National Security Presidential Memorandum-20 defines three tier levels for launch approval of space nuclear systems. The two main factors determining the tier level are the total quantity and type of radioactive sources and the probability of any member of the public receiving doses above certain thresholds. The total quantity of radioactive sources is compared with International Atomic Energy Agency transportation regulations. The dose probability is determined by the product of three terms: 1) the probability of a launch accident occurring; 2) the probability of a release of radioactive material given an accident; and 3) the probability of exceeding the dose threshold to any member of the public given a release. This paper provides a methodology for evaluating these values and applies this methodology to an example mission as a demonstration. For the example mission, a preliminary tier determination of Tier III was concluded.
Multiple-input/multiple-output (MIMO) vibration control often relies on a least-squares solution utilizing a matrix pseudo-inverse. While this is simple and effective for many cases, it lacks flexibility in assigning preference to specific control channels or degrees of freedom (DOFs). For example, the user may have some DOFs where accuracy is very important and other DOFs where accuracy is less important. This chapter shows a method for assigning weighting to control channels in the MIMO vibration control process. These weights can be constant or frequency-dependent functions depending on the application. An algorithm is presented for automatically selecting DOF weights based on a frequency-dependent data quality metric to ensure the control solution is only using the best, linear data. An example problem is presented to demonstrate the effectiveness of the weighted solution.
With the amount of neuromorphic tools and frame-works growing in number, we recognize a need to increase interoperability within our field. As an illustration of this, we explore linking two independently constructed tools. Specifically, we detail the construction of an a execution backend based on STACS: Simulation Tool for Asynchronous Cortical Streams for the Fugu spiking neural algorithms framework. STACS extends the computational scope of Fugu, enabling fast simulation of large-scale neural networks. Combining these two tools is shown to be mutually beneficial, ultimately enabling more functionality than either tool on its own. We discuss design considerations, in-cluding recognizing the advantages of straightforward standards. Further, we provide some benchmark results showing drastic improvements in execution time.
Although fire events inside nuclear power plants (NPPs) are infrequent, when they occur, they can affect the safe operation of the plant if there is not sufficient protection addressing the risk. As mitigation for fire events, NPPs have comprehensive fire protection systems intended to reduce the likelihood of a fire event and the associated consequences. An electrical arcing fault involving components made of aluminum is one such hazard that could lead to a significant consequence. Because the original evaluation of high-energy arcing faults (HEAF) was performed on components made of copper, there is an interest in understanding the effects of aluminum in these incidents. The nuclear regulatory commission (NRC) has led a series of HEAF experiments at a facility near Philadelphia, PA, in conjunction with the national institute of standards and technology (NIST), European and Japanese partners, and Sandia National Laboratories (SNL). To capture a range of different HEAF events, Sandia has provided high-speed visible and IR videography from multiple angles during this series of experiments. One of the data products provided by Sandia is the combination and synchronization of infrared and visible data from the multiple cameras used in the tests. This multispectral fusion of information (visible, MWIR, and LWIR) allows the customer to visualize the tests and understand when different events happen in the 2 to 4 second duration of a test. The presentation will dissect three experiments and describe the different events occurring during their duration. The presentation will compare the behavior of equipment that contains aluminum components versus the ones containing copper or steel. Finally, data from a switchgear experiment will be presented to complement the bus duct data.
Additive manufacturing has ushered in a new paradigm of bottom-up materials-by-design of spatially non-uniform materials. Functionally graded materials have locally tailored compositions to provide optimized global properties and performance. In this letter, we propose an opportunity for the application of graded magnetic materials as lens elements for charged particle optics. A Hiperco50/Hymu80 (FeCo-2 V/Fe-80Ni-5Mo) graded magnetic alloy was successfully additively manufactured via Laser Directed Energy Deposition with spatially varying magnetic properties. The compositional gradient is then applied using computational simulations to demonstrate how a tailored material can enhance the magnetic performance of a critical, image-forming component of a transmission electron microscope.
Comparison of pure sinusoidal vibration to random vibration or combinations of the two is an important and useful subject for dynamic testing. The objective of this chapter is to succinctly document the technical background for converting a sine-sweep test specification into an equivalent random vibration test specification. The information can also be used in reverse, i.e., to compare a random vibe spec with a sine-sweep, although that is less common in practice. Because of inherent assumptions involved in such conversions, it is always preferable to test to original specifications and conduct this conversion when other options are impractical. This chapter outlines the theoretical premise and relevant equations. An example of implementation with hypothetical but realistic data is provided that captures the conversion of a sinusoid to an equivalent ASD. The example also demonstrates how to account for the rate of sine-sweep to the duration of the random vibration. A significant content of this chapter is the discussion on the statistical distribution of peaks in a narrow-band random signal and the consequences of that on the damage imparted to a structure. Numerical simulations were carried out to capture the effect of various combinations of narrow-band random and pure sinusoid superimposed on each other. The consequences of this are captured to provide guidance on accuracy and conservatism.
Deep neural networks for automatic target recognition (ATR) have been shown to be highly successful for a large variety of Synthetic Aperture Radar (SAR) benchmark datasets. However, the black box nature of neural network approaches raises concerns about how models come to their decisions, especially when in high-stake scenarios. Accordingly, a variety of techniques are being pursued seeking to offer understanding of machine learning algorithms. In this paper, we first provide an overview of explainability and interpretability techniques introducing their concepts and the insights they produce. Next we summarize several methods for computing specific approaches to explainability and interpretability as well as analyzing their outputs. Finally, we demonstrate the application of several attribution map methods and apply both attribution analysis metrics as well as localization interpretability analysis to six neural network models trained on the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset to illustrate the insights these methods offer for analyzing SAR ATR performance.
We use complete polarization tomography of photon pairs generated in semiconductor metasurfaces via spontaneous parametric down-conversion to show how bound states in the continuum resonances affect the polarization state of the emitted photons.
Traditional electronics assemblies are typically packaged using physically or chemically blown potted foams to reduce the effects of shock and vibration. These potting materials have several drawbacks including manufacturing reliability, lack of internal preload control, and poor serviceability. A modular foam encapsulation approach combined with additively manufactured (AM) silicone lattice compression structures can address these issues for packaged electronics. These preloaded silicone lattice structures, known as foam replacement structures (FRSs), are an integral part of the encapsulation approach and must be properly characterized to model the assembly stresses and dynamics. In this study, dynamic test data is used to validate finite element models of an electronics assembly with modular encapsulation and a direct ink write (DIW) AM silicone FRS. A variety of DIW compression architectures are characterized, and their nominal stress-strain behavior is represented with hyperfoam constitutive model parameterizations. Modeling is conducted with Sierra finite element software, specifically with a handoff from assembly preloading and uniaxial compression in Sierra/Solid Mechanics to linear modal and vibration analysis in Sierra/Structural Dynamics. This work demonstrates the application of this advanced modeling workflow, and results show good agreement with test data for both static and dynamic quantities of interest, including preload, modal, and vibration response.
Deep neural networks (DNNs) achieve state-of-the-art performance in video anomaly detection. However, the usage of DNNs is limited in practice due to their computational overhead, generally requiring significant resources and specialized hardware. Further, despite recent progress, current evaluation criteria of video anomaly detection algorithms are flawed, preventing meaningful comparisons among algorithms. In response to these challenges, we propose (1) a compression-based technique referred to as Spatio-Temporal N-Gram Prediction by Partial Matching (STNG PPM) and (2) simple modifications to current evaluation criteria for improved interpretation and broader applicability across algorithms. STNG PMM does not require specialized hardware, has few parameters to tune, and is competitive with DNNs on multiple benchmark data sets in video anomaly detection.
Numerical simulations were performed in 3D Cartesian coordinates to examine the post-detonation processes produced by the detonation of a 12 mm-diameter hemispherical PETN explosive charge in air. The simulations captured air dissociation by the Mach 20+ shock, chemical equilibration, and afterburning using finite-rate chemical kinetics with a skeletal chemical reaction mechanism. The Becker-Kistiakowsky-Wilson real-gas equation of state is used for the gas-phase. A simplified programmed burn model is used to seamlessly couple the detonation propagation through the explosive charge to the post-detonation reaction processes inside the fireball. Four charge sizes were considered, including diameters of 12 mm, 38 mm, 120 mm, and 1200 mm. The computed blast, shock structures, and chemical composition within the fireball agree with literature. The evolution of the flow at early times is shown to be gas dynamic driven and nearly self-similar when the time and space was scaled. The flow fields were azimuthally averaged and a mixing layer analysis was performed. The results show differences in the temperature and chemical composition with increasing charge size, implying a transition from a chemical kinetic-limited to a mixing-limited regime.
Before residential photovoltaic (PV) systems are interconnected with the grid, various planning and impact studies are conducted on detailed models of the system to ensure safety and reliability are maintained. However, these model-based analyses can be time-consuming and error-prone, representing a potential bottleneck as the pace of PV installations accelerates. Data-driven tools and analyses provide an alternate pathway to supplement or replace their model-based counterparts. In this article, a data-driven algorithm is presented for assessing the thermal limitations of PV interconnections. Using input data from residential smart meters, and without any grid models or topology information, the algorithm can determine the nameplate capacity of the service transformer supplying those customers. The algorithm was tested on multiple datasets and predicted service transformer capacity with >98% accuracy, regardless of existing PV installations. This algorithm has various applications from model-free thermal impact analysis for hosting capacity studies to error detection and calibration of existing grid models.
The radar generalized image quality equation (RGIQE) is a metric used to measure both monostatic and bistatic synthetic aperture radar (BSAR) image quality, it is a function of signal-to-noise ratio (SNR) and 2-D bandwidth. The 2-D bandwidth is equal to the area of the transfer function's (TF) passband region. With the exception of side-looking monostatic geometries, almost all monostatic and bistatic geometries have skewed passband shapes when waveform frequency parameters remain unchanged from pulse to pulse. Most synthetic aperture radar (SAR) applications require a rectangular-shaped passband region, this is achieved by inscribing a rectangular region within the skewed intrinsic passband region. Increasing skewness results in less inscription area reducing 2-D bandwidth, image SNR, and thus RGIQE capacity. In this article, a waveform with frequency agility is used to rectify the skewness that degrades RGIQE capacity. By changing the waveform's center frequency and instantaneous bandwidth from pulse to pulse in a particular manner, the intrinsic passband region can be de-skewed. The de-skewed shape maximizes the inscription area thus maximizing 2-D bandwidth, image SNR, and RGIQE capacity. Three examples are given in this article, one monostatic geometry, and two bistatic geometries. RGIQE capacity is increased by 52.02%, 44.42%, and 79.09% for the three examples.
We investigate the kinetics and report the time-resolved concentrations of key chemical species in the oxidation of tetrahydrofuran (THF) at 7500 torr and 450-675 K. Experiments are carried out using high-pressure multiplexed photoionization mass spectrometry (MPIMS) combined with tunable vacuum ultraviolet radiation from the Berkely Lab Advanced Light Source. Intermediates and products are quantified using reference photoionization (PI) cross sections, when available, and constrained by a global carbon balance tracking approach at all experimental temperatures simultaneously for the species without reference cross sections. From carbon balancing, we determine time-resolved concentrations for the ROO˙ and ˙OOQOOH radical intermediates, butanedial, and the combined concentration of ketohydroperoxide (KHP) and unsaturated hydroperoxide (UHP) products stemming from the ˙QOOH + O2 reaction. Furthermore, we quantify a product that we tentatively assign as fumaraldehyde, which arises from UHP decomposition via H2O or ˙OH + H loss. The experimentally derived species concentrations are compared with model predictions using the most recent literature THF oxidation mechanism of Fenard et al., (Combust. Flame, 2018, 191, 252-269). Our results indicate that the literature mechanism significantly overestimates THF consumption and the UHP + KHP concentration at our conditions. The model predictions are sensitive to the rate coefficient for the ROO˙ isomerization to ˙QOOH, which is the gateway for radical chain propagating and branching pathways. Comparisons with our recent results for cyclopentane (Demireva et al., Combust. Flame, 2023, 257, 112506) provide insights into the effect of the ether group on reactivity and highlight the need to determine accurate rate coefficients of ROO˙ isomerization and subsequent reactions.
Determining the thermal response of energetic materials at high densities can be difficult when pressure dependent reactions occur within the interior of the material. At high temperatures, reactive components such as hexahydro-l,3,5-tri-nitro-l,3,5-triazine (RDX), ammonium perchlorate (AP), and hydroxyl-terminated polybutadiene (HTPB) decompose and interact. The decomposition products accumulate near defects where internal pressure ultimately causes mechanical damage with closed pores transitioning into open pores. Gases are no longer confined locally; instead, they freely migrate between open pores and ultimately escape into the surrounding headspace or vent. Recently we have developed a universal cookoff model (UCM) coupled to a micromechanics pressurization (MMP) model to address pressure-dependent reactions that occur within the interior of explosives. Parameters for the UCM/MMP model are presented for an explosive and two propellants that contain similar portions of both aluminum (Al) and a binder. The explosive contains RDX and the propellants contain AP with no RDX. One of the propellants contains small amounts of curing catalysts and a burn modifier whereas the other propellant does not. We found that the cookoff behavior of the two propellants behave similarly leading and conclude that small amounts of catalysts or burn modifiers do not influence cookoff behavior appreciably. Kinetic parameters for the UCM/MMP models were obtained from the Sandia Instrumented Thermal Ignition (SITI) experiment. Validation is done with data from other laboratories.
Network Operation Centers (NOCs) and Security Operation Centers (SOCs) play a critical role in addressing a wide range of threats in critical infrastructure systems such as the electric grid. However, when considering the electric grid and related industrial control systems (ICSs), visibility into the information technology (IT), operational technology (OT), and underlying physical process systems are often disconnected and standalone. As the electric grid becomes increasingly cyber-physical and faces dynamic, cyber-physical threats, it is vital that cyber-physical situational awareness (CPSA) across the interconnected system is achieved. In this paper, we review existing NOC and SOC capabilities and visualizations, motivate the need for CPSA, and define design principles with example visualizations for a next-generation grid cyber-physical integrated SOC (CP-ISOC).
The Explosive Destruction System (EDS) V31 containment vessel was procured by the US Army Recovered Chemical Materiel Directorate (RCMD) as a third-generation system used to destroy chemical munitions. It is the fifth individual 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 this vessel, based on the code case, is twenty-four (24) pounds 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 24lbs of Composition C-4 (30 lb 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 of Composition C-4 (24 lb TNT equivalent). Qualification test (3) consisted of a 12-pack of regular, right circular cylinders, distributed evenly inside the vessel (totaling 19.2 lb of C-4, or 24 lb TNT equivalent). The ASME certification was based exclusively on the analytical predictions because the data required for certification cannot be obtained through testing. Strains through the thickness of the wall and on the inside surface of the cylinder were required and could only be obtained through analysis. Strain gages were placed on the outside of the vessel in three locations and the displacement of the door was measured using a Photonic Doppler Velocimetry (PDV) system. These measured values are compared to analytical predictions to help ensure the accuracy of the predicted strains and displacements throughout the rest of the model.
We present a novel design of a III-V-on-silicon heterogeneously integrated tunable ring laser, achieving >80 nanometers of tuning bandwidth, the widest conceived using only two rings, fostering many applications such as spectroscopy and beam steering.
The diesel-piloted dual-fuel compression ignition combustion strategy is well-suited to accelerate the decarbonization of transportation by adopting hydrogen as a renewable energy carrier into the existing internal combustion engine with minimal engine modifications. Despite the simplicity of engine modification, many questions remain unanswered regarding the optimal pilot injection strategy for reliable ignition with minimum pilot fuel consumption. The present study uses a single-cylinder heavy-duty optical engine to explore the phenomenology and underlying mechanisms governing the pilot fuel ignition and the subsequent combustion of a premixed hydrogen-air charge. The engine is operated in a dual-fuel mode with hydrogen premixed into the engine intake charge with a direct pilot injection of n-heptane as a diesel pilot fuel surrogate. Optical diagnostics used to visualize in-cylinder combustion phenomena include high-speed IR imaging of the pilot fuel spray evolution as well as high-speed HCHO* and OH* chemiluminescence as indicators of low-temperature and high-temperature heat release, respectively. Three pilot injection strategies are compared to explore the effects of pilot fuel mass, injection pressure, and injection duration on the probability and repeatability of successful ignition. The thermodynamic and imaging data analysis supported by zero-dimensional chemical kinetics simulations revealed a complex interplay between the physical and chemical processes governing the pilot fuel ignition process in a hydrogen containing charge. Hydrogen strongly inhibits the ignition of pilot fuel mixtures and therefore requires longer injection duration to create zones with sufficiently high pilot fuel concentration for successful ignition. Results show that ignition typically tends to rely on stochastic pockets with high pilot fuel concentration, which results in poor repeatability of combustion and frequent misfiring. This work has improved the understanding on how the unique chemical properties of hydrogen pose a challenge for maximization of hydrogen's energy share in hydrogen dual-fuel engines and highlights a potential mitigation pathway.
The Rydberg dipole blockade has emerged as the standard mechanism to induce entanglement between neutral-Atom qubits. In these protocols, laser fields that couple qubit states to Rydberg states are modulated to implement entangling gates. Here we present an alternative protocol to implement entangling gates via Rydberg dressing and a microwave-field-driven spin-flip blockade [Y.-Y. Jau, Nat. Phys. 12, 71 (2016)1745-247310.1038/nphys3487]. We consider the specific example of qubits encoded in the clock states of cesium. An auxiliary hyperfine state is optically dressed so that it acquires partial Rydberg character. It thus acts as a proxy Rydberg state, with a nonlinear light shift that plays the role of blockade strength. A microwave-frequency field coupling a qubit state to this dressed auxiliary state can be modulated to implement entangling gates. Logic gate protocols designed for the optical regime can be imported to this microwave regime, for which experimental control methods are more robust. We show that unlike the strong dipole-blockade regime usually employed in Rydberg experiments, going to a moderate-spin-flip-blockade regime results in faster gates and smaller Rydberg decay. We study various regimes of operations that can yield high-fidelity two-qubit entangling gates and characterize their analytical behavior. In addition to the inherent robustness of microwave control, we can design these gates to be more robust to laser amplitude and frequency noises at the cost of a small increase in Rydberg decay.
Gaseous hydrogen is known to embrittle most steels, including the steels used in natural gas pipelines. As injection of hydrogen into the existing natural gas infrastructure is considered globally by the pipeline industry, the structural integrity of pipelines transporting gaseous hydrogen must be investigated. Hydrogen Extremely Low Probability of Rupture (HELPR) is a publicly available and open-source probabilistic fatigue and fracture mechanics toolkit recently developed at Sandia National Laboratories. HELPR is intended to incorporate the influence of hydrogen into structural integrity assessments of natural gas transmission and distribution infrastructure. HELPR utilizes engineering models, such as those specified in ASME B31.12 and API 579, with relatively low computational costs to perform large sample ensembles, enabling estimation of performance distributions including low probability tail estimates. Leveraging the probabilistic capabilities built into HELPR, the sensitivity of fatigue and fracture calculations to specific modeling parameters on performance margins can be quantified. Through applying HELPR’s probabilistic capabilities to realistic scenarios, the impact of uncertainty in specific model parameter descriptions on performance margins, such as cycles to unstable crack growth or rupture in gaseous hydrogen, can be characterized; this same approach can then be used to assess the impact of reducing uncertainty sources on the resulting performance metrics, margins, and associated risks. A few industry-motivated scenarios are used to demonstrate this approach.
Our present electric power grid maximizes spinning inertia of fossil fuel generators (inherent energy storage) to meet stability and performance requirements. Our goal is to begin to investigate the replacement of the large spinning inertia of fossil fuel generators with energy storage systems (ESS) including information flow as a necessary part of the renewable energy sources (RES) and subject to certain criteria. General criteria metrics include: energy storage, information flow, estimation, communication links, central versus decentralized, etc. Our focus is on evaluating the Fisher Information Equivalency (FIE) metric as a multi-criteria trade-off cost function for the minimization of ESS options and information flow. This paper begins with a formal conceptual definition of an infinite bus. Then a simple example of a One Machine Infinite Bus (OMIB) system with a Unified Power Flow Controller (UPFC) to demonstrate the FIE-based approach to minimize the ESS. A second more detailed example of several spinning machines are included with representative power electronic and ESS for RES that are attached to the electric power grid. A simple trade-study begins to highlight requirements to support large penetration of RES. Keep in mind for a large scale high penetration of RES will require large investments in ESS which we want to minimize.
Plenoptic background-oriented schlieren is a diagnostictechnique that enables the measure-ment of three-dimensional refractive gradients by a combination of background-oriented schlieren and a plenoptic light field camera. This plenoptic camera is a modification of a traditional camera via the insertion of an array of microlenses between the imaging lens and digital sensor. This allows the collection of both spatial and angular information on the incoming light rays and therefore provides three-dimensional information about the imaged scene. Background-oriented schlieren requires a relatively simple experimental configurationincludingonlyacameraviewing a patterned background through the density field of interest. By using a plenoptic camera to capture background-oriented schlieren images the optical distortion created by density gradients in three dimensions can be measured. This chapter is intended to review critical developments in plenoptic background-oriented schlieren imaging and provide an outlook for future applications of this measurement technique.
Underground caverns in salt formations are promising geologic features to store hydrogen (H2) because of salt's extremely low permeability and self-healing behavior.Successful salt-cavern H2 storage schemes must maximize the efficiency of cyclic injection-production while minimizing H2 loss through adjacent damaged salt.The salt cavern storage community, however, has not fully understood the geomechanical behaviors of salt rocks driven by quick operation cycles of H2 injection-production, which may significantly impact the cost-effective storage-recovery performance.Our field-scale generic model captures the impact of combined drag and back stressing on the salt creep behavior corresponding to cycles of compression and extension, which may lead to substantial loss of cavern volumes over time and diminish the cavern performance for H2 storage.Our preliminary findings address that it is essential to develop a new salt constitutive model based on geomechanical tests of site-specific salt rock to probe the cyclic behaviors of salt both beneath and above the dilatancy boundary, including reverse (inverse transient) creep, the Bauschinger effect and fatigue.