Publications

Living document updating the 5.14 version, SAND2023-03603 of May 2023.

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. In this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.

We explore the use of reduced physics models for efficient kinetic particle simulations of space charge limited (SCL) emission in inner magnetically insulated transmission lines (inner MITLs), with application to Sandia National Laboratories' Z machine. We propose a drift kinetic (guiding center) model of electron motion in place of a fully kinetic model and electrostatic-magnetostatic fields in place of electromagnetic fields. The validity of these approximations is suggested by the operational parameters of the Z machine, namely, current pulse lengths of order 100 ns compared with Larmor periods typically smaller than 10-11 s, typical Larmor radii of a few (tens) of microns (magnetic fields of tens to hundreds of Tesla) compared with MITL dimensions of a few centimeters, and transient time of light waves along the inner MITL of order a fraction of a nanosecond. Guiding center orbits eliminate the fast electron gyromotion, which enables the use of tens to hundreds of times larger time steps in the numerical particle advance. Electrostatic-magnetostatic fields eliminate the Courant-Friedrichs-Lewy (CFL) numerical stability limit on the time step and allow the use of higher grid resolutions or, alternatively, larger time steps in the fields advance. Overall, potential computational cost savings of tens to hundreds of times exists. The applicability of the reduced physics models is examined on two problems. First, in the simulation of space charge limited emission of electrons from the cathode surface due to high electric fields in a radial inner MITL geometry with a short load. In particular, it is shown that a drift kinetic-based particle-in-cell (PIC) model with electrostatic-magnetostatic fields is able to accurately reproduce well-known physics of electron vortex formation, spatially and temporally. Second, deeper understanding is gained of the mechanism behind vortex formation in this MITL geometry by considering an exemplar problem of an electron block of charge. This simpler setup reveals that the main mechanism of vortex formation can be attributed to pure drift motion of the electrons, that is, the (fully kinetic) gyromotion of the electrons is inessential to the process. This exemplar problem also suggests a correlation of the spatial dimensions of vortices to the thickness of the electron layer, as observed in SCL simulations. It also confirms that the electromagnetic nature of the fields does not play an essential role. Finally, an improved hybrid fully kinetic and drift kinetic model for electron motion is proposed, as means of capturing finite Larmor radius (FLR) effects; the particular FLR physics that is missed by the drift kinetic model is the particle-wall interaction. By initializing SCL emitted electrons as fully kinetic and later transitioning them to drift kinetic, according to simple criteria, the accuracy of SCL simulations can be improved, while preserving the potential for computational efficiency.

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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.

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 injection and storage of anthropogenic CO2 in the subsurface is being deployed as a climate change mitigation tool; however, diagenetic-paragenetic heterogeneity in sandstone reservoirs often contributes to interval specific chemomechanical changes that affect injection and can increase leakage risk. Here, we address reservoir heterogeneities’ impact on chemomechanical changes in a macroporous-dominated lithofacies of Morrow B sandstone, a formation containing several diagenetically-distinct hydraulic facies while undergoing enhanced oil recovery (EOR) and carbon dioxide (CO2) sequestration. We performed three flow-through experiments using a CO2-charged or uncharged formation water combined with four indirect tensile strength tests per post-test sample. We then used the microstructure and paragenetic sequence to understand chemomechanical weakening with key observations as follows: dissolution of carbonates and feldspars changed porosity; increased permeability led to reclassifying each sample in a different hydraulic flow unit; decreased ultrasonic velocity; and did not lead to a loss of tensile strength. Tensile strength maintenance occurred due to the low abundance and minor dissolution of siderite, the stability of quartz, and the relative position of diagenetic ankerite within feldspar. This macroporous-dominated lithofacies is the primary reservoir for the Morrow B Sandstone, and is analogous to other porous sandstone reservoirs. It represents an end-member of a chemomechanically low-risk siliceous CO2 sequestration and CO2-EOR reservoir.

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A direct numerical simulation (DNS) campaign is deployed for a series of confined downward oriented, non-isothermal turbulent impinging jet configurations. A baseline Reynolds number of 9960 is obtained through a precursor DNS pipe flow simulation (Reτ=505). Three jet temperature configurations (confinement height to nozzle diameter of three) enter a cylindrical domain that share ambient and impingement plate temperatures (298.15K). The range of jet temperatures are crafted such that the ratio of inlet to ambient density varies from unity to 0.52, showcasing the effect of density disparity on flow characteristics such as core collapse, radial mixing of momentum and energy, near-wall stagnation behavior, wall-jet profiles, and large-scale vortical structures. Surface quantities provided include mean radial heat flux and wall-shear stress profiles, and heat flux histograms at select radial stations. Results showcase increased radial normal stresses for higher temperature jets that support increased mixing, resulting in large-scale recirculation structures that are smaller, while retaining similar normalized radial wall profiles for shear stress, heat flux and pressure. Radial plots for wall shear stress and Nusselt number showcase strong radial decay as compared to previous configurations that share similar jet and ambient temperatures. For the 373.15 K case, a Gaussian-like histogram for heat fluxes at the impingement plate transitions to a log-normal profile as radial distances increase. In contrast, the 573.15 K configuration displays a bi-modal heat flux characteristic at the impingement plate, and in similar manner to the moderate temperature counterpart, transitions to a log-normal profile at larger radial distances.

Code verification plays an important role in establishing the credibility of computational simulations by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, the numerical solution to integral equations incurs multiple interacting sources of numerical error, as well as other challenges, which render traditional code-verification approaches ineffective. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources for the method-of-moments implementation of the combined-field integral equation. We demonstrate the effectiveness of these approaches for cases with and without coding errors.

Update to prior 5.14 user manual. I think updates are minor and mostly in the Johnson-cook section. In there those updates are more writing and less on technical changes.

Calibrated measurements of lightning optical emissions are critical for both quantifying the impacts of lightning in our atmosphere and devising detection instruments with sufficient dynamic range capable of yielding close to 100% detection efficiency. However, to date, there is only a limited number of investigations that have attempted to take such calibrated measurements. In this work, we report the power radiated by lightning in both visible and infrared bands, assuming isotropic emission, and accounting for atmospheric absorption. More precisely, we report peak radiated power and total radiated energy in the combined visible plus near-infrared range (VNIR, 0.34–1.1 μm), around the Hα line (652–667 nm), and for the 2–2.5 μm infrared band. The estimated peak power and total energy radiated by negative cloud-to-ground return strokes in the VNIR range is 130 MW and 20 kJ, respectively. Additionally, we detected peak radiated powers of 12 and 0.19 MW in the Hα and infrared bands, respectively. We cross-reference the optical data set with peak current reported by a lightning detection network. The resulting trend is that optical power emitted around the Hα line scales with peak return stroke current according to a power law with exponent equal to 1.25. This trend, which should be approximately true across the entire visible spectrum, can be attributed to the plasma negative differential resistance of the lightning return stroke channel. We conclude by discussing the challenges in performing calibrated measurements of lightning optical power in different bands and comparing the results with previously-collected data with different experimental setups, observation conditions, and calibration methods.

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.

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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 applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

Previous studies have shown that selective laser flash sintering (SLFS) can be initiated in dielectrics that exhibit ionic or electronic conduction at high temperature. These materials required high laser powers to reach the temperatures where electrical conduction is sufficient to initiate SLFS. In this study, SLFS in lanthanum chromite (LC), an intrinsic electronic conductor with high conductivity, and lanthanum strontium chromite (LSC), which is doped to further increase electronic conductivity, were investigated with a focus on understanding the initiation mechanisms. Results show that the initiation of SLFS in LC and LSC occurs when electronic charge carriers are activated and flow to the electrode where the current is measured. A combination of carriers produced at the electrode, temperature-activated intrinsic charge carriers, and extrinsic charge carriers present in LSC due to doping are responsible for the facile initiation of SLFS.

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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.

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.