Publications

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The nuclear accident consequence analysis code MACCS has traditionally modeled dispersion during downwind transport using a Gaussian plume segment model. MACCS is designed to estimate consequence measures such as air concentrations and ground depositions, radiological doses, and health and economic impacts on a statistical basis over the course of a year to produce annualaveraged output measures. The objective of this work is to supplement the Gaussian atmospheric transport and diffusion (ATD) model currently in MACCS with a new option using the HYSPLIT model. HYSPLIT/MACCS coupling has been implemented, with HYSPLIT as an alternative ATD option. The subsequent calculations in MACCS use the HYSPLIT-generated air concentration, and ground deposition values to calculate the same range of output quantities (dose, health effects, risks, etc.) that can be generated when using the MACCS Gaussian ATD model. Based on the results from the verification test cases, the implementation of the HYSPLIT/MACCS coupling is confirmed. This report contains technical details of the HYSPLIT/MACCS coupling and presents a benchmark analysis using the HYSPLIT/MACCS coupling system. The benchmark analysis, which involves running specific scenarios and sensitivity studies designed to examine how the results generated by the traditional MACCS Gaussian plume segment model compare to the new, higher fidelity HYSPLIT/MACCS modeling option, demonstrates the modeling results that can be obtained by using this new option. The comparisons provided herein can also help decision-makers evaluate the potential benefit of using results based on higher fidelity modeling with the additional computational burden needed to perform the calculations. Three sensitivity studies to investigate the potential impact of alternative modeling options, regarding 1) input meteorological data set, 2) method to estimate stability class, and 3) plume dispersion model for larger distances, on consequence results were also performed. The results of these analyses are provided and discussed in this report.

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The Department of Energy (DOE) is the owner and part operator of multiple facilities in Northern California. The facilities include those located at Lawrence Livermore National Laboratory (LLNL), Lawrence Berkeley National Laboratory (LBNL), Sandia National Laboratories/California (SNL/CA) and SLAC National Accelerator Laboratory (SLAC) among other sites. Through their operations, the facilities generate hazardous waste and, thereby, are subject to the requirements of Chapter 31 of the Title 22 California Code of Regulations, Waste Minimization. The Northern California sites are primarily research and development facilities in the areas relating to national security, high-energy physics, engineering, bioscience and environmental health and safety disciplines. As mentioned above these DOE facilities are primarily research and development facilities. The hazardous wastes generated may be associated with operations that range in size from small, bench scale R&D to major maintenance and operations waste streams. Therefore, even though this document breaks down the waste streams based on California Waste Codes (CWC), the quantities of waste within one waste code category could be from many different locations and dissimilar processes. Because of the nature of the work at the sites, it is not economically feasible to try to implement source reduction measures for every process that generates a portion of the waste stream. This document identifies the processes that generate the major portion of the waste within an identified major waste stream and reports on progress made toward source reduction. In accomplishing the mission, it is DOE’s goal to eliminate waste generation and emissions giving priority to those that may present the greatest risk to human health and the environment.

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The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verfication/validation analysis of weapons systems. This document provides a user's guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.

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Operability thresholds that differentiate between functional RP-87 exploding bridge wire (EBW) detonators and nonfunctional RP-87 EBW detonators (duds) were determined by measuring the time delay between initiation and early wall movement (function time). The detonators were inserted into an externally heated hollow cylinder of aluminum and fired with current flow from a charged capacitor using an exploding bridge wire (EBW initiated). Functioning detonators responded like unheated pristine detonators when the function time was 4 μs or less. The operability thresholds of the detonators were characterized with a simple decomposition cookoff model calibrated using a modified version of the Sandia Instrumented Thermal Ignition (SITI) experiment. These thresholds are based on the calculated state of the PETN when the detonators fire. The operability threshold is proportional to the positive temperature difference (ΔT) between the maximum temperature within the PETN and the onset of decomposition (∼406 K). The temperature difference alone was not sufficient to define the operability threshold. The operability threshold was also proportional to the time that the PETN had been at elevated temperatures. That is, failure was proportional to both temperature and reaction rate. The reacted gas fraction is used in the current work for the reaction correlation. Melting of PETN also had a significant effect on the operability threshold. Detonator failure occurred when the maximum temperature exceeded the nominal melting point of PETN (414 K) for 45±5 s or more.

There are many scenarios in which a mobile agent may not want its path to be predictable. Examples include preserving privacy or confusing an adversary. However, this desire for deception can conflict with the need for a low path cost. Optimal plans such as those produced by RRT∗ may have low path cost, but their optimality makes them predictable. Similarly, a deceptive path that features numerous zig-zags may take too long to reach the goal. We address this trade-off by drawing inspiration from adversarial machine learning. We propose a new planning algorithm, which we title Adversarial RRT*. Adversarial RRT∗ attempts to deceive machine learning classifiers by incorporating a predicted measure of deception into the planner cost function. Adversarial RRT∗ considers both path cost and a measure of predicted deceptiveness in order to produce a trajectory with low path cost that still has deceptive properties. We demonstrate the performance of Adversarial RRT*, with two measures of deception, using a simulated Dubins vehicle. We show how Adversarial RRT∗ can decrease cumulative RNN accuracy across paths to 10%, compared to 46% cumulative accuracy on near-optimal RRT∗ paths, while keeping path length within 16% of optimal. We also present an example demonstration where the Adversarial RRT∗ planner attempts to safely deliver a high value package while an adversary observes the path and tries to intercept the package.

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Corynebacterium glutamicum has been successfully employed for the industrial production of amino acids and other bioproducts, partially due to its native ability to utilize a wide range of carbon substrates. We demonstrated C. glutamicum as an efficient microbial host for utilizing diverse carbon substrates present in biomass hydrolysates, such as glucose, arabinose, and xylose, in addition to its natural ability to assimilate lignin-derived aromatics. As a case study to demonstrate its bioproduction capabilities, L-lactate was chosen as the primary fermentation end product along with acetate and succinate. C. glutamicum was found to grow well in different aromatics (benzoic acid, cinnamic acid, vanillic acid, and p-coumaric acid) up to a concentration of 40 mM. Besides, ¹³C-fingerprinting confirmed that carbon from aromatics enter the primary metabolism via TCA cycle confirming the presence of β-ketoadipate pathway in C. glutamicum. ¹³C-fingerprinting in the presence of both glucose and aromatics also revealed coumarate to be the most preferred aromatic by C. glutamicum contributing 74 and 59% of its carbon for the synthesis of glutamate and aspartate respectively. ¹³C-fingerprinting also confirmed the activity of ortho-cleavage pathway, anaplerotic pathway, and cataplerotic pathways. Finally, the engineered C. glutamicum strain grew well in biomass hydrolysate containing pentose and hexose sugars and produced L-lactate at a concentration of 47.9 g/L and a yield of 0.639 g/g from sugars with simultaneous utilization of aromatics. Succinate and acetate co-products were produced at concentrations of 8.9 g/L and 3.2 g/L, respectively. Our findings open the door to valorize all the major carbon components of biomass hydrolysate by using C. glutamicum as a microbial host for biomanufacturing.

Demonstration of broadband nanosecond coherent anti-Stokes Raman scattering (CARS) using a burst-mode-pumped noncolinear optical parametric oscillator (NOPO) has been achieved at a pulse repetition rate of 40 kHz. The NOPO is pumped with the 355-nm output of a burst-mode Nd:YAG laser at 50 mJ/pulse for 45 pulses and produces an output centered near 607 nm, with a bandwidth of 370 cm−1 at energies of 5 mJ/pulse. A planar BOXCARS phase matching scheme uses the broadband NOPO output as the Stokes beam and the narrowband 532-nm burst-mode output for the two CARS pump beams for single-laser-shot nitrogen thermometry in near adiabatic H2/air flames at temperatures up to 2200 K.

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Silicon spin qubits satisfy the necessary criteria for quantum information processing. However, a demonstration of high-fidelity state preparation and readout combined with high-fidelity single- and two-qubit gates, all of which must be present for quantum error correction, has been lacking. We use a two-qubit Si/SiGe quantum processor to demonstrate state preparation and readout with fidelity greater than 97%, combined with both singleand two-qubit control fidelities exceeding 99%. The operation of the quantum processor is quantitatively characterized using gate set tomography and randomized benchmarking. Our results highlight the potential of silicon spin qubits to become a dominant technology in the development of intermediate-scale quantum processors.

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We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-To-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a > 10× gain in energy efficiency over state-of-The-Art digital and analog inference accelerators.

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The SPECTACULAR model is a development extension of the Simplified Potential Energy Clock (SPEC) model. Both models are nonlinear viscoelastic constitutive models used to predict a wide range of time-dependent behaviors in epoxies and other glass-forming materials. This report documents the procedures used to generate SPECTACULAR calibrations for two particulate-filled epoxy systems, 828/CTBN/DEA/GMB and 828/DEA/GMB. No previous SPECTACULAR or SPEC calibration exists for 828/CTBN/DEA/GMB, while a legacy SPEC calibration exists for 828/DEA/GMB. To generate the SPECTACULAR calibrations, a step-by-step procedure was executed to determine parameters in groups with minimal coupling between parameter groups. This procedure has often been deployed to calibrate SPEC, therefore the resulting SPECTACULAR calibration is backwards compatible with SPEC (i.e. none of the extensions specific to SPECTACULAR are used). The calibration procedure used legacy Sandia experimental data stored on the Polymer Properties Database website. The experiments used for calibration included shear master curves, isofrequency temperature sweeps under oscillatory shear, the bulk modulus at room temperature, the thermal strain during a temperature sweep, and compression through yield at multiple temperatures below the glass transition temperature. Overall, the calibrated models fit the experimental data remarkably well. However, the glassy shear modulus varies depending on the experiment used to calibrate it. For instance, the shear master curve, isofrequency temperature sweep under oscillatory shear, and the Young's modulus in glassy compression yield values for the glassy shear modulus at the reference temperature that vary by as much as 15 %. Also, for 828/CTBN/DEA/GMB, the temperature dependence of the glassy shear modulus when fit to the Young's modulus at different temperatures is approximately four times larger than when it is determined from the isofrequency temperature sweep under oscillatory shear. For 828/DEA/GMB, the temperature dependence of the shear modulus determined from the isofrequency temperature sweep under oscillatory shear accurately predicts the Young's modulus at different temperatures. When choosing values for the shear modulus, fitting the glassy compression data was prioritized. The new and legacy calibrations for 828/DEA/GMB are similar and appear to have been calibrated from the same data. However, the new calibration improves the fit to the thermal strain data. In addition to the standard calibrations, development calibrations were produced that take advantage of development features of SPECTACULAR , including an updated equilibrium Helmholtz free energy that eliminates undesirable behavior found in previous work. In addition to the previously mentioned experimental data, the development calibrations require data for the heat capacity during a stress-free temperature sweep to calibrate thermal terms.

Fuel costs and emissions in maritime ports are an opportunity for transportation energy efficiency improvement and emissions reduction efforts. Ocean-going vessels, harbor craft, and cargo handling equipment are still major contributors to air pollution in and around ports. Diesel engine costs continually increase as tighter criteria pollutant regulations come into effect and will continue to do so with expected introduction of carbon emission regulations. Diesel fuel costs will also continue to rise as requirements for cleaner fuels are imposed. Both aspects will increase the cost of diesel-based power generation on the vessel and on shore. Although fuel cells have been used in many successful applications, they have not been technically or commercially validated in the port environment. One opportunity to do so was identified in Honolulu Harbor at the Young Brothers Ltd. wharf. At this facility, barges sail regularly to and from neighboring islands and containerized diesel generators provide power for the reefers while on the dock and on the barge during transport, nearly always at part load. Due to inherent efficiency characteristics of fuel cells and diesel generators, switching to a hydrogen fuel cell power generator was found to have potential emissions and cost savings. Deployment in Hawaii showed the unit needed greater reliability in the start-up sequence, as well as an improved interface to the end-user, thereby presenting opportunities for repairing/upgrading the unit for deployment in another locale. In FY2018, the unit was repaired and upgraded based on the Hawaii experience, and another deployment site was identified for another 6-month deployment of the 100 kW MarFC.

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Vertical gallium nitride (GaN) p-n diodes have garnered significant interest for use in power electronics where high-voltage blocking and high-power efficiency are of concern. In this article, we detail the growth and fabrication methods used to develop a large area (1 mm2) vertical GaN p-n diode capable of a 6.0-kV breakdown. We also demonstrate a large area diode with a forward pulsed current of 3.5 A, an 8.3-mΩ·cm2 differential specific ON-resistance, and a 5.3-kV reverse breakdown. In addition, we report on a smaller area diode (0.063 mm2) that is capable of 6.4-kV breakdown with a differential specific ON-resistance of 10.2 m·Ω·cm2, when accounting for current spreading through the drift region at a 45° angle. Finally, the demonstration of avalanche breakdown is shown for a 0.063-mm2 diode with a room temperature breakdown of 5.6 kV. These results were achieved via epitaxial growth of a 50-μm drift region with a very low carrier concentration of < 1×1015 cm-3 and a carefully designed four-zone junction termination extension.

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Wind energy can provide renewable, sustainable electricity to rural Native homes and power schools and businesses. It can even provide tribes with a source of income and economic development. The purpose of this research is to determine the potential for deploying community and utility-scale wind renewable technologies on Turtle Mountain Band of Chippewa tribal lands. Ideal areas for wind technology development were investigated, based on wind resources, terrain, land usage, and other factors. This was done using tools like the National Renewable Energy Laboratory Wind Prospector, in addition to consulting tribal members and experts in the field. The result was a preliminary assessment of wind energy potential on Turtle Mountain lands, which can be used to justify further investigation and investment into determining the feasibility of future wind technology projects.

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The Department of Energy (DOE) is the owner of multiple facilities in Northern California. The facilities include Lawrence Livermore National Laboratory (LLNL), Lawrence Berkeley National Laboratory (LBNL), Sandia National Laboratories/California (SNL/CA) and SLAC National Accelerator Laboratory (SLAC) among other sites. Through their operations, the facilities generate hazardous waste and, thereby, are subject to the requirements of Chapter 31 of the Title 22 California Code of Regulations, Waste Minimization. The Northern California sites are primarily research and development facilities in the areas relating to national security, high-energy physics, bioscience and the environment.

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