Publications

This is the presentation, that goes with a previously-approved, Information-Released abstract of the same title,

In this work, thermogravimetric analysis (TGA) was performed on samples of a carbon fiber epoxy composite, a glass fiber epoxy composite, and a mixed carbon fiber/glass fiber epoxy composite, as well on each constituent material (polymer epoxy, carbon fibers and glass fibers). TGA was conducted for heating rates from 1-20 C/min with purified purge gases of nitrogen and dry air. For the fiberglass composite, we find that ~70% of the material remains after heating in air to 1200 C. For the carbon fiber epoxy composite, we observe greater mass loss as the carbon fibers can oxidize, leaving little material by the end of the test. The mixed composite, which has a 2:1 ratio of glass fibers to carbon fibers, experienced a total mass loss between the two other composites. By determining the relationship between the thermal decomposition of a composite material and its constituent materials, we can predict the fire behavior of novel composites during the material design phase.

Although visualizations are a useful tool for helping people to understand information, they can also have unintended effects on human cognition. This is especially true for uncertain information, which is difficult for people to understand. Prior work has found that different methods of visualizing uncertain information can produce different patterns of decision making from users. However, uncertainty can also be represented via text or numerical information, and few studies have systematically compared these types of representations to visualizations of uncertainty. We present two experiments that compared visual representations of risk (icon arrays) to numerical representations (natural frequencies) in a wildfire evacuation task. Like prior studies, we found that different types of visual cues led to different patterns of decision making. In addition, our comparison of visual and numerical representations of risk found that people were more likely to evacuate when they saw visualizations than when they saw numerical representations. These experiments reinforce the idea that design choices are not neutral: seemingly minor differences in how information is represented can have important impacts on human risk perception and decision making.

The primary purpose of this document is to outline the progress made on the LDRD titled “Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms” in FY22 and FY23. In this LDRD, we explored the usage of compression-based analytics to identify anomalous activity in video. We developed a novel algorithm, Spatio-Temporal N-Gram PPM (STNG PPM) that accounts for spatially and temporally aware anomalies in video. We extracted features using motions vectors from video as well as operating on the raw features. STNG PPM is comparable to many deep learning approaches but does not require specialized hardware (GPUs) to run efficiently. We also examine the evaluation metrics and propose novel measures addressing faults in the current evaluation measures.

Abstract not provided.

In this report, we document the process related to developing a regional geologic model of a 605 x 1334 km area centered around Utah and encompassing surrounding states. This model is developed to test the effect that composition of a model has on the generation of synthetic data with the intent of using this information to improve upon full waveform moment tensor inversions. We compare observed data from three seismic events and five stations to the synthetic data generated by a preliminary model derived from a geologic framework model (GFM) developed by the USGS. The synthetic data and observed data comparisons indicate that our preliminary model performs well at smaller offset distances in the northern and central sections of the model. However, the southern stations consistently display synthetic data P- and S-wave arrival times that do not match the observed data arrival times, indicating that the velocity structure of the southern part of the model especially is inaccurate.

Laser powder bed fusion (LPBF) Additive Manufacturing (AM) has the potential to enable the production of components with novel designs and material properties unachievable otherwise. However, process repeatability is a challenge, making qualification ill-defined and greatly reducing the utility of what could be an important manufacturing technology. In this work, a combination of modeling, uncertainty quantification (UQ), and experimentation are used in an effort to predict and bound the range of possible outcomes of the LPBF process. Quantities of interest predicted are melt pool dimensions, microstructure features, and mechanical distortions. A combination of high fidelity thermal-fluid models, microstructure growth models, and reduced fidelity, rapid thermal and mechanical models are used. Uncertainty propagation techniques are used to predict probability distributions of quantities of interest from estimates of process uncertainties. Repeated experiments are done to quantify observed probability distributions and compared to predicted distributions to determine if predictions are precise and accurate. Novel modeling methods are microstrucutre characterization techniques are also discussed. It is found that high fidelity models do a generally good job bounding experimentally observed melt pool morphologies for both bead-on-plate and powder bed cases. Microstructure models are able to bound a number of experimentally observed microstructure statistics, but with low precision due to challenges with calibrating the microstructure growth model parameters. A developed modified inherent strain distortion model does not accurately predict observed distortions. A lumped laser distortion model shows promise in being both accurately and precisely bounding observed outcomes from the deflection comb build, but requires further evaluation on more builds and geometries.

Quantum point contacts (QPC) are the building blocks of quantum dot qubits and semiconducting quantum electrical metrology circuits. QPCs also make highly sensitive electrical amplifiers with the potential to operate in the quantum-limited regime. Though the inherent operational bandwidth of QPCs can eclipse the THz regime, the impedance mismatch with the external circuitry limits the operational frequency to a few kHz. Lumped-element impedance-matching circuits are successful only up to a few hundreds of MHz in frequency. QPCs are characterised by a complex impedance consisting of quantized resistance, capacitance, and inductance elements. Characterising the complex admittance at higher frequencies and understanding the coupling of QPC to other circuit elements and electromagnetic environments will provide valuable insight into its sensing and backaction properties. In this work, we couple a QPC galvanically to a superconducting stub tuner impedance matching circuit realised in a coplanar waveguide architecture to enhance the operation frequency into the GHz regime and investigate the electrical amplification and complex admittance characteristics. The device, operating at ~1.96 $GHz$ exhibits a conductance sensitivity of 2.92 X 10^-5(e²/h)/$\sqrt{Hz}$ with a bandwidth of 13 $MHz$. Besides, the RF reflected power unambiguously reveals the complex admittance characteristics of the QPC, shining more light on the behaviour of quantum tunnel junctions at higher operational frequencies.

Securing satellite groundstations against cyber-attacks is vital to national security missions. However, these cyber threats are constantly evolving. As vulnerabilities are discovered and patched, new vulnerabilities are discovered and exploited. In order to automate the process of discovering existing vulnerabilities and the means to exploit them, a reinforcement learning framework is presented in this report. We demonstrate that this framework can learn to successfully navigate an unknown network and detect nodes of interest despite the presence of a moving target defense. The agent then exfiltrates a file of interest from the node as quickly as possible. This framework also incorporates a defensive software agent that learns to impede the attacking agents progress. This setup allows for the agents to work against each other and improve their abilities. We anticipate that this capability will help uncover unforeseen vulnerabilities and the means to mitigate them. The modular nature of the framework enables users to swap out learning algorithms and modify the reward functions in order to adapt the learning tasks to various use cases and environments. Several algorithms, viz., tabular Q learning, deep Q networks, proximal policy optimization, advantage actor-critic, generative adversarial imitation learning, are explored for the agents and the results highlighted. The agent learns to solve the tasks in a light-weight abstract environment. Once the agent learns to perform sufficiently well, it can be deployed in a minimega virtual machine environment (or a real network) with wrappers that map abstract actions to software commands. The agent also uses a local representation of the actions called a ‘slot-mechanism’. This allows the agent to learn in a certain network and generalize it to different networks. The defensive agent learns to predict the actions taken by an offensive agent and uses that information to anticipate the threat. This information can then either be used to raise an alarm or to take actions to thwart the attack. We believe that with the appropriate reward design, a representative environment, and action set, this framework can be generalized to tackle other cybersecurity tasks. By sufficiently training these agents, we can anticipate vulnerabilities leading to robust future designs. We can also deploy automated defensive agents that can help secure satellite groundstation and their vital national security missions.

This paper describes a process for forming a buried field shield in GaN by an etch-and-regrowth process, which is intended to protect the gate dielectric from high fields in the blocking state. GaN trench MOSFETs made at Sandia serve as the baseline to show the limitations in making a trench gated device without a method to protect the gate dielectric. Device data coupled with simulations show device failure at 30% of theoretical breakdown for devices made without a field shield. Implementation of a field shield reduces the simulated electric field in the dielectric to below 4 MV/cm at breakdown, which eliminates the requirement to derate the device in order to protect the dielectric. For realistic lithography tolerances, however, a shield-to-channel distance of 0.4 μm limits the field in the gate dielectric to 5 MV/cm and requires a small margin of device derating to safeguard a long-term reliability and lifetime of the dielectric.

Abstract not provided.

The wave energy control competition established a benchmark problem which was offered as an open challenge to the wave energy system control community. The competition had two stages: In the first stage, competitors used a standard wave energy simulation platform (WEC-Sim) to evaluate their controllers while, in the second stage, competitors were invited to test their controllers in a real-time implementation on a prototype system in a wave tank. The performance function used was based on converted energy across a range of standard sea states, but also included aspects related to economic performance, such as peak/average power, peak force, etc. This paper compares simulated and experimental results and, in particular, examines if the results obtained in a linear system simulation are borne out in reality. Overall, within the scope of the device tested, the range of sea states employed, and the performance metric used, the conclusion is that high-performance WEC controllers work well in practice, with good carry-over from simulation to experimentation. However, the availability of a good WEC mathematical model is deemed to be crucial.

Using DTW for HPC / LDMS data clustering

Fracture prognosis and characterization efforts require knowledge of crack tip position and the Stress Intensity Factors (SIFs) acting in the vicinity of the crack. Here, we present an efficient numerical approach to infer both of these characteristics under a consistent theoretical framework from noisy, unstructured displacement data. The novel approach utilizes the separability of the asymptotic linear elastic fracture mechanics fields to expedite the search for crack tip position and is particularly useful for noisy displacement data. The manuscript begins with an assessment of the importance of accurately locating crack tip position when quantifying the SIFs from displacement data. Next, the proposed separability approach for quickly inferring crack tip position is introduced. Comparing to the widely used displacement correlation approach, the performance of the separability approach is assessed. Cases involving both noisy data and systematic deviation from the asymptotic linear elastic fracture mechanics model are considered, e.g. inelastic material behavior and finite geometries. An open source python implementation of the proposed approach is available for use by those doing field and laboratory work involving digital image correlation and simulations, e.g. finite element, discrete element, molecular dynamics and peridynamics, where the crack tip position is not explicitly defined.

The modern global economy relies heavily on carbon-based products that are derived from petroleum, which presents sustainability, resource management, and greenhouse gas exacerbated climate change challenges. Due to these challenges, there is the need for a global industrial transition towards green and sustainable production. Microbial production of valuable chemicals from renewable biomass represents one promising route. However, high-volume low-value products such as commodity chemicals are still difficult to make profitable. One fundamental bottleneck is a waste of more than 1/3 of the feedstock carbon as CO₂ in the fermentation process. Here the project focuses on fundamentally reconfiguring the metabolism to reduce CO₂ loss in central metabolic pathways thereby also improving bioproduct yields. Here we present technologies to prevent CO₂ loss and balance reducing equivalents within the cell to enable complete conversion of glucose from renewable feedstocks into bioproducts.

Frontal polymerization involves the propagation of a thermally driven polymerization wave through a monomer solution to rapidly generate high-performance polymeric materials with little energy input. The balance between latent catalyst activation and sufficient reactivity to sustain a front can be difficult to achieve and often results in systems with poor storage lives. This is of particular concern for frontal ring-opening metathesis polymerization (FROMP) where gelation occurs within a single day of resin preparation due to the highly reactive nature of Grubbs-type catalysts. In this report we demonstrate the use of encapsulated catalysts to provide remarkable latency to frontal polymerization systems, specifically using the highly active dicyclopentadiene monomer system. Negligible differences were observed in the frontal velocities or thermomechanical properties of the resulting polymeric materials. FROMP systems with encapsulated catalyst particles are shown with storage lives exceeding 12 months and front rates that increase over a well-characterized 2 month period. Moreover, the modularity of this encapsulation method is demonstrated by encapsulating a platinum catalyst for the frontal polymerization of silicones by using hydrosilylation chemistry.

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. An extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

Decarbonization efforts highlight hydrogen as an attractive alternative to fossil fuels, but its tendency to embrittle structural metals demands careful consideration when designing hydrogen infrastructure. Moreover, the mechanisms by which hydrogen degrades these materials are still being elucidated. The current work develops new computational tools to quantify the different contributions of hydrogen to the energy barrier of cross-slip, a key deformation mechanism. Novel features are implemented to a line tension model, which include the use of non-singular dislocation interactions, character-dependent dislocation energies and simulations of the constriction configurations. A new molecular dynamics technique is developed to calculate the interaction energy between the partials of a dissociated dislocation via fixing the centers of mass of the regions below and above the Shockley partials and performing time-averaged calculations. Hydrogen is found to impact the stacking fault width of dislocations in different ways depending on their characters: it decreases for dislocations with a character θ > 30°, remains unchanged for θ = 30° and increases for θ < 30°. The latter regime is a newly identified mechanism by which hydrogen inhibits cross-slip. Moreover, formation of nano-hydrides is predicted to occur around screw dislocations for high hydrogen concentrations, a phenomenon previously identified only in dislocations with an edge component. If nano-hydrides develop, their influence extending the equilibrium stacking fault width and increasing both the constriction and cross-slip energy barriers dominate over all other hydrogen contributions. The theory and tools developed will pave the way towards a comprehensive understanding of hydrogen-dislocation interactions in structural metals.

The carbon phase diagram is rich with polymorphs which possess very different physical and optical properties ideal for different scientific and engineering applications. An understanding of the dynamically driven phase transitions in carbon is particularly important for applications in inertial confinement fusion, as well as planetary and meteorite impact histories. Experiments on the Z Pulsed Power Facility at Sandia National Laboratories generate dynamically compressed high-pressure states of matter with exceptional uniformity, duration, and size that are ideal for investigations of fundamental material properties. X-ray diffraction (XRD) is an important material physics measurement because it enables direct observation of the strain and compression of the crystal lattice, and it enables the detection and identification of phase transitions. Several unique challenges of dynamic compression experiments on Z prevent using XRD systems typically utilized at other dynamic compression facilities, so novel XRD diagnostics have been designed and implemented. We performed experiments on Z to shock compress carbon (pyrolytic graphite) samples to pressures of 150–320 GPa. The Z-Beamlet Laser generated Mn-Heα (6.2 keV) X-rays to probe the shock-compressed carbon sample, and the new XRD diagnostics measured changes in the diffraction pattern as the carbon transformed into its high-pressure phases. Quantitative analysis of the dynamic XRD patterns in combination with continuum velocimetry information constrained the stability fields and melting of high-pressure carbon polymorphs.

This project pursued a novel strategy for incorporating multiple qubits per ion into ion-trap based quantum computing (ITQC) involving Ca⁺ and Ba⁺. By forming molecular complexes of these cations with molecular-scale cages, we hypothesized that molecular energy levels could be incorporated into quantum computing while retaining key properties of the atomic ions intact. We experimented with a variety of molecular cages and found that Na⁺, K⁺, Rb⁺, Ca²⁺, Sr²⁺, and Ba²⁺ could be captured and brought into the gas phase efficiently by imbedding them inside [2.2.2]-benzocryptand. IR and UV spectra of these cage complexes are sensitive to the size and charge state of the ion, reporting on the structures and binding properties of the cage complexes. UV photofragmentation of the Ba²⁺-Acetate^-1-BzCrypt complex produces Ba⁺-BzCrypt, the complex targeted for exploration in the original hypothesis. Follow-on funding is needed to pursue the spectroscopy of this complex as a target for ITQC.

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The understanding of power flow plasmas is important as we look towards next generation pulsed power (NGPP) as current losses could prohibit the goals of that facility. Therefore, it is important to have accurate diagnostics of the plasma parameters on the current machines, which can be used to help inform and improve simulations. Having these plasma parameters will help validate models and simulations to provide confidence when they are expanded to conditions relevant to NGPP. One important plasma parameter that can be measured is the electron density, which can be measured by photonic Doppler velocimetry (PDV). A PDV system has several key advantages over other interferometers by measuring relatively low densities (> 1 × 10¹⁵ cm^-2) with both spatial and temporal resolution. Experiments were performed on the Mykonos pulsed power machine, which is a 1 MA sub scale machine in which recent platforms have been developed to explore current densities relevant to the inner magnetically insulated transmission line (MITL) on the Z machine. Experiments were performed on two different platforms, the thin foil platform and the Mykonos parallel plate platform (MP3). In addition, a combination of both single-point and multi-point measurements were used. The single-point measurements proved to be very promising, providing a clear increase in density at about 70 ns into the current rise on thin foil experiments up to about 5 × 10¹⁷ cm^-3 before the probe stopped providing signal. While we did also see returns from multi-point measurements on both platforms, the signals were not as easy to interpret due to strong background effects. However, they do show initial promise for this diagnostic to measure density at several points across a 1 mm gap. These measurements provide insights in how to improve the diagnostic so that it can provide useful information on power flow relevant experiments.

This report documents the generation of a mechanism to predict the inclusion of carbon soot particles in a high explosive flow. The mechanism includes gasification and oxidation reactions, formation, sublimation, radiation, and agglomeration. Each part of the mechanism is derived from properties in the literature. The influence of each part of the mechanism is explored using simple, example simulations consisting of a 12 mm diameter 2,4,6-Trinitrotoluene charge detonated in ambient air. The mechanism has not been quantitatively compared to experiments. Additional efforts will be required to tune and validate it, which will require continued advancements in experimental diagnostics and simulation techniques.