The impact on the morphology nanoceramic materials generated from group 4 metal alkoxides ([M(OR)4]) and the same precursors modified by 6,6′-(((2-hydroxyethyl)azanediyl)bis(methylene))bis(2,4-di-tert-butylphenol) (referred to as H3-AM-DBP2 (1)) was explored. The products isolated from the 1:1 stoichiometric reaction of a series of [M(OR)4] where M = Ti, Zr, or Hf; OR = OCH(CH3)2(OPri); OC(CH3)3(OBut); OCH2C(CH3)3(ONep) with H3-AM-DBP2 proved, by single crystal X-ray diffraction, to be [(ONep)Ti(k4(O,O′,O′′,N)-AM-DBP2)] (2), [(OR)M(μ(O)-k3(O′,O′′,N)-AM-DBP2)]2 [M = Zr: OR = OPri, 3·tol; OBut, 4·tol; ONep, 5·tol; M = Hf: OR = OBut, 6·tol; ONep, 7·tol]. The product from each system led to a tetradentate AM-DBP2 ligand and retention of a parent alkoxide ligand. For the monomeric Ti derivative (2), the metal was solved in a trigonal bipyramidal geometry, whereas for the Zr (3-5) and Hf (6, 7) derivatives a symmetric dinuclear complex was formed where the ethoxide moiety of the AM-DBP2 ligand bridges to the other metal center, generating an octahedral geometry. High quality density functional theory level gas-phase electronic structure calculations on compounds 2-7 using Gaussian 09 were used for meaningful time dependent density functional theory calculations in the interpretation of the UV-vis absorbance spectral data on 2-7. Nanoparticles generated from the solvothermal treatment of the ONep/AM-DBP2 modified compounds (2, 5, 7) in comparison to their parent [M(ONep)4] were larger and had improved regularity and dispersion of the final ceramic nanomaterials.
The effects of ultra-low glass transition temperature (Tg) phosphate glass (Pglass) on the thermal, morphological, rheological, mechanical, and crystallization properties of hybrid Pglass/poly(eththylene terephthalate)(PET) were investigated. Nano- and micro-scale distribution of the Pglass in the PET polymer matrix was observed. The polydispersed Pglass in the PET matrix functioned as a nucleation agent, resulting in increasing crystallization temperature. The Pglass in the PET matrix decreased the Tg, indicating a plasticizing effect of the Pglass in the hybrids that was confirmed by the significantly decreased complex viscosity of the PET matrix. In addition, with increasing temperature, a non-terminal behavior of the viscoelastic properties occurred due to the hybrid structural changes and improved miscibility of the hybrid components. In conclusion, the obtained solid-state variable temperature 31P and 1H NMR spectroscopy results showed strong Pglass concentration dependency of the interactions at the PET-Pglass interface.
International Conference on Optical MEMS and Nanophotonics
Sarma, Raktim S.; De Ceglia, Domenico; Nookala, Nishant; Vincenti, Maria A.; Campione, Salvatore; Wolf, Omri; Scalora, Michael; Belkin, Mikhail; Brener, Igal B.
We experimentally demonstrate a novel approach of using coupling between a leaky mode resonance and intersubband transitions in semiconductor quantum wells to realize a hybrid dielectric-semiconductor metasurface with high second-harmonic conversion efficiency and increased bandwidth.
The image below is a false-color rendering of a previously published cross-track stereo Synthetic Aperture Radar (SAR) height map. A gray-scale version of this image appeared in the following UUR document.
Some long-outstanding technical challenges exist that continue to be of hindrance to fully harnessing the unique investigative advantages of nuclear magnetic resonance (NMR) spectroscopy in the in situ investigation of rechargeable battery chemistry. For instance, the conducting materials and circuitry necessary for an operational battery always deteriorate the coil-based NMR sensitivity when placed inside the coil, and the shape mismatch between them leads to low sample filling factors and even higher detection limits. We report, herein, a novel and successful adaptation of stripline NMR detection that integrates seamlessly NMR detection with the construction of an electrochemical device in general, or a battery in particular, which leads to an in situ electrochemical NMR technique with much higher detection sensitivity, higher sample filling factor, and which is particularly suitable for mass-limited samples.
A patient in the United States has been diagnosed with Ebola. Fear and panic kicks in across the country, and hospitals are inundated with hundreds of people, some infected with the highly contagious disease and others not. Blood tests are needed for positive diagnoses, but the diagnostic labs are overwhelmed with blood samples to test, and staff are overworked and stressed. Infected people need to be quarantined and treated, but it's hard to find rooms to quarantine so many patients. Sick people who need triage and regular care for other emergencies are afraid to go to hospitals for fear of Ebola, which has a 50% fatality rate. And since hospitals are so overwhelmed, sick people often stay home, infecting heathy people around them; the U.S. is now in the grips a full-blown Ebola outbreak. Sandia's high-performance computers simulated such a nightmare scenario recently, and with good reason. An Ebola outbreak in the United States could be devastating if hospitals are not prepared. When an Ebola outbreak in West Africa became a global concern in 2014, health advisers were alarmed at the length of time it took to properly diagnose infected people. In rural areas in Liberia, for example, blood samples from ailing people would be sent to a laboratory for testing, but the closest lab was hundreds of miles away through difficult and sometimes impassable roads. In more urban areas, blood samples would be sent to nearby labs, but those labs were often already overburdened by the sheer volume of samples to test. Staff at some treatment centers were unaware that a lab a little farther away might have the capacity to take in more samples. Meanwhile, undiagnosed infected people were unknowingly spreading the disease to many others around them, worsening the outbreak. The U.S. Defense Threat Reduction Agency (DTRA) and Centers for Disease Control and Prevention (CDC) posed a serious question: how do we improve blood-sample transportation routes in Liberia to ensure that samples taken from ill people are tested as quickly as possible, ensuring a proper diagnosis and faster treatment? Sandia scientists, already experts in transportation modeling for nuclear materials, quickly swarmed on this problem. The Sandia Ebola response team immediately set out to collect data from the region using available maps and local information, and transformed the raw data to GIS maps. Then, applying Sandia transportation routing algorithms, the team identified the optimal routes to get blood samples to the best laboratory for testing, even if that lab was not geographically the closest. The models also showed the best possible locations for mobile diagnostic laboratories that would better support the very rural regions that were most affected by the Ebola outbreak.
Solar thermochemical hydrogen (STCH) production is one avenue for converting sunlight into hydrogen through concentrating solar thermal technology. STCH is a two-step redox process that begins with concentrated sunlight to thermally reduce a metal oxide around 1500 °C leaving it in an oxygen deficient form. Subsequent exposure of the reduced metal oxide to steam at lower temperature reoxidizes the material and produces hydrogen. The efficiency of this process is dependent on the metal oxide material thermodynamic properties and cycle operating conditions.
With the increased scale expected on future leadership-class systems, detailed information about the resource usage and performance of MPI message matching provides important insights into how to maintain application performance on next-generation systems. However, obtaining MPI message matching performance data is often not possible without significant effort. A common approach is to instrument an MPI implementation to collect relevant statistics. While this approach can provide important data, collecting matching data at runtime perturbs the application's execution, including its matching performance, and is highly dependent on the MPI library's matchlist implementation. In this paper, we introduce a trace-based simulation approach to obtain detailed MPI message matching performance data for MPI applications without perturbing their execution. Using a number of key parallel workloads and microbenchmarks, we demonstrate that this simulator approach can rapidly and accurately characterize matching behavior. Specifically, we use our simulator to collect several important statistics about the operation of the MPI posted and unexpected queues. For example, we present data about search lengths and the duration that messages spend in the queues waiting to be matched. Data gathered using this simulation-based approach have significant potential to aid hardware designers in determining resource allocation for MPI matching functions and provide application and middleware developers with insight into the scalability issues associated with MPI message matching.
This report serves as the executive summary to the comprehensive document that describes the software, control logic, and operational functions of the Pacific DC Intertie (PDCI) Oscillation Damping Controller. The purpose of the damping controller (DCON) is to mitigate inter-area oscillations in the Western Interconnection (WI) by active improvement of oscillatory mode damping using phasor measurement unit (PMU) feedback to modulate power flow in the PDCI. This report provides the high level descriptions, diagrams, and charts to receive a basic understanding of the organization and structure of the DCON software. This report complements the much longer comprehensive software document, and it does not include any proprietary information as the more comprehensive report does. The level of detail provided by the comprehensive report on the software documentation is intended to assist with the process needed to obtain compliance for North American Electric Reliability Corporation Critical Infrastructure Protection (NERC-CIP) as a Bulk energy system Cyber Asset (BCA) device. That report organizes, summarizes, and presents the charts, figures, and flow diagrams that detail the organization and function of the damping controller software. The PDCI Wide-Area Damping Controller is the result of a collaboration between Sandia National Laboratories (SNL), Bonneville Power Administration (BPA), Montana Tech University (MTU), and the Department of Energy (DOE).
The Vanguard program informally began in January 2017 with the submission of a white paper entitled "Sandia's Vision for a 2019 Arm Testbed" to NNSA headquarters. The program proceeded in earnest in May 2017 with an announcement by Doug Wade (Director, Office of Advanced Simulation and Computing and Institutional R&D at NNSA) that Sandia National Laboratories (Sandia) would host the first Advanced Architecture Prototype platform based on the Arm architecture. In August 2017, Sandia formed a Tri-lab team chartered to develop a robust HPC software stack for Astra to support the Vanguard program goal of demonstrating the viability of Arm in supporting ASC production computing workloads. This document describes the high-level Vanguard program goals, the Vanguard-Astra project acquisition plan and procurement up to contract placement, the initial software stack environment planned for the Vanguard-Astra platform (Astra), a description of how the communities of users will utilize the platform during the transition from the open network to the classified network, and initial performance results.
Structural dynamic testing is a common method for determining if the design of a component of a system will mechanically fail when deployed into its field environment. To satisfy the test's goal, the mechanical stresses must be replicated. Structural dynamic testing is commonly executed on a shaker table or a shock apparatus such as a drop table or a resonant plate. These apparatus impart a force or load on the component through a test fixture that connects the unit under test to the apparatus. Because the test fixture is directly connected to the unit under test, the fixture modifies the structural dynamics of the system, thus varying the locations and relative levels of stress on the unit under test. This may lead to a false positive or negative indication if the unit under test will fail in its field environment depending on the environment and the test fixture. This body of research utilizes topology optimization using the Plato software to design a test fixture that attaches to the unit under test that matches the dynamic impedance of the next level of assembly. The optimization's objective function is the difference between the field configuration and the laboratory configuration's frequency response functions. It was found that this objective function had many local minima and posed difficulties in converging to an acceptable solution. A case study is presented that uses this objective function and although the results are not perfect, they are quantifiably better than the current method of using a sufficiently stiff fixture.
The software team that develops Turbo FRMAC (TF) at Sandia National Labs has continued to look for technologies to add Cloud-enabling features to Turbo FRMAC. The Amazon AppStream service has now matured into a viable low-cost solution with quick turnaround potential to create a Cloud version of Turbo FRMAC. This service would allow both a Desktop and Cloud version of Turbo FRMAC to exist without duplicate efforts to support both instances. The only software needed to run is a modern Web Browser — no downloads and no installation necessary.
Sandia National Laboratories has tested and evaluated a digitizer, the SMART24B, manufactured by Geotech Instruments, LLC. These digitizers are used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as power consumption, input impedance, sensitivity, full scale, self-noise, dynamic range, system noise, response, passband, and timing. The SMART24B is Geotech's datalogger intended for borehole deployment in their digitizer product line. The SMART24B is available with either 3 or 6 channels at 24 bit resolution. The digitizer is to be deployed in boreholes, therefore are a minimum number of connections required on the digitizer case as datalogger utilizes a distribution panel, mounted up-hole, serving to breakout power, GPS, serial communications and ethernet connections.
This document serves to guide a researcher through the process of running the Weather Research and Forecasting (WRF) model and incorporating observations into coarse resolution reanalysis products to model atmospheric conditions at high (50 m) resolution. This documentation is specific to WRF and the WRF Preprocessing System (WPS) version 3.8.1 and the Objective Analysis (OBSGRID) code released on April 8, 2016. Output from WRF serves as an input into the Time-Domain Atmospheric Acoustic Propagation Suite (TDAAPS) which performs staggered-grid finite difference modeling of the acoustic velocity pressure system to produce Green's functions through these atmospheric models.
A series of experiments were performed with the objective of achieving an extreme thermal environment by creating a fire whirl in an enclosure in facilities at the Thermal Test Complex (TTC) at Sandia National Laboratories. The motivation for the experiments is based on results from previous experiments performed at Sandia in an igloo representing a mock weapon's storage facility. In that test series, a fire whirl developed within the igloo resulting in extremely high heat flux levels. This environment was created with a pool fire of 4.6-m in diameter and was not under controlled, repeatable conditions. The objective of the current tests is to have the ability to create this environment in a repeatable controlled environment at a smaller scale, namely with a pool fire not above 3-m diameter effectively, thereby allowing for repeatable, cost-effective testing. In FY15, six tests were conducted in the Crosswind Test Facility (XTF), using a 1.77 m square pan. In FY16, three tests were conducted in the Fire Laboratory for Accreditation of Modeling by Experiment (FLAME) using a 3-m diameter pan. Both of these test series utilized the same enclosure. In FY17, a single test was performed in XTF using a 2.7 m square pan using a modified enclosure which included a ceiling. All tests used Jet-A as the fuel. The wind speed and gap width of the enclosure were varied for the FY15 XTF tests and the gap width and effect of insulation on the enclosure walls were varied for the FY16 FLAME tests. Fuel regression rates, heat flux, and gas velocity measurements were obtained. The results from the FY15 and FY16 test series indicate that fuel regression rates and peak heat flux levels are a factor of two higher than non-fire whirl pool fires of equivalent diameter. The results from the FY17 test using an enclosure with a ceiling met the objective of the test series by achieving temperatures of nearly 1400°C and heat flux levels of 400 kW/m2.
Multivariate time-series datasets are intrinsic to the study of dynamic, naturalistic behavior, such as in the applications of finance and motion video analysis. Statistical models provide the ability to identify event patterns in these data under conditions of uncertainty, but researchers must be able to evaluate how well a model uses available information in a dataset for clustering decisions and for uncertainty information. The Hidden Markov Model (HMM) is an established method for clustering time-series data, where the hidden states of the HMM are the clusters. We develop novel methods for quantifying the uncertainty of the performance of and for visualizing the clustering performance and uncertainty of fitting a HMM to multivariate time-series data. We explain the usefulness of uncertainty quantification and visualization with evaluating the performance of clustering models, as well as how information exploitation of time-series datasets can be enhanced. We implement our methods to cluster patterns of scanpaths from raw eye tracking data.
Social network graph models are data structures representing entities (often people, corporations, or accounts) as "vertices" and their interactions as "edges" between pairs of vertices. These graphs are most often total-graph models — the overall structure of edges and vertices in a bidirectional or directional graph are described in global terms and the network is generated algorithmically. We are interested in "egocentrie or "agent-based" models of social networks where the behavior of the individual participants are described and the graph itself is an emergent phenomenon. Our hope is that such graph models will allow us to ultimately reason from observations back to estimated properties of the individuals and populations, and result in not only more accurate algorithms for link prediction and friend recommendation, but also a more intuitive understanding of human behavior in such systems than is revealed by previous approaches. This report documents our preliminary work in this area; we describe several past graph models, two egocentric models of our own design, and our thoughts about the future direction of this research.
We present single-sided 3D image reconstruction and neutron spectrum of non-nuclear material interrogated with a deuterium-tritium neutron generator. The results presented here are a proof-of-principle of an existing technique previously used for nuclear material, applied to non-nuclear material. While we do see excess signatures over background, they do not have the expected form and are currently un-identified.
Recent work done at the University of Florida (UF) revealed a tremendously enhanced germanium diffusion process along silicon/silicon dioxide interfaces during oxidizing anneals, allowing for the controlled formation of Si quantum wires. This project seeks to further explore this unusual germanium behavior during oxidation for the purpose of forming unique and useful nano and quantum structures. Specifically, we propose here to demonstrate for the first time that this phenomenon can be extended to realize OD Si nanostructures through the oxidation of axially heterostructured vertical Si/SiGe pillars. Such structures could be of great interest for applications in integrated optoelectronics, beyond Moore's Law computing, and quantum computing.
The description and notes describe and scope the activities performed under this PHS. All hazards have been identified. Questions are answered correctly and, as necessary, rationale or clarification is provided. All hazards in the HA have been analyzed, including the identification of controls for each hazard. l have reviewed this PHS and concur that its contents are accurate and complete.
The description and notes describe and scope the activities performed under this PHS. All hazards have been identified. Questions are answered correctly and, as necessary, rationale or clarification is provided. All hazards in the HA have been analyzed, including the identification of controls for each hazard. l have performed the above reviews and concur that those items are complete and accurate.
Aerosol jet printing (AJP) is a promising microscale additive manufacturing technology for emerging applications in printed and flexible electronics. However, the more widespread adoption of this emerging technique is hindered by a limited fundamental understanding of the process. This work focuses on a critical and underappreciated aspect of the process, the interaction between drying induced by the sheath gas and impaction. Combining focused experiments with support from numerical modeling, it is shown that these effects have a dramatic impact on key outputs of the process, including deposition rate, resolution, and morphology. These effects can amplify minor changes in ink composition or atomization yield, increasing process sensitivity and drift. Moreover, these effects can confound strategies for in-line process monitoring and control based on empirical observables. Strategies to directly manipulate this annular drying phenomenon are presented, providing a viable tool to tailor and study the process. This work clarifies coupled effects of printer design, ink formulation, and print parameters, establishing a more robust theoretical framework for understanding the AJP process and advancing the maturity of this promising technology.
Seismic signals are composed of the seismic waves (phases) that reach a sensor, similar to the way speech signals are composed of phonemes that reach a listener's ear. Large/small seismic events near/far from a sensor are similar to loud/quiet speakers with high/low-pitched voices. We leverage ideas from speech recognition for the classification of seismic phases at a seismic sensor. Seismic Phase ID is challenging due to the varying paths and distances an event takes to reach a sensor, but there is consistent structure of the makeup (e.g. ordering) of the different phases arriving at the sensor.
Current loss in magnetically insulated transmission lines (MITLs) was investigated using data from experiments conducted on Z and Mykonos. Data from experiments conducted on Z were used to optimize an ion diode current loss model that has been implemented into the transmission line circuit model of Z. Details on the current loss model and comparisons to data from Z experiments have been previously published in a peer-reviewed journal [Hutsel, et al., Phys. Rev. Accel. Beams 21, 030401]. Dedicated power flow experiments conducted on Mykonos investigated current loss in a millimeter-scale anode-cathode gap MITL operated at lineal current densities greater than 410 kA/cm and with electric field stresses in excess of 240 kV/cm where it is expected that both anode and cathode plasmas are formed. The experiment MITLs were exposed to varying vacuum conditions; including vacuum pressure at shot time, time under vacuum, and vacuum storage protocols. The results indicate that the vacuum conditions have an effect on current loss in high lineal current density MITLs.
GaN-on-Si combines the wide bandgap advantages of GaN with the cost and scaling advantages of Si. Sputtered A1N is an attractive nucleation layer material because it reduces Al diffusion into the Si and eliminates a time-intensive preconditioning step in the GaN growth process, but is limited by the poor film quality of PVD A1N films deposited on Si substrates. Sputtering also offers a large degree of control over A1N film properties, including control of the intrinsic stress using substrate biasing. Doping the A1N films with Sc improves the lattice match to A1GaN and GaN films by expanding the a-axis and c-axis lattice parameters. A1N and A10.88Sc0.12N films have been grown on silicon, metal, and sapphire substrates and characterized for properties such as stress, grain size, roughness, and film orientation for use as nucleation layers for MOCVD GaN growth.
Traditional Monte Carlo particle transport codes are expected to run inefficiently on next-generation architectures as they are memory-intensive and highly divergent. Since electrons and photons also behave differently, the future for coupled electron-photon radiation transport looks even worse. This project describes preliminary efforts to improve the performance of Monte Carlo particle transport codes when using accelerators like the graphics processing unit (GPU). Two key issues are addressed: how to handle memory-intensive tallies, and how to reduce divergence. Tallying on the GPU can be done efficiently by post-processing particle data, or by using a feature called warp shuffle for summing scores in parallel during the simulation. Reducing divergence is possible by using an event-based algorithm for particle tracking instead of the traditional history-based one. Although performance tests presented in this work show that the history-based algorithm generally outperformed the event-based one for simple problems, this outcome will likely change as the complexity of the code increases.
Stress corrosion cracks (SCC) represent a major concern for the structural integrity of engineered metal structures. In hazardous or restricted-access environments, remote detection of corrosion or SCC frequently relies on visual methods; however, with standard VT-1 visual inspection techniques, probabilities of SCC detection are low. Here, we develop and evaluate an improved optical sensor for SCC in restricted access-environments by combining a robotically controlled camera/fiber-optic based probe with software-based super-resolution imaging (SRI) techniques to increase image quality and detection of SCC. SRI techniques combine multiple images taken at different viewing angles, locations, or rotations, to produce a single higher- resolution composite image. We have created and tested an imaging system and algorithms for combining optimized, controlled camera movements and super- resolution imaging, improving SCC detection probabilities, and potentially revolutionizing techniques for remote visual inspections of any type.
This report explores the potential for reducing the fields and the quality factor within a system cavity by introducing microwave absorbing materials. Although the concept of introducing absorbing (lossy) materials within a cavity to drive the interior field levels down is well known, increasing the loading into a complex weapon cavity specifically for improved electromagnetic performance has not, in general, been considered, and this will be the subject of this work. We compare full-wave simulations to experimental results, demonstrating the applicability of the proposed method.
Current designs for spent fuel transportation casks cannot ensure a cask's integrity during shipment, nor is there any verifiable means of maintaining continuity of knowledge (CoK) on a cask's contents. Spent fuel destined for encapsulation plants or geological repositories requires additional containment and surveillance (C/S) measures during shipment. Following final safeguards accountancy measurements on spent fuel assemblies, the shipment of verified assemblies will require unprecedented reliance on maintaining CoK on the fuel inside transport casks. Such increased reliance is due to the lack of reverification of spent fuel following encapsulation into disposal canisters and by meeting the requirement of dual C/S measures during such fuel shipments according to recommendations made by the Application of Safeguards to Geological Repositories (ASTOR) International Atomic Energy Agency (IAEA) expert group. By designing spent fuel transportation casks with effective seals integrated into their design, CoK can be more effectively maintained than by ad hoc C/S measures because seal integration ensures that a cask has not been tampered with. Externally applied seals might not be able to provide such assurance for currently designed spent fuel transportation casks, although some combination of seals, detectors, and/or a technology that can verify canister integrity might provide this assurance. This paper examines the design criteria for integrating safeguards seals into transportation casks and provides recommendations for near-term applications.
The rise of low-power neuromorphic hardware has the potential to change high-performance computing; however much of the focus on brain-inspired hardware has been on machine learning applications. A low-power solution for solving partial differential equations could radically change how we approach large-scale computing in the future. The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by grid cells in the brain. The second method tracks the densities of random walkers at each spatial location directly. We present the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions. Finally, we present implementations of these algorithms on neuromorphic hardware.
This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on developing a sampling capability that can handle the challenges of generating samples from nuclear cross-section data. The covariance information between energy groups tends to be very ill-conditioned and thus poses a problem using traditional methods for generated correlated samples. This report outlines a method that addresses the sample generation from cross-section matrices. The treatment allows one to assume the cross sections are distributed with a multivariate normal distribution, lognormal distribution, or truncated normal distribution.
This report analyzes data from multi-arm caliper (MAC) surveys taken at the Bryan Mound Strategic Petroleum Reserve site to determine baseline statistics for the original innermost cemented casing or the subsequent installed liner. Along with analyzing the internal diameters from the MAC surveys, this analysis looks to approximate casing weight, an important metric for determining the strength of well sections. Casing weight is calculated for each section, survey, and well. Results from the analysis show most wells reflect the dimensions in the original as-built drawings. There are, however, a few exceptions. Some well sections have calculated wall thicknesses outside API tolerance. In addition, some well section depths differ from the as-built drawings. All results are discussed on a well-by-well basis. Where applicable, information from this report should be used to update as-built drawings and aid in creating more accurate well models for future studies.
In recent years, seismicity rates in the US have dramatically risen due to increased activity in onshore oil and gas production. This project attempts to tie observations about induced seismicity to dehydration reactions in laumontite, a common mineral found in fault gouge in crystalline basement formations. It is the hypothesis of this study that in addition to pressurerelated changes in the in situ stress state, the injection of wastewater pushes new fluids into crystalline fault fracture networks that are not in chemical equilibrium with the mineral assemblages, particularly laumontite in fault gouge. Experiments were conducted under hydrothermal conditions where samples of laumontite were exposed to NaC1 brines at different pH values. After exposure to different fluid chemistries for 8 weeks at 90° C, we did not observe substantial alteration of laumontite. In hydrostatic compaction experiments, all samples deformed similarly in the presence of different fluids. Pore pressure decreases were observed at the start of a 1 week hold at 85° C in a 1M NaC1 pH 3 solution, suggesting that acidic fluids might stabilize pore pressures in basement fault networks. Friction experiments on laumontite and kaolinite powders showed both materials have similar coefficients of friction. Mixtures with partial kaolinite content showed a slight decrease in the coefficient of friction, which could be sufficient to trigger slip on critically stressed basement faults.
Quantum-size-controlled photoelectrochemical (QSC-PEC) etching, which uses quantum confinement effects to control size, can potentially enable the fabrication of epitaxial quantum nanostructures with unprecedented accuracy and precision across a wide range of materials systems. However, many open questions remain about this new technique, including its limitations and broader applicability. In this project, using an integrated experimental and theoretical modeling approach, we pursue a greater understanding of the time-dependent QSC-PEC etch process and to uncover the underlying mechanisms that determine its ultimate accuracy and precision. We also seek to broaden our understanding of the scope of its ultimate applicability in emerging nanostructures and nanodevices.
The retina plays an important role in animal vision --- namely to pre-process visual information before sending it to the brain. The goal of this LDRD was to develop models of motion-sensitive retinal cells for the purpose of developing retinal-inspired algorithms to be applied to real-world data specific to Sandia's national security missions. We specifically focus on detection of small, dim moving targets amidst varying types of clutter or distractor signals. We compare a classic motion-sensitive model, the Hassenstein-Reichardt model, to a model of the OMS (object motion- sensitive) cell, and find that the Reichardt model performs better under continuous clutter (e.g. white noise) but is very sensitive to particular stimulus conditions (e.g. target velocity). We also demonstrate that lateral inhibition, a ubiquitous characteristic of neural circuitry, can effect target-size tuning, improving detection specifically of small targets.
This work is to characterize the mechanical performances of the selected composites with four different overlap lengths of 0.25 in, 0.5 in, 0,75 in and 1.0 in. The composite materials in this study were one carbon composite (AS4C/UF3662) and one glass (E-glass/UF3662) composite. They both had the same resin of UF 3362, but with different fibers of carbon AS4C and E-glass. The mechanical loading in this study was limited to the quasi-static loading of 2 mm/min, which was equivalent to 5x10(-4) strain rate. Digital cameras were set up to record images during the mechanical testing. The full-field deformation data obtained from Digital Image Correlation (DIC) and the side view of the specimens were used to understand the different failure modes of the composites. The maximum load and the ultimate strength with consideration of the location of the failure for the different overlap lengths were compared and plotted together to understand the effect of the overlap lengths on the mechanical performance of the overlapped composites.
Imaging diagnostics that utilize coherent light, such as digital in-line holography, are important for object sizing and tracking applications. However, in explosive, supersonic, or hypersonic environments, gas-phase shocks impart imaging distortions that obscure internal objects. To circumvent this problem, some research groups have conducted experiments in vacuum, which inherently alters the physical behavior. Other groups have utilized single-shot flash x-ray or high-speed synchrotron x-ray sources to image through shock-waves. In this work, we combine digital in-line holography with a phase conjugate mirror to reduce the phase distortions caused by shock-waves. The technique operates by first passing coherent light through the shock-wave phase-distortion and then a phase-conjugate mirror. The phase-conjugate mirror is generated by a four-wave mixing process to produce a return beam that has the exact opposite phase-delay as the forward beam. Therefore, by passing the return beam back through the phase-distortion, the phase delays picked up during the initial pass are canceled, thereby producing improved coherent imaging. In this work, we implement phase conjugate digital in-line holography (PCDIH) for the first time with a nanosecond pulse-burst laser and ultra-high-speed cameras. This technique enables accurate measurement of the three-dimensional position and velocity of objects through shock-wave distortions at video rates up to 5 MHz. This technology is applied to improve three-dimensional imaging in a variety of environments from imaging supersonic shock-waves through turbulence, sizing objects through laser-spark plasma-generated shock-waves, and tracking explosively generated hypersonic fragments. Theoretical foundations and additional capabilities of this technique are also discussed.
Existing models for most materials do not describe phase transformations and associated lattice dy- namics (kinetics) under extreme conditions of pressure and temperature. Dynamic x-ray diffraction (DXRD) allows material investigations in situ on an atomic scale due to the correlation between solid-state structures and their associated diffraction patterns. In this LDRD project we have devel- oped a nanosecond laser-compression and picosecond-to-nanosecond x-ray diffraction platform for dynamically-compressed material studies. A new target chamber in the Target Bay in building 983 was commissioned for the ns, kJ Z-Beamlet laser (ZBL) and the 0.1 ns, 250 J Z-Petawatt (ZPW) laser systems, which were used to create 8-16 keV plasma x-ray sources from thin metal foils. The 5 ns, 15 J Chaco laser system was converted to a high-energy laser shock driver to load material samples to GPa stresses. Since laser-to-x-ray energy conversion efficiency above 10 keV is low, we employed polycapillary x-ray lenses for a 100-fold fluence increase compared to a conventional pinhole aperture while simultaneously reducing the background significantly. Polycapillary lenses enabled diffraction measurements up to 16 keV with ZBL as well as diffraction experiments with ZPW. This x-ray diffraction platform supports experiments that are complementary to gas guns and the Z facility due to different strain rates. Ultimately, there is now a foundation to evaluate DXRD techniques and detectors in-house before transferring the technology to Z. This page intentionally left blank.
Stochastic optimization deals with making highly reliable decisions under uncertainty. Chance constraints are a crucial tool of stochastic optimization to develop mathematical optimization models; they form the backbone of many important national security data science applications. These include critical infrastructure resiliency, cyber security, power system operations, and disaster relief management. However, existing algorithms to solve chance-constrained optimization models are severely limited by problem size and structure. In this investigative study, we (i) develop new algorithms to approximate chance-constrained optimization models, (ii) demonstrate the application of chance-constraints to a national security problem, and (iii) investigate related stochastic optimization problems. We believe our work will pave way for new research is stochastic optimization as well as secure national infrastructures against unforeseen attacks.
Modeling material and component behavior using finite element analysis (FEA) is critical for modern engineering. One key to a credible model is having an accurate material model, with calibrated model parameters, which describes the constitutive relationship between the deformation and the resulting stress in the material. As such, identifying material model parameters is critical to accurate and predictive FEA. Traditional calibration approaches use only global data (e.g. extensometers and resultant force) and simplified geometries to find the parameters. However, the utilization of rapidly maturing full-field characterization techniques (e.g. Digital Image Correlation (DIC)) with inverse techniques (e.g. the Virtual Feilds Method (VFM)) provide a new, novel and improved method for parameter identification. This LDRD tested that idea: in particular, whether more parameters could be identified per test when using full-field data. The research described in this report successfully proves this hypothesis by comparing the VFM results with traditional calibration methods. Important products of the research include: verified VFM codes for identifying model parameters, a new look at parameter covariance in material model parameter estimation, new validation techniques to better utilize full-field measurements, and an exploration of optimized specimen design for improved data richness.
The high-level objective of this project is to solve national-security problems associated with petroleum use, cost, and environmental impacts by enabling more efficient use of natural-gas-fueled internal combustion engines. An improved science-base on end-gas autoignition, or “knock,” is required to support engineering of more efficient engine designs through predictive modeling. An existing optical diesel engine facility is retrofitted for natural gas fueling with laser-spark-ignition combustion to provide in-cylinder imaging and pressure data under knocking combustion. Zero-dimensional chemical-kinetic modeling of autoignition, adiabatically constrained by the measured cylinder pressure, isolates the role of autoignition chemistry. OH* chemiluminescence imaging reveals six different categories of knock onset that depend on proximity to engine surfaces and the in-cylinder deflagration. Modeling results show excellent prediction regardless of the knock category, thereby validating state-of-the-art kinetic mechanisms. The results also provide guidance for future work to build a science base on the factors that affect the deflagration rate.