principle, become favorable, even without bulk elastic relaxations. Additional evidence for a small elbow energy is that our computed change in the Au(111) surface stress tensor caused by the (√ 3 × 22) reconstruction is considerably smaller than previous estimates.

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real-time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high-fidelity modeling in real-time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time-critical decision-making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high-fidelity computations in real-time.

The high-pressure dynamic response of titanium dioxide (TiO 2) is not only of interest because of its numerous industrial applications but also because of its structural similarities to silica (SiO 2). We performed plate impact experiments in a two-stage light gas gun, at peak stresses from 64 to 221 GPa to determine the TiO 2 response along the Hugoniot. The lower stress experiment at 64 GPa shows an elastic behavior followed by an elastic-plastic transition, whereas the high stress experiments above 64 GPa show a single wave structure. Previous shock studies have shown the presence of high-pressure phases (HPP) I (26 GPa) and HPP II (100 GPa); however, our data suggest that the HPP I phase is stable up to 150 GPa. Using a combination of data from our current study and our previous Z-data, we determine that TiO 2 likely melts on the Hugoniot at 157 GPa. Furthermore, our data confirm that TiO 2 is not highly incompressible as shown by a previous study.