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.
Research interest in developing computing systems that represent logic states using quantum mechanical observables has only increased in the few decades since its inception. While quantum computers, with Josephson junction based qubits, have now been commercially available in the last three years, there is also significant research initiative to develop scalable quantum computers with so-called donor qubits. B.E. Kane first published on a device implementation of a silicon-based quantum computer in 1998, which sparked a wave of follow-on advances due to the attractive nature of silicon-based computing[7]. Nearly all commercial computing systems using classical binary logic are fabricated using a silicon substrate and it is inarguably the most mature material system for semiconductor devices, so that coupling classical and quantum bits on a single substrate is possible. The process of growing and processing silicon crystals into wafers is extremely robust and leads to minimal impurities or structural defects.
This project focused on providing a fundamental mechanistic understanding of the complex degradation mechanisms associated with Pellet/Clad Debonding (PCD) through the use of a unique suite of novel synthesis of surrogate spent nuclear fuel, in-situ nanoscale experiments on surrogate interfaces, multi-modeling, and characterization of decommissioned commercial spent fuel. The understanding of a broad class of metal/ceramic interfaces degradation studied within this project provided the technical basis related to the safety of high burn-up fuel, a problem of interest to the DOE.
This document archives the results developed by the Lab Directed Research and Development (LDRD) project sponsored by Sandia National Laboratories (SNL). In this work, it is shown that SNL has developed the first known high-energy hyperspectral computed tomography system for industrial and security applications. The main results gained from this work include dramatic beam-hardening artifact reduction by using the hyperspectral reconstruction as a bandpass filter without the need for any other computation or pre-processing; additionally, this work demonstrated the ability to use supervised and unsupervised learning methods on the hyperspectral reconstruction data for the application of materials characterization and identification which is not possible using traditional computed tomography systems or approaches.
We report on the fabrication and characterization of nanocrystalline ZnO films for use as a random laser physical unclonable function (PUF). Correlation between processing conditions and film microstructure will be made to optimize the lasing properties and random response. We will specifically examine the repeatability and security of PUFs demonstrated in this novel system. This demonstration has promise to impact many of Sandia's core missions including counterfeit detection.
Pressure losses and aerosol collection efficiencies were measured for fibrous filter materials at air-flow rates consistent with high efficiency filtration (hundreds of cubic feet per minute). Microfiber filters coated with nanofibers were purchased and fabricated into test assemblies for a 12-inch duct system designed to mimic high efficiency filtration testing of commercial and industrial processes. Standards and specifications for high efficiency filtration were studied from a variety of institutions to assess protocols for design, testing, operations and maintenance, and quality assurance (e.g., DOE, ASHRAE, ASME). Three materials with varying Minimum Efficiency Reporting Values (MERV) were challenged with sodium chloride aerosol. Substantial filter loading was observed where aerosol collection efficiencies and pressure losses increased during experiments. Filter designs will be optimized and characterized in subsequent years of this study. Additional testing will be performed with higher hazard aerosols at Oak Ridge National Laboratory.
This project targeted a full-field understanding of the conversion of plastic work into heat using advanced diagnostics (digital image correlation, DIC, combined with infrared, IR, imaging). This understanding will act as a catalyst for reformulating the prevalent simplistic model, which will ultimately transform Sandia's ability to design for and predict thermomechanical behavior, impacting national security applications including nuclear weapon assessments of accident scenarios. Tensile 304L stainless steel dogbones are pulled in tension at quasi-static rates until failure and full-field deformation and temperature data are captured, while accounting for thermal losses. The IR temperature fields are mapped onto the DIC coordinate system (Lagrangian formulation). The resultant fields are used to calculate the Taylor-Quinney coefficient, β, at two strain rates rates (0.002 s-1 and 0.08 s-1) and two temperatures (room temperature, RT, and 250°C).
The Near-Field Scanning Optical Microscope (NSOM) was used to image a wide array samples using a variety of standard and non-standard operating conditions on a custom system built in Org. 5625. The ability of this technique to produce high-quality images was assessed during this one-year LDRD. To obtain details about the devices imaged, as well as the experimental details, please refer to the classified report from the project manager, Rich Dondero, or the NSP IA lead, Kristina Czuchlewski.
A coupled electrochemical/thermochemical cycle was investigated to produce hydrogen from renewable resources. Like a conventional thermochemical cycle, this cycle leverages chemical energy stored in a thermochemical working material that is reduced thermally by solar energy. However, in this concept, the stored chemical energy only needs to be partially capable of splitting steam to produce hydrogen. To push the reaction to completion, a proton-conducting membrane is employed to separate hydrogen as it is produced, thus shifting the thermodynamics toward further hydrogen production. This novel coupled-cycle concept provides several benefits. First, the required oxidation enthalpy of the reversible thermochemical material is reduced, enabling the process to occur at lower temperatures. Second, removing the requirement for spontaneous steam splitting widens the scope of materials compositions, allowing for less expensive/more abundant elements to be used. Lastly, thermodynamics calculations suggest that this concept can potentially reach higher efficiencies than photovoltaic-to-electrolysis hydrogen production methods. This Exploratory Express LDRD involved assessing the practical feasibility of the proposed coupled cycle. A test stand was designed and constructed and proton-conducting membranes were synthesized. An LDRD plus-up of $10k enabled the remediation of a membrane sealing issue and enabled testing with an improved membrane. However, the membrane proved too thick for efficient proton conduction, and there were insufficient funds to continue. While the full proof of concept was not achieved, the individual components of the experiment were validated and new capabilities that can be leveraged by a variety of programs were developed.
In this work we propose an approach for accelerating Uncertainty Quantification (UQ) analysis in the context of Multifidelity applications. In the presence of complex multiphysics applications, which often require a prohibitive computational cost for each evaluation, multifidelity UQ techniques try to accelerate the convergence of statistics by leveraging the in- formation collected from a larger number of a lower fidelity model realizations. However, at the-state-of-the-art, the performance of virtually all the multifidelity UQ techniques is related to the correlation between the high and low-fidelity models. In this work we proposed to design a multifidelity UQ framework based on the identification of independent important directions for each model. The main idea is that if the responses of each model can be represented in a common space, this latter can be shared to enhance the correlation when the samples are drawn with respect to it instead of the original variables. There are also two main additional advantages that follow from this approach. First, the models might be correlated even if their original parametrizations are chosen independently. Second, if the shared space between models has a lower dimensionality than the original spaces, the UQ analysis might benefit from a dimension reduction standpoint. In this work we designed this general framework and we also tested it on several test problems ranging from analytical functions for verification purpose, up to more challenging application problems as an aero-thermo-structural analysis and a scramjet flow analysis.
Verification results for Sierra/SM using inexact reference solutions have often exhibited unsatisfactory convergence behavior. With an understanding of the convergence behavior for these types of tests, one can avoid falsely attributing pathologies of the test with incorrectness of the code. Simple theoretical results highlight that for an inexact reference solution two conditions must be met to observe asymptotic convergence. These conditions, and the resulting types of convergence behaviors, are further illustrated with graphical examples depicting the exact, inexact reference, and sequence of numerical solutions as vectors (in a function space). A stress concentration problem is adopted to contrast convergence behaviors when using inexact (classical linear elastic) and exact (manufactured) reference solutions. Convergence is not initially attained with the classical solution. Convergence with the manufactured solution indicates the convergence failure with the classical reference did not result from code error and provides insight on how for this problem asymptotic convergence could be attained with the classical reference solution by modifying the computational models.
A reduced order modeling capability has been developed to reduce the computational burden associated with time-domain solutions of structural dynamic models with linear viscoelastic materials. The discretized equations-of-motion produce convolution integrals resulting in a linear system with nonviscous damping forces. The challenge associated with the reduction of nonviscously damped, linear systems is the selection and computation of the appropriate modal basis to perform modal projection. The system produces a nonlinear eigenvalue problem that is challenging to solve and requires use of specialized algorithms not readily available in commercial finite element packages. This SAND report summarizes the LDRD discoveries of a reduction scheme developed for monolithic finite element models and provides preliminary investigations to extensions of the method using component mode synthesis. In addition, this report provides a background overview of structural dynamic modeling of structures with linear viscoelastic materials, and provides an overview of a new code capability in Sierra Structural Dynamics to output the system level matrices computed on multiple processors.