Publications

Prescriptive approaches for the cybersecurity of digital nuclear instrumentation and control (I&C) systems can be cumbersome and costly. These considerations are of particular concern for advanced reactors that implement digital technologies for monitoring, diagnostics, and control. A risk-informed performance-based approach is needed to enable the efficient design of secure digital I&C systems for nuclear power plants. This paper presents a tiered cybersecurity analysis (TCA) methodology as a graded approach for cybersecurity design. The TCA is a sequence of analyses that align with the plant, system, and component stages of design. Earlier application of the TCA in the design process provides greater opportunity for an efficient graded approach and defense-in-depth. The TCA consists of three tiers. Tier 1 is design and impact analysis. In Tier 1 it is assumed that the adversary has control over all digital systems, components, and networks in the plant, and that the adversary is only constrained by the physical limitations of the plant design. The plant's safety design features are examined to determine whether the consequences of an attack by this cyber-enabled adversary are eliminated or mitigated. Accident sequences that are not eliminated or mitigated by security by design features are examined in Tier 2 analysis. In Tier 2, adversary access pathways are identified for the unmitigated accident sequences, and passive measures are implemented to deny system and network access to those pathways wherever feasible. Any systems with remaining susceptible access pathways are then examined in Tier 3. In Tier 3, active defensive cybersecurity architecture features and cybersecurity plan controls are applied to deny the adversary the ability to conduct the tasks needed to cause a severe consequence. Tier 3 is not performed in this analysis because of the design maturity required for this tier of analysis.

Abstract not provided.

Robot manipulation of the environment often uses force feedback control approaches such as impedance control. Impedance controllers can be designed to be passive and work well while coupled to a variety of dynamic environments. However, in the presence of a high gear ratio and compliance in manipulator links, non-passive system properties may result in force feedback instabilities when coupled to certain environments. This necessitates an approach that ensures stability when using impedance control methods to interact with a wide range of environments. We propose a method for improving stability and steady-state convergence of an impedance controller by using a deep neural network to map a damping impedance control parameter. In this paper, a dynamic model and impedance controlled simulated system are presented and used for analyzing the coupled dynamic behavior in worst case environments. This simulation environment is used for Nyquist analysis and closed-loop stability analysis to algorithmically determine updated impedance damping parameters that secures stability and desired performance. The deep neural network inputs utilized present impedance control parameters and environmental dynamic properties to determine an updated value of damping that improves performance. In a data set of 10,000 combinations of control parameters and environmental dynamics, 20.3% of all the cases result in instability or do not meet convergence criterion. Our deep neural network improves this and reduces instabilities and failed control performance to 2.29%. The design of the network architecture to achieve this improvement is presented and compared to other architectures with their respective performances.

As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

Here we examine models for particle curtain dispersion using drag based formalisms and their connection to streamwise pressure difference closures. Focusing on drag models, we specifically demonstrate that scaling arguments developed in DeMauro et. al. [1] using early time drag modeling can be extended to include late time particle curtain dispersion behavior by weighting the dynamic portion of the drag relative velocity e.g. (Formula Presented) by the inverse of the particle volume fraction to the ¼th power. The additional parameter e.g. α introduced in this scaling is related to the model drag parameters by employing an early-time latetime matching argument. Comparison with the scaled measurements of DeMauro et. al. suggest that the proposed modification is an effective formalism. Next, the connection between drag-based models and streamwise pressure difference-based expressions is explored by formulating simple analytical models that verify an empirical (Daniel and Wagner [2]) upstream-downstream expression. Though simple, these models provide physics-based approached describing shock particle curtain interaction behavior.

Polymers are widely used as damping materials in vibration and impact applications. Liquid crystal elastomers (LCEs) are a unique class of polymers that may offer the potential for enhanced energy absorption capacity under impact conditions over conventional polymers due to their ability to align the nematic phase during loading. Being a relatively new material, the high rate compressive properties of LCEs have been minimally studied. Here, we investigated the high strain rate compression behavior of different solid LCEs, including cast polydomain and 3D-printed, preferentially oriented monodomain samples. Direct ink write (DIW) 3D printed samples allow unique sample designs, namely, a specific orientation of mesogens with respect to the loading direction. Loading the sample in different orientations can induce mesogen rotation during mechanical loading and subsequently different stress-strain responses under impact. We also used a reference polymer, bisphenol-A (BPA) cross-linked resin, to contrast LCE behavior with conventional elastomer behavior.

We demonstrate the use of low-temperature grown GaAs (LT-GaAs) metasurface as an ultrafast photoconductive switching element gated with 1550 nm laser pulses. The metasurface is designed to enhance a weak two-step photon absorption at 1550 nm, enabling THz pulse detection.

Two-dimensional (2D) layered oxides have recently attracted wide attention owing to the strong coupling among charges, spins, lattice, and strain, which allows great flexibility and opportunities in structure designs as well as multifunctionality exploration. In parallel, plasmonic hybrid nanostructures exhibit exotic localized surface plasmon resonance (LSPR) providing a broad range of applications in nanophotonic devices and sensors. A hybrid material platform combining the unique multifunctional 2D layered oxides and plasmonic nanostructures brings optical tuning into the new level. In this work, a novel self-assembled Bi2MoO6 (BMO) 2D layered oxide incorporated with plasmonic Au nanoinclusions has been demonstrated via one-step pulsed laser deposition (PLD) technique. Comprehensive microstructural characterizations, including scanning transmission electron microscopy (STEM), differential phase contrast imaging (DPC), and STEM tomography, have demonstrated the high epitaxial quality and particle-in-matrix morphology of the BMO-Au nanocomposite film. DPC-STEM imaging clarifies the magnetic domain structures of BMO matrix. Three different BMO structures including layered supercell (LSC) and superlattices have been revealed which is attributed to the variable strain states throughout the BMO-Au film. Owing to the combination of plasmonic Au and layered structure of BMO, the nanocomposite film exhibits a typical LSPR in visible wavelength region and strong anisotropy in terms of its optical and ferromagnetic properties. This study opens a new avenue for developing novel 2D layered complex oxides incorporated with plasmonic metal or semiconductor phases showing great potential for applications in multifunctional nanoelectronics devices. [Figure not available: see fulltext.]

High reliability (Hi-Rel) electronics for mission critical applications are handled with extreme care; stress testing upon full assembly can increase a likelihood of degrading these systems before their deployment. Moreover, novel material parts, such as wide bandgap semiconductor devices, tend to have more complicated fabrication processing needs which could ultimately result in larger part variability or potential defects. Therefore, an intelligent screening and inspection technique for electronic parts, in particular gallium nitride (GaN) power transistors, is presented in this paper. We present a machine-learning-based non-intrusive technique that can enhance part-selection decisions to categorize the part samples to the population's expected electrical characteristics. This technique provides relevant information about GaN HEMT device characteristics without having to operate all of these devices at the high current region of the transfer and output characteristics, lowering the risk of damaging the parts prematurely. The proposed non-intrusive technique uses a small signal pulse width modulation (PWM) of various frequencies, ranging from 10 kHz to 500 kHz, injected into the transistor terminals and the corresponding output signals are observed and used as training dataset. Unsupervised clustering techniques with K-means and feature dimensional reduction through principal component analysis (PCA) have been used to correlate a population of GaN HEMT transistors to the expected mean of the devices' electrical characteristic performance.

The focus of this study was to demonstrate the vaporphase halogenation of Si(100) and subsequently evaluate the inhibiting ability of the halogenated surfaces toward atomic layer deposition (ALD) of aluminum oxide (Al2O3). Hydrogen-terminated silicon ⟨100⟩ (H−Si(100)) was halogenated using N-chlorosuccinimide (NCS), N-bromosuccinimide (NBS), and N-iodosuccinimide (NIS) in a vacuum-based chemical process. The composition and physical properties of the prepared monolayers were analyzed by using X-ray photoelectron spectroscopy (XPS) and contact angle (CA) goniometry. These measurements confirmed that all three reagents were more effective in halogenating H−Si(100) over OH−Si(100) in the vapor phase. The stability of the modified surfaces in air was also tested, with the chlorinated surface showing the greatest resistance to monolayer degradation and silicon oxide (SiO2) generation within the first 24 h of exposure to air. XPS and atomic force microscopy (AFM) measurements showed that the succinimide-derived Hal-Si(100) surfaces exhibited blocking ability superior to that of H− Si(100), a commonly used ALD resist. This halogenation method provides a dry chemistry alternative for creating halogen-based ALD resists on Si(100) in near-ambient environments.

Multiple rotors on single structures have long been proposed to increase wind turbine energy capture with no increase in rotor size, but at the cost of additional mechanical complexity in the yaw and tower designs. Standard turbines on their own very-closely-spaced towers avoid these disadvantages but create a significant disadvantage; for some wind directions the wake turbulence of a rotor enters the swept area of a very close downwind rotor causing low output, fatigue stress, and changes in wake recovery. Knowing how the performance of pairs of closely spaced rotors varies with wind direction is essential to design a layout that maximizes the useful directions and minimizes the losses and stress at other directions. In the current work, the high-fidelity large-eddy simulation (LES) code Exa-Wind/Nalu-Wind is used to simulate the wake interactions from paired-rotor configurations in a neutrally stratified atmospheric boundary layer to investigate performance and feasibility. Each rotor pair consists of two Vestas V27 turbines with hub-to-hub separation distances of 1.5 rotor diameters. The on-design wind direction results are consistent with previous literature. For an off-design wind direction of 26.6°, results indicate little change in power and far-wake recovery relative to the on-design case. At a direction of 45.0°, significant rotor-wake interactions produce an increase in power but also in far-wake velocity deficit and turbulence intensity. A severely off-design case is also considered.

Molten Salt Reactor (MSR) systems can be divided into two basic categories: liquid-fueled MSRs in which the fuel is dissolved in the salt, and solid-fueled systems such as the Fluoride-salt-cooled High-temperature Reactor (FHR). The molten salt provides an impediment to fission product release as actinides and many fission products are soluble in molten salt. Nonetheless, under accident conditions, some radionuclides may escape the salt by vaporization and aerosol formation, which may lead to release into the environment. We present recent enhancements to MELCOR to represent the transport of radionuclides in the salt and releases from the salt. Some soluble but volatile radionuclides may vaporize and subsequently condense to aerosol. Insoluble fission products can deposit on structures. Thermochimica, an open-source Gibbs Energy Minimization (GEM) code, has been integrated into MELCOR. With the appropriate thermochemical database, Thermochimica provides the solubility and vapor pressure of species as a function of temperature, pressure, and composition, which are needed to characterize the vaporization rate and the state of the salt with fission products. Since thermochemical databases are still under active development for molten salt systems, thermodynamic data for fission product solubility and vapor pressure may be user specified. This enables preliminary assessments of fission product transport in molten salt systems. In this paper, we discuss modeling of soluble and insoluble fission product releases in a MSR with Thermochimica incorporated into MELCOR. Separate-effects experiments performed as part of the Molten Salt Reactor Experiment in which radioactive aerosol was released are discussed as needed for determining the source term.

Unlike traditional base excitation vibration qualification testing, multi-axis vibration testing methods can be significantly faster and more accurate. Here, a 12-shaker multiple-input/multiple-output (MIMO) test method called intrinsic connection excitation (ICE) is developed and assessed for use on an example aerospace component. In this study, the ICE technique utilizes 12 shakers, 1 for each boundary condition attachment degree of freedom to the component, specially designed fixtures, and MIMO control to provide an accurate set of loads and boundary conditions during the test. Acceleration, force, and voltage control provide insight into the viability of this testing method. System field test and ICE test results are compared to traditional single degree of freedom specification development and testing. Results indicate the multi-shaker ICE test provided a much more accurate replication of system field test response compared with single degree of freedom testing.

This work proposes a method of designing adaptive controllers for reliable and stable operation of a Grid-Forming Inverter (GFI) during black-start. Here, the characteristic loci method has been primarily used for guiding the adaptation and tuning of the control parameters, based on a thorough sensitivity analysis of the system over a desired frequency bandwidth. The control hierarchy comprises active-reactive (P-Q) power support, voltage regulation, current control, and frequency recovery over the sequence of various events during black-starting. These events comprise energization of transformers and different types of loads, alongside post-fault recovery. The developed method has been tested in a 75 MVA inverter system, which is simulated in PSCAD®. The inverter energizes static and induction motor loads, besides transformers. This system has also been subjected to a line-ground fault for validating the robustness of the proposed adaptive control structure in post-fault recovery.

The structure-property linkage is one of the two most important relationships in materials science besides the process-structure linkage, especially for metals and polycrystalline alloys. The stochastic nature of microstructures begs for a robust approach to reliably address the linkage. As such, uncertainty quantification (UQ) plays an important role in this regard and cannot be ignored. To probe the structure-property linkage, many multi-scale integrated computational materials engineering (ICME) tools have been proposed and developed over the last decade to accelerate the material design process in the spirit of Material Genome Initiative (MGI), notably crystal plasticity finite element model (CPFEM) and phase-field simulations. Machine learning (ML) methods, including deep learning and physics-informed/-constrained approaches, can also be conveniently applied to approximate the computationally expensive ICME models, allowing one to efficiently navigate in both structure and property spaces effortlessly. Since UQ also plays a crucial role in verification and validation for both ICME and ML models, it is important to include UQ in the picture. In this paper, we summarize a few of our recent research efforts addressing UQ aspects of homogenized properties using CPFEM in a big picture context.

Kolmogorov's theory of turbulence assumes that the small-scale turbulent structures in the energy cascade are universal and are determined by the energy dissipation rate and the kinematic viscosity alone. However, thermal fluctuations, absent from the continuum description, terminate the energy cascade near the Kolmogorov length scale. Here, we propose a simple superposition model to account for the effects of thermal fluctuations on small-scale turbulence statistics. For compressible Taylor-Green vortex flow, we demonstrate that the superposition model in conjunction with data from direct numerical simulation of the Navier-Stokes equations yields spectra and structure functions that agree with the corresponding quantities computed from the direct simulation Monte Carlo method of molecular gas dynamics, verifying the importance of thermal fluctuations in the dissipation range.

A comprehensive study of the mechanical response of a 316 stainless steel is presented. The split-Hopkinson bar technique was used to evaluate the mechanical behavior at dynamic strain rates of 500 s⁻¹, 1500 s⁻¹, and 3000 s⁻¹ and temperatures of 22 °C and 300 °C under tension and compression loading, while the Drop-Hopkinson bar was used to characterize the tension behavior at an intermediate strain rate of 200 s⁻¹. The experimental results show that the tension and compression flow stress are reasonably symmetric, exhibit positive strain rate sensitivity, and are inversely dependent on temperature. The true failure strain was determined by measuring the minimum diameter of the post-test tension specimen. The 316 stainless steel exhibited a ductile response, and the true failure strain increased with increasing temperature and decreased with increasing strain rate.

Measurements of gas-phase temperature and pressure in hypersonic flows are important for understanding gas-phase fluctuations which can drive dynamic loading on model surfaces and to study fundamental compressible flow turbulence. To achieve this capability, femtosecond coherent anti-Stokes Raman scattering (fs CARS) is applied in Sandia National Laboratories’ cold-flow hypersonic wind tunnel facility. Measurements were performed for tunnel freestream temperatures of 42–58 K and pressures of 1.5–2.2 Torr. The CARS measurement volume was translated in the flow direction during a 30-second tunnel run using a single computer-controlled translation stage. After broadband femtosecond laser excitation, the rotational Raman coherence was probed twice, once at an early time where the collisional environment has not affected the Raman coherence, and another at a later time after the collisional environment has led to significant dephasing of the Raman coherent. The gas-phase temperature was obtained primarily from the early-probe CARS spectra, while the gas-phase pressure was obtained primarily from the late-probe CARS spectra. Challenges in implementing fs CARS in this facility such as changes in the nonresonant spectrum at different measurement location are discussed.

Despite state-of-the-art deep learning-based computer vision models achieving high accuracy on object recognition tasks, x-ray screening of baggage at checkpoints is largely performed by hand. Part of the challenge in automation of this task is the relatively small amount of available labeled training data. Furthermore, realistic threat objects may have forms or orientations that do not appear in any training data, and radiographs suffer from high amounts of occlusion. Using deep generative models, we explore data augmentation techniques to expand the intra-class variation of threat objects synthetically injected into baggage radiographs using openly available baggage x-ray datasets. We also benchmark the performance of object detection algorithms on raw and augmented data.

When exposed to mechanical environments such as shock and vibration, electrical connections may experience increased levels of contact resistance associated with the physical characteristics of the electrical interface. A phenomenon known as electrical chatter occurs when these vibrations are large enough to interrupt the electric signals. It is critical to understand the root causes behind these events because electrical chatter may result in unexpected performance or failure of the system. The root causes span a variety of fields, such as structural dynamics, contact mechanics, and tribology. Therefore, a wide range of analyses are required to fully explore the physical phenomenon. This paper intends to provide a better understanding of the relationship between structural dynamics and electrical chatter events. Specifically, electrical contact assembly composed of a cylindrical pin and bifurcated structure were studied using high fidelity simulations. Structural dynamic simulations will be performed with both linear and nonlinear reduced-order models (ROM) to replicate the relevant structural dynamics. Subsequent multi-physics simulations will be discussed to relate the contact mechanics associated with the dynamic interactions between the pin and receptacle to the chatter. Each simulation method was parametrized by data from a variety of dynamic experiments. Both structural dynamics and electrical continuity were observed in both the simulation and experimental approaches, so that the relationship between the two can be established.

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that can be used for provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. Also, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. We demonstrate the details of the dataset events and dependency analysis.

Multiple Input Multiple Output (MIMO) vibration testing provides the capability to expose a system to a field environment in a laboratory setting, saving both time and money by mitigating the need to perform multiple and costly large-scale field tests. However, MIMO vibration test design is not straightforward oftentimes relying on engineering judgment and multiple test iterations to determine the proper selection of response Degree of Freedom (DOF) and input locations that yield a successful test. This work investigates two DOF selection techniques for MIMO vibration testing to assist with test design, an iterative algorithm introduced in previous work and an Optimal Experiment Design (OED) approach. The iterative-based approach downselects the control set by removing DOF that have the smallest impact on overall error given a target Cross Power Spectral Density matrix and laboratory Frequency Response Function (FRF) matrix. The Optimal Experiment Design (OED) approach is formulated with the laboratory FRF matrix as a convex optimization problem and solved with a gradient-based optimization algorithm that seeks a set of weighted measurement DOF that minimize a measure of model prediction uncertainty. The DOF selection approaches are used to design MIMO vibration tests using candidate finite element models and simulated target environments. The results are generalized and compared to exemplify the quality of the MIMO test using the selected DOF.

The V31 containment vessel was procured by the US Army Recovered Chemical Material Directorate (RCMD) as a third-generation EDS containment vessel. It is the fifth EDS vessel to be fabricated under Code Case 2564 of the 2019 ASME Boiler and Pressure Vessel Code, which provides rules for the design of impulsively loaded vessels. The explosive rating for the vessel, based on the code case, is 24 lb (11 kg) TNT-equivalent for up to 1092 detonations. This report documents the results of explosive tests that were performed on the vessel at Sandia National Laboratories in Albuquerque, New Mexico to qualify the vessel for field operations use. There were three design basis configurations for qualification testing. Qualification test (1) consisted of a simulated M55 rocket motor and warhead assembly of 24 lb (11 kg) of Composition C-4 (30 lb [14 kg] TNT equivalent). This test was considered the maximum load case, based on modeling and simulation methods performed by Sandia prior to the vessel design phase. Qualification test (2) consisted of a regular, right circular cylinder, unitary charge, located central to the vessel interior of 19.2 lb (8.72 kg) of Composition C-4 (24 lb [11 kg] TNT equivalent). Qualification test (3) consisted of a 12-pack of regular, right circular cylinders of 2 lb (908 g) each, distributed evenly inside the vessel (totaling 19.2 lb [8.72 kg] of C-4, or 24 lb [11 kg] TNT equivalent). All vessel acceptance criteria were met.

Experiments were conducted on a wave tank model of a bottom raised oscillating surge wave energy converter (OSWEC) model in regular waves. The OSWEC model shape was a thin rectangular flap, which was allowed to pitch in response to incident waves about a hinge located at the intersection of the flap and the top of the supporting foundation. Torsion springs were added to the hinge in order to position the pitch natural frequency at the center of the wave frequency range of the wave maker. The flap motion as well as the loads at the base of the foundation were measured. The OSWEC was modeled analytically using elliptic functions in order to obtain closed form expressions for added mass and radiation damping coefficients, along with the excitation force and torque. These formulations were derived and reported in a previous publication by the authors. While analytical predictions of the foundation loads agree very well with experiments, large discrepancies are seen in the pitch response close to resonance. These differences are analyzed by conducting a sensitivity study, in which system parameters, including damping and added mass values, are varied. The likely contributors to the differences between predictions and experiments are attributed to tank reflections, standing waves that can occur in long, narrow wave tanks, as well as the thin plate assumption employed in the analytical approach.