Publications

This document presents the process for a new method developed for the characterization of the delayed gamma-ray radiation fields in pulse reactors like the Annular Core Research Reactor (ACRR) and the Fueled Ring External Cavity (FREC-II). The environments used to test this method in the ACRR were FF, LB44, PLG and CdPoly, and the environments used in the FREC-II were FF with rods-down, FF with rods-up, CdPoly with rods-down and CdPoly with rods-up. All environment configurations used the same fission product gamma-ray source energy spectrum. This method required the fission sites located in the MCNP KCODE source tapes. A FORTRAN script was written to translate and extract the coordinates for the fission sites. The 10K fission sites were then input it into an MCNP SOURCE mode script. Using a MATLAB script, a parametric analysis was done, and it helped determine that 10K fission sites are an appropriate number of coordinates to converge to the correct answer. The method gave excellent results and was tested in the ACRR, FREC-II and White Sands Missile Range (WSMR). This method can be applied to other pulse research reactors as well.

We provide an overview of the experimental techniques, measurement modalities, and diverse applications of the quantum diamond microscope (QDM). The QDM employs a dense layer of fluorescent nitrogen-vacancy (NV) color centers near the surface of a transparent diamond chip on which a sample of interest is placed. NV electronic spins are coherently probed with microwaves and optically initialized and read out to provide spatially resolved maps of local magnetic fields. NV fluorescence is measured simultaneously across the diamond surface, resulting in a wide-field, two-dimensional magnetic field image with adjustable spatial pixel size set by the parameters of the imaging system. NV measurement protocols are tailored for imaging of broadband and narrowband fields, from DC to GHz frequencies. Here we summarize the physical principles common to diverse implementations of the QDM and review example applications of the technology in geoscience, biology, and materials science.

Tin-germanium alloys are increasingly of interest as optoelectronic and thermoelectric materials as well as materials for Li/Na ion battery electrodes. However, the lattice incompatibility of bulk Sn and Ge makes creating such alloys challenging. By exploiting the unique strain tolerance of nanosized crystals, we have developed a facile synthetic method for homogeneous SnxGe1-x alloy nanocrystals with composition varying from essentially pure Ge to 95% Sn while still maintaining the cubic structure.

An electrodeposition process for void-free bottom-up filling of sub-millimeter scale through silicon vias (TSVs) with Cu is detailed. The 600 μm deep and nominally 125 μm diameter metallized vias were filled with Cu in less than 7 hours under potentiostatic control. The electrolyte is comprised of 1.25 mol/L CuSO4 - 0.25 mol/L CH3SO3H with polyether and halide additions that selectively suppress metal deposition on the free surface and side walls. A brief qualitative discussion of the procedures used to identify and optimize the bottom-up void-free feature filling is presented.

Understanding the impact of distributed photovoltaic (PV) resources on various elements of the distribution feeder is imperative for their cost effective integration. A year-long quasi-static time series (QSTS) simulation at 1-second granularity is often necessary to fully study these impacts. However, the significant computational burden associated with running QSTS simulations is a major challenge to their adoption. In this paper, we propose a fast scalable QSTS simulation algorithm that is based on a linear sensitivity model for estimating voltage-related PV impact metrics of a three-phase unbalanced, nonradial distribution system with various discrete step control elements including tap changing transformers and capacitor banks. The algorithm relies on computing voltage sensitivities while taking into account all the effects of discrete controllable elements in the circuit. Consequently, the proposed sensitivity model can accurately estimate the state of controllers at each time step and the number of control actions throughout the year. For the test case of a real distribution feeder with 2969 buses (5469 nodes), 6 load/PV time series power profiles, and 9 voltage regulating elements including controller delays, the proposed algorithm demonstrates a dramatic time reduction, more than 180 times faster than traditional QSTS techniques.

The curing of diglycidyl ether of bisphenol A (DGEBA) epoxy with diethanolamine (DEA) is studied. DEA has three reactive groups, a secondary amine hydrogen and two hydroxyls. The secondary amine reacts rapidly, forming an adduct containing tertiary amines, epoxides and hydroxyls. The epoxides and hydroxyls then react in the presence of the amines to crosslink and vitrify the epoxy in the “gelation” reaction. The gelation reaction, the subject of this study, is not simple. The reaction exhibits unusual dependencies on both temperature and degree of cure. Previously, the general mechanisms of this curing process were explored by a number of us. In the present paper, both differential scanning calorimetry (DSC) and isothermal microcalorimetry (IMC) are used to determine a number of characteristic times associated with the reaction. The characteristic times show that the reaction rate has different functional forms at different temperatures and extents of reaction. This results from the reaction rate not depending solely upon the temperature and over-all extent-of-reaction. The concentration of a number of auxiliary reactive species that are generated in the course of the reaction (as well as their mobility and steric hindrance) appear to be key factors in defining the reaction kinetics. The dependence of the final network structure on cure schedule for this type of tertiary amine activated reaction is then discussed in the context of the literature. Finally, in the Supplementary Material, Kamal-like functions are fit to the isothermal reaction kinetics, with the reader cautioned in applying the functions to non-isothermal cures.

Widespread permafrost thaw in response to changing climate conditions has the potential to dramatically impact ecosystems, infrastructure, and the global carbon budget. Ambient seismic noise techniques allow passive subsurface monitoring that could provide new insights into permafrost vulnerability and active-layer processes. Using nearly 2 years of continuous seismic data recorded near Fairbanks, Alaska, we measured relative velocity variations that showed a clear seasonal cycle reflecting active-layer freeze and thaw. Relative to January 2014, velocities increased up to 3% through late spring, decreased to −8% by late August, and then gradually returned to the initial values by the following winter. Velocities responded rapidly (over ~2 to 7 days) to discrete hydrologic events and temperature forcing and indicated that spring snowmelt and infiltration events from summer rainfall were particularly influential in propagating thaw across the site. Velocity increases during the fall zero-curtain captured the refreezing process and incremental ice formation. Looking across multiple frequency bands (3–30 Hz), negative relative velocities began at higher frequencies earlier in the summer and then shifted lower when active-layer thaw deepened, suggesting a potential relationship between frequency and thaw depth; however, this response was dependent on interstation distance. Bayesian tomography returned 2-D time-lapse images identifying zones of greatest velocity reduction concentrated in the western side of the array, providing insight into the spatial variability of thaw progression, soil moisture, and drainage. This study demonstrates the potential of passive seismic monitoring as a new tool for studying site-scale active-layer and permafrost thaw processes at high temporal and spatial resolution.

Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy, is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed frame- work of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters, within a Bayesian inference context.We focus specifically on a polynomial chaos representation with addi- tive modification of existing model parameters, enabling a nonintrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors’ contributions to predictive uncer- tainty. The framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem.

Emerging memory devices, such as resistive crossbars, have the capacity to store large amounts of data in a single array. Acquiring the data stored in large-capacity crossbars in a sequential fashion can become a bottleneck. We present practical methods, based on sparse sampling, to quickly acquire sparse data stored on emerging memory devices that support the basic summation kernel, reducing the acquisition time from linear to sub-linear. The experimental results show that at least an order of magnitude improvement in acquisition time can be achieved when the data are sparse. In addition, we show that the energy cost associated with our approach is competitive to that of the sequential method.

This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110-km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model's strong aerosol-related effective radiative forcing (ERFari+aci = −1.65 W/m2) and high equilibrium climate sensitivity (ECS = 5.3 K).

In magneto-inertial-fusion experiments, energy losses such as a radiation need to be well controlled in order to maximize the compressional work done on the fuel and achieve thermonuclear conditions. One possible cause for high radiation losses is high-Z material mixing from the target components into the fuel. In this work, we analyze the effects of mix on target performance in Magnetized Liner Inertial Fusion (MagLIF) experiments at Sandia National Laboratories. Our results show that mix is likely produced from a variety of sources, approximately half of which originates during the laser heating phase and the remainder near stagnation, likely from the liner deceleration. By changing the "cushion" component of MagLIF targets from Al to Be, we achieved a 10× increase in neutron yield, a 60% increase in ion temperature, and an ∼50% increase in fuel energy at stagnation.

Flame-sampling experiments, especially in conjunction with laminar low-pressure premixed flames, are routinely used in combustion chemistry studies to unravel the identities and quantities of key intermediates and their pathways. In many instances, however, an unambiguous interpretation of the experimental and modeling results is hampered by the uncertainties about the probe-induced, perturbed temperature profile. To overcome this limitation, two-dimensional perturbations of the temperature field caused by sampling probes with different geometries have been investigated using synchrotron-based X-ray fluorescence spectroscopy. In these experiments, which were performed at the 7-BM beamline of the Advanced Photon Source (APS) at the Argonne National Laboratory, a continuous beam of hard X-rays at 15keV was used to excite krypton atoms that were added in a concentration of 5vol.-% to the unburnt gas mixture and the resulting krypton fluorescence at 12.65keV was subsequently collected. The highly spatially resolved signal was converted into the local flame temperature to obtain temperature fields at various burner-probe separations as functions of the distance to the burner surface and the radial distance from the centerline. Multiple measurements were performed with different probe geometries and because of the observed impact on the temperature profiles the results clearly revealed the need to specify the sampling probe design to enable quantitative and meaningful comparisons of modeling results with flame-sampled mole fraction data.

Society depends on scientists and engineers to help address critical challenges in national security, efficient and sustainable energy systems, environmental stewardship and other complex areas of global importance. In the U.S., national laboratories and universities have traditionally served as research and development engines and sources of critical expertise in service to the nation.

Abstract not provided.

The term photonic wire laser is now widely used for lasers with transverse dimensions much smaller than the wavelength. As a result, a large fraction of the mode propagates outside the solid core. Here, we propose and demonstrate a scheme to form a coupled cavity by taking advantage of this unique feature of photonic wire lasers. In this scheme, we used quantum cascade lasers with antenna-coupled third-order distributed feedback grating as the platform. Inspired by the chemistry of hybridization, our scheme phase-locks multiple such lasers by π coupling. With the coupled-cavity laser, we demonstrated several performance metrics that are important for various applications in sensing and imaging: a continuous electrical tuning of ~10 GHz at ~3.8 THz (fractional tuning of ~0.26%), a good level of output power (~50–90 mW of continuous-wave power) and tight beam patterns (~100 of beam divergence).

As batteries become more prevalent in grid energy storage applications, the controllers that decide when to charge and discharge become critical to maximizing their utilization. Controller design for these applications is based on models that mathematically represent the physical dynamics and constraints of batteries. Unrepresented dynamics in these models can lead to suboptimal control. Our goal is to examine the state-of-the-art with respect to the models used in optimal control of battery energy storage systems (BESSs). This review helps engineers navigate the range of available design choices and helps researchers by identifying gaps in the state-of-the-art. BESS models can be classified by physical domain: state-of-charge (SoC), temperature, and degradation. SoC models can be further classified by the units they use to define capacity: electrical energy, electrical charge, and chemical concentration. Most energy based SoC models are linear, with variations in ways of representing efficiency and the limits on power. The charge based SoC models include many variations of equivalent circuits for predicting battery string voltage. SoC models based on chemical concentrations use material properties and physical parameters in the cell design to predict battery voltage and charge capacity. Temperature is modeled through a combination of heat generation and heat transfer. Heat is generated through changes in entropy, overpotential losses, and resistive heating. Heat is transferred through conduction, radiation, and convection. Variations in thermal models are based on which generation and transfer mechanisms are represented and the number and physical significance of finite elements in the model. Modeling battery degradation can be done empirically or based on underlying physical mechanisms. Empirical stress factor models isolate the impacts of time, current, SoC, temperature, and depth-of-discharge (DoD) on battery state-of-health (SoH). Through a few simplifying assumptions, these stress factors can be represented using regularization norms. Physical degradation models can further be classified into models of side-reactions and those of material fatigue. This article demonstrates the importance of model selection to optimal control by providing several example controller designs. Simpler models may overestimate or underestimate the capabilities of the battery system. Adding details can improve accuracy at the expense of model complexity, and computation time. Our analysis identifies six gaps: deficiency of real-world data in control literature, lack of understanding in how to balance modeling detail with the number of representative cells, underdeveloped model uncertainty based risk-averse and robust control of BESS, underdevelopment of nonlinear energy based SoC models, lack of hysteresis in voltage models used for control, lack of entropy heating and cooling in thermal modeling, and deficiency of knowledge in what combination of empirical degradation stress factors is most accurate. These gaps are opportunities for future research.

While peak shaving is commonly used to reduce power costs, chemical process facilities that can reduce power consumption on demand during emergencies (e.g., extreme weather events) bring additional value through improved resilience. For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We present a grid‐centric mixed‐integer stochastic programming framework to determine the value of DR for improving grid resilience in place of capital investments that can be cost prohibitive for system operators. We formulate problems using both a linear approximation and a nonlinear alternating current power flow model. Our numerical results with both models demonstrate that DR can be used to reduce the capital investment necessary for resilience, increasing the value that chemical process facilities bring through DR. However, the linearized model often underestimates the amount of DR needed in our case studies. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. AIChE J , 65: e16508, 2019

Photodetection plays a key role in basic science and technology, with exquisite performance having been achieved down to the single-photon level. Further improvements in photodetectors would open new possibilities across a broad range of scientific disciplines and enable new types of applications. However, it is still unclear what is possible in terms of ultimate performance and what properties are needed for a photodetector to achieve such performance. Here, we present a general modeling framework for photodetectors whereby the photon field, the absorption process, and the amplification process are all treated as one coupled quantum system. The formalism naturally handles field states with single or multiple photons as well as a variety of detector configurations and includes a mathematical definition of ideal photodetector performance. The framework reveals how specific photodetector architectures introduce limitations and tradeoffs for various performance metrics, providing guidance for optimization and design.

We invert far-field infrasound data for the equivalent seismoacoustic time-domain moment tensor to assess the effects of variable atmospheric models and source phenomena. The infrasound data were produced by a series of underground chemical explosions that were conducted during the Source Physics Experiment (SPE), which was originally designed to study seismoacoustic signal phenomena. The first goal is to investigate the sensitivity of the inversion to the variability of the estimated atmospheric model. The second goal is to determine the relative contribution of two presumed source mechanisms to the observed infrasonic wavefield. Rather than using actual atmospheric observations to estimate the necessary atmospheric Green’s functions, we build a series of atmospheric models that rely on publicly available, regional-scale atmospheric observations. The atmospheric observations are summarized and interpolated onto a 3D grid to produce a model of sound speed at the time of the experiment. For each of four SPE acoustic datasets that we invert, we produced a suite of three atmospheric models for each chemical explosion event, based on 10 yrs of meteorological data: an average model, which averages the atmospheric conditions for 10 yrs prior to each SPE event, as well as two extrema models. To parameterize the inversion, we assume that the source of infrasonic energy results from the linear combination of explosion-induced surface spall and linear seismic-to-elastic mode conversion at the Earth’s free surface. We find that the inversion yields relatively repeatable results for the estimated spall source. Conversely, the estimated isotropic explosion source is highly variable. This suggests that 1) the majority of the observed acoustic energy is produced by the spall and/or 2) our modeling of the elastic energy, and the subsequent conversion to acoustic energy, is too simplistic.

Large-scale collaborative scientific software projects require more knowledge than any one person typically possesses. This makes coordination and communication of knowledge and expertise a key factor in creating and safeguarding software quality, without which we cannot have sustainable software. However, as researchers attempt to scale up the production of software, they are confronted by problems of awareness and understanding. This presents an opportunity to develop better practices and tools that directly address these challenges. To that end, we conducted a case study of developers of the Trilinos project. We surveyed the software development challenges addressed and show how those problems are connected with what they know and how they communicate. Based on these data, we provide a series of practicable recommendations, and outline a path forward for future research.

Applications involving quantum physics are becoming an increasingly important area for electromagnetic engineering. To address practical problems in these emerging areas, appropriate numerical techniques must be utilized. However, the unique needs of many of these applications require new computational electromagnetic solvers to be developed. The A-4:1. formulation is a novel approach that can address many of these needs. This formulation utilizes equations developed in terms of the magnetic vector potential (A) and electric scalar potential (t.). The resulting equations overcome many of the limitations of traditional solvers and are ideal for coupling to quantum mechanical calculations. In this work, the A-4. formulation is extended by developing time domain integral equations suitable for multiscale perfect electric conducting objects. These integral equations can be stably discretized and constitute a robust numerical technique that is a vital step in addressing the needs of many emerging applications. To validate the proposed formulation, numerical results are presented which demonstrate the stability and accuracy of the method.

Energy storage systems are flexible resources that accommodate and mitigate variability and uncertainty in the load and generation of modern power systems. We present a stochastic optimization approach for sizing and scheduling an energy storage system (ESS) for behind-the-meter use. Specifi-cally, we investigate the use of an ESS with a solar photovoltaic (PV) system and a generator in islanded operation tasked with balancing a critical load. The load and PV generation are uncertain and variable, so forecasts of these variables are used to determine the required energy capacity of the ESS as well as the schedule for operating the ESS and the generator. When the forecasting uncertainties can be fit to normal distributions, the probabilistic load balancing constraint can be reformulated as a linear inequality constraint, and the resulting optimization problem can be solved as a linear program. Finally, we present results from a case study considering the balancing of the critical load of a water treatment plant in islanded operation.

Adversary sophistication in the cyber domain is a constantly growing threat. As more systems become accessible from the Internet, the risk of breach, exploitation, and malice grows. To thwart reconnaissance and exploitation, Moving Target Defense (MTD) has been researched and deployed in various systems to modify the threat surface of a system. Tools are necessary to analyze the security, reliability, and resilience of their information systems against cyber-Attack and measure the effectiveness of the MTD technologies. Today's security analyses utilize (1) real systems such as computers, network routers, and other network equipment; (2) computer emulations (e.g., virtual machines); and (3) simulation models separately. In this paper, we describe the progress made in developing and utilizing hybrid Live, Virtual, Constructive (LVC) environments for the evaluation of a set of MTD technologies. The LVC methodology has been most rooted in the Modeling Simulation (MS) work of the Department of Defense. With the recent advances in virtualization and software-defined networking, Sandia has taken the blueprint for LVC and extended it by crafting hybrid environments of simulation, emulation, and human-in-The-loop. Furthermore, we discuss the empirical analysis of MTD technologies and approaches with LVC-based experimentation, incorporating aspects that may impact an operational deployment of the MTD under evaluation.

Operational Technology (OT) networks existed well before the dawn of the Internet, and had enjoyed security through being air-gapped and isolated. However, the interconnectedness of the world has found its way into these OT networks, exposing their vulnerabilities for cyber attacks. As the global Internet continues to grow, it becomes more and more embedded with the physical world. The Internet of Things is one such example of how IT is blurring the cyber-physical boundaries. The eventuality will be a convergence of IT and OT. Until that day comes, cyber practitioners must still deal with the primitive security features of OT networks, maintain a foothold on enterprise and cloud networks, and attempt to instill sound security practices in burgeoning IoT networks. In this paper, we propose a new method to bring cyber security to OT and IoT-based networks, through Multi-Agent Systems (MAS). MAS are flexible enough to integrate with fixed legacy networks, such as ICS, as well with be burned into newer devices and software, such as IoT and IT networks. In this paper, we discuss the features of MAS, the opportunities that exist to benefit cyber security, and a proposed architecture for a OT-based MAS.

Adversary sophistication in the cyber domain is a constantly growing threat. As more systems become accessible from the Internet, the risk of breach, exploitation, and malice grows. To thwart reconnaissance and exploitation, Moving Target Defense (MTD) has been researched and deployed in various systems to modify the threat surface of a system. Tools are necessary to analyze the security, reliability, and resilience of their information systems against cyber-Attack and measure the effectiveness of the MTD technologies. Today's security analyses utilize (1) real systems such as computers, network routers, and other network equipment; (2) computer emulations (e.g., virtual machines); and (3) simulation models separately. In this paper, we describe the progress made in developing and utilizing hybrid Live, Virtual, Constructive (LVC) environments for the evaluation of a set of MTD technologies. The LVC methodology has been most rooted in the Modeling Simulation (MS) work of the Department of Defense. With the recent advances in virtualization and software-defined networking, Sandia has taken the blueprint for LVC and extended it by crafting hybrid environments of simulation, emulation, and human-in-The-loop. Furthermore, we discuss the empirical analysis of MTD technologies and approaches with LVC-based experimentation, incorporating aspects that may impact an operational deployment of the MTD under evaluation.

Recently there has been a large interest in achieving metasurface resonances with large quality factors. In this article, we examine metasurfaces that comprised a finite number of magnetic dipoles oriented parallel or orthogonal to the plane of the metasurface and determine analytic formulas for their resonances’ quality factors. These conditions are experimentally achievable in finite-size metasurfaces made of dielectric cubic resonators at the magnetic dipole resonance. Our results show that finite metasurfaces made of parallel (to the plane) magnetic dipoles exhibit low quality factor resonances with a quality factor that is independent of the number of resonators. More importantly, finite metasurfaces made of orthogonal (to the plane) magnetic dipoles lead to resonances with large quality factors, which ultimately depend on the number of resonators comprising the metasurface. In particular, by properly modulating the array of dipole moments by having a distribution of resonator polarizabilities, one can potentially increase the quality factor of metasurface resonances even further. These results provide design guidelines to achieve a sought quality factor applicable to any resonator geometry for the development of new devices such as photodetectors, modulators, and sensors.

As part of the Source Physics Experiment (SPE) Phase I shallow chemical detonation series, multiple surface and borehole active-source seismic campaigns were executed to perform high resolution imaging of seismic velocity changes in the granitic substrate. Cross-correlation data processing methods were implemented to efficiently and robustly perform semi-automated change detection of first-arrival times between campaigns. The change detection algorithm updates the arrival times, and consequently the velocity model, of each campaign. The resulting tomographic imagery reveals the evolution of the subsurface velocity structure as the detonations progressed.

Energy storage is a unique grid asset in that it is capable of providing a number of grid services. In market areas, these grid services are only as valuable as the market prices for the services provided. This paper formulates the optimization problem for maximizing energy storage revenue from arbitrage and frequency regulation in the CAISO market. The optimization algorithm was then applied to three years of historical market data (2014-2016) at 2200 nodes to quantify the locational and time-varying nature of potential revenue. The optimization assumed perfect foresight, so it provides an upper bound on the maximum expected revenue. Since California is starting to experience negative locational marginal prices (LMPs) because of increased renewable generation, the optimization includes a duty cycle constraint to handle negative LMPs. The results show that participating in frequency regulation provides approximately 3.4 times the revenue of arbitrage. In addition, arbitrage potential revenue is highly location-specific. Since there are only a handful of zones for frequency regulation, the distribution of potential revenue from frequency regulation is much tighter.

This paper focuses on a transmission system with a high penetration of converter-interfaced generators participating in its primary frequency regulation. In particular, the effects on system stability of widespread misconfiguration of frequency regulation schemes are considered. Failures in three separate primary frequency control schemes are analyzed by means of time domain simulations where control action was inverted by, for example, negating controller gain. The results indicate that in all cases the frequency response of the system is greatly deteriorated and, in multiple scenarios, the system loses synchronism. It is also shown that including limits to the control action can mitigate the deleterious effects of inverted control configurations.

Nuisance and false alarms are prevalent in modern physical security systems and often overwhelm the alarm station operators. Deep learning has shown progress in detection and classification tasks, however, it has rarely been implemented as a solution to reduce the nuisance and false alarm rates in a physical security systems. Previous work has shown that transfer learning using a convolutional neural network can provide benefit to physical security systems by achieving high accuracy of physical security targets [10]. We leverage this work by coupling the convolutional neural network, which operates on a frame-by-frame basis, with temporal algorithms which evaluate a sequence of such frames (e.g. video analytics). We discuss several alternatives for performing this temporal analysis, in particular Long Short-Term Memory and Liquid State Machine, and demonstrate their respective value on exemplar physical security videos. We also outline an architecture for developing an ensemble learner which leverages the strength of each individual algorithm in its aggregation. The incorporation of these algorithms into physical security systems creates a new paradigm in which we aim to decrease the volume of nuisance and false alarms in order to allow the alarm station operators to focus on the most relevant threats.

Physical security systems (PSS) and humans are inescapably tied in the current physical security paradigm. Yet, physical security system evaluations often end at the console that displays information to the human. That is, these evaluations do not account for human-in-The-loop factors that can greatly impact performance of the security system, even though methods for doing so are well-established. This paper highlights two examples of methods for evaluating the human component of the current physical security system. One of these methods is qualitative, focusing on the information the human needs to adequately monitor alarms on a physical site. The other of these methods objectively measures the impact of false alarm rates on threat detection. These types of human-centric evaluations are often treated as unnecessary or not cost effective under the belief that human cognition is straightforward and errors can be either trained away or mitigated with technology. These assumptions are not always correct, are often surprising, and can often only be identified with objective assessments of human-system performance. Thus, taking the time to perform human element evaluations can identify unintuitive human-system weaknesses and can provide significant cost savings in the form of mitigating vulnerabilities and reducing costly system patches or retrofits to correct an issue after the system has been deployed.

Unmanned aircraft system (UAS) technologies have gained immense popularity in the commercial sector and have enabled capabilities that were not available just a short time ago. Once limited to the domain of highly skilled hobbyists or precision military instruments, consumer UAS are now widespread due to increased computational power, manufacturing techniques, and numerous commercial applications. The rise of consumer UAS and the low barrier to entry necessary to utilize these systems provides an increased potential for using a UAS as a delivery platform for malicious intent. This creates a new security concern which must be addressed. The contribution presented in this work is the realization of counter UAS security technology concepts viewed through the traditional security framework and the associated challenges to such a framework.

In this work, we provide an economic analysis of using behind-the-meter (BTM) energy storage systems (ESS) for time-of-use (TOU) bill management together with power factor correction. A nonlinear optimization problem is formulated to find the optimal ESS's charge/discharge operating scheme that minimizes the energy and demand charges while correcting the power factor of the utility customers. The energy storage's state of charge (SOC) and inverter's power factor (PF) are considered in the constraints of the optimization. The problem is then transformed to a Linear Programming (LP) problem and formulated using Pyomo optimization modeling language. Case studies are conducted for a waste water treatment plant (WWTP) in New Mexico.

Utilizing historical utility outage data, an approach is presented to optimize investments which maximize reliability, i.e., minimize System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) metrics. This method is designed for distribution system operators (DSOs) to improve reliability through small investments. This approach is not appropriate for large system planning and investments (e.g. new transmission lines or generation) since further economic and stability concerns are required for this type of analysis. The first step in the reliability investment optimization is to create synthetic outage data sets for a future year based on probability density functions of historical utility outage data. Once several (likely hundreds of) future year outage scenarios are created, an optimization model is used to minimize the synthetic outage SAIDI and SAIFI norm (other metrics could also be used). The results from this method can be used for reliability system planning purposes and can inform DSOs which investments to pursue to improve their reliability metrics.

This report provides a summary of notes for building and running the Sandia Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) code. SCEPTRE is a general purpose C++ code for solving the Boltzmann transport equation in serial or parallel using unstructured spatial finite elements, multigroup energy treatment, and a variety of angular treatments including discrete ordinates and spherical harmonics. Either the first-order form of the Boltzmann equation or one of the second-order forms may be solved. SCEPTRE requires a small number of open-source Third Party Libraries (TPL) to be available, and example scripts for building these TPL's are provided. The TPL's needed by SCEPTRE are Trilinos, boost, and netcdf. SCEPTRE uses an autoconf build system, and a sample configure script is provided. Running the SCEPTRE code requires that the user provide a spatial finite-elements mesh in Exodus format and a cross section library in a format that will be described. SCEPTRE uses an xml-based input, and several examples will be provided.

We report that over the past century, and particularly since the outset of the Cold War, wargames (interactive simulations used to evaluate aspects of tactics, operations, and strategy) have become an integral means for militaries and policy-makers to evaluate how strategic decisions are made related to nuclear weapons strategy and international security. Furthermore, these methods have also been applied beyond the military realm, to examine phenomena as varied as elections, government policy, international trade, and supply-chain mechanics. Today, a renewed focus on wargaming combined with access to sophisticated and inexpensive drag-and-drop digital game development frameworks and new cloud computing architectures have democratized the ability to enable massive multiplayer gaming experiences. With the integration of simulation tools and experimental methods from a variety of social science disciplines, a science-based experimental gaming approach has the potential to transform the insights generated from gaming by creating human-derived, large-n datasets for replicable, quantitative analysis. In the following, we outline challenges associated with contemporary simulation and wargaming tools, investigate where scholars have searched for game data, and explore the utility of new experimental gaming and data analysis methods in both policy-making and academic settings.

On November 28, 2018 at approximately 4:17pm the arsenic monitor in the Acid Waste Neutralization (AWN) room located in 858N was registering a concentration above the permit level of 51ppb as stated in ABCWUA Permit 2069G Daily Composite Limit. 100ml samples had been drawn from the waste stream at - 6pm November 28, 2018. The samples were analyzed, results received on November 29, 2018 confirmed an arsenic concentration above the permit level.

Energy storage systems are flexible resources that accommodate and mitigate variability and uncertainty in the load and generation of modern power systems. We present a stochastic optimization approach for sizing and scheduling an energy storage system (ESS) for behind-the-meter use. Specifi-cally, we investigate the use of an ESS with a solar photovoltaic (PV) system and a generator in islanded operation tasked with balancing a critical load. The load and PV generation are uncertain and variable, so forecasts of these variables are used to determine the required energy capacity of the ESS as well as the schedule for operating the ESS and the generator. When the forecasting uncertainties can be fit to normal distributions, the probabilistic load balancing constraint can be reformulated as a linear inequality constraint, and the resulting optimization problem can be solved as a linear program. Finally, we present results from a case study considering the balancing of the critical load of a water treatment plant in islanded operation.

In this work, we provide an economic analysis of using behind-the-meter (BTM) energy storage systems (ESS) for time-of-use (TOU) bill management together with power factor correction. A nonlinear optimization problem is formulated to find the optimal ESS's charge/discharge operating scheme that minimizes the energy and demand charges while correcting the power factor of the utility customers. The energy storage's state of charge (SOC) and inverter's power factor (PF) are considered in the constraints of the optimization. The problem is then transformed to a Linear Programming (LP) problem and formulated using Pyomo optimization modeling language. Case studies are conducted for a waste water treatment plant (WWTP) in New Mexico.

Many membrane distillation models have been created to simulate the heat and mass exchange process involved but most of the literature only validates models to a couple of cases with minor configuration changes. Tools are needed that allow tradeoffs between many configurations. The multiconfiguration membrane distillation model handles many configurations. This report introduces membrane distillation, provides theory, and presents the work to verify and validate the model against experimental data from Colorado School of Mines and a lower resolution model created at the National Renewable Energy Laboratory. Though more data analysis and testing are needed, an initial look at the model to experimental comparisons indicates that the model correlates to the data well but that design comparisons are likely to be incorrect across a broad range of configurations. More accurate quantification of heat and mass transfer through computational fluid mechanics is suggested.

Coupling interests in small modular reactors (SMR) as efficient and effective method to meet increasing energy demands with a growing aversion to cost and schedule overruns traditionally associated with the current fleet of commercial nuclear power plants (NPP), SMRs are attractive because they offer a significant relative cost reduction to current-generation nuclear reactors—increasing their appeal around the globe. Sandia's Global Nuclear Assurance and Security (GNAS) research perspective reframes the discussion around the "complex risk" of SMRs to address interdependencies between safety, safeguards, and security. This systems study provides technically rigorous analysis of the safety, safeguards, and security risks of SMR technologies. The aim of this research is three-fold. The first aim is to provide analytical evidence to support safety, safeguards, and security claims related to SMRs (Study Report Volume I). Second, this study aims to introduce a systems-theoretic approach for exploring interdependencies between the technical evaluations (Study Report Volume II). The third aim is to demonstrate Sandia's capability for timely, rigorous, and technical analysis to support emerging complex GNAS mission objectives.

The Thermal-Mechanical Failure project conducted in FY 2018 was divided into three sub projects: 1. Calibration of the uniaxial response of 304L stainless steel specimens at three temperatures (20, 150 and 310°C) and two strain rates (2 x 10^-4 and 8 x 10^-2 s^-1); 2. Measurements of the fraction of plastic work that is converted to heat (Taylor-Quinney parameter) for 304L stainless steel. This fraction is usually assumed to be 0.95 in analysis because data is only available for a few materials; 3. Comparison of the predicted responses by isotropic and kinematic hardening plasticity models in a couple of simplified structural problems. One problem is a can crush followed by pressurization and is loosely associated with a crush-and-burn scenario. The other problem consists of a drop scenario of a thin-walled cylinder that carries a cantilevered internal mass.

We perform a joint inversion of absolute and differential P and S body waves, gravity measurements, and surface wave dispersion curves for the 3-D P- and S-wave velocity structure of the Nevada National Security Site (NNSS) and vicinity. Data from earthquakes, past nuclear tests, and other active source chemical explosive experiments, such as the Source Physics Experiments (SPE), are combined with surface wave phase and group speed measurements from ambient noise, source interferometry, and active source experiments to construct a 3-D velocity model of the site with resolvable structures as fine as 6 km horizontal and 2 km vertically. Results compare favorably with previous studies and expand and extend the knowledge of the 3-D structure of the region.

The ECP/VTK-m project is providing the core capabilities to perform scientific visualization on Exascale architectures. The ECP/VTK-m project fills the critical feature gap of performing visualization and analysis on processors like graphics-based processors and many integrated core. The results of this project will be delivered in tools like ParaView, Vislt, and Ascent as well as in stand-alone form. Moreover, these projects are depending on this ECP effort to be able to make effective use of ECP architectures.

The Nevada National Security Site (NNSS) will serve as the geologic setting for a Source Physics Experiment (SPE) program. The SPE will provide ground truth data to create and improve strong ground motion and seismic S-wave generation and propagation models. The NNSS was chosen as the test bed because it provides a variety of geologic settings ranging from relatively simple to very complex. Each series of SPE testing will comprise the setting and firing of explosive charges (source) placed in a central bore hole at varying depths and recording ground motions in instrumented bore holes located in two rings around the source positioned at different radii. Modeling using advanced simulation codes will be performed both a priori and after each test to predict ground response and to improve models based on acquired field data, respectively. A key component in the predictive capability and ultimate validation of the models is the full understanding of the intervening geology between the source and the instrumented bore holes including the geomechanical behavior of the site rock/structural features. This report presents a limited scope of work for an initial phase of primarily unconfined compression testing. Samples tested came from the U-15n core hole, which was drilled in granitic rock (quartz monzonite). The core hole was drilled at the location of the central SPE borehole, and thus represents material in which the explosive charges will be detonated. The U-15n location is the site of the first SPE, in Area 15 of the NNSS.

The Nevada National Security Site (NNSS) serves as the geologic setting for a Source Physics Experiment (SPE) program. The SPE provides ground truth data to create and improve strong ground motion and seismic S-wave generation and propagation models. The NNSS was chosen as the test bed because it provides a variety of geologic settings ranging from relatively simple to very complex. Each series of SPE testing will comprise the setting and firing of explosive charges (source) placed in a central borehole at varying depths and recording ground motions in instrumented boreholes located in two rings around the source, positioned at different radii. Modeling using advanced simulation codes will be performed both before and after each test to predict ground response and to improve models based on acquired field data, respectively. A key component in the predictive capability and ultimate validation of the models is the full understanding of the intervening geology between the source and the instrumented boreholes including the geomechanical behavior of the site's rock/structural features. This report summarizes unconfined compression testing (UCS) from coreholes U-15n#12 and U-15n#13 and compares those datasets to UCS results from coreholes U-15n and U-15n#10. U-15n#12 corehole was drilled at -60° to the horizontal and U-15n#13 was drilled vertically in granitic rock (quartz monzonite) after the third SPE shot. Figure 1 illustrates at the surface, U 15n#12 and U-15n#13 coreholes were approximately 30 meters and 10 meters from the central SPE borehole (U-15n) respectively. Corehole U-15n#12 intersects the central SPE borehole (U 15n) at a core depth of 174 feet (approximately 150 feet vertical depth). The location of U 15n#12 and U-15n#13 is the site of the first, second and third SPE's, in Area 15 of the NNSS.

Direct Shear (DS) and Triaxial Shear (FCT) tests from Core holes U-15n and U-15n#10 are part of a larger material characterization effort for the Source Physics Experiment (SPE) project. This larger effort encompasses characterizing a granite body from Nevada both before and after each SPE shot. Core hole U-15n is the vertically oriented source hole for all SPE shots; pre shot core was taken from this hole for DS and FCT testing. After two SPE shots were executed, an inclined core hole (U-15n#10) was drilled; both DS and FCT tests were conducted from this core hole. The first shot (SPE-1) conducted on May 3, 2011 was a calibration shot. SPE-1 was an order of magnitude smaller than the second shot (SPE-2). After SPE-2 was conducted on October 25, 2011 the aforementioned inclined core hole (U-15n#10) was drilled. At its bottom, the inclined core hole intersects the source hole. The third shot (SPE-3) occurred on July 24, 2012. Vertical and inclined core holes were drilled post SPE-3 and specimens will soon be selected for geomechanical characterization. At the time of this writing, work is ongoing at Nevada in preparation for the fourth SPE shot (SPE-4).

Dynamic Brazilian tension (DBR) tests from core hole U-15n are part of a larger material characterization effort for the Source Physics Experiment (SPE) project. This larger effort encompasses characterizing Climax Stock granite rock from the Nevada National Security Site (NNSS) both before and after each SPE shot. The current test series includes DBR tests on dry intact granite and fault material at depths of -85 and -150 ft.

Triaxial compression tests from core hole U-15n are part of a larger material characterization effort for the Source Physics Experiment (SPE) project. This larger effort encompasses characterizing Climax Stock granite rock from the Nevada National Security Site (NNSS) both before and after each SPE shot. The current test series includes triaxial compression tests on dry and saturated intact granite and fault material at 100, 200, 300, and 400 MPa confining pressure.

The Nevada National Security Site (NNSS) serves as the geologic setting for a Source Physics Experiment (SPE) program. The SPE provides ground truth data to create and improve strong ground motion and seismic S-wave generation and propagation models. The NNSS was chosen as the test bed because it provides a variety of geologic settings ranging from relatively simple to very complex. Each series of SPE testing will comprise the setting and firing of explosive charges (source) placed in a central borehole at varying depths and recording ground motions in instrumented boreholes located in two rings around the source, positioned at different radii. Modeling using advanced simulation codes will be performed both before and after each test to predict ground response and to improve models based on acquired field data, respectively. A key component in the predictive capability and ultimate validation of the models is the full understanding of the intervening geology between the source and the instrumented boreholes including the geomechanical behavior of the site's rock/structural features. This memorandum reports on an initial phase of unconfined compression testing from corehole U-15n#10. Specimens tested came from the U-15n#10 core hole, which was drilled at -60° to the horizontal in granitic rock (quartz monzonite) after the second SPE shot (SPE-2). Figure 1 illustrates at the surface, the core hole was approximately 90 feet from the central SPE borehole. Corehole U 15n#10 intersects the central SPE borehole (U-15n) at a core depth of 170 feet (approximately 150 feet vertical depth) which is within the highly damaged zone of SPE-2. The U-15n#10 location is the site of the first, second and third SPE's, in Area 15 of the NNSS.

Efficiency in requirements engineering and management (REM) for complex hardware systems is desirable to reduce program impacts, such as schedule and budget. Sandia National Labs (SNL) investigated external state-of-the-practice REM to capture insights, recommendations, and best practices from external entities on several REM topics. Twenty-one at-will participants contributed responses to closed- and open-ended questions. The results were synthesized and are provided herein. The results help SNL and others to understand where its practices are current; what trends, approaches, or processes in REM might be beneficial if implemented or introduced; what challenges might be avoided; where efficiencies might be realized; and which practices are still maturing or evolving in industry and academia, so that SNL can stay abreast of these developments.

Fingerprint sensing is pervasive in the cellular telecommunications market. Current commercial fingerprint sensors utilize capacitive scanning. This work focuses on the design, fabrication and characterization of post-complementary-metal-oxide-semiconductor (CMOS) compatible piezoelectric micro-machined ultrasonic transducers for use as ultrasonic pixels to improve robustness to contamination and allow for sub-epidermis scans. Ultrasonic pixels are demonstrated at frequencies ranging from 100 kHz to 800 kHz with several electrode coverages and styles to identify trends.

A facility for continuously monitoring the thermal and elastic performance of materials under exposure to ion beam irradiation has been designed and commissioned. By coupling an all-optical, non-contact, non-destructive measurement technique known as transient grating spectroscopy (TGS) to a 6 MV tandem ion accelerator, bulk material properties may be measured at high fidelity as a function of irradiation exposure and temperature. Ion beam energies and optical parameters may be tuned to ensure that only the properties of the ion-implanted surface layer are interrogated. This facility provides complementary capabilities to the set of facilities worldwide which have the ability to study the evolution of microstructure in situ during radiation exposure, but lack the ability to measure bulk-like properties. Here, the measurement physics of TGS, design of the experimental facility, and initial results using both light and heavy ion exposures are described. Lastly, several short- and long-term upgrades are discussed which will further increase the capabilities of this diagnostic.

The nuclear incident at the Fukushima Daiichi nuclear power plant has created a strong push for accident-tolerant fuel cladding to replace current zirconium-based cladding. A current near-term focus on iron-chromium-aluminum (FeCrAl) alloys. Laser-welded FeCrAl samples (C35MN, C37M, and C35M10 TC) were subjected to three different post-weld heat treatment regimes: 650 °C for 5 h, 850 °C for 1 h, and 850 °C for 5 h. Here, the samples were then analyzed using optical light microscopy, micro-hardness indentation, and scanning electron microscopy coupled with energy-dispersive spectroscopy and electron backscatter diffraction. The base microstructure of C37M and C35M10 TC experienced significant grain coarsening outside the fusion zone due to the applied post-weld heat treatments, whereas Nb-rich precipitation in C35MN limited grain growth compared with the other alloys studied.

Solid-state reaction kinetics on atomic length scales have not been heavily investigated due to the long times, high reaction temperatures, and small reaction volumes at interfaces in solid-state reactions. All of these conditions present significant analytical challenges in following reaction pathways. Herein we use in situ and ex situ X-ray diffraction, in situ X-ray reflectivity, high-angle annular dark field scanning transmission electron microscopy, and energy-dispersive X-ray spectroscopy to investigate the mechanistic pathways for the formation of a layered (Pb_0.5Sn_0.5Se)_1+δ(TiSe₂)_m heterostructure, where m is the varying number of TiSe₂ layers in the repeating structure. Thin film precursors were vapor deposited as elemental-modulated layers into an artificial superlattice with Pb and Sn in independent layers, creating a repeating unit with twice the size of the final structure. At low temperatures, the precursor undergoes only a crystallization event to form an intermediate (SnSe₂)_1+γ(TiSe₂)_m(PbSe)_1+δ(TiSe₂)_m superstructure. At higher temperatures, this superstructure transforms into a (Pb_0.5Sn_0.5Se)_1+δ(TiSe₂)_m alloyed structure. The rate of decay of superlattice reflections of the (SnSe₂)_1+γ(TiSe₂)_m(PbSe)_1+δ(TiSe₂)_m superstructure was used as the indicator of the progress of the reaction. Here, we show that increasing the number of TiSe₂ layers does not decrease the rate at which the SnSe₂ and PbSe layers alloy, suggesting that at these temperatures it is reduction of the SnSe₂ to SnSe and Se that is rate limiting in the formation of the alloy and not the associated diffusion of Sn and Pb through the TiSe₂ layers.

As computing power rapidly increases, quickly creating a representative and accurate discretization of complex geometries arises as a major hurdle towards achieving a next generation simulation capability. Component definitions may be in the form of solid (CAD) models or derived from 3D computed tomography (CT) data, and creating a surface-conformal discretization may be required to resolve complex interfacial physics. The Conformal Decomposition Finite Element Methods (CDFEM) has been shown to be an efficient algorithm for creating conformal tetrahedral discretizations of these implicit geometries without manual mesh generation. In this work we describe an extension to CDFEM to accurately resolve the intersections of many materials within a simulation domain. This capability is demonstrated on both an analytical geometry and an image-based CT mesostructure representation consisting of hundreds of individual particles. Effective geometric and transport properties are the calculated quantities of interest. Solution verification is performed, showing CDFEM to be optimally convergent in nearly all cases. Representative volume element (RVE) size is also explored and per-sample variability quantified. Relatively large domains and small elements are required to reduce uncertainty, with recommended meshes of nearly 10 million elements still containing upwards of 30% uncertainty in certain effective properties. This work instills confidence in the applicability of CDFEM to provide insight into the behaviors of complex composite materials and provides recommendations on domain and mesh requirements.

This work explores the current performance and scaling of a fully-implicit stabilized unstructured finite element (FE) variational multiscale (VMS) capability for large-scale simulations of 3D incompressible resistive magnetohydrodynamics (MHD). The large-scale linear systems that are generated by a Newton nonlinear solver approach are iteratively solved by preconditioned Krylov subspace methods. The efficiency of this approach is critically dependent on the scalability and performance of the algebraic multigrid preconditioner. This study considers the performance of the numerical methods as recently implemented in the second-generation Trilinos implementation that is 64-bit compliant and is not limited by the 32-bit global identifiers of the original Epetra-based Trilinos. The study presents representative results for a Poisson problem on 1.6 million cores of an IBM Blue Gene/Q platform to demonstrate very large-scale parallel execution. Additionally, results for a more challenging steady-state MHD generator and a transient solution of a benchmark MHD turbulence calculation for the full resistive MHD system are also presented. These results are obtained on up to 131,000 cores of a Cray XC40 and one million cores of a BG/Q system.

A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. In conclusion, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.

We study regression using functional predictors in situations where these functions contains both phase and amplitude variability. In other words, the functions are misaligned due to errors in time measurements, and these errors can significantly degrade both model estimation and prediction performance. The current techniques either ignore the phase variability, or handle it via preprocessing, that is, use an off–the–shelf technique for functional alignment and phase removal. We develop a functional principal component regression model which has a comprehensive approach in handling phase and amplitude variability. The model utilizes a mathematical representation of the data known as the square–root slope function. These functions preserve the L² norm under warping and are ideally suited for simultaneous estimation of regression and warping parameters. Furthermore, using both simulated and real–world data sets, we demonstrate our approach and evaluate its prediction performance relative to current models. In addition, we propose an extension to functional logistic and multinomial logistic regression.

Heat release that leads to thermal runaway of lithium-ion batteries begins with decomposition reactions associated with lithiated graphite. We broadly review the observed phenomena related to lithiated graphite electrodes and develop a comprehensive model that predicts with a single parameter set and with reasonable accuracy measurements over the available temperature range with a range of graphite particle sizes. The model developed in this work uses a standardized total heat release and takes advantage of a revised dependence of reaction rates and the tunneling barrier on specific surface area. The reaction extent is limited by inadequate electrolyte or lithium. Calorimetry measurements show that heat release from the reaction between lithiated graphite and electrolyte accelerates above ~200°C, and the model addresses this without introducing additional chemical reactions. This method assumes that the electron-tunneling barrier through the solid electrolyte interphase (SEI) grows initially and then becomes constant at some critical magnitude, which allows the reaction to accelerate as the temperature rises by means of its activation energy. Phenomena that could result in the upper limit on the tunneling barrier are discussed. The model predictions with two candidate activation energies are evaluated through comparisons to calorimetry data, and recommendations are made for optimal parameters.

Emerging memory devices, such as resistive crossbars, have the capacity to store large amounts of data in a single array. Acquiring the data stored in large-capacity crossbars in a sequential fashion can become a bottleneck. We present practical methods, based on sparse sampling, to quickly acquire sparse data stored on emerging memory devices that support the basic summation kernel, reducing the acquisition time from linear to sub-linear. The experimental results show that at least an order of magnitude improvement in acquisition time can be achieved when the data are sparse. Finally, in addition, we show that the energy cost associated with our approach is competitive to that of the sequential method.

A methanesulfonic acid (MSA) electrolyte with a single suppressor additive was used for potentiostatic bottom-up filling of copper in mesoscale through silicon vias (TSVs). Conversly, galvanostatic deposition is desirable for production level full wafer plating tools as they are typically not equipped with reference electrodes which are required for potentiostatic plating. Potentiostatic deposition was used to determine the over-potential required for bottom-up TSV filling and the resultant current was measured to establish a range of current densities to investigate for galvanostatic deposition. Galvanostatic plating conditions were then optimized to achieve void-free bottom-up filling in mesoscale TSVs for a range of sample sizes.

With Non-Volatile Memories (NVMs) beginning to enter the mainstream computing market, it is time to consider how to secure NVM-equipped computing systems. Recent Meltdown and Spectre attacks are evidence that security must be intrinsic to computing systems and not added as an afterthought. Processor vendors are taking the first steps and are beginning to build security primitives into commodity processors. One security primitive that is associated with the use of emerging NVMs is memory encryption. Memory encryption, while necessary, is very challenging when used with NVMs because it exacerbates the write endurance problem. Secure architectures use cryptographic metadata that must be persisted and restored to allow secure recovery of data in the event of power-loss. Specifically, encryption counters must be persistent to enable secure and functional recovery of an interrupted system. However, the cost of ensuring and maintaining persistence for these counters can be significant. In this paper, we propose a novel scheme to maintain encryption counters without the need for frequent updates. Our new memory controller design, Osiris, repurposes memory Error-Correction Codes (ECCs) to enable fast restoration and recovery of encryption counters. To evaluate our design, we use Gem5 to run eight memory-intensive workloads selected from SPEC2006 and U.S. Department of Energy (DoE) proxy applications. Compared to a write-Through counter-cache scheme, on average, Osiris can reduce 48.7% of the memory writes (increase lifetime by 1.95x), and reduce the performance overhead from 51.5% (for write-Through) to only 5.8%. Furthermore, without the need for backup battery or extra power-supply hold-up time, Osiris performs better than a battery-backed write-back (5.8% vs. 6.6% overhead) and has less write-Traffic (2.6% vs. 5.9% overhead).

The term photonic wire laser is now widely used for lasers with transverse dimensions much smaller than the wavelength. As a result, a large fraction of the mode propagates outside the solid core. Here, we propose and demonstrate a scheme to form a coupled cavity by taking advantage of this unique feature of photonic wire lasers. In this scheme, we used quantum cascade lasers with antenna-coupled third-order distributed feedback grating as the platform. Inspired by the chemistry of hybridization, our scheme phase-locks multiple such lasers by π coupling. Alongside the coupled-cavity laser, we demonstrated several performance metrics that are important for various applications in sensing and imaging: a continuous electrical tuning of ~10 GHz at ~3.8 THz (fractional tuning of ~0.26%), a good level of output power (~50–90 mW of continuous-wave power) and tight beam patterns (~10⁰ of beam divergence).

Nanocrystalline metals offer significant improvements in structural performance over conventional alloys. However, their performance is limited by grain boundary instability and limited ductility. Solute segregation has been proposed as a stabilization mechanism, however the solute atoms can embrittle grain boundaries and further degrade the toughness. In the present study, we confirm the embrittling effect of solute segregation in Pt-Au alloys. However, more importantly, we show that inhomogeneous chemical segregation to the grain boundary can lead to a new toughening mechanism termed compositional crack arrest. Energy dissipation is facilitated by the formation of nanocrack networks formed when cracks arrested at regions of the grain boundaries that were starved in the embrittling element. This mechanism, in concert with triple junction crack arrest, provides pathways to optimize both thermal stability and energy dissipation. A combination of in situ tensile deformation experiments and molecular dynamics simulations elucidate both the embrittling and toughening processes that can occur as a function of solute content.

In the present study, three boundary-layer stability codes are compared based on hypersonic high-enthalpy boundary-layer flows around a blunted 7 deg half-angle cone. The code-to-code comparison is conducted between the following codes: the Nonlocal Transition analysis code of the DLR, German Aerospace Center (DLR); the Stability and Transition Analysis for hypersonic Boundary Layers code of VirtusAero LLC; and the VKI Extensible Stability and Transition Analysis code of the von Kármán Institute for Fluid Dynamics. The comparison focuses on the role of real-gas effects on the second-mode instability, in particular the disturbance frequency, and deals with the question on how far not accounting for real-gas effects compromises the stability analysis. Here, the experimental test cases for the comparison are provided by the DLR High Enthalpy Shock Tunnel Göttingen and the Japan Aerospace Exploration Agency High Enthalpy Shock Tunnel. The focus of the comparison between the stability results and the measurements is, besides real-gas effects, the influence of uncertainties in the mean flow on the stability analysis.

In this paper, we present heavy ion and proton data on AlGaN highvoltage HEMTs showing Single Event Burnout, Total Ionizing Dose, and Displacement Damage responses. These are the first such data for materials of this type. Two different designs of the epitaxial structure were tested for Single Event Burnout (SEB). The default layout design showed burnout voltages that decreased rapidly with increasing LET, falling to about 25% of nominal breakdown voltage for ions with LET of about 34 MeV·cm2/mg for both structures. Samples of the device structure with lower AlN content were tested with varying gate-drain spacing and revealed an improved robustness to heavy ions, resulting in burnout voltages that did not decrease up to at least 33.9 MeV·cm2/mg. Failure analysis showed there was consistently a point, location random, where gate and drain had been shorted. Oscilloscope traces of terminal voltages and currents during burnout events lend support to the hypothesis that burnout events begin with a heavy ion strike in the vulnerable region between gate and drain. This subsequently initiates a cascade of events resulting in damage that is largely manifested elsewhere in the device. This hypothesis also suggests a path for greatly improving the susceptibility to SEB as development of this technology goes forward. Lastly, testing with 2.5 MeV protons showed only minor changes in device characteristics.

The Center for Computing Research (CCR) at Sandia National Laboratories organizes an active and productive summer program each year, in coordination with the Computer Science Research Institute (CSRI) and Cyber Engineering Research Institute (CERI). CERI focuses on open, exploratory research in cyber security in partnership with academia, industry, and government, and provides collaborators an accessible portal to Sandia's cybersecurity experts and facilities. Moreover, CERI provides an environment for visionary, threat-informed research on national cyber challenges. CSRI brings university faculty and students to Sandia National Laboratories for focused collaborative research on DOE computer and computational science problems. CSRI provides a mechanism by which university researchers learn about problems in computer and computational science at DOE Laboratories. Participants conduct leading - edge research, interact with scientists and engineers at the laboratories, and help transfer the results of their research to programs at the labs.

This paper provides experimental evidence for the chemical structures of aliphatically substituted and bridged polycyclic aromatic hydrocarbon (PAH) species in gas-physe combustion environments. The identification of these single- and multicore aromatic species, which have been hypothesized to be important in PAH growth and soot nucleation, was made possible through a combination of sampling gaseous constituents from an atmospheric pressure inverse coflow diffusion flame of ethylene and high-resolution tandem mass spectrometry (MS-MS). In these experiments, the flame-sampled components were ionized using a continuous VUV lamp at 10.0 eV and the ions were subsequently fragmented through collisions with Ar atoms in a collision-induced dissociation (CID) process. The resulting fragment ions, which were separated using a reflectron time-of-flight mass spectrometer, were used to extract structural information about the sampled aromatic compounds. The high-resolution mass spectra revealed the presence of alkylated single-core aromatic compounds and the fragment ions that were observed correspond to the loss of saturated and unsaturated units containing up to a total of 6 carbon atoms. Furthermore, the aromatic structures that form the foundational building blocks of the larger PAHs were identified to be smaller single-ring and pericondensed aromatic species with repetitive structural features. For demonstrative purposes, details are provided for the CID of molecular ions at masses 202 and 434. Insights into the role of the aliphatically substituted and bridged aromatics in the reaction network of PAH growth chemistry were obtained from spatially resolved measurements of the flame. The experimental results are consistent with a growth mechanism in which alkylated aromatics are oxidized to form pericondensed ring structures or react and recombine with other aromatics to form larger, potentially three-dimensional, aliphatically bridged multicore aromatic hydrocarbons.

Modern digital hardware and software designs are increasingly complex but are themselves only idealizations of a real system that is instantiated in, and interacts with, an analog physical environment. Insights from physics, formal methods, and complex systems theory can aid in extending reliability and security measures from pure digital computation (itself a challenging problem) to the broader cyber-physical and out-of-nominal arena. Example applications to design and analysis of high-consequence controllers and extreme-scale scientific computing illustrate the interplay of physics and computation. In particular, we discuss the limitations of digital models in an analog world, the modeling and verification of out-of-nominal logic, and the resilience of computational physics simulation. A common theme is that robustness to failures and attacks is fostered by cyber-physical system designs that are constrained to possess inherent stability or smoothness. This chapter contains excerpts from previous publications by the authors.

Sandia National Laboratories performed a 6-month effort to stand up a "zero-entry" cyber range environment for the purpose of providing self-directed practice to augment transmedia learning across diverse media and/or devices that may be part of a loosely coupled, distributed ecosystem. This 6-month effort leveraged Minimega, an open-source Emulytics™ (emulation + analytics) tool for launching and managing virtual machines in a cyber range. The proof of concept addressed a set of learning objectives for cybersecurity operations by providing three, short "zero-entry" exercises for beginner, intermediate, and advanced levels in network forensics, social engineering, penetration testing, and reverse engineering. Learners provided answers to problems they explored in networked virtual machines. The hands-on environment, Cyber Scorpion, participated in a preliminary demonstration in April 2017 at Ft. Bragg, NC. The present chapter describes the learning experience research and software development effort for a cybersecurity use case and subsequent lessons learned. It offers general recommendations for challenges which may be present in future learning ecosystems.

Mixed, augmented, and virtual reality holds promise for many securityrelated applications including physical security systems. When combined with models of a site, an augmented reality (AR) approach can be designed to enhance knowledge and understanding of the status of the facility. The present chapter describes how improved modeling and simulation will increase situational awareness by blurring the lines among the use of tools for analysis, rehearsal, and training-especially when coupled with immersive interaction experiences offered by augmented reality. We demonstrate how the notion of a digital twin can blur these lines. We conclude with challenges that must be overcome when applying digital twins, advanced modeling, and augmented reality to the design and development of next-generation physical security systems.

Deep neural networks are often computationally expensive, during both the training stage and inference stage. Training is always expensive, because back-propagation requires high-precision floating-pointmultiplication and addition. However, various mathematical optimizations may be employed to reduce the computational cost of inference. Optimized inference is important for reducing power consumption and latency and for increasing throughput. This chapter introduces the central approaches for optimizing deep neural network inference: pruning "unnecessary" weights, quantizing weights and inputs, sharing weights between layer units, compressing weights before transferring from main memory, distilling large high-performance models into smaller models, and decomposing convolutional filters to reduce multiply and accumulate operations. In this chapter, using a unified notation, we provide a mathematical and algorithmic description of the aforementioned deep neural network inference optimization methods.

An implicit, low-dissipation, low-Mach, variable density control volume finite element formulation is used to explore foundational understanding of numerical accuracy for large-eddy simulation applications on hybrid meshes. Detailed simulation comparisons are made between low-order hexahedral, tetrahedral, pyramid, and wedge/prism topologies against a third-order, unstructured hexahedral topology. Using smooth analytical and manufactured low-Mach solutions, design-order convergence is established for the hexahedral, tetrahedral, pyramid, and wedge element topologies using a new open boundary condition based on energy-stable methodologies previously deployed within a finite-difference context. A wide range of simulations demonstrate that low-order hexahedral- and wedge-based element topologies behave nearly identically in both computed numerical errors and overall simulation timings. Moreover, low-order tetrahedral and pyramid element topologies also display nearly the same numerical characteristics. Although the superiority of the hexahedral-based topology is clearly demonstrated for trivial laminar, principally-aligned flows, e.g., a 1x2x10 channel flow with specified pressure drop, this advantage is reduced for non-aligned, turbulent flows including the Taylor–Green Vortex, turbulent plane channel flow (Re^τ395), and buoyant flow past a heated cylinder. With the order of accuracy demonstrated for both homogenous and hybrid meshes, it is shown that solution verification for the selected complex flows can be established for all topology types. Although the number of elements in a mesh of like spacing comprised of tetrahedral, wedge, or pyramid elements increases as compared to the hexahedral counterpart, for wall-resolved large-eddy simulation, the increased assembly and residual evaluation computational time for non-hexahedral is offset by more efficient linear solver times. Lastly, most simulation results indicate that modest polynomial promotion provides a significant increase in solution accuracy.

Herein we present details of the design, simulation, and performance of a 100-GW linear transformer driver (LTD) cavity at Sandia National Laboratories. The cavity consists of 20 "bricks." Each brick is comprised of two 80 nF, 100 kV capacitors connected electrically in series with a custom, 200 kV, three-electrode, field-distortion gas switch. The brick capacitors are bipolar charged to ±100 kV for a total switch voltage of 200 kV. Typical brick circuit parameters are 40 nF capacitance (two 80 nF capacitors in series) and 160 nH inductance. The switch electrodes are fabricated from a WCu alloy and are operated with breathable air. Over the course of 6,556 shots the cavity generated a peak electrical current and power of 1.03 MA (±1.8%) and 106 GW (±3.1%). Experimental results are consistent (to within uncertainties) with circuit simulations for normal operation, and expected failure modes including prefire and late-fire events. New features of this development that are reported here in detail include: (1) 100 ns, 1 MA, 100-GW output from a 2.2 m diameter LTD into a 0.1 Ω load, (2) high-impedance solid charging resistors that are optimized for this application, and (3) evaluation of maintenance-free trigger circuits using capacitive coupling and inductive isolation.

In this project we studied undoped Ge/SiGe heterostructure field-effect transistors, which had a very wide hole density range from 1x10¹⁰cm^-2 to 3.5x10¹¹ cm^-2 tunable by (negative) gate voltage. At low temperatures reasonably high carriers mobility of about 3.4x10⁵ cm²/Vs was achieved.

Post-polymerization reactions of Diels-Alder polyphenylene with ring-substituted benzoyl chloride derivatives using triflic acid as the catalyst, effected selective Friedel-Crafts acylation of the lateral phenyl groups attached to the polyphenylene backbone. Using 4-(trifluoromethyl) benzoyl chloride gave a polymer with increased hydrophobicity. Using 4-fluorobenzoyl chloride afforded lateral 4-(fluorobenzoyl)phenyl substituents, which were further functionalized by nucleophilic aromatic substitution of the reactive fluoro substituent by 4-methoxyphenol.

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expression (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities.In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naïve solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naïve solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.

The use of self–assembling, pre–polymer materials in 3D printing is rare, due to difficulties of facilitating printing with low molecular weight species and preserving their reactivity and/or functions on the macroscale. Akin to 3D printing of small molecules, examples of extrusion–based printing of pre–polymer thermosets are uncommon, arising from their limited rheological tuneability and slow reactions kinetics. The direct ink write (DIW) 3D printing of a two–part resin, Epon 828 and Jeffamine D230, using a self–assembly approach is reported. Through the addition of self–assembling, ureidopyrimidinone–modified Jeffamine D230 and nanoclay filler, suitable viscoelastic properties are obtained, enabling 3D printing of the epoxy–amine pre–polymer resin. A significant increase in viscosity is observed, with an infinite shear rate viscosity of approximately two orders of magnitude higher than control resins, in addition to, an increase in yield strength and thixotropic behavior. As a result, printing of simple geometries is demonstrated with parts showing excellent interlayer adhesion, unachievable using control resins.

With current lithium ion batteries optimized for performance under relatively low charge rate conditions, implementation of XFC has been hindered by drawbacks including Li plating, kinetic polarization, and heat dissipation. This project will utilize model-informed design of 3-D hierarchical electrodes to tune key XFC related variables like 1) bulk porosity/tortuosity 2) vertical pore diameter, spacing, and lattice 3) crystallographic orientation of graphite particles relative to exposed surfaces 4) interfacial chemistry of the graphite surfaces through "artificial sEr formation using ALD 5) current collector surface roughness (aspect ratio, roughness factor, etc.). A key aspect of implementing novel electrodes is characterizing them in relevant settings. For this project, ultimately led out of University of Michigan by Neil Dasgupta, that includes both coin cell and 2+ Ah pouch cell testing, as well as comparison testing against baselines. Sandia National Labs will be conducting detailed cell characterization on iterative versions/improvements of the model-based hierarchical electrodes, as well as COTS cells for baseline comparisons. Key metrics include performance under fast charge conditions, as well as the absence or degree of lithium plating. Sandia will use their unique high precision cycling and rapid EIS capabilities to accurately characterize performance and any lithium plating during 6C charging and beyond, coupling electrochemical observations with cell teardown. Sandia will also design custom fixturing to cool cells during rapid charge, to decouple any kinetic effects brought about by cell heating and allow comparisons between different cells and charge rates. Using these techniques, Sandia will assess HOH electrodes from the University of Michigan, as well as aiding in iterative model and electrode design.

The uncontrolled interaction of a quantum system with its environment is detrimental for quantum coherence. For quantum bits in the solid state, decoherence from thermal vibrations of the surrounding lattice can typically only be suppressed by lowering the temperature of operation. Here, we use a nano-electro-mechanical system to mitigate the effect of thermal phonons on a spin qubit - the silicon-vacancy colour centre in diamond - without changing the system temperature. By controlling the strain environment of the colour centre, we tune its electronic levels to probe, control, and eventually suppress the interaction of its spin with the thermal bath. Strain control provides both large tunability of the optical transitions and significantly improved spin coherence. Finally, our findings indicate the possibility to achieve strong coupling between the silicon-vacancy spin and single phonons, which can lead to the realisation of phonon-mediated quantum gates and nonlinear quantum phononics.

Optical nonlocalities are elusive and hardly observable in traditional plasmonic materials like noble and alkali metals. Here we report experimental observation of viscoelastic nonlocalities in the infrared optical response of epsilon-near-zero nanofilms made of low-loss doped cadmium-oxide. The nonlocality is detectable thanks to the low damping rate of conduction electrons and the virtual absence of interband transitions at infrared wavelengths. We describe the motion of conduction electrons using a hydrodynamic model for a viscoelastic fluid, and find excellent agreement with experimental results. The electrons' elasticity blue-shifts the infrared plasmonic resonance associated with the main epsilon-near-zero mode, and triggers the onset of higher-order resonances due to the excitation of electron-pressure modes above the bulk plasma frequency. We also provide evidence of the existence of nonlocal damping, i.e., viscosity, in the motion of optically-excited conduction electrons using a combination of spectroscopic ellipsometry data and predictions based on the viscoelastic hydrodynamic model.

Emerging sequencing technologies are allowing us to characterize environmental, clinical and laboratory samples with increasing speed and detail, including real-time analysis and interpretation of data. One example of this is being able to rapidly and accurately detect a wide range of pathogenic organisms, both in the clinic and the field. Genomes can have radically different GC content however, such that accurate sequence analysis can be challenging depending upon the technology used. Here, we have characterized the performance of the Oxford MinION nanopore sequencer for detection and evaluation of organisms with a range of genomic nucleotide bias. We have diagnosed the quality of base-calling across individual reads and discovered that the position within the read affects base-calling and quality scores. Finally, we have evaluated the performance of the current state-of-the-art neural network-based MinION basecaller, characterizing its behavior with respect to systemic errors as well as context- and sequence-specific errors. Overall, we present a detailed characterization the capabilities of the MinION in terms of generating high-accuracy sequence data from genomes with a wide range of nucleotide content. This study provides a framework for designing the appropriate experiments that are the likely to lead to accurate and rapid field-forward diagnostics.

Methanol is a benchmark for understanding tropospheric oxidation, but is underpredicted by up to 100% in atmospheric models. Recent work has suggested this discrepancy can be reconciled by the rapid reaction of hydroxyl and methylperoxy radicals with a methanol branching fraction of 30%. However, for fractions below 15%, methanol underprediction is exacerbated. Theoretical investigations of this reaction are challenging because of intersystem crossing between singlet and triplet surfaces – ∼45% of reaction products are obtained via intersystem crossing of a pre-product complex – which demands experimental determinations of product branching. Here we report direct measurements of methanol from this reaction. A branching fraction below 15% is established, consequently highlighting a large gap in the understanding of global methanol sources. These results support the recent high-level theoretical work and substantially reduce its uncertainties.

U-Pu-Zr alloys are considered ideal metallic fuels for experimental breeder reactors because of their superior material properties and potential for increased burnup performance. However, significant constituent redistribution has been observed in these alloys when irradiated, or subject to a thermal gradient, resulting in inhomogeneity of both composition and phase, which, in turn, alters the fuel performance. The hybrid Potts-phase field method is reformulated for ternary alloys in a thermal gradient and utilized to simulate and predict constituent redistribution and phase transformations in the U-Pu-Zr nuclear fuel system. Simulated evolution profiles for the U-16Pu-23Zr (at. pct) alloy show concentric zones that are compared with published experimental results; discrepancies in zone size are attributed to thermal profile differences and assumptions related to the diffusivity values used. Twenty-one alloys, over the entire ternary compositional spectrum, are also simulated to investigate the effects of alloy composition on constituent redistribution and phase transformations. The U-40Pu-20Zr (at. pct) alloy shows the most potential for compositional uniformity and phase homogeneity, throughout a thermal gradient, while remaining in the compositional range of feasible alloys.

We study semiconductor hyperbolic metamaterials (SHMs) at the quantum limit experimentally using spectroscopic ellipsometry as well as theoretically using a new microscopic theory. The theory is a combination of microscopic density matrix approach for the material response and Green’s function approach for the propagating electric field. Our approach predicts absorptivity of the full multilayer system and for the first time allows the prediction of in-plane and out-of-plane dielectric functions for every individual layer constructing the SHM as well as effective dielectric functions that can be used to describe a homogenized SHM.

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This report describes the progress on the validation of the development of MELCOR Sodium Chemistry (NAC) package. The primary focus for this report is to ensure that the implementation of the CONTAIN-LMR sodium models into MELCOR is correctly done. Thus, the verification test is to conduct the code-to-code comparison with MELCOR and CONTAIN-LMR. Last year we had reported the development of NAC package which included three sodium models: spray fire, pool fire and atmospheric chemistry. The first 2 models were completed and additional improvement for these two models were done this year to allow upward spray capability and various functional capability for modeling the pool fire experiment better, respectively. This year, the atmospheric chemistry implementation has been progressed to a point for testing in the presence of water vapor (modeled as ideal gas) as a part of the two-condensable option model in the CONTAIN- LMR. The user's guide and reference manual for the NAC package including these improvements are described in a separate document being published as a part of the MELCOR 2.2 release. For this report, we would discuss the experimental validation using the implemented spray fire and pool fire models. A code-to-code comparison with CONTAIN-LMR is described for a spray fire experiment. Note that the atmospheric chemistry model has not fully implemented due to the absence of the two condensable option. Only the chemical reactions between the sodium aerosol and water vapor can be modeled. ACKNOWLEDGEMENTS This work was overseen and managed by Matthew R. Denman (Sandia National Laboratories). In addition, we appreciate that Chris Faucett for developing experimental data and provided the initial input decks as a part of the MELCOR assessment report development for U.S. Nuclear Regulatory Commission's project. This work is supported by the Office of Nuclear Energy of the U.S. Department of Energy work package number AT-17SN170204 and NT-185N05030102.

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Spontaneous Raman scattering images of liquid and near liquid methane released through 1 and 1.25 mm diameter orifices were taken using a pulsed planar laser sheet. The methane back pressure was varied between 2 and 6 bar_abs with methane temperatures between 130 and 220 K. Analysis of the Raman images resulted in the planar concentration and temperature fields of the methane jets. The measured methane concentration was compared with empirical relationships for warm gas releases and found to be in agreement in terms of centerline concentration decay rate, self-similarity, and half-width decay rate. Comparisons were then made for anticipated real-world CNG and LNG releases showing similar extents of flammable mass for the two fuel options. Measured images were compared to a cold gas release model, which showed good agreement over the range of methane release temperatures, pressures, and nozzle sizes. The collected measurements provide validation of this cold release model which will be used to model additional scenarios and inform LNG safety codes and standards.

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