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.
Dynamic compression studies have been used to study the nucleation kinetics of water to ice VII for decades. Diagnostics such as photon Doppler velocimetry, transmission loss, and imaging have been used to measure pressure/density, and phase fraction, while temperature has remained the difficult thermodynamic property to quantify. In this work, we measured pressure/density and implemented a diagnostic to measure the temperature. In doing so the temperature shows quasi-isentropically compressed liquid water forms ice at pressures below the previously defined metastable limit, and the liquid phase is not hypercoooled as previously thought above that limit. Instead, the latent heat raises the temperature to the liquid-ice-VII melt line, where it remains with increasing pressure. We propose a hypothesis to corroborate these results with previous work on dynamic compression freezing. These results provide constraints for nucleation models, and suggest this technique be used to investigate phase transitions in other materials.
This report describes research and development (R&D) activities conducted during Fiscal Year 2023 (FY23) in the Advanced Fuels and Advanced Reactor Waste Streams Strategies work package in the Spent Fuel Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Energy (DOE). This report is focused on evaluating and cataloguing Advanced Reactor Spent Nuclear Fuel (AR SNF) and Advanced Reactor Waste Streams (ARWS) and creating Back-end Nuclear Fuel Cycle (BENFC) strategies for their disposition. The R&D team for this report is comprised of researchers from Sandia National Laboratories and Enviro Nuclear Services, LLC.
This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan—University at Buffalo Research Center’s Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. Here in this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier–Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.
Work accomplished: Collected and compared historic data for the 1993 Rock Valley earthquake sequence; Compared preliminary and prior location work from different location algorithms, phase pick sets, station constellations, and velocity models; Selected a common set of stations that could be used across all location methods for consistency; Reviewed 8 different sets of phase picks and converged on a single, reviewed set of picks for all common stations; Evaluated four pre-existing regional velocity models and incorporated new and preliminary results for five new velocity models that provide information on the very shallow (< 2km) structure near station RTPP; Compared location results from different methods while using the common sets of picks, stations, and velocity models
Real-time monitoring of a research nuclear reactor, a system in which all generated power is dissipated to the environment, can be performed via analysis of the heat rejection from the cooling system. Given an inlet water temperature and flow rate, the reactor power can be well-approximated from the outlet water temperature; however, the instrumentation to measure outlet conditions may not be robust or accurate. If we know how a cooling tower performs from historical data, but cannot measure the outlet temperature, a mathematical representation of the system can be inverted to obtain the outlet water temperature that describes the cooling capacity. Unfortunately, model inversion processes are computationally expensive. To address this, an artificial neural network (ANN) is implemented to assess the performance of a multi-cell cooling tower for a nuclear reactor. This approach leverages the Merkel model to obtain an extensive data set describing performance of the cooling tower cells throughout a wide array of potential operating conditions. The Merkel model is expressed as a function of four parameters: the inlet and outlet water temperatures, inlet air wet bulb temperature, and ratio of liquid-to-gas mass flow rates (L/G), which together provide a non-dimensional number indicative of cooling tower performance, called the Merkel integral. Computing a 4-dimensional data structure that describes finite combinations of the Merkel integral, an inverse model is then generated using an ANN to determine the cell outlet water temperature from the other three model parameters along with the computed Merkel integral. Compared to traditional model inversion methods, the ANN reduces the computational time by approximately 4 orders of magnitude, with effectively no sacrifice to solution accuracy, and could be applied for different cooling towers in the event the performance curve is known. Finally, three use cases of the ANN are then reviewed: (1) determining the cell outlet water temperatures when gas flow at rated conditions (GFRC) is known, (2) performing the prior case without knowledge of the GRFC, and (3) assessing performance differences between the individual tower cells.
Electrothermal instability plays an important role in applications of current-driven metal, creating striations (which seed the magneto-Rayleigh-Taylor instability) and filaments (which provide a more rapid path to plasma formation). However, the initial formation of both structures is not well understood. Simulations show for the first time how a commonly occurring isolated defect transforms into the larger striation and filament, through a feedback loop connecting current and electrical conductivity. Simulations have been experimentally validated using defect-driven self-emission patterns.
Using three-dimensional (3D) magnetohydrodynamic simulations, we study how a pit on a metal surface evolves when driven by intense electrical current density j. Redistribution of j around the pit initiates a feedback loop: j both reacts to and alters the electrical conductivity σ, through Joule heating and hydrodynamic expansion, so that j and σ are constantly in flux. Thus, the pit transforms into larger striation and filament structures predicted by the electrothermal instability theory. Both structures are important in applications of current-driven metal: Here, the striation constitutes a density perturbation that can seed the magneto-Rayleigh-Taylor instability, while the filament provides a more rapid path to plasma formation, through 3D j redistribution. Simulations predict distinctive self-emission patterns, thus allowing for experimental observation and comparison.
We present a highly diagonal “split-well resonant-phonon” (SWRP) active region design for GaAs/Al0.3Ga0.7As terahertz quantum cascade lasers (THz-QCLs). Negative differential resistance is observed at room temperature, which indicates the suppression of thermally activated leakage channels. The overlap between the doped region and the active level states is reduced relative to that of the split-well direct-phonon (SWDP) design. The energy gap between the lower laser level (LLL) and the injector is kept at 36 meV, enabling a fast depopulation of the LLL. Within this work, we investigated the temperature performance and potential of this structure.
Sapphire (Al2O3) is a major constituent of the Earth's mantle and has significant contributions to the field of high-pressure physics. Constraining its Hugoniot over a wide pressure range and identifying the location of shock-driven phase transitions allows for development of a multiphase equation of state and enables its use as an impedance-matching standard in shock physics experiments. In this paper we present measurements of the principal Hugoniot and sound velocity from direct impact experiments using magnetically launched flyers on the Z machine at Sandia National Laboratories. The Hugoniot was constrained for pressures from 0.2–2.1 TPa and a four-segment piecewise linear shock-velocity–particle-velocity fit was determined. First-principles molecular dynamics simulations were conducted and agree well with the experimental Hugoniot. Sound-speed measurements identified the onset of melt between 450 and 530 GPa, and the Hugoniot fit refined the onset to 525 ± 13 GPa. A phase diagram which incorporates literature diamond-anvil cell data and melting measurements is presented.
Gas intercalation into clay interlayers may result in hydrogen loss in the geological storage of hydrogen; a phenomenon that has not been fully understood and quantified. Here we use metadynamics molecular simulations to calculate the free energy landscape of H2 intercalation into montmorillonite interlayers and the H2 solubility in the confined water; in comparison with results obtained for CO2. The results indicate that H2 intercalation into hydrated interlayers is thermodynamically unfavorable while CO2 intercalation can be favorable. H2 solubility in hydrated clay interlayers is in the same order of magnitude as that in bulk water and therefore no over-solubility effect due to nanoconfinement is observed - in striking contrast with CO2. These results indicate that H2 loss and leakage through hydrated interlayers due to intercalation in a subsurface storage system, if any, is limited.
Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350-500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. We also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.
Previous studies of the cantilevered pipeline conveying fluid system have included motion-limiting constraints in the form of trilinear springs. While this is desirable in experimental scenarios, it may not be representative of real-world applications. Therefore, here, this study focuses on multi-segmented motion-limiting constraints. As this type of motion-limiting constraint has not been investigated with a cantilevered pipeline system, a wide variety of outer and inner constraint stiffness and constraint gap sizes are investigated in this study to gain a comprehensive understanding of how the multi-segmented constraints affect the dynamics of the cantilevered pipeline. In this effort, bifurcation diagrams, phase portraits, Poincare maps, time histories, and power spectra are used to investigate the dynamics of the system, and the fluid flow speeds where dynamic characteristics are considered. In general, it is found that critical flow speeds like when the pipe sticks in the constraints are reduced as the constraint stiffnesses are increased. Additionally, the sticking flow speed occurred at lower flow speeds as the gap sizes of the inner and outer constraints decrease, and a larger constraint offset results in a smaller inner gap size leading to critical behaviors occurring at earlier flow speeds.
ALEGRA is a multiphysics finite-element shock hydrodynamics code, under development at Sandia National Laboratories since 1990. Fully coupled multiphysics capabilities include transient magnetics, magnetohydrodynamics, electromechanics, and radiation transport. Importantly, ALEGRA is used to study hypervelocity impact, pulsed power devices, and radiation effects. The breadth of physics represented in ALEGRA is outlined here, along with simulated results for a selected hypervelocity impact experiment.
Fluorinated graphite materials are of interest for an assortment of applications and can be synthesized under a variety of synthetic conditions from many different types of carbon. Due to such variations, structural disorders in the form of defects and polymorphism are often present. Here, we investigate the impact of local structural variations on the C-F bond dissociation energies (BDEs) in carbon-based fluoride materials using density functional theory (DFT) computational methods. Employing fluorographene (FG) cluster models, we determine the impact of different C-F bonding configurations in the core of each platelet on the equilibrium BDEs for each C-F bond. The introduction of structural disorder decreases the first C-F BDE by approximately 1 eV compared to the canonical arrangement of axial C-F bonds ordered as in a network of cyclohexane “chairs”. Variability of calculated BDEs among the different polymorphs decreases upon subsequent F removal. Common structural tendencies of the adiabatic defluorination pathways for each polymorph are identified. Our analysis suggests that at F/C ratios near 1.0, disorder in the local structure can play a significant role in the energetics of the initial carbon fluoride defluorination and that the influence of this configurational disorder diminishes with decreasing F/C ratios.
Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas.
Spotte-Smith, Evan W.C.; Blau, Samuel M.; Barter, Daniel; Leon, Noel J.; Hahn, Nathan T.; Redkar, Nikita S.; Zavadil, Kevin R.; Liao, Chen; Persson, Kristin A.
Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions are critical for a range of technological applications. For instance, in metal-ion batteries, spontaneous electrolyte degradation controls electrode passivation and battery cycle life. Here, to improve our ability to elucidate electrochemical reactivity, we for the first time combine computational chemical reaction network (CRN) analysis based on density functional theory (DFT) and differential electrochemical mass spectroscopy (DEMS) to study gas evolution from a model Mg-ion battery electrolyte-magnesium bistriflimide (Mg(TFSI)2) dissolved in diglyme (G2). Automated CRN analysis allows for the facile interpretation of DEMS data, revealing H2O, C2H4, and CH3OH as major products of G2 decomposition. These findings are further explained by identifying elementary mechanisms using DFT. While TFSI-is reactive at Mg electrodes, we find that it does not meaningfully contribute to gas evolution. The combined theoretical-experimental approach developed here provides a means to effectively predict electrolyte decomposition products and pathways when initially unknown.