Publications

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Several sources of interest often generate both low-frequency acoustic and seismic signals due to energy propagation through the atmosphere and the solid Earth. Seismic and acoustic observations are associated with a wide range of sources, including earthquakes, volcanoes, bolides, chemical and nuclear explosions, ocean noise, and others. The fusion of seismic and acoustic observations contributes to a better understanding of the source, both in terms of constraining source location and physics, as well as the seismic to acoustic coupling of energy. In this review, we summarize progress in seismoacoustic data processing, including recent developments in open-source data availability, low-cost seismic and acoustic sensors, and large-scale deployments of collocated sensors from 2010 to 2022. Similarly, we outline the recent advancements in modeling efforts for both source characteristics and propagation dynamics. Finally, we highlight the advantages of fusing multiphenomenological signals, focusing on current and future techniques to improve source detection, localization, and characterization efforts. This review aims to serve as a reference for seismologists, acousticians, and others within the growing field of seismoacoustics and multiphenomenology research.

Reversible logic schemes using flux solitons (fluxons) on long Josephson junctions (LJJs) have recently been proposed. The attraction of the fluxon is that it propagates ballistically along an LJJ until it encounters a change in the character of the LJJ, often a designed circuit element. Logic gates involve fluxons interacting with circuit elements and with other fluxons. However, testing of ballistic fluxon circuits requires other circuits outside the logic family to direct and control fluxon motion. We discuss two such non-reversible fluxon control circuits. First, the polarity filter gate is a simple non-reversible gate that allows one polarity of fluxon to pass, while reflecting the other polarity. In the off state both polarities reflect. Second, the polarity separator generalizes on the polarity filter concept and allows separation of the two fluxon polarities into different LJJs. We discuss simulations of these structures and possible applications.

IT Service Management (ITSM) is an inimitable, ever-changing practice that is critical to all far-reaching organizations, including Sandia National Laboratories. With recent developments in technology and society – spurred by major events like the advent of ChatGPT and the pandemic – it is as important as ever to consider the implications and opportunities for ITSM. This report strategically synthesizes existing information about ITSM structures and examines the current state of Sandia’s service management entity: CCHD. Then, it looks at the emerging state of the world, culminating in CCHD-tailored recommendations for continual service improvement (CSI). Ultimately, the biggest matters to address are staff tenure, ticket documentation, and self-service facilitation. The next step might be to introduce AI to service channels in a non-system-invasive manner, namely a chatbot on the main CCHD page. All would serve to enhance end-user experiences, and by proxy Sandia’s output.

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Tribal energy development is a complex multi-faceted topic. The objective of this work is to identify common themes across tribal solar energy deployment projects, focusing specifically on lessons learned and recommendations. Identifying these commonalities and learning from the experiences of tribes that have embarked in energy development efforts can help to inform the development of future tribal solar energy projects. A thematic qualitative analysis approach was used to analyze project reports and presentations for 41 tribal solar deployment projects (only within the contiguous United States) funded by the Office of Indian Energy Policy and Programs, applying a framework of success factors developed from the literature. The results of the qualitative analysis are described in four discrete parts: comprehensive and inclusive planning, fostering partnerships and collaboration, building capacity, and exercising and advancing tribal sovereignty. Each of the overarching themes inform recommendations for tribes to promote the success of solar projects.

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Earthquakes have repeatedly been shown to produce inaudible acoustic signals (< 20 Hz), otherwise known as infrasound. These signals can propagate hundreds to thousands of kilometers and still be detected by ground-based infrasound arrays depending on the source strength, distance between source and receiver, and atmospheric conditions. Another type of signal arrival at infrasound arrays is the seismic induced motion of the sensor itself, or ground-motion-induced sensor noise. Measured acoustic and seismic waves produced by earthquakes can provide insight into properties of the earthquake such as magnitude, depth, and focal mechanism, as well as information about the local lithology and atmospheric conditions. Large earthquakes that produce strong acoustic signals detected at distances greater than 100 km are the most commonly studied; however, more recent studies have found that smaller magnitude earthquakes (Mw < 2:0) can be detected at short ranges. In that vein, this study will investigate the ability for a long-term deployment of infrasound sensors (deployed as part of the Source Physics Experiments [SPE] from 2014 to 2020) to detect both seismic and infrasonic signals from earthquakes at local ranges (< 50 km). Methods used include a combination of spectral analysis and automated array processing, supported by U.S. Geological Survey earthquake bulletins. This investigation revealed no clear acoustic detections for short range earthquakes. However, secondary infrasound from an Mw 7.1 earthquake over 200 km away was detected. Important insights were also made regarding the performance of the SPE networks including detections of other acoustic sources such as bolides and rocket launches. Finally, evaluation of the infrasound arrays is performed to provide insight into optimal deployments for targeting earthquake infrasound.

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Factor Fixing (FF) is a common method for reducing the number of model parameters to lower computational cost. FF typically starts with distinguishing the insensitive parameters from the sensitive and pursues uncertainty quantification (UQ) on the resulting reduced-order model, fixing each insensitive parameter at a fixed value. There is a need, however, to expand such a common approach to consider the effects of decision choices in the FF-UQ procedure on metrics of interest. Therefore, to guide the use of FF and increase confidence in the resulting dimension-reduced model, we propose a new adaptive framework consisting of four principles: (a) re-parameterize the model first to reduce obvious non-identifiable parameter combinations, (b) focus on decision relevance especially with respect to errors in quantities of interest (QoI), (c) conduct adaptive evaluation and robustness assessment of errors in the QoI across FF choices as sample size increases, and (d) reconsider whether fixing is warranted. The framework is demonstrated on a spatially-distributed water quality model. The error in estimates of QoI caused by FF can be estimated using a Polynomial Chaos Expansion (PCE) surrogate model. Built with 70 model runs, the surrogate is computationally inexpensive to evaluate and can provide global sensitivity indices for free. For the selected catchment, just two factors may provide an acceptably accurate estimate of model uncertainty in the average annual load of Total Suspended Solids (TSS), suggesting that reducing the uncertainty in these two parameters is a priority for future work before undertaking further formal uncertainty quantification.

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Self-healing or self-assembling power systems that rely on local measurements for decision making can provide significant resilience benefits, but they also must include safeguards that prevent the system from self-assembling into an undesirable configuration. One potential undesirable configuration would be the formation of closed loops for which the system was not designed, a situation that can arise any time that two intentional-island systems can be connected in more than one place, e.g., if tie-line breakers are included in the self-assembling system. This paper discusses the unintentional loop formation problem in self-assembling systems and presents a method for mitigating it. This method involves calculating the correlation or the mean absolute error (MAE) between the two local frequency measurements made on either side of a line relay. The correlation and MAE between these frequencies changes significantly between the loop and non-loop cases, and this difference can be used for loop detection. This article presents and explains the method in detail, presents evidence that the method's underlying assumptions are valid, and demonstrates in PSCAD two implementations of the method. The paper concludes with a discussion of the strengths and weaknesses of the proposed method.

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This work shows that the static random access memory (SRAM) error rate for a commercial 65-nm device in a dose rate environment can be highly dependent upon the integrated dose (dose rate × pulse duration). While the typical metric for such testing is dose rate upset (DRU) level in rad(Si)/s, a series of dose rate experiments at Little Mountain Test Facility (LMTF) shows dependence on the integrated dose. The error rate is also found to be dependent on the core voltage, and the preradiation value of the bits. We believe that these effects are explained by a well charge depletion caused by gamma ray photocurrent.

We present a graph neural network approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the ideal crystal structure, without the need to create defected atomic structure models as input. Here we used density functional theory reference data for vacancy defects in oxides, to train a defect graph neural network (dGNN) model that replaces the density functional theory supercell relaxations otherwise required for each symmetrically unique crystal site. Interfaced with thermodynamic calculations of reduction entropies and associated free energies, the dGNN model is applied to the screening of oxides in the Materials Project database, connecting the zero-kelvin defect enthalpies to high-temperature process conditions relevant for solar thermochemical hydrogen production and other energy applications. The dGNN approach is applicable to arbitrary structures with an accuracy limited principally by the amount and diversity of the training data, and it is generalizable to other defect types and advanced graph convolution architectures. It will help to tackle future materials discovery problems in clean energy and beyond.

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The classical Drude model provides an accurate description of the plasma resonance of three-dimensional materials, but only partially explains two-dimensional systems where quantum mechanical effects dominate such as P:δ layers - atomically thin sheets of phosphorus dopants in silicon that induce electronic properties beyond traditional doping. Previously it was shown that P:δ layers produce a distinct Drude tail feature in ellipsometry measurements. However, the ellipsometric spectra could not be properly fit by modeling the δ layer as a discrete layer of classical Drude metal. In particular, even for large broadening corresponding to extremely short relaxation times, a plasma resonance feature was anticipated but not evident in the experimental data. In this work, we develop a physically accurate description of this system, which reveals a general approach to designing thin films with intentionally suppressed plasma resonances. Our model takes into account the strong charge-density confinement and resulting quantum mechanical description of a P:δ layer. We show that the absence of a plasma resonance feature results from a combination of two factors: (i) the sharply varying charge-density profile due to strong confinement in the direction of growth; and (ii) the effective mass and relaxation time anisotropy due to valley degeneracy. The plasma resonance reappears when the atoms composing the δ layer are allowed to diffuse out from the plane of the layer, destroying its well-confined two-dimensional character that is critical to its distinctive electronic properties.

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The goal of this report is to provide insight to the development of vt-tv, a C++ HPC visualization tool designed for insightful analysis of load-balancing metrics in the DARMA toolkit. In particular, it delves into its modular data model and diverse usage scenarios, emphasizing adaptability and efficiency.

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the global maximum likelihood estimator from local minima. Simultaneously, a recent trend in theoretical computer science and numerical linear algebra leverages randomization to solve very large, hard problems. The typical approach is to use randomization for a fast approximation and determinism for refinement to yield effective algorithms with theoretical guarantees. Two popular algorithms for Poisson CPD reflect that emergent dichotomy: CP Alternating Poisson Regression is a deterministic algorithm and Generalized Canonical Polyadic decomposition makes use of stochastic algorithms in several variants. This work extends recent work to develop two new methods that leverage randomized and deterministic algorithms for improved accuracy and performance.

This report summarizes the international collaborations conducted by Sandia funded by the US Department of Energy Office (DOE) of Nuclear Energy (DOE-NE) Spent Fuel and Waste Science & Technology (SFWST) as part of the Sandia National Laboratories Salt R&D and Salt International work packages. This report satisfies the level-three milestone M3SF-23SN010303062. Several stand-alone sections make up this summary report, each completed by the participants. The sections discuss granular salt reconsolidation (KOMPASS), engineered barriers (RANGERS), numerical model comparison (DECOVALEX) and an NEA Salt Club working group on the development of scenarios as part of the performance assessment development process. Finally, we summarize events related to the US/German Workshop on Repository Research, Design and Operations.

Motion primitives allow for application of discrete search algorithms to rapidly produce trajectories in complex continuous space. The maneuver automaton (MA) provides an elegant formulation for creating a primitive library based on trims and maneuvers. However, performance is fundamentally limited by the contents of the primitive library. If the library is too sparse, performance can be poor in terms of path cost, whereas a library that is too large can increase run time. This work outlines new methods for using genetic algorithms to prune a primitive library. The proposed methods balance the path cost and planning time while maintaining the reachability of the MA. The genetic algorithm in this paper evaluates and mutates populations of motion primitive libraries to optimize both objectives. Here, we illustrate the performance of these methods with a simulated study using a nonlinear medium-fidelity F-16 model. We optimize a library with the presented algorithm for obstacle-free navigation and a nap-of-the-Earth navigation task. In the obstacle-free navigation task, we show a tradeoff of a 10.16% higher planning cost for a 96.63% improvement in run time. In the nap-of-the-Earth task, we show a tradeoff of a 9.712% higher planning cost for a 92.06% improvement in run time.

Understanding and controlling nanoscale interface phenomena, such as band bending and secondary phase formation, is crucial for electronic device optimization. In granular metal (GM) studies, where metal nanoparticles are embedded in an insulating matrix, the importance of interface phenomena is frequently neglected. Here, we demonstrate that GMs can serve as an exemplar system for evaluating the role of secondary phases at interfaces through a combination of x-ray photoemission spectroscopy (XPS) and electrical transport studies. We investigated SiN_x as an alternative to more commonly used oxide-insulators, as SiN_x-based GMs may enable high temperature applications when paired with refractory metals. Comparing Co-SiN_x and Mo-SiN_x GMs, we found that, in the tunneling-dominated insulating regime, Mo-SiN_x had reduced metal-silicide formation and orders-of-magnitude lower conductivity. XPS measurements indicate that metal-silicide and metal-nitride formation are mitigatable concerns in Mo-SiN_x. Given the metal-oxide formation seen in other GMs, SiN_x is an appealing alternative for metals that readily oxidize. Furthermore, SiN_x provides a path to metal-nitride nanostructures, potentially useful for various applications in plasmonics, optics, and sensing.

Recent work on atomic-precision dopant incorporation technologies has led to the creation of both boron and aluminum δ -doped layers in silicon with densities above the solid solubility limit. We use density functional theory to predict the band structure and effective mass values of such δ layers, first modeling them as ordered supercells. Structural relaxation is found to have a significant impact on the impurity band energies and effective masses of the boron layers, but not the aluminum layers. However, disorder in the δ layers is found to lead to a significant flattening of the bands in both cases. We calculate the local density of states and doping potential for these δ -doped layers, demonstrating that their influence is highly localized with spatial extents at most 4 nm. We conclude that acceptor δ -doped layers exhibit different electronic structure features dependent on both the dopant atom and spatial ordering. This suggests prospects for controlling the electronic properties of these layers if the local details of the incorporation chemistry can be fine-tuned.

While radiation is known to degrade AlGaN/GaN high-electron-mobility transistors (HEMTs), the question remains on the extent of damage governed by the presence of an electrical field in the device. In this study, we induced displacement damage in HEMTs in both ON and OFF states by irradiating with 2.8 MeV Au4+ ion to fluence levels ranging from 1.72 × 10 10 to 3.745 × 10 13 ions cm−2, or 0.001-2 displacement per atom (dpa). Electrical measurement is done in situ, and high-resolution transmission electron microscopy (HRTEM), energy dispersive x-ray (EDX), geometrical phase analysis (GPA), and micro-Raman are performed on the highest fluence of Au4+ irradiated devices. The selected heavy ion irradiation causes cascade damage in the passivation, AlGaN, and GaN layers and at all associated interfaces. After just 0.1 dpa, the current density in the ON-mode device deteriorates by two orders of magnitude, whereas the OFF-mode device totally ceases to operate. Moreover, six orders of magnitude increase in leakage current and loss of gate control over the 2-dimensional electron gas channel are observed. GPA and Raman analysis reveal strain relaxation after a 2 dpa damage level in devices. Significant defects and intermixing of atoms near AlGaN/GaN interfaces and GaN layer are found from HRTEM and EDX analyses, which can substantially alter device characteristics and result in complete failure.

Experiments with trapped ions and neutral atoms typically employ optical modulators in order to control the phase, frequency, and amplitude of light directed to individual atoms. These elements are expensive, bulky, consume substantial power, and often rely on free-space I/O channels, all of which pose scaling challenges. To support many-ion systems like trapped-ion quantum computers or miniaturized deployable devices like clocks and sensors, these elements must ultimately be microfabricated, ideally monolithically with the trap to avoid losses associated with optical coupling between physically separate components. In this work we design, fabricate, and test an optical modulator capable of monolithic integration with a surface-electrode ion trap. These devices consist of piezo-optomechanical photonic integrated circuits configured as multi-stage Mach-Zehnder modulators that are used to control the intensity of light delivered to a single trapped ion on a separate chip. We use quantum tomography employing hundreds of multi-gate sequences to enhance the sensitivity of the fidelity to the types and magnitudes of gate errors relevant to quantum computing and better characterize the performance of the modulators, ultimately measuring single qubit gate fidelities that exceed 99.7%.

This work explores the influence of blend composition, network architecture, and hydrogen bonding on the material properties of crosslinked epoxy networks, focusing on the glass transition temperature (Tg) and Young’s modulus (Y). We used coarse-grained molecular dynamics simulations to simulate varying compositions of stiff and flexible components in epoxy monomer blends with varying excess of curative. We find that, without hydrogen bonding, networks of any composition show a monotonically increasing Tg with decreasing excess curative, consistent with theory. In contrast, we find that when hydrogen bonding is introduced, the binary blend networks show significant enhancement in Tg for lightly crosslinked systems. This result contributes to an explanation of the anomalous Tg behavior observed experimentally in these systems. We further find that Y is generally enhanced by hydrogen bonds, especially below Tg, demonstrating that hydrogen bonding has a significant influence on mechanical properties and can allow access to other desirable dynamic behavior, especially self-healing.

The Sandia Mechanics Challenge (SMC) provides the solid-mechanics community a forum for assessing its ability to predict mechanical behavior in structures and materials through a blind, round-robin format. Computationalists are asked to predict the behavior of an unfamiliar geometry given experimental calibration data, their predictions are compared to experimental measurements of the SMC scenario, and then the participants assess and compare their approaches, documenting their findings. The SMC broadens the scope of Sandia-hosted benchmarking problems that previously focused on ductile failure through the Sandia Fracture Challenges, enabling an enduring, community-wide self-assessment of predictive capabilities for a variety of mechanics topics. The SMC is part of the Structural Reliability Partnership, which offers other benchmarking challenges hosted by several participating institutions.

Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. In this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.

Researchers have the potential to be exposed to a wide variety of hazards inherent to the equipment they use and maintain. When equipment does not function as expected, researchers sometimes reach out to their vendors for assistance. Early diagnostic or troubleshooting interactions between researcher and vendor are often conducted over the telephone and can lead to researchers performing work outside of their area of expertise and exposure to unknown hazards. This type of interaction significantly contributed to an incident where during diagnostic activities a researcher accidentally contacted, and discharged, a capacitor in an X-ray diffraction instrument. While this incident did not produce a serious injury, if the capacitor discharge path had occurred hand-to-hand across the heart, a serious injury may have been possible.

Magnetized liner inertial fusion (MagLIF) is a magneto-inertial-fusion concept that is studied on the 20-MA, 100-ns rise time Z Pulsed Power Facility at Sandia National Laboratories. Given the relative success of the platform, there is a wide interest in studying the scaled performance of this concept at a next-generation pulsed-power facility that may produce peak currents upward of 60 MA. An important aspect that requires more research is the instability dynamics of the imploding MagLIF liner, specifically how instabilities are initially seeded. It has been shown in magnetized 1-MA thin-foil liner Z-pinch implosion simulations that a Hall interchange instability (HII) effect can provide an independent seeding mechanism for helical magneto-Rayleigh–Taylor instabilities. Here in this paper, we explore this instability at higher peak currents for MagLIF using 2D discontinuous Galerkin PERSEUS simulations, an extended magneto-hydrodynamics code, which includes Hall physics. Our simulations of scaled MagLIF loads show that the growth rate of the HII is invariant to the peak current, suggesting that studies at 20-MA are directly relevant to 60-MA class machines.

We report significantly enhanced device performance in long wavelength interband cascade lasers (ICLs) by employing a recently proposed innovative quantum well (QW) active region containing strained InAsP layers. These ICLs were able to operate at wavelengths near 14.4 μm, the longest ever demonstrated for III-V interband lasers, implying great potential of ICLs to cover an even wider wavelength range. Also, by applying the aforesaid QW active region configuration on ICLs at relatively short wavelengths, ICLs were demonstrated at a low threshold current density (e.g., 13 A/cm2 at 80 K) and at temperatures up to 212 K near 12.4 μm, more than 50 K higher than the previously reported ICLs with the standard W-shape QW active region at similar wavelengths. This suggests that the QW active region with InAsP layers can be used to improve device performance at the shorter wavelengths.

This memorandum investigates the Callahan crushed salt constitutive model developed and used at Sandia National Laboratories for geomechanics applications. The formulation is reviewed, calibration against a novel long-term experiment with complex loading history is performed and validation against other experiments is attempted. Areas of improvement and deficiencies are identified that support the need for an alternative or updated constitutive model for crushed salt.

Electronic structure calculations on small systems such as H2, H2O, LiH, and BeH2 with chemical accuracy are still a challenge for the current generation of noisy intermediate-scale quantum (NISQ) devices. One of the reasons is that due to the device limitations, only minimal basis sets are commonly applied in quantum chemical calculations, which allows one to keep the number of qubits employed in the calculations at a minimum. However, the use of minimal basis sets leads to very large errors in the computed molecular energies as well as potential energy surface shapes. One way to increase the accuracy of electronic structure calculations is through the development of small basis sets better suited for quantum computing. In this work, we show that the use of adaptive basis sets, in which exponents and contraction coefficients depend on molecular structure, provides an easy way to dramatically improve the accuracy of quantum chemical calculations without the need to increase the basis set size and thus the number of qubits utilized in quantum circuits. As a proof of principle, we optimize an adaptive minimal basis set for quantum computing calculations on an H2 molecule, in which exponents and contraction coefficients depend on the H-H distance, and apply it to the generation of H2 potential energy surface on IBM-Q quantum devices. The adaptive minimal basis set reaches the accuracy of the double-zeta basis sets, thus allowing one to perform double-zeta quality calculations on quantum devices without the need to utilize twice as many qubits in simulations. This approach can be extended to other molecular systems and larger basis sets in a straightforward manner.

Earlier studies have proven how ducted fuel injection (DFI) substantially reduces soot for low- and mid-load conditions in heavy-duty engines, without significant adverse effects on other emissions. Nevertheless, no comprehensive DFI study exists showing soot reductions at high- and full-load conditions. This study investigated DFI in a single-cylinder, 1.7-L, optical engine from low- to full-load conditions with a low-net-carbon fuel consisting of 80% renewable diesel and 20% biodiesel. Over the tested load range, DFI reduced engine-out soot by 38.1-63.1% compared to conventional diesel combustion (CDC). This soot reduction occurred without significant detrimental effects on other emission types. Thus, DFI reduced the severity of the soot-NOx tradeoff at all tested conditions. While DFI delivered considerable soot reductions in the present study, previous DFI studies at low- and mid-load conditions delivered larger soot reductions (>90%) compared to CDC operation at the same conditions. Therefore, the DFI configuration used here has been deemed nonoptimal (in terms of parameters such as the injector-spray and piston geometries), and several improvements are recommended for future studies with high-load DFI. These improvements include employing better spray-duct alignment, a deeper piston bowl with a smaller injector umbrella angle, and a fuel injector that opens and closes faster. The study also suggests future research to make DFI ready for commercialization, such as metal-engine tests to ensure desirable DFI performance over an engine's complete speed/load map. Overall, this study supports the continued development and commercialization of DFI to meet upcoming emissions regulations for heavy-duty vehicles. Specifically, multicylinder engine experiments and CFD simulations should be utilized to optimize the performance and clarify the full potential of DFI.

Here, we report deposition of hematite/Pd thin films on silicon wafers via radio frequency (RF) magnetron sputtering and subsequent characterization for future in situ neutron reflectometry studies. Following deposition, the hematite/Pd thin films were characterized as prepared and after annealing in air for 2h at 400, 500, and 600 °C, respectively. Raman spectroscopy, grazing incidence x-ray diffraction, and neutron reflectometry (NR) were used to characterize the structure and chemical compositions of the thin films. The results indicate that pure α-Fe₂O₃ (hematite) films were produced, free from other iron oxide phases and impurities. NR data reveal that one intermediate layer between the Pd layer and the hematite layer was formed during sputtering deposition processes. The fitted scattering length density (SLD) of the as-deposited hematite layer is 70% of the theoretical SLD value, indicating that the grains are loosely packed in the RF-deposited hematite films. After annealing at elevated temperatures, the hematite films show increased SLD values but remain comparable to that of preannealed.

Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.

On a high level, the larger project in question, HydroGEN, aims to develop software used for finding equations of state (EOS) to optimize catalyst configuration for H₂ production through water splitting. In particular, this summer project focused on solving the nonlinear equations used in fitting the equations. This problem involved using Python to solve a linear system with nonlinear constraints. In order for this to be achieved, Pyomo was used to build a model and the solver Ipopt, interior point optimizer, was used. Pyomo is a Python-based language developed at Sandia; it is an optimization modeling language. Rather than solving the entire problem at once, a toy problem was created, simplifying the problem down to the most important focus. This problem had a known solution, comparable to the calculated solution to assess accuracy and as progress was made towards finding solutions, complexity was gradually added to the problem. After building and solving the toy problem, it was found that it gave reasonably accurate solutions, better compared to the two existing solvers previously used with this project in terms of functionality. The solver is now ready for implementation into the project’s main software.

While land-based wind energy has become economically competitive with traditional energy generation sources in the U.S., offshore wind is not. For floating offshore wind this difference is even more substantial where the levelized cost of energy (LCOE) is projected to be around 3-5 times more expensive than land-based wind. The turbine capital costs represent around 50% of the LCOE for land-based wind sites, but the increased system costs for floating offshore wind reduce this to 20%. The platform and mooring costs are the single largest contributor to the LCOE for floating offshore wind where their mass must counteract the overturning moment caused by the turbine’s thrust force. Despite the high costs of the platform and relatively low cost of the turbine, current offshore wind turbines are designed essentially the same as for land-based sites. Reducing the LCOE is the greatest challenge to realize the benefits of sustained development of floating offshore wind in the U.S. Reducing the complicated system costs of floating offshore wind will enable the industry to continue to grow and outpace current projections if reduced cost curves can be reached. The ideal wind energy system would remove all mass and cost that is not directly capturing energy from the wind. For floating offshore wind energy systems, this objective is even more significant as increased mass above the water level must be supported by larger and more expensive floating platforms. For this reason, vertical-axis wind turbines (VAWTs) are ideal for floating offshore sites and have several advantages over horizontal-axis wind turbines (HAWTs) at this scale. Large VAWTs offer opportunities for improved energy capture over HAWTs as single units and with reduced wind plant aerodynamic losses through enhanced wake recovery. Additionally, the platform-level placement of the VAWT drivetrain greatly reduces the demands placed on the floating platform and its mass and cost. The ARCUS vertical-axis wind turbine concept (U.S. 11,421,650 B2) is an iteration beyond traditional Darrieus-type VAWTs that replaces the rigid tower with blades that are bent into shape and held in place with tensioned center supports, like a bow. The ARCUS design has been shown to further decrease the VAWT rotor mass properties, with a 50% reduction over traditional Darrieus VAWTs quantified in the ATLANTIS program. The ARCUS VAWT’s efficient use of material for the rotor and turbine support structures combined with its lowered center of gravity enables a tension-leg platform (TLP) with simplified installation procedures. TLPs have been an emerging platform architecture in the Oil and Gas industry and demonstrated to have the lowest hull mass requirements while maintaining stability with minimal roll and pitch deflections in operation. A 22 MW ARCUS turbine has been designed with a three-column TLP that enables quayside integration of the turbine while maintaining system stability during tow-out and installation and having optimal mass and cost properties. A comprehensive analysis shows the optimal ARCUS TLP system design minimizes LCOE through efficient material usage and increased energy capture to yield a competitive LCOE estimate of $\$$55/MWh. A comparison with a reference HAWT, having the same swept area, quantifies the advantages that helped to produce this improved LCOE for the ARCUS concept: (1) 30% reduction in total turbine mass, (2) 70% reduction in turbine center of gravity, and (3) 45% increase in energy production over what is optimal for a HAWT. Intellectual property has been generated through the ATLANTIS program providing opportunities to further reduce the LCOE and improve the performance of the ARCUS turbine and TLP system, expanding the list of innovations to support commercial development of the ARCUS concept.

Porous liquids (PLs) based on the zeolitic imidazole framework ZIF-8 are attractive systems for carbon capture since the hydrophobic ZIF framework can be solvated in aqueous solvent systems without porous host degradation. However, solid ZIF-8 is known to degrade when exposed to CO2 in wet environments, and therefore the long-term stability of ZIF-8-based PLs is unknown. Through aging experiments, the long-term stability of a ZIF-8 PL formed using the water, ethylene glycol, and 2-methylimidazole solvent system was systematically examined, and the mechanisms of degradation were elucidated. The PL was found to be stable for several weeks, with no ZIF framework degradation observed after aging in N2 or air. However, for PLs aged in a CO2 atmosphere, formation of a secondary phase occurred within 1 day from the degradation of the ZIF-8 framework. From the computational and structural evaluation of the effects of CO2 on the PL solvent mixture, it was identified that the basic environment of the PL caused ethylene glycol to react with CO2 forming carbonate species. These carbonate species further react within the PL to degrade ZIF-8. The mechanisms governing this process involves a multistep pathway for PL degradation and lays out a long-term evaluation strategy of PLs for carbon capture. Additionally, it clearly demonstrates the need to examine the reactivity and aging properties of all components in these complex PL systems in order to fully assess their stabilities and lifetimes.

Here, using a quantum computer to speed up one step in a textbook approach to generating random numbers proves to be a savvy strategy, and one that could make good use of quantum computers that will be available in the near future.

A predictive thermodynamic model is utilized for the calculation of fuel properties of oxymethylene dimethyl ethers (OME_3–4), surrogates for gasoline, diesel and aviation fuel, as well as alcohol blends with gasoline and diesel. The alcohols used for these blends are methanol, ethanol, propanol, butanol and pentanol; their mixing ratio ranges from 10 to 50% by volume. The model is based on the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) equation of state (EoS) and Vapor Liquid Equilibrium (VLE) calculations at constant temperature, density and composition. The model includes the association term, with the assumption of two association sites (2B scheme), to enable the modeling of alcohols. The pure-component parameters are estimated based on the Group Contribution (GC) method of various sources, as well as a parametrization model specifically designed for the case of OME_3–4. The results of the computational model for the density, vapor pressure and distillation curves at various conditions, including high-pressure, high-temperature (HPHT), are compared to experimental and computational data available in the literature. In the cases where no measurements are available for the surrogates, experimental data for the corresponding target fuel are used, taking into consideration the inherent deviation in properties between real and surrogate fuel. Overall, the results are in good agreement with the data from the literature, with the average deviation not exceeding 12% for temperature (Kelvin) on the distillation curves, 10% for density and 46% for vapor pressure and the general trend being captured successfully. The use of different pure component parameter estimation techniques can further improve the prediction quality in the cases of OME3–4 and the aviation fuel surrogate, especially for the vapor pressure, leading to an average deviation lower than 18%. These results demonstrate the predictive capabilities of the model, which extend to a wide range of fuel types and pressure/temperature conditions. Through this investigation, the present work aims to establish the limits of applicability of this thermodynamic property prediction methodology.

Here, the effects of gamma and proton irradiation, and of forward bias minority carrier injection, on photo-response were investigated for InAsSb/AlAsSb pBn mid-wave infrared (MWIR) detectors with an engineered majority-carrier barrier. Room-temperature gamma irradiation had an insignificant effect on 77 K photo-response. Gamma irradiation at 77 K detector temperature, however, decreased in situ photo-response by 19% after a cumulative dose of ~ 500 krad(Si). Subsequent forward bias minority carrier injection had no effect on photo-response. The 77 K detectors irradiated with 30 MeV protons up to 2 Mrad(Si) had photo-response degraded by up to 70%, but here forward bias minority carrier (hole) injection caused up to 12% recovery that persisted more than 30 min. These results suggest a mitigation strategy for maintaining the photo-response of similar detectors in radiation environments that cause displacement damage defects.

Astra, deployed in 2018, was the first petascale supercomputer to utilize processors based on the ARM instruction set. The system was also the first under Sandia's Vanguard program which seeks to provide an evaluation vehicle for novel technologies that with refinement could be utilized in demanding, large-scale HPC environments. In addition to ARM, several other important first-of-a-kind developments were used in the machine, including new approaches to cooling the datacenter and machine. This article documents our experiences building a power measurement and control infrastructure for Astra. While this is often beyond the control of users today, the accurate measurement, cataloging, and evaluation of power, as our experiences show, is critical to the successful deployment of a large-scale platform. While such systems exist in part for other architectures, Astra required new development to support the novel Marvell ThunderX2 processor used in compute nodes. In addition to documenting the measurement of power during system bring up and for subsequent on-going routine use, we present results associated with controlling the power usage of the processor, an area which is becoming of progressively greater interest as data centers and supercomputing sites look to improve compute/energy efficiency and find additional sources for full system optimization.

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ∼5 kJ molH2−1) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ molH2−1) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

The Proliferation Resistance Optimization (PRO-X) program is actively supporting the design of new-build nuclear systems by identifying intrinsic characteristics or design choices to minimize the potential for diversion or production of weapons-usable nuclear material. The PRO-X program looks to optimize the safety, security, and performance of the fuel cycle infrastructure for a wide array of reactor types, including research reactors, small modular reactors (SMRs), and large advanced reactors (ARs).

Vanadium dioxide (VO2) undergoes a metal-insulator phase transition at ∼70 °C and has attracted substantial interest for potential applications in electronics, including those in neuromorphic computing. The vanadium-oxygen system has a rather complicated phase diagram, and controlling the stoichiometry and the phase of thin films of vanadium oxides is a well-known challenge. We explore the novel combination of two methods of VO2 thin film deposition using off-axis RF magnetron sputtering on (100)- and (111)-oriented yttria-stabilized zirconia (YSZ) substrates: reactive sputtering of vanadium in an oxygen environment and sputtering of vanadium metal followed by oxidation to VO2. Interestingly, the reactive sputtering process on both substrate orientations yields the metastable semiconducting VO2 (B) phase, which is structurally stabilized by the YSZ surface. The metal sputtering and oxidation process on YSZ produces mainly the equilibrium monoclinic (or M1) phase of VO2 that exhibits a metal-insulator transition. Using this method, we obtained thin films of (010)-textured polycrystalline VO2 (M1) that show a metal-insulator transition with an on/off ratio larger than 1000.

The penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment-friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind-integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast-acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39-bus test system.

Here, phase formation and stability of five component compositionally complex rare earth zirconates (5RE₂Zr₂O₇) were investigated by X-ray diffraction and electron microprobe analysis. Zirconates with different rare earth compositions (LaNdSmEuDy, LaNdSmEuYb, LaNdEuErYb, LaNdDyErYb, SmEuDyYHo, LaYHoErYb, and DyYHoErYb) were synthesized at 1700°C and 2000°C by the solid-state method to investigate the effect of A-site site disorder (δ_A) on phase stability. Increased site disorder results from mixed cation occupancy with localized crystallographic strain and bond disorder. Compositions LaNdSmEuDy (δ_A = 4.6) and LaNdSmEuYb (δ_A = 6.0) produced a single pyrochlore phase and compositions SmDyYHoErYb (δ_A = 2.8), LaYHoErYb (δ_A = 6.2), and DyYHoErYb (δ_A = 1.7) produced a single fluorite phase. High δ_A compositions LaNdEuErYb (δ_A = 6.9) and LaNdDyErYb (δ_A = 7.2) produced a pyrochlore and fluorite phase mixture at 1700°C. Single phase was obtained for the latter composition at 2000°C. Of the single phase compositions calcined at 1700°C, LaNdSmEuYb and LaYHoErYb (both with largest δ_A) showed decomposition to mixed fluorite and pyrochlore phases during lower temperature anneals, indicating entropic stabilization. Comparison with prior work shows a temperature dependence of the critical δ_A for phase stability, and compositions near it are expected to be entropy stabilized.

Ga₂O₃ films were deposited on (100) MgAl₂O₄ spinel substrates at 550, 650, 750, and 850 °C using metal-organic chemical vapor deposition and investigated using x-ray diffraction and transmission electron microscopy. A phase-pure γ-Ga₂O₃-based material having an inverse spinel structure was formed at 850 °C; a mixture of the γ-phase and β-Ga₂O₃ was detected in films grown at 750 °C. Only β-Ga₂O₃ was determined in the films deposited at 650 and 550 °C. A β- to γ-phase transition occurred from the substrate/film interface during growth at 750 °C. The growth and stabilization of the γ-phase at the outset of film growth at 850 °C was affected by the substantial Mg and Al chemical interdiffusion from the MgAl₂O₄ substrate observed in the energy-dispersive x-ray spectrum. Further, atomic-scale investigations via scanning transmission electron microscopy of the films grown at 750 and 850 °C revealed a strong tetrahedral site preference for Ga and an octahedral site preference for Mg and Al. It is postulated that the occupation of these atoms in these particular sites drives the β-Ga₂O₃ to γ-phase transition and markedly enhances the thermal stability of the latter phase at elevated temperatures.

Modern density functional theory (DFT) is a powerful tool for accurately predicting self-consistent material properties such as equations of state, transport coefficients and opacities in high energy density plasmas, but it is generally restricted to conditions of local thermodynamic equilibrium (LTE) and produces only averaged electronic states instead of detailed configurations. We propose a simple modification to the bound-state occupation factor of a DFT-based average-atom model that captures essential non-LTE effects in plasmas—including autoionization and dielectronic recombination—thus extending DFT-based models to new regimes. Finally, we then expand the self-consistent electronic orbitals of the non-LTE DFT-AA model to generate multi-configuration electronic structure and detailed opacity spectra. This article is part of the theme issue ‘Dynamic and transient processes in warm dense matter’.

Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.

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Presentation of damage detection and experimental design approaches for structures under frequency-domain dynamics

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The Sandia National Laboratories, in California (Sandia/CA) is a research and development facility, owned by the U.S. Department of Energy’s National Nuclear Security Administration agency (DOE/NNSA). The laboratory is located in the City of Livermore (the City) and is comprised of approximately 410 acres. The Sandia/CA facility is operated by National Technology and Engineering Solutions of Sandia, LLC (NTESS) under a contract with the DOE/NNSA. The DOE/ NNSA’s Sandia Field Office (SFO) oversees the operations of the site. North of the Sandia/CA facility is the Lawrence Livermore National Laboratory (LLNL), in which Sandia/CA’s sewer system combines with before discharging to the City’s Publicly Owned Treatment Works (POTW) for final treatment and processing. The City’s POTW authorizes the wastewater discharge from Sandia/CA via the assigned Wastewater Discharge Permit #1251 (the Permit), which is issued to the DOE/NNSA’s main office for Sandia National Laboratories, located in New Mexico (Sandia/NM). The Permit requires the submittal of this Monthly Sewer Monitoring Report to the City by the twenty-fifth day of each month.

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Many highly pixelated organic scintillator detection systems would benefit from independent readout of each scintillator pixel. Recent advances in Silicon Photomultiplier (SiPM) technology makes this goal feasible, however the data acquisition from potentially hundreds or thousands of channels requires a low-cost and compact solution. For pixelated neutron detection with organic scintillators, the capability to distinguish between neutron and gamma interactions using Pulse Shape Discrimination (PSD) is required along with pulse charge and time of arrival. The TOFPET2 ASIC from PETsys Electronics is a 64-channel readout chip providing pulse time and charge integration measurements from SiPMs, and is specifically designed for time-of-flight positron-emission tomography. Using an 8 × 8 array of 6 mm × 6 mm J-series SiPMs from SensL/OnSemi (ArrayJ-60035-64P-PCB), we have studied the energy and PSD performance of the TOFPET2 ASIC using a 4 × 4 array of 6 mm × 6 mm × 30 mm trans-Stilbene crystals from Inrad Optics and a custom SiPM routing board from PETsys Electronics. Using a time-over-threshold method, we measure a maximum PSD figure-of-merit of approximately 1.2 at 478 keV (the Compton edge of 662 keV) for a J-series SiPM operating at an over-voltage of 3V.

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It is necessary to establish confidence in high-consequence codes containing an extensive suite of physics algorithms in the regimes of interest. Verification problems allow code developers to assess numerical accuracy and increase confidence that specific sets of model physics were implemented correctly in the code. The two main verification techniques are code verification and solution verification. In this work, we present verification problems that can be used in other codes to increase confidence in simulations of relativistic beam transport. Specifically, we use the general plasma code EMPIRE to model and compare with the analytical solution to the evolution of the outer radial envelope of a relativistic charged particle beam. We also outline a benchmark test of a relativistic beam propagating through a vacuum and pressurized gas cell, and present the results between EMPIRE and the hybrid code GAZEL. Further, we discuss the subtle errors that were caught with these problems and detail lessons learned.

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Plastic scintillators are widely used as radiation detection media in homeland security and nuclear physics applications. Their attributes include low cost, scalability to large detector volumes, and additive compounding to enable additional material and detection features, such as pulse shape discrimination (PSD), gamma-ray spectroscopy, aging resistance, and coincidence timing. However, traditional chemically cured plastic scintillators (CCS) require long reaction times, and hazardous wet chemical procedures performed by specially trained personnel, and can leave residual monomer, resulting in deleterious optical and material properties. Here, we synthesize melt blended scintillators (MBSs) in 2.5 days using easily accessible solid-state compounding of commercially-available poly(styrene) with 30-60 wt% fluorene-based compound 'P2' to create monolithic detectors with < 100 ppm residual monomer, in several form factors. The best scintillation performance was recorded for 60 wt% P2 in Styron 665, including gamma-ray light yield 139% of EJ- 200 commercial scintillator and PSD figure of merit (FOM) value of 2.65 at 478 keVee, approaching P2 organic glass scintillator (OGS). The capability of MBS to generate fog-resistant scintillators and poly(methyl methacrylate) (PMMA)-based scintillators for use in challenging environments is also demonstrated.

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Proceedings of the 2023 Improving Scientific Software Conference

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We develop an adaptive method for quantum state preparation that utilizes randomness as an essential component and that does not require classical optimization. Instead, a cost function is minimized to prepare a desired quantum state through an adaptively constructed quantum circuit, where each adaptive step is informed by feedback from gradient measurements in which the associated tangent space directions are randomized. We provide theoretical arguments and numerical evidence that convergence to the target state can be achieved for almost all initial states. We investigate different randomization procedures and develop lower bounds on the expected cost function change, which allows for drawing connections to barren plateaus and for assessing the applicability of the algorithm to large-scale problems.

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Micro ribonucleic acids (miRNA) give our immune systems the ability to recognize viruses and other pathogens by their complementary single-stranded RNA (ssRNA) produced in the reproduction of the pathogen in our cells. When miRNA of a specific sequence is detected in a cell sample, it can be assumed that the immune system is activated and attempting to track down the infection. This pathway can be utilized to diagnose infection from a pathogen before the individual even develops symptoms, aiding in early disease detection and proper treatment. One of the ways that we can detect miRNA is through an assay of clustered regularly interspaced short palindromic repeats or “CRISPR” and the bacterial protein Cas13a. This report details discoveries made while attempting to optimize this assay for miRNA detection. After looking at several different factors within the assay, it was determined that some factors, such as reporter type and metallic ion concentration, are more impactful on the overall assay sensitivity than other factors, such as the overall concentration of Cas13a, CRISPR RNA (crRNA), or ssRNA reporter. It was also discovered that different sequences with different lengths require renewed optimization efforts, as each target has a unique binding affinity determined by the sequence length and composition. This information is crucial in the development of point of care molecular detection devices as they become sensitive enough to identify pathogens before they spread.

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High-altitude balloons carrying infrasound sensor payloads can be leveraged toward monitoring efforts to provide some advantages over other sensing modalities. On 10 July 2020, three sets of controlled surface explosions generated infrasound waves detected by a high-altitude floating sensor. One of the signal arrivals, detected when the balloon was in the acoustic shadow zone, could not be predicted via propagation modeling using a model atmosphere. Considering that the balloon’s horizontal motion showed direct evidence of gravity waves, we examined their role in infrasound propagation. Implementation of gravity wave perturbations to the wind field explained the signal detection and aided in correctly predicting infrasound travel times. Our results show that the impact of gravity waves is negligible below 20 km altitude; however, their effect is important above that height. The results presented here demonstrate the utility of balloon-borne acoustic sensing toward constraining the source region of variability, as well as the relevance of complexities surrounding infrasound wave propagation at short ranges for elevated sensing platforms.

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Avoiding stress concentrations is essential to achieve robust parts since failure tends to originate at such concentrations. With recent advances in multimaterial additive manufacturing, it is possible to alter the stress (or strain) distribution by adjusting the material properties in selected locations. Here, we investigate the use of grayscale digital light processing (g-DLP) 3D printing to create modulus gradients around areas of high stress. These gradients prevent failure by redistributing high stresses (or strains) to the neighboring material. The improved material distributions are calculated using finite element analysis. The much-enhanced properties are demonstrated experimentally for thin plates with circular, triangular, and elliptical holes. This work suggests that multimaterial additive manufacturing techniques like g-DLP printing provide a unique opportunity to create tougher engineering materials and parts.

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