Publications

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The war to establish cyber supremacy continues, and the literature is crowded with strictly technical cyber security measures. We present the results of a three year LDRD project using Linkography, a methodology new to the field of cyber security, we establish the foundation necessary to track and profile the microbehavior of humans attacking cyber systems. We also propose ways to leverage this understanding to influence and deceive these attackers. We studied the science of linkography, applied it to the cyber security domain, implemented a software package to manage linkographs, generated the preprocessing blocks necessary to ingest raw data, produced machine learning models, created ontology refinement algorithms and prototyped a web application for researchers and practitioners to apply linkography. Machine learning produced some of our key results: We trained and validated multinomial classifiers with a real world data set and predicted the attacker's next category of action with 86 to 98% accuracy; dimension reduction techniques indicated that the linkography-based features were among the most powerful. We also discovered ontology refinement algorithms that advanced the state of the art in linkography in general and cyber security in particular. We conclude that linkography is a viable tool for cyber security; we look forward to expanding our work to other data sources and using our prediction results to enable adversary deception techniques.

Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristics-based scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidth-constrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.

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Sandia National Laboratories’ California site is celebrating its 60th anniversary (1956 to 2016), and high performance computing has been a key enabler for its scientists and engineers throughout much of its history. Since its founding, Sandia California has helped pioneer the use of HPC platforms including hosting Sandia’s first Cray-1 supercomputer in the 1970s and supporting development of scalable cluster computing platforms to create a new paradigm for cost-effective supercomputing in the 1990s. Recent decades of investment in creation of scalable application frameworks for scientific computing have also enabled new generations of modeling and simulation codes. These resources have facilitated computational analysis of complex phenomena in diverse applications spanning national defense, energy, and homeland security. Today, Sandia California researchers work with partners in academia, industry, and national labs to evolve the state-of-the-art in HPC, modeling, and data analysis (including foundational capabilities for exascale computing platforms) and apply them in transformational ways. Research efforts include mitigating the effects of silent hardware failures that can jeopardize the results of large-scale computations, developing exascale-capable asynchronous task-parallel programming models and runtime systems, formulating new techniques to better explore and analyze extreme-scale data sets, and increasing our understanding of materials and chemical sciences which has applications spanning nuclear weapons stockpile stewardship to more efficient automobile engines. The following section highlights some of these research and applications projects and further illustrates the breadth of our HPC capabilities.

Optical diagnostics play a central role in dynamic compression research. Currently, streak cameras are employed to record temporal and spectroscopic information in single-event experiments, yet are limited in several ways; the tradeoff between time resolution and total record duration is one such limitation. This project solves the limitations that streak cameras impose on dynamic compression experiments while reducing both cost and risk (equipment and labor) by utilizing standard high-speed digitizers and commercial telecommunications equipment. The missing link is the capability to convert the set of experimental (visible/x-ray) wavelengths to the infrared wavelengths used in telecommunications. In this report, we describe the problem we are solving, our approach, our results, and describe the system that was delivered to the customer. The system consists of an 8-channel visible-to-infrared converter with > 2 GHz 3-dB bandwidth.

The purpose of this Failure Modes and Effects Analysis (FMEA) is chosen to examine the potential failures of the systems which could result in an overspeed event with a potential for flying debris from the wind turbine blades. The FMEA method was chosen to examine the turbine hydraulic system because two important turbine protective features use hydraulic pressure to function: the blade pitching system, and the brake. The objective of the FMEA was to determine if the two safety features have a likely common failure point, or if the safety systems can be individually credited for protection of the turbine.

Particle methods in computational physics are useful for modeling the motion of fluids and solids subject to large deformations. Under these conditions, mesh-based approaches often fail due to decreasing element quality leading to inaccuracy and instability. The developed software package called Moab investigates and prototypes next-generation particle methods, focusing on rigorous error analysis and active error minimization strategies during the computation. The present work discusses examples calculations representative of real engineering problems with quantified and maximized accuracy while demonstrating the potential for meeting engineering performance requirements.

This work supports Airborne Intelligence, Surveillance, and Reconnaissance (ISR) for tactical situational awareness in challenging environments with modified imaging LIDAR (light detection and ranging). LIDAR produces an irradiance-based scene with high, three-dimensional, spatial resolution; differentiating reflecting surfaces and surface textures not just for target detection, but also target recognition. LIDAR is generally prevented from working through all weather; as the traditional source wavelengths are scattered and/or absorbed by fog, clouds, and dust known as degraded visual environments (DVEs). This work identifies and quantifies improved optical wavelength regimes and polarization strategies that should open this otherwise denied operating window for LIDAR. We demonstrate modified imaging LIDAR's utility and ability to produce images in environments that have been challenging for traditional LIDAR (fog, dust) systems. We utilize a state-of-the-art Geiger mode avalanche photodiode (GMAPD) 32X32 detecting array for imaging with an integrated fast timing circuit ROIC per imaging detector pixel. This GMAPD is equivalent to 1024 radar receivers and produces a 3-D point cloud scene for each

Enhancement-mode Si/SiGe electron quantum dots have been pursued extensively by many groups for their potential in quantum computing. Most of the reported dot designs utilize multiple metal-gate layers and use Si/SiGe heterostructures with Ge concentration close to 30%. Here, we report the fabrication and low-temperature characterization of quantum dots in the Si/Si0.8Ge0.2 heterostructures using only one metal-gate layer. We find that the threshold voltage of a channel narrower than 1 μm increases as the width decreases. The higher threshold can be attributed to the combination of quantum confinement and disorder. We also find that the lower Ge ratio used here leads to a narrower operational gate bias range. The higher threshold combined with the limited gate bias range constrains the device design of lithographic quantum dots. We incorporate such considerations in our device design and demonstrate a quantum dot that can be tuned from a single dot to a double dot. The device uses only a single metal-gate layer, greatly simplifying device design and fabrication.

We report the successful growth of high-quality SrO films on highly-ordered pyrolytic graphite (HOPG) and single-layer graphene by molecular beam epitaxy. The SrO layers have (001) orientation as confirmed by X-ray diffraction (XRD) while atomic force microscopy measurements show continuous pinhole-free films having rms surface roughness of <1.5 Å. Transport measurements of exfoliated graphene after SrO deposition show a strong dependence between the Dirac point and Sr oxidation. Subsequently, the SrO is leveraged as a buffer layer for more complex oxide integration via the demonstration of (001) oriented SrTiO3 grown atop a SrO/HOPG stack.

This study offers experimental observation of the effect of low strain conditions (ε < 10%) on abnormal grain growth (AGG) in Nickel-200. At such conditions, stored mechanical energy is low within the microstructure enabling one to observe the impact of increasing mechanical deformation on the early onset of AGG compared to a control, or nondeformed, equivalent sample. The onset of AGG was observed to occur at specific pairings of compressive strain and annealing temperature and an empirical relation describing the influence of thermal exposure and strain content was developed. The evolution of low-Σ coincident site lattice (CSL) boundaries and overall grain size distributions are quantified using electron backscatter diffraction preceding, at onset and during ensuing AGG, whereby possible mechanisms for AGG in the low strain regime are offered and discussed.

The SMASH (Sandia Matlab AnalysiS Hierarchy) toolbox is a collection of MATLAB code for data analysis. The toolbox contains general purpose functions, custom class definitions, and self-contained programs aimed at the needs of experimental physicists working in pulsed power research. The initial release (version 1.0) supports file access, signal/image analysis, and user interface creation; custom graphics and generic system tools are also provided. Much of the package is object oriented, encouraging users to build new capabilities from established classes. Future releases will continue this goal, expanding capabilities and streamlining application development.

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