Publications

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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.

Six degree of freedom shaker tests are becoming more popular as they save testing time because they test a component in multiple directions in one test rather than executing multiple tests in one direction at a time. However, there are several difficulties in conducting a component six degree of freedom shaker test in a way that adequately replicates the component field stress. One difficulty is knowing if a classical rigid test fixture will produce component modes that span the displacement space of the component in the field environment. If the modes of the component while attached to a rigid fixture do not span the space of the component in the field environment, then the test will be unable to replicate that motion and corresponding stresses. This paper will examine the motion of the Removable Component of the BARC hardware in an field assembly and calculate the modal projection error expected by executing a six degree of freedom shaker test on a rigid fixture. The paper will conclude by examining the data and comparing it to the pre-test predictions of error calculated by the modal projection error.

We describe efforts in generating synthetic malware samples that have specified behaviors that can then be used to train a machine learning (ML) algorithm to detect behaviors in malware. The idea behind detecting behaviors is that a set of core behaviors exists that are often shared in many malware variants and that being able to detect behaviors will improve the detection of novel malware. However, empirically the multi-label task of detecting behaviors is significantly more difficult than malware classification, only achieving on average 84% accuracy across all behaviors as opposed to the greater than 95% multi-class or binary accuracy reported in many malware detection studies. One of the difficulties in identifying behaviors is that while there are ample malware samples, most data sources do not include behavioral labels, which means that generally there is insufficient training data for behavior identification. Inspired by the success of generative models in improving image processing techniques, we examine and extend a 1) conditional variational auto-encoder and 2) a flow-based generative model for malware generation with behavior labels. Initial experiments indicate that synthetic data is able to capture behavioral information and increase the recall of behaviors in novel malware from 32% to 45% without increasing false positives and to 52% with increased false positives.

Researchers have shown that the dynamic field environment for a component may not be represented well by a component level single Degree-of-Freedom shaker environmental test. Here we demonstrate for a base mounted component, a controlled six Degree-of-Freedom component level shaker test. The field response power spectral densities are well simulated by the component response on the six Degree-of-Freedom shaker. The component is the Removable Component from the boundary condition challenge problem. The field environment was established with the component mounted in the AWE Modal Analysis Test Vehicle during an acoustic test. Interesting mileposts during the process of achieving the controlled component response are discussed.

Microstructure reconstruction is a long-standing problem in experimental and computational materials science, for which numerous attempts have been made to solve. However, the majority of approaches often treats microstructure as discrete phases, which, in turn, reduces the quality of the resulting microstructures and limits its usage to the computational level of fidelity, but not the experimental level of fidelity. In this work, we applied our previously proposed approach [41] to generate synthetic microstructure images at the experimental level of fidelity for the UltraHigh Carbon Steel DataBase (UHCSDB) [13].

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.

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.

The Sandia Critical Experiments (SCX) Program provides a specialized facility for performing water moderated and reflected critical experiments with UO₂ fuel rod arrays. A history of safe reactor operations and flexibility in reactor core configuration has resulted in the completion of several benchmark critical experiment evaluations that are published in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The LEUCOMP-THERM-078 and LEU-COMP-THERM-080 evaluations from the handbook provide similar cases for reference. The set of experiments described here were performed using the Seven Percent Critical Experiment (7uPCX) fuel to measure the effects of decreasing the fuel-to-water volume ratio on the critical array size. This was accomplished by using fuel loading patterns to effectively increase the pitch of the fuel arrays in the assembly. The fuel rod pitch variations provided assembly configurations that ranged from strongly undermoderated to slightly overmoderated.

Multi-shaker testing is used to represent the response of a structure to a complex operational load in a laboratory setting. One promising method of multi-shaker testing is Impedance Matched Multi-Axis Testing (IMMAT). IMMAT targets responses at discrete measurement points to control the multiple shaker input excitations, resulting in a laboratory response representative of the expected operational response at the controlled measurement points. However, the relationship between full-field operational responses and the full-field IMMAT response has not been thoroughly explored. Poorly chosen excitation positions may match operational responses at the control points, but over or under excite uncontrolled regions of the structure. Additionally, the effectiveness of the IMMAT method on the whole test structure could depend on the type of operational excitation. Spatially distributed excitations, such as acoustic loading, may be difficult to reproduce over the whole test structure in a lab setting using the point force IMMAT excitations. This work will simulate operational and IMMAT responses of a lab-scale structure to analyze the accuracy of IMMAT at uncontrolled regions of the structure. Determination of the effect of control locations and operational locations on the IMMAT method will lead to better test design and improved predictive capabilities.

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Degraded visual environments like fog pose a major challenge to safety and security because light is scattered by tiny particles. We show that by interpreting the scattered light it is possible to detect, localize, and characterize objects normally hidden in fog. First, a computationally efficient light transport model is presented that accounts for the light reflected and blocked by an opaque object. Then, statistical detection is demonstrated for a specified false alarm rate using the Neyman-Pearson lemma. Finally, object localization and characterization are implemented using the maximum likelihood estimate. These capabilities are being tested at the Sandia National Laboratory Fog Chamber Facility.

Phenomenology-Informed (PI) Machine Learning is introduced to address the unique challenges faced when applying modern machine-learning object recognition techniques to the SAR domain. PI-ML includes a collection of data normalization and augmentation techniques inspired by successful SAR ATR algorithms designed to bridge the gap between simulated and real-world SAR data for use in training Convolutional Neural Networks (CNNs) that perform well in the low-noise, feature-dense space of camera-based imagery. The efficacy of PI-ML will be evaluated using ResNet, EfficientNet, and other networks, using both traditional training techniques and all-SAR transfer learning.

Several recent workshops conducted by the DOE Advanced Scientific Computing Research program have established the fact that the complexity of developing applications and executing them on high-performance computing (HPC) systems is rising at a rate which will make it nearly impossible to continue to achieve higher levels of performance and scalability. Absent an alternative approach to managing this ever-growing complexity, HPC systems will become increasingly difficult to use. A more holistic approach to designing and developing applications and managing system resources is required. This paper outlines a research strategy for managing the increasing the complexity by providing the programming environment, software stack, and hardware capabilities needed for autonomous resource management of HPC systems. Developing portable applications for a variety of HPC systems of varying scale requires a paradigm shift from the current approach, where applications are painstakingly mapped to individual machine resources, to an approach where machine resources are automatically mapped and optimized to applications as they execute. Achieving such automated resource management for HPC systems is a daunting challenge that requires significant sustained investment in exploring new approaches and novel capabilities in software and hardware that span the spectrum from programming systems to device-level mechanisms. This paper provides an overview of the functionality needed to enable autonomous resource management and optimization and describes the components currently being explored at Sandia National Laboratories to help support this capability.

Phenomenology-Informed (PI) Machine Learning is introduced to address the unique challenges faced when applying modern machine-learning object recognition techniques to the SAR domain. PI-ML includes a collection of data normalization and augmentation techniques inspired by successful SAR ATR algorithms designed to bridge the gap between simulated and real-world SAR data for use in training Convolutional Neural Networks (CNNs) that perform well in the low-noise, feature-dense space of camera-based imagery. The efficacy of PI-ML will be evaluated using ResNet, EfficientNet, and other networks, using both traditional training techniques and all-SAR transfer learning.

Software development for high-performance scientific computing continues to evolve in response to increased parallelism and the advent of on-node accelerators, in particular GPUs. While these hardware advancements have the potential to significantly reduce turnaround times, they also present implementation and design challenges for engineering codes. We investigate the use of two strategies to mitigate these challenges: the Kokkos library for performance portability across disparate architectures, and the DARMA/vt library for asynchronous many-task scheduling. We investigate the application of Kokkos within the NimbleSM finite element code and the LAMÉ constitutive model library. We explore the performance of DARMA/vt applied to NimbleSM contact mechanics algorithms. Software engineering strategies are discussed, followed by performance analyses of relevant solid mechanics simulations which demonstrate the promise of Kokkos and DARMA/vt for accelerated engineering simulators.

Abstract not provided.

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.

Abstract not provided.

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.

We measure the photoelastic constants of piezo-optomechanical photonic integrated circuits incorporating a specially formulated, silicon-depleted silicon nitride thin films using a laser doppler vibrometer to calibrate the strain produced by the integrated piezoelectric actuators.