Publications

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This report focuses on the two primary goals set forth in Sandia’s TAFI effort, referred to here under the name Kebab. The first goal is to overlay a trajectory onto a large database of historical trajectories, all with very different sampling rates than the original track. We demonstrate a fast method to accomplish this, even for databases that hold over a million tracks. The second goal is to then demonstrate that these matched historical trajectories can be used to make predictions about unknown qualities associated with the original trajectory. As part of this work, we also examine the problem of defining the qualities of a trajectory in a reproducible way.

The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.

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China is endeavoring to build nuclear power plants (NPPs) in numerous countries around the globe - an initiative that has the potential to strengthen Chinas political and economic influences on those countries. This study provides an overview of the situation and considers the issues involved in such partnerships with China. In order to assess Chinas ability to follow through with its agreements, this study also presents a technical review of its NPP production capability.

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The outer core of the Earth is composed primarily of liquid iron, and the inner core boundary is governed by the intersection of the melt line and the geotherm. While there are many studies on the thermodynamic equation of state for solid iron, the equation of state of liquid iron is relatively unexplored. We use dynamic compression to diagnose the high‐pressure liquid equation of state of iron by utilizing the shock‐ramp capability at Sandia National Laboratories’ Z‐Machine. This technique enables measurements of material states off the Hugoniot by initially shocking samples and subsequently driving a further, shockless compression. Planetary studies benefit greatly from isentropic, off‐Hugoniot experiments since they can cover pressure‐temperature (P‐T) conditions that are close to adiabatic profiles found in planetary interiors. We used this method to drive iron to P‐T conditions similar to those of the Earth’s outer‐inner core boundary, along an elevated‐temperature isentrope in the liquid from 275 GPa to 400 GPa. We derive the equation of state using a hybrid backward integration – forward Lagrangian technique on particle velocity traces to determine the pressure‐density history of the sample. Our results are in excellent agreement with SESAME 92141, a previously published equation of state table. With our data and previous experimental data on liquid iron we provide new information on the iron melting line and derive new parameters for a Vinet‐based equation of state. The table and our parameterized equation of state are applied to provide an updated means of modeling the pressure, mass, and density of liquid iron cores in exoplanetary interiors.

This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.

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Background: Using a thin-walled tube torsion test to characterize a material’s shear response is a well-known technique; however, the thin walled specimen tends to buckle before reaching large shear deformation and failure. An alternative technique is the surface stress method (Nadai 1950; Wu et al. J Test Eval 20:396–402, 1992), which derives a shear stress-strain curve from the torque-angular displacement relationship of a solid cylindrical bar. The solid bar torsion test uniquely stabilizes the deformation which allows us to control and explore very large shear deformation up to failure. However, this method has rarely been considered in the literature, possibly due to the complexity of the analysis and experimental issues such as twist measurement and specimen uniformity. Objective: In this investigation, we develop a method to measure the large angular displacement in the solid bar torsion experiments to study the large shear deformation of two common engineering materials, Al6061-T6 and SS304L, which have distinctive hardening behaviors. Methods: Modern stereo-DIC methods were applied to make deformation measurements. The large angular displacement of the specimen posed challenges for the DIC analysis. An analysis method using multiple reference configurations and transformation of deformation gradient is developed to make the large shear deformation measurement successful. Results: We successfully applied the solid bar torsion experiment and the new analysis method to measure the large shear deformation of Al6061-T6 and SS304L till specimen failure. The engineering shear strains at failure are on the order of 2–3 for Al6061-T6 and 3–4 for SS304L. Shear stress-strain curves of Al6061-T6 and SS304L are also obtained. Conclusions: Solid bar torsion experiments coupled with 3D-DIC technique and the new analysis method of deformation gradient transformation enable measurement of very large shear deformation up to specimen failure.

Background:: Subcritical crack growth can occur in a brittle material when the stress intensity factor is smaller than the fracture toughness if an oxidizing agent (such as water) is present at the crack tip. Objective:: We present a novel bi-material beam specimen which can measure environmentally assisted crack growth rates. The specimen is “self-loaded” by residual stress and requires no external loading. Methods:: Two materials with different coefficient of thermal expansion are diffusion bonded at high temperature. After cooling to room temperature a subcritical crack is driven by thermal residual stresses. A finite element model is used to design the specimen geometry in terms of material properties in order to achieve the desired crack tip driving force. Results:: The specimen is designed so that the crack driving force decreases as the crack extends, thus enabling the measurement of the crack velocity versus driving force relationship with a single test. The method is demonstrated by measuring slow crack growth data in soda lime silicate glass and validated by comparison to previously published data. Conclusions:: The self-loaded nature of the specimen makes it ideal for measuring the very low crack velocities needed to predict brittle failure at long lifetimes.

This report represents completion of milestone deliverable M2SF-21SN010309012 “Annual Status Update for OWL and Waste Form Characteristics” that provides an annual update on status of fiscal year (FY 2020) activities for the work package SF-20SN01030901 and is due on January 29, 2021. The Online Waste Library (OWL) has been designed to contain information regarding United States (U.S.) Department of Energy (DOE)-managed (as) high-level waste (DHLW), spent nuclear fuel (SNF), and other wastes that are likely candidates for deep geologic disposal, with links to the current supporting documents for the data (when possible; note that no classified or official-use-only (OUO) data are planned to be included in OWL). There may be up to several hundred different DOE-managed wastes that are likely to require deep geologic disposal. This draft report contains versions of the OWL model architecture for vessel information (Appendix A) and an excerpt from the OWL User’s Guide (Appendix B and SNL 2020), which are for the current OWL Version 2.0 on the Sandia External Collaboration Network (ECN).

Laboratory research can expose workers to a wide variety of chemical hazards. Researchers must not only take personal responsibility for their safety but also inevitably rely on coworkers to also work safely. The foundations for protocols, requirements, and behaviors come from our history and lessons learned from others. For that reason, here, a recent incident is examined in which a researcher suffered hydrofluoric acid (HF) burns while working with an inorganic digestion mixture of aqueous HF (8%) and nitric acid (HNO₃, 58%). HF education is critical for workers because delays in treatment, improper treatment, and delay of symptoms are all factors in unfavorable outcomes in case reports. Furthermore, while the potential severity of the incident was elevated due to bypassed engineered controls and lack of proper personal protective equipment, only minor injuries were sustained. We discuss the results of a causal analysis of the incident that revealed areas of improvement in protocols, personal protective equipment, and emergency response that could help prevent similar accidents from occurring. We also present simple improvements that anyone can implement to reduce the potential consequences of an accident, based upon our lessons learned.

Non-aqueous redox flow batteries (RFBs) offer the possibility of higher voltage and a wider working temperature range than their aqueous counterpart. Here, we optimize the established 2.26 V Fe(bpy)3(BF4)2/Ni(bpy)3(BF4)2 asymmetric RFB to lessen capacity fade and improve energy efficiency over 20 cycles. We also prepared a family of substituted Fe(bpyR)3(BF4)2 complexes (R = -CF3, -CO2Me, -Br, -H, -tBu, -Me, -OMe, -NH2) to potentially achieve a higher voltage RFB by systematically tuning the redox potential of Fe(bpyR)3(BF4)2, from 0.94 V vs. Ag/AgCl for R = OMe to 1.65 V vs. Ag/AgCl for R = CF3 (ΔV = 0.7 V). A series of electronically diverse symmetric and asymmetric RFBs were compared and contrasted to study electroactive species stability and efficiency, in which the unsubstituted Fe(bpy)3(BF4)2 exhibited the highest stability as a catholyte in both symmetric and asymmetric cells with voltage and coulombic efficiencies of 94.0% and 96.5%, and 90.7% and 80.7%, respectively.

Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence to the global optimum. While remaining as one of the most popular optimization methods, its capability is hindered by the size of data, the dimensionality of the considered problem, and the nature of sequential optimization. These scalability issues are intertwined with each other and must be tackled simultaneously. In this work, we propose the Scalable3-BO framework, which employs sparse GP as the underlying surrogate model to scope with Big Data and is equipped with a random embedding to efficiently optimize high-dimensional problems with low effective dimensionality. The Scalable3-BO framework is further leveraged with asynchronous parallelization feature, which fully exploits the computational resource on HPC within a computational budget. As a result, the proposed Scalable3-BO framework is scalable in three independent perspectives: with respect to data size, dimensionality, and computational resource on HPC. The goal of this work is to push the frontiers of BO beyond its well-known scalability issues and minimize the wall-clock waiting time for optimizing high-dimensional computationally expensive applications. We demonstrate the capability of Scalable3-BO with 1 million data points, 10,000-dimensional problems, with 20 concurrent workers in an HPC environment.

Industrial and security communities leverage x-ray computed tomography for several applications in non-destructive evaluation such as material detection and metrology. Many of these applications ultimately reach a limit as most x-ray systems have a nonlinear mathematical operator due to the Bremsstrahlung radiation emitted from the x-ray source. This work proposes a design of a multi-metal pattered anode coupled with a hyperspectral X-ray detector to improve spatial resolution, absorption signal, and overall data quality for various quantitative. The union of a multi-metal pattered anode x-ray source with an energy-resolved photon counting detector permits the generation and detection of a preferential set of X-ray energy peaks. When photons about the peaks are detected, while rejecting photons outside this neighborhood, the overall quality of the image is improved by linearizing the operator that defines the image formation. Additionally, the effective X-ray focal spot size allows for further improvement of the image quality by increasing resolution. Previous works use machine learning techniques to analyze the hyperspectral computed tomography signal and reliably identify and discriminate a wide range of materials based on a material's composition, improving data quality through a multi-material pattern anode will further enhance these identification and classification methods. This work presents initial investigations of a multi-metal patterned anode along with a hyperspectral detector using a general-purpose Monte Carlo particle transport code known as PHITS version 3.24. If successful, these results will have tremendous impact on several nondestructive evaluation applications in industry, security, and medicine.

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. To solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.