Publications

Ducted fuel injection (DFI) is a strategy to improve fuel/charge-gas mixing in direct-injection compression-ignition engines. DFI involves injecting fuel along the axis of a small tube in the combustion chamber, which promotes the formation of locally leaner mixtures in the autoignition zone relative to conventional diesel combustion. Previous work has demonstrated that DFI is effective at curtailing engine-out soot emissions across a wide range of operating conditions. This study extends previous investigations, presenting engine-out emissions and efficiency trends between ducted two-orifice and ducted four-orifice injector tip configurations. For each configuration, parameters investigated include injection pressure, injection duration, intake manifold pressure, intake manifold temperature, start of combustion timing, and intake-oxygen mole fraction. For both configurations and across all parameters, DFI reduced engine-out soot emissions compared to conventional diesel combustion, with little effect on other emissions and engine efficiency. Emissions trends for both configurations were qualitatively the same across the parameters investigated. The four-duct configuration had higher thermal efficiency and indicated-specific engine-out nitrogen oxide emissions but lower indicated-specific engine-out hydrocarbon and carbon monoxide emissions than the two-duct assembly. Both configurations achieved indicated-specific engine-out emissions for both soot and nitrogen oxides that comply with current on- and off-road heavy-duty regulations in the United States without exhaust-gas aftertreatment at an intake-oxygen mole fraction of 12%. High-speed in-cylinder imaging of natural soot luminosity shows that some conditions include a second soot-production phase late in the cycle. The probability of these late-cycle events is sensitive to both the number of ducted sprays and the operating conditions.

Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent molecules. We present a machine-learned electron density model based on a Euclidean neural network framework that includes a built-in understanding of equivariance to model explicitly solvated double-stranded DNA. By training the machine learning model using molecular fragments that sample the key DNA and solvent interactions, we show that the model predicts electron densities of arbitrary systems of solvated DNA accurately, resolves polarization effects that are neglected by classical force fields, and captures the physics of the DNA-solvent interaction at the ab initio level.

Recent findings suggest that ions are strongly correlated in atmospheric pressure plasmas if the ionization fraction is sufficiently high ( ≳ 10 − 5 ). A consequence is that ionization causes disorder-induced heating (DIH), which triggers a significant rise in ion temperature on a picosecond timescale. This is followed by a rise in the neutral gas temperature on a longer timescale of up to nanoseconds due to ion-neutral temperature relaxation. The sequence of DIH and ion-neutral temperature relaxation suggests a new mechanism for ultrafast neutral gas heating. Previous work considered only the case of an instantaneous ionization pulse, whereas the ionization pulse extends over nanoseconds in many experiments. Here, molecular dynamics simulations are used to analyze the evolution of ion and neutral gas temperatures for a gradual ionization over several nanoseconds. The results are compared with published experimental results from a nanosecond pulsed discharge, showing good agreement with a measurement of fast neutral gas heating.

A dry etching process to transfer the pattern of a photonic integrated circuit design for high-speed laser communications is described. The laser stack under consideration is a 3.2-µm-thick InGaAs/InAlAs/InAlGaAs epitaxial structure grown by molecular beam epitaxy. The etching was performed using Cl2-based inductively-coupled-plasma and reactive-ion-etching (ICP-RIE) reactors. Four different recipes are presented in two similar ICP-RIE reactors, with special attention paid to the etched features formed with various hard mask compositions, in-situ passivations, and process temperatures. The results indicate that it is possible to produce high-aspect-ratio features with sub-micron separation on this multilayer structure. Additionally, the results of the etching highlight the tradeoffs involved with the corresponding recipes.

NasGen provides a path for migration of structural models from NASTRAN bulk data format (BDF) into both an Exodus mesh file and an ASCII input file for Sierra Structural Dynamics (Salinas) and Solid Mechanics (Presto).

Coulomb drag experiments have been an essential tool to study strongly interacting low-dimensional systems. Historically, this effect has been explained in terms of momentum transfer between electrons in the active and the passive layer. We report Coulomb drag measurements between laterally coupled GaAs/AlGaAs quantum wires in the multiple one-dimensional (1D) sub-band regime that break Onsager's reciprocity upon both layer and current direction reversal, in contrast to prior 1D Coulomb drag results. The drag signal shows nonlinear current-voltage (I-V) characteristics, which are well characterized by a third-order polynomial fit. These findings are qualitatively consistent with a rectified drag signal induced by charge fluctuations. However, the nonmonotonic temperature dependence of this drag signal suggests that strong electron-electron interactions, expected within the Tomonaga-Luttinger liquid framework, remain important and standard interaction models are insufficient to capture the qualitative nature of rectified 1D Coulomb drag.

The On-Line Waste Library is a website that contains information regarding United States Department of Energy-managed high-level waste, spent nuclear fuel, and other wastes that are likely candidates for deep geologic disposal, with links to supporting documents for the data. This report provides supporting information for the data for which an already published source was not available.

This report describes qualitative economic effects of storing, transporting, and disposing of spent high assay low-enriched uranium nuclear fuel.

Chromium self-diffusion through stainless steel (SS) matrix and along grain boundaries is an important mechanism controlling SS structural materials corrosion. Cr diffusion in austenitic SS was simulated using canonical ab initio molecular dynamics with realistic models of type-316 SS bulk, with and without Cr vacancies, and a low-energy Σ3 twin boundary typically observed at active corrosion sites. Cr self-diffusion coefficients at 750 and 850 °C calculated using Einstein's diffusion equation are 4.2 × 10−6 and 8.1 × 10−6 Å2 ps−1 in pristine bulk, 3.8 × 10−3 and 5.5 × 10−3 Å2 ps−1 in bulk including Cr vacancies, and 9.5 × 10−2 and 1.0 × 10−1 Å2 ps−1 at a Σ3[1 1 1]60° twin boundary.

Presented in this document is a portion of the tests that exist in the Sierra Thermal/Fluids verificationtest suite. Each of these tests is run nightly with the Sierra/TF code suite and the results of the testchecked under mesh refinement against the correct analytic result. For each of the tests presented in thisdocument the test setup, derivation of the analytic solution, and comparison of the code results to theanalytic solution is provided.

Perovskite solar cells (PSCs) are emerging photovoltaic (PV) technologies capable of matching power conversion efficiencies (PCEs) of current PV technologies in the market at lower manufacturing costs, making perovskite solar modules (PSMs) cost competitive if manufactured at scale and perform with minimal degradation. PSCs with the highest PCEs, to date, are lead halide perovskites. Lead presents potential environmental and human health risks if PSMs are to be commercialized, as the lead in PSMs are more soluble in water compared to other PV technologies. Therefore, prior to commercialization of PSMs, it is important to highlight, identify, and establish the potential environmental and human health risks of PSMs as well as develop methods for assessing the potential risks. Here, we identify and discuss a variety of international standards, U.S. regulations, and permits applicable to PSM deployment that relate to the potential environmental and human health risks associated with PSMs. The potential risks for lead and other hazardous material exposures to humans and the environment are outlined which include water quality, air quality, human health, wildlife, land use, and soil contamination, followed by examples of how developers of other PV technologies have navigated human health and environmental risks previously. Potential experimentation, methodology, and research efforts are proposed to elucidate and characterize potential lead leaching risks and concerns pertaining to fires, in-field module damage, and sampling and leach testing of PSMs at end of life. Lastly, lower technology readiness level solutions to mitigate lead leaching, currently being explored for PSMs, are discussed. PSMs have the potential to become a cost competitive PV technology for the solar industry and taking steps toward understanding, identifying, and creating solutions to mitigate potential environmental and human health risks will aid in improving their commercial viability.

Tests from the Sierra Structural Dynamics verification test suite are reviewed. Each is run nightly and the results of the test checked versus the correct analytic result. For each of the tests presented in this document the test setup, derivation of the analytic solution, and comparison of the Sierra code results to the analytic solution is provided. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems.

The Integrated Tiger Series (ITS) generates a database containing energy deposition data. This data, when stored on an Exodus file, is not typically suitable for analysis within Sierra Mechanics for finite element analysis. The its2sierra tool maps data from the ITS database to the Sierra database.

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of theASC fire environment simulation project. The fire environment simulation project is directed atcharacterizing both open large-scale pool fires and building enclosure fires. Fuego represents theturbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, andabsorption coefficient model portion of the simulation software.

Testing of a compact Bremsstrahlung diode was performed at the High Energy Radiation Megavolt Electron Source III (HERMES-III) was performed at Sandia National Laboratories in November, 2023. The compact diode described here is the first prototype diode in a campaign to optimize a Bremsstrahlung diode in terms of size and dose production. The goal was to test the diode at about 13MV, and the experiment realized between 10-12MV at the diode. Modeling and simulation of this geometry was performed

As ferroelectric hafnium zirconium oxide (HZO) becomes more widely utilized in ferroelectric microelectronics, integration impacts of intentional and nonintentional dielectric interfaces and their effects upon the ferroelectric film wake-up (WU) and circuit parameters become important to understand. In this work, the effect of the addition of a linear dielectric aluminum oxide, Al2O3, below a ferroelectric Hf0.58Zr0.42O2 film in a capacitor structure for FeRAM applications with niobium nitride (NbN) electrodes was measured. Depolarization fields resulting from the linear dielectric is observed to induce a reduction of the remanent polarization of the ferroelectric. Addition of the aluminum oxide also impacts the WU of the HZO with respect to the cycling voltage applied. Intricately linked to the design of a FeRAM 1C/1T cell, the metal-ferroelectric-insulator-metal (MFIM) devices are observed to significantly shift charge related to the read states based on aluminum oxide thickness and WU cycling voltage. A 33% reduction in the separation of read states are measured, which complicates how a memory cell is designed and illustrates the importance of clean interfaces in devices.

Quantifying the radioactive sources present in gamma spectra is an ever-present and growing national security mission and a time-consuming process for human analysts. While machine learning models exist that are trained to estimate radioisotope proportions in gamma spectra, few address the eventual need to provide explanatory outputs beyond the estimation task. In this work, we develop two machine learning models for a NaI detector measurements: one to perform the estimation task, and the other to characterize the first model’s ability to provide reasonable estimates. To ensure the first model exhibits a behavior that can be characterized by the second model, the first model is trained using a custom, semi-supervised loss function which constrains proportion estimates to be explainable in terms of a spectral reconstruction. The second auxiliary model is an out-of-distribution detection function (a type of meta-model) leveraging the proportion estimates of the first model to identify when a spectrum is sufficiently unique from the training domain and thus is out-of-scope for the model. In demonstrating the efficacy of this approach, we encourage the use of meta-models to better explain ML outputs used in radiation detection and increase trust.

Living document updating the 5.16 version, SAND2023-09919 of September 2023.

Cast Monel alloys are used in many industrial applications that require a combination of good mechanical properties and excellent resistance to corrosion. Despite relative widespread use, there has been limited prior research investigating the fundamental composition–structure–property relationships. In this work, microstructural characterization, thermal analysis, electron probe microanalysis, tensile testing, and Varestraint testing were used to assess the effects of variations in nominal composition on the solidification path, microstructure, mechanical properties, and solidification cracking susceptibility of cast Monel alloys. It was found that Si segregation caused the formation of silicides at the end of solidification in grades containing at least 3 wt pct Si. While increases to Si content led to significant improvements in strengthening due to the precipitation of β1-Ni3Si, the silicide eutectics acted as crack nucleation sites during tensile loading which severely reduced ductility. The solidification cracking susceptibility of low-Si Monel alloys was found to be relatively low. However, increases to Si concentration and the onset of associated eutectic reactions increased the solidification temperature range and drastically reduced cracking resistance. Increases in the Cu and Mn concentrations were found to reduce the solubility limit of Si in austenite which promoted additional eutectic formation and exacerbated the reductions in ductility and/or weldability.

The role to which a realistic inflow turbulent boundary layer (TBL) influences transient and mean large-scale pool fire quantities of interest (QoIs) is numerically investigated. High-fidelity, low-Mach large-eddy simulations that activate low-dissipation, unstructured numerics are conducted using an unsteady flamelet combustion modeling approach with mutiphysics coupling to soot and participating media radiation transport. Three inlet profile configurations are exercised for a large-scale, high-aspect rectangular pool that is oriented perpendicular to the flow direction: a time-varying, TBL inflow profile obtained from a periodic precursor simulation, the time-mean of the transient TBL, and a steady power-law inflow profile that replicates the mean TBL crosswind velocity of 10.0 m/s at a vertical height of 10 m. Results include both qualitative transient flame evolution and quantitative flame shape with ground-level temperature and convective/radiative heat flux profiles. While transient fire events, which are driven by burst-sweep TBL coupling, such as blow-off and reattachment are vastly different in the TBL case (contributing to increased root mean square QoI fluctuation prediction and disparate flame lengths), mean surface QoI magnitudes are similar. Quadrant analysis demonstrates that the TBL configuration modifies burst-sweep phenomena at windward pool locations, while leeward recovery is found. Positive fluctuations of convective heat flux correlate with fast moving fluid away from the pool surface due to intermittent combustion events.

Developing atomically synergistic bifunctional catalysts relies on the creation of colocalized active atoms to facilitate distinct elementary steps in catalytic cycles. Herein, we show that the atomically-synergistic binuclear-site catalyst (ABC) consisting of Zn^δ+ -O-Cr⁶⁺ on zeolite SSZ-13 displays unique catalytic properties for iso-stoichiometric co-conversion of ethane and CO₂. Ethylene selectivity and utilization of converted CO₂ can reach 100 % and 99.0% under 500 °C at ethane conversion of 9.6%, respectively. In-situ/ex-situ spectroscopic studies and DFT calculations reveal atomic synergies between acidic Zn and redox Cr sites. Zn^δ+ (0 < δ < 2) sites facilitate β-C-H bond cleavage in ethane and the formation of Zn-H^δ- hydride, thereby the enhanced basicity promotes CO₂ adsorption/activation and prevents ethane C-C bond scission. The redox Cr site accelerates CO₂ dissociation by replenishing lattice oxygen and facilitates H₂O formation/desorption. This study presents the advantages of the ABC concept, paving the way for the rational design of novel advanced catalysts.

Granular matter takes many paths to pack in natural and industrial processes. The path influences the packing microstructure, particularly for frictional grains. We perform discrete element modeling simulations of different paths to construct packings of frictional spheres. Specifically, we explore four stress-controlled protocols implementing packing expansions and compressions in various combinations thereof. We characterize the eventual packed states through their dependence of the packing fraction and coordination number on packing pressure, identifying non-monotonicities with pressure that correlate with the fraction of frictional contacts. These stress-controlled, bulk-like particle simulations access very low-pressure packings, namely, the marginally stable limit, and demonstrate the strong protocol dependence of frictional granular matter.

The high-pressure compaction of three dimensional granular packings is simulated using a bonded particle model (BPM) to capture linear elastic deformation. In the model, grains are represented by a collection of point particles connected by bonds. A simple multibody interaction is introduced to control Poisson's ratio and the arrangement of particles on the surface of a grain is varied to model both high- and low-frictional grains. At low pressures, the growth in packing fraction and coordination number follow the expected behavior near jamming and exhibit friction dependence. As the pressure increases, deviations from the low-pressure power-law scaling emerge after the packing fraction grows by approximately 0.1 and results from simulations with different friction coefficients converge. These results are compared to predictions from traditional discrete element method simulations which, depending on the definition of packing fraction and coordination number, may only differ by a factor of two. As grains deform under compaction, the average volumetric strain and asphericity, a measure of the change in the shape of grains, are found to grow as power laws and depend heavily on the Poisson's ratio of the constituent solid. Larger Poisson's ratios are associated with less volumetric strain and more asphericity and the apparent power-law exponent of the asphericity may vary. The elastic properties of the packed grains are also calculated as a function of packing fraction. In particular, we find the Poisson's ratio near jamming is 1/2 but decreases to around 1/4 before rising again as systems densify.

The epitaxial development and characterization of metamorphic “GaSb-on-silicon” buffers as substrates for antimonide devices is presented. The approach involves the growth of a spontaneously and fully relaxed GaSb metamorphic buffer in a primary epitaxial reactor, and use of the resulting “GaSb-on-silicon” wafer to grow subsequent layers in a secondary epitaxial reactor. The buffer growth involves four steps—silicon substrate preparation for oxide removal, nucleation of AlSb on silicon, growth of the GaSb buffer, and finally capping of the buffer to prevent oxidation. This approach on miscut silicon substrates leads to a buffer with negligible antiphase domain density. The growth of this buffer is based on inducing interfacial misfit dislocations between an AlSb nucleation layer and the underlying silicon substrate, which results in a fully relaxed GaSb buffer. A 1 μm thick GaSb layer buffer grown on silicon has ~9.2 × 10⁷ dislocations/cm². The complete lack of strain in the epitaxial structure allows subsequent growths to be accurately lattice matched, thus making the approach ideal for use as a substrate. Here we characterize the GaSb-on-silicon wafer using high-resolution x-ray diffraction and transmission electron microscopy. The concept’s feasibility is demonstrated by growing interband cascade light emitting devices on the GaSb-on-silicon wafer. The performance of the resulting LEDs on silicon approaches that of counterparts grown lattice matched on GaSb.

3D integration of multiple microelectronic devices improves size, weight, and power while increasing the number of interconnections between components. One integration method involves the use of metal bump bonds to connect devices and components on a common interposer platform. Significant variations in the coefficient of thermal expansion in such systems lead to stresses that can cause thermomechanical and electrical failures. More advanced characterization and failure analysis techniques are necessary to assess the bond quality between components. Frequency domain thermoreflectance (FDTR) is a nondestructive, noncontact testing method used to determine thermal properties in a sample by fitting the phase lag between an applied heat flux and the surface temperature response. The typical use of FDTR data involves fitting for thermal properties in geometries with a high degree of symmetry. In this work, finite element method simulations are performed using high performance computing codes to facilitate the modeling of samples with arbitrary geometric complexity. A gradient-based optimization technique is also presented to determine unknown thermal properties in a discretized domain. Using experimental FDTR data from a GaN-diamond sample, thermal conductivity is then determined in an unknown layer to provide a spatial map of bond quality at various points in the sample.