This project has developed models of variability of performance to enable robust design and certification. Material variability originating from microstructure has significant effects on component behavior and creates uncertainty in material response. The outcomes of this project are uncertainty quantification (UQ) enabled analysis of material variability effects on performance and methods to evaluate the consequences of microstructural variability on material response in general. Material variability originating from heterogeneous microstructural features, such as grain and pore morphologies, has significant effects on component behavior and creates uncertainty around performance. Current engineering material models typically do not incorporate microstructural variability explicitly, rather functional forms are chosen based on intuition and parameters are selected to reflect mean behavior. Conversely, mesoscale models that capture the microstructural physics, and inherent variability, are impractical to utilize at the engineering scale. Therefore, current efforts ignore physical characteristics of systems that may be the predominant factors for quantifying system reliability. To address this gap we have developed explicit connections between models of microstructural variability and component/system performance. Our focus on variability of mechanical response due to grain and pore distributions enabled us to fully probe these influences on performance and develop a methodology to propagate input variability to output performance. This project is at the forefront of data-science and material modeling. We adapted and innovated from progressive techniques in machine learning and uncertainty quantification to develop a new, physically-based methodology to address the core issues of the Engineering Materials Reliability (EMR) research challenge in modeling constitutive response of materials with significant inherent variability and length-scales.
Sandia National Laboratories has tested and evaluated various infrasound shrouds, produced Doug Seastrand and Gary Walker, DOE staff in Las Vegas, Nevada, intended for a Hyperion 5201W digital infrasound sensor. The purpose of these shrouds is to improve the infrasound sensor’s attenuation of incoherent signals, specifically those caused by wind passing by the sensor. The purpose of the shroud evaluation was to measure amplitude and phase of the sensors with a variety of shroud designs attached and determine whether there is any appreciable changes in amplitude and/or phase response over the IMS passband for infrasound applications, 0.02 Hz to 4.0 Hz. These shrouds utilize a tubule design that directs and mixes airflow from ports radially distributed at approximately 90 degree offsets in an attempt to minimize wind-generated signal due to Bernoulli effect caused by airflow passing by ports perpendicular to the wind.
Triangle counting is a foundational graph-analysis kernel in network science. It has also been one of the challenge problems for the 'Static Graph Challenge'. In this work, we propose a novel, hybrid, parallel triangle counting algorithm based on its linear algebra formulation. Our framework uses MPI and Cilk to exploit the benefits of distributed-memory and shared-memory parallelism, respectively. The problem is partitioned among MPI processes using a two-dimensional (2D) Cartesian block partitioning. One-dimensional (1D) rowwise partitioning is used within the Cartesian blocks for shared-memory parallelism using the Cilk programming model. Besides exhibiting very good strong scaling behavior in almost all tested graphs, our algorithm achieves the fastest time on the 1.4B edge real-world twitter graph, which is 3.217 seconds, on 1,092 cores. In comparison to past distributed-memory parallel winners of the graph challenge, we demonstrate a speed up of 2.7× on this twitter graph. This is also the fastest time reported for parallel triangle counting on the twitter graph when the graph is not replicated.
This report documents the initial testing of the Sandia Parallel Aerodynamics and Reentry Code (SPARC) to directly simulate hypersonic, turbulent boundary layer flow over a sharp 7- degree half-angle cone. This type of computation involves a tremendously large range of scales both in time and space, requiring a large number of grid cells and the efficient utilization of a large pool of resources. The goal of the simulation is to mimic and verify a wind tunnel experiment that seeks to measure the turbulent surface pressure fluctuations. These data are necessary for building a model to predict random vibration loading in the reentry flight environment. A low-dissipation flux scheme in SPARC is used on a 2.7 billion cell mesh to capture the turbulent fluctuations in the boundary layer flow. The grid is divided into 115200 partitions and simulated using the Knight's Landings (KNL) partition of the Trinity system. The parallel performance of SPARC is explored on the Trinity system, as well as some of the other new architectures. Extracting data from the simulation shows good agreement with the experiment as well as a colleague's simulation. The data provide a guide for which a new model can be built for better prediction of the reentry random vibration loads.
Atmospheric ice affects Earth's radiative properties and initiates most precipitation. Growing ice typically requires a particle, often airborne mineral dust, e.g., to catalyze freezing of supercooled cloud droplets. How chemistry, structure and morphology determine the ice-nucleating ability of minerals remains elusive. Not surprisingly, poor understanding of a erosol-cloud interactions is a major source of uncertainty in climate models. In this project, we combine d optical microscopy with atomic force microscopy to explore the mechanisms of initial ice formation on alkali feldspar, a mineral proposed to dominate ice nucleation in Earth's atmosphere. When cold air becomes supersaturated with respect to water, we discovered that supercooled liquid water condenses at steps without having to overcome a nucleation barrier, and subsequently freezes quickly. Our results imply that steps, common even on macroscopically flat feldspar surfaces, can accelerate water condensation followed by freezing, thus promoting glaciation and dehydration of mixed - phase clouds. Motivated by the fact that current climate simulations do not properly account for feldspar's extreme efficiency to nucleate ice, we modified DOE's climate model, the Energy Exascale Earth System Model (E3SM), to increase the activation of ice nucleation on feldspar dust. This included adding a new aerosol tracer into the model and updating the ice nucleation parameterization, based on Classical Nucleation Theory, for multiple mineral dust tracers. Although t he se modifications have little impact on global averages , predictions of regional averages can be strongly affected .
Interval Assignment (IA) means selecting the number of mesh edges for each CAD curve. IIA is a discrete algorithm over integers. A priority queue iteratively selects compatible sets of intervals to increase in lock-step by integers. In contrast, the current capability in Cubit is floating-point Linear Programming with Branch-and-Bound for integerization (BBIA).
The failure of subsurface seals (i.e., wellbores, shaft and drift seals in a deep geologic nuclear waste repository) has important implications for US Energy Security. The performance of these cementitious seals is controlled by a combination of chemical and mechanical forces, which are coupled processes that occur over multiple length scales. The goal of this work is to improve fundamental understanding of cement-geomaterial interfaces and develop tools and methodologies to characterize and predict performance of subsurface seals. This project utilized a combined experimental and modeling approach to better understand failure at cement-geomaterial interfaces. Cutting-edge experimental methods and characterization methods were used to understand evolution of the material properties during chemo-mechanical alteration of cement-geomaterial interfaces. Software tools were developed to model chemo-mechanical coupling and predict the complex interplay between reactive transport and solid mechanics. Novel, fit-for-purpose materials were developed and tested using fundamental understanding of failure processes at cement-geomaterial interfaces.
Local electromagnetic probing was developed to allow investigation of a variety of devices in noisy electrical environments. The quality and applicability of this technique was assessed during this one year LDRD. To obtain details about the experimental setup, the devices imaged, and the experimental details, please refer to the classified report from the project manager, Will Zortman, or the NSP IA lead, Kristina Czuchlewski.
In this report we describe an enhanced methodology for performing stochastic Bayesian inversions of atmospheric trace gas inversions that allows the time variation of model parameters to be inferred. We use measurements of methane atmospheric mixing ratio made in Livermore, California along with atmospheric transport modeling and published prior estimates of emissions to estimate the regional emissions of methane and the temporal variations in inferred bias parameters. We compute Bayesian model evidence and continuous rank probability score to optimize the model with respect to temporal resolution. Using two different emissions inventories, we perform inversions for a series of models with increasing temporal resolution in the model bias representation. We show that temporal variation in the model bias can improve the model fit and can also increase the likelihood that the parameterization is appropriate, as measured by the Bayesian model evidence.
The overall goal of this work was to advance the integrated workflow capabilities to provide the weapon system analysts the tools to construct, communicate and robustly execute end-to-end computational simulation models and analysis. Specifically, this includes developing and running automated, CompSim workflows that map model inputs to responses from large-scale studies executed on a distributed, heterogenous computing environment (e.g., CAD applications on Windows laptop to Sierra FEM codes on Trinity within a single workflow). The workflows developed enable ensemble calculations in support of ND program decisions, including sensitivity analysis, optimization and uncertainty quantification.
This report details initial investigations of neural network surrogate models for combustion applications. The models are assessed with respect to averaged predictive fidelity over a test data set as well as with respect to the accuracy of resolving landmark statistics of interest, as a function of increasing training data volume.
Remote sensing (RS) data collection capabilities are rapidly evolving hyper-spectrally (sensing more spectral bands), hyper-temporally (faster sampling rates) and hyper-spatially (increasing number of smaller pixels). Accordingly, sensor technologies have outpaced transmission capa- bilities introducing a need to process more data at the sensor. While many sophisticated data processing capabilities are emerging, power and other hardware requirements for these approaches on conventional electronic systems place them out of context for resource constrained operational environments. To address these limitations, in this research effort we have investigated and char- acterized neural-inspired architectures to determine suitability for implementing RS algorithms In doing so, we have been able to highlight a 100x performance per watt improvement using neu- romorphic computing as well as developed an algorithmic architecture co-design and exploration capability.
Many shock experiments, whether impact, laser, or magnetically driven, use reflected optical light from shocked samples to diagnose their material properties. Specifically, optical velocimetry diagnostics, which do not require absolute power measurements, are regularly used to obtain equation-of-state information of materials. However, new diagnostics will be necessary to expand the realm of measured material properties, and many useful diagnostic techniques do require absolute measurements. Thus, it is important to understand what happens at the reflective surface of shock experiments, and the effect scattering has on the light collection of optical probes. To this end, we present results from experiments done to observe the behavior of a reflected beam from a specular coating on an optical window during shock impact. We find that the specular condition of the coating is adversely affected by the shock front, but this can be mitigated by minimizing roughness on the surface preceding the coating.
This report describes the potential of a novel class of materials—α-ZrW2O8, Zr2WP2O12, and related compounds that contract upon amorphization as possible radionuclide waste-forms. The proposed ceramic waste-forms would consist of zoned grains, or sintered ceramics with center- loaded radionuclides and barren shells. Radiation-induced amorphization would result in core shrinkage but would not fracture the shells or overgrowths, maintaining isolation of the radionuclide. In this report, we have described synthesis techniques to produce phase-pure forms of the materials, and how to fully densify those materials. Structural models for the materials were developed and validated using DFPT approaches, and radionuclide substitution was evaluated; U(IV), Pu(IV), Tc(IV) and Tc(VII) all readily substitute into the material structures. MD modeling indicated that strain associated with radiation-induced amorphization would not affect the integrity of surrounding crystalline materials, and these results were validated via ion beam experimental studies. Finally, we have evaluated the leach rates of the barren materials, as determined by batch and flow-through reactor experiments. ZrW2O8 leaches rapidly, releasing tungstate while Zr is retained as a solid oxide or hydroxide. Tungsten release rates remain elevated over time and are highly sensitive to contact times, suggesting that this material will not be an effective waste-form. Conversely, tungsten releases rates from Zr2WP2O12 rapidly drop, show little dependence on short-term changes in fluid contact time, and in over time, become tied to P release rates. The results presented here suggest that this material may be a viable waste-form for some hard-to-handle radionuclides such as Pu and Tc.
This progress report describes work done in FY19 at Sandia National Laboratories (SNL) to assess the localized corrosion performance of container/cask materials used in the interim storage of spent nuclear fuel (SNF). Of particular concern is stress corrosion cracking (SCC), by which a through-wall crack could potentially form in a canister outer wall over time intervals that are shorter than possible dry storage times. Work in FY19 refined our understanding of the chemical and physical environment on canister surfaces and evaluated the relationship between chemical and physical environment and the form and extent of corrosion that occurs.
This report summarizes the methods and algorithms that were developed on the Sandia National Laboratory LDRD project entitled "Polynomial Chaos methods in Xyce for Embedded Uncertainty Quantification in Circuit Analysis", which was project 200265 and proposal 2019-0817. As much of our work has been published in other reports and publications, this report gives a brief summary. Those who are interested in the technical details are encouraged to read the full published results and also contact the report authors for the status of follow-on projects.
This research aims to develop brain-inspired solutions for reliable and adaptive autonomous navigation in systems that have limited internal and external sensors and may not have access to reliable GPS information. The algorithms investigated and developed by this project was performed in the context of Sandas A4H (autonomy for hypersonics) mission campaign. These algorithms were additionally explored with respect to their suitability for implementation on emerging neuromorphic computing hardware technology. This project is premised on the hypothesis that brain-inspired SLAM (simultaneous localization and mapping) algorithms may provide an energy-efficient, context-flexible approach to robust sensor-based, real-time navigation.
This report documents work done at the Sandia Ion Beam Laboratory to develop a capability to produce 14 Me neutrons at levels sufficient for testing radiation effects on electronic materials and components. The work was primarily enabled by a laboratory directed research and development (LDRD) project. The main elements of the work were to optimize target lifetime, test a new thin- film target design concept to reduce tritium usage, design and construct a new target chamber and beamline optimized for high-flux tests, and conduct tests of effects on electronic devices and components. These tasks were all successfully completed. The improvements in target performance and target chamber design have increased the flux and fluence of 14 MV neutrons available at the test location by several orders of magnitude. The outcome of the project is that a new capability for testing radiation-effects on electronic components from 14 MeV neutrons is now available at Sandia National Laboratories. This capability has already been extensively used for many qualification and component evaluation and development tests.