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.
Distributed acoustic sensing (DAS) has a demonstrated potential for wide-scale and continuous in situ monitoring of near-surface environmental and anthropogenic processes. DAS is attractive for development as a multi-geophysical observatory due to the prevalence of existing fiber infrastructure in regions with environmental, cultural, or strategic significance. To evaluate the efficacy of this technology for monitoring of polar environmental processes, we collected DAS data from a 37-km long section of seafloor telecommunications fiber located on the continental shelf of the Beaufort Sea, Alaska. This experiment spanned eight, one-week, seasonally-distributed periods across two years. This was the first ever deployment of seafloor DAS beneath sea ice, and the first deployment in any marine environment to span multiple seasons. We recorded a variety of environmental and anthropogenic signals with demonstrable utility for the study of sea ice dynamics and tracking of ocean vessels and ice-traversing vehicles.
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.
This report summarizes the measurement and analysis results of initial performance demonstrator wafers patterned at LumArray in January 2023 in accordance with the project Statement of Work.
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 primary purpose of this project was to evaluate alternative gas mixtures to sulfur hexafluoride (SF6) developed for high voltage power delivery applications for use in high voltage spark gap switches. These SF6 alternatives lower global warming potential emissions and enable improvements to the pressure-voltage design space. A combined experimental, computational, and theoretical study was used to quantify the impact of persistent breakdown products on the breakdown distribution of SF6-replacement gas mixtures. Viable SF6 replacements suitable for use in spark gap switches were studied to enable performance and agility improvements for next-generation pulsed power research relevant to national security missions. Experimental campaign included establishing parameters of switch gases as function of concentration. Various concentrations and pressures were tested for trends in breakdown voltage, repeatability, and durability, and breakdown constituents. A zero-dimensional plasma global model was used to simulate the plasma arc decay and recombination process in spark-gap switches relevant to the Z machine. Finally, a complete and consistent set of electron-neutral collision cross-sections for the novel insulating gas C4F7N is reported.
Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition that leveraging device-level noise as a source of controlled stochasticity represents an exciting prospect of achieving brain-like capabilities in probabilistic neural algorithms, but the reality of integrating stochastic devices with deterministic devices in an already-challenging neuromorphic circuit design process is formidable. Here, we explore how the brain combines different signaling modalities into its neural circuits as well as consider the implications of more tightly integrated stochastic, analog, and digital circuits. Further, by acknowledging that a fully CMOS implementation is the appropriate baseline, we conclude that if mixing modalities is going to be successful for neuromorphic computing, it will be critical that device choices consider strengths and limitations at the overall circuit level.
A discussion of systems like ChatGPT, what their legal issues may be, how they are affecting society, and what the ethical considerations of their existence and use are.
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.
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.
Modeling of phenomena such as anomalous transport via fractional-order differential equations has been established as an effective alternative to partial differential equations, due to the inherent ability to describe large-scale behavior with greater efficiency than fully resolved classical models. In this review article, we first provide a broad overview of fractional-order derivatives with a clear emphasis on the stochastic processes that underlie their use. We then survey three exemplary application areas — subsurface transport, turbulence, and anomalous materials — in which fractional-order differential equations provide accurate and predictive models. For each area, we report on the evidence of anomalous behavior that justifies the use of fractional-order models, and survey both foundational models as well as more expressive state-of-the-art models. We also propose avenues for future research, including more advanced and physically sound models, as well as tools for calibration and discovery of fractional-order models.
The ion velocity distribution functions of thermonuclear plasmas generated by spherical laser direct drive implosions are studied using deuterium-tritium (DT) and deuterium-deuterium (DD) fusion neutron energy spectrum measurements. A hydrodynamic Maxwellian plasma model accurately describes measurements made from lower temperature (<10 keV), hydrodynamiclike plasmas, but is insufficient to describe measurements made from higher temperature more kineticlike plasmas. The high temperature measurements are more consistent with Vlasov-Fokker-Planck (VFP) simulation results which predict the presence of a bimodal plasma ion velocity distribution near peak neutron production. These measurements provide direct experimental evidence of non-Maxwellian ion velocity distributions in spherical shock driven implosions and provide useful data for benchmarking kinetic VFP simulations.
A critical mission need exists to develop new materials that can withstand extreme environments and multiple sequential threats. High entropy materials, those containing 5 or more metals, exhibit many exciting properties which would potentially be useful in such situations. However, a particularly hard challenge in developing new high entropy materials is determining a priori which compositions will form the desired single phase material. The project outlined here combined several modeling and experimental techniques to explore several structure-property-relationships of high entropy ceramics in an effort to better understand the connection between their compositional components, their observed properties, and stability. We have developed novel machine learning algorithms which rapidly predict stable high entropy ceramic compositions, identified the stability interplay between configurational entropy and cation defects, and tested the mechanical stability of high entropy oxides using the unique capabilities at the Dynamic Compression Sector facility and the Saturn accelerator.
Deep learning (DL) models have enjoyed increased attention in recent years because of their powerful predictive capabilities. While many successes have been achieved, standard deep learning methods suffer from a lack of uncertainty quantification (UQ). While the development of methods for producing UQ from DL models is an active area of current research, little attention has been given to the quality of the UQ produced by such methods. In order to deploy DL models to high-consequence applications, high-quality UQ is necessary. This report details the research and development conducted as part of a Laboratory Directed Research and Development (LDRD) project at Sandia National Laboratories. The focus of this project is to develop a framework of methods and metrics for the principled assessment of UQ quality in DL models. This report presents an overview of UQ quality assessment in traditional statistical modeling and describes why this approach is difficult to apply in DL contexts. An assessment on relatively simple simulated data is presented to demonstrate that UQ quality can differ greatly between DL models trained on the same data. A method for simulating image data that can then be used for UQ quality assessment is described. A general method for simulating realistic data for the purpose of assessing a model’s UQ quality is also presented. A Bayesian uncertainty framework for understanding uncertainty and existing metrics is described. Research that came out of collaborations with two university partners are discussed along with a software toolkit that is currently being developed to implement the UQ quality assessment framework as well as serve as a general guide to incorporating UQ into DL applications.
Developed in 2018, PathTrace is a software package built with the intention of making path analysis simple and intuitive. PathTrace is a top-down pathway analysis software where a user is able to explore vulnerable pathways into a facility. The intention of utilizing a software tool like PathTrace is to characterize an existing physical protection system (PPS) and to upgrade the system to achieve a high level of response interruption, or probability of interruption (PI) of the adversary. There are four steps for conducting path analysis using PathTrace. The first step is to identify an image to use to build the model and scale the model within PathTrace using a section of known distance (wall or fence perimeter, for example). The scaling process will produce a grid of cells through which the user is able to build a model. The second step is to fill out the grid of cells with four categories of materials: Barriers, Detection Areas, Jumps, and Targets. These materials apply associated delay and detection values to the cells in which they are applied. The third step is to represent the adversary and response forces. The adversaries are represented by their capabilities in interacting with the materials identified in step two, and the response is represented by how quickly they will be able to respond to an adversary attack. Finally, the user is able to take all of the information from the previous three steps and perform a Most Vulnerable Path (MVP) analysis. In this stage, the user is able to visualize vulnerable adversary pathways and reason about how to upgrade these pathways to provide a high level of PI.
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.
