Distributed Phase Plates (DPP) are used in laser experiments to create homogenous intensity distributions of a distinct shape at the location of the laser focus. Such focal shaping helps with controlling the intensity that is impeding on the target. To efficiently use a DPP, the exact size and shape of the focal distribution is of critical importance. We recorded direct images of the focal distribution with ideal continuous-wave (CW) alignment lasers and with laser pulses delivered by the Z-Beamlet facility. As necessary to protect the imaging sensors, laser pulses will not be performed by full system shots, but rather with limited energy on so-called 'rod-shots', in which Z-Beamlet's main amplifiers do not engage. The images are subsequently analyzed for characteristic radii and shape. All characterizations were performed at the Pecos target area of Sandia with a lens of 3.2 m focal length.
This is an addendum to the Sierra/SolidMechanics 4.54 User's Guide that documents additional capabilities available only in alternate versions of the Sierra/SolidMechanics (Sierra/SM) code. These alternate versions are enhanced to provide capabilities that are regulated under the U.S. Department of State's International Traffic in Arms Regulations (ITAR) export control rules. The ITAR regulated codes are only distributed to entities that comply with the ITAR export control requirements. The ITAR enhancements to Sierra/SM include material models with an energy-dependent pressure response (appropriate for very large deformations and strain rates) and capabilities for blast modeling. This document is an addendum only; the standard Sierra/SolidMechanics 4.54 User's Guide should be referenced for most general descriptions of code capability and use.
Presented in this document are tests that exist in the Sierra/SolidMechanics example problem suite, which is a subset of the Sierra/SM regression and performance test suite. These examples showcase common and advanced code capabilities. A wide variety of other regression and verification tests exist in the Sierra/SM test suite that are not included in this manual.
Presented in this document are the theoretical aspects of capabilities contained in the Sierra/SM code. This manuscript serves as an ideal starting point for understanding the theoretical foundations of the code. For a comprehensive study of these capabilities, the reader is encouraged to explore the many references to scientific articles and textbooks contained in this manual. It is important to point out that some capabilities are still in development and may not be presented in this document. Further updates to this manuscript will be made as these capabilities come closer to production level.
Sierra/SolidMechanics (Sierra/SM) is a Lagrangian, three-dimensional finite element analysis code for solids and structures subjected to extensive contact and large deformations, encompassing explicit and implicit dynamic as well as quasistatic loading regimes. This document supplements the primary Sierra/SM 4.54 User's Guide, describing capabilities specific to Goodyear analysis use cases, including additional implicit solver options, material models, finite element formulations, and contact settings.
Accurate modeling of viscoelasticity remains an important consideration for a variety of materials (e.g. polymers and inorganic glasses). As such, over the previous decades a substantial body of work has been dedicated to developing appropriate constitutive models for viscoelasticity ranging from initial considerations of linear thermoviscoelasticity to more complex non-linear formulations incorporating fictive temperatures or potential energy clocks including the use of both internal state variable(ISV) and hereditary integral representations. Nonetheless, relatively limited (in comparison to plasticity) attention has been paid to the numerical integration of such schemes. In terms of integral based formulations, Taylor et al. first considered the problem of the integration of a linear viscoelasticity model. That work focused on the integration of the hereditary integrals and demonstrated improved performance of the new scheme with a custom finite element code over an existing finite difference reference. Chambers and Becker, using a free volume based shift factor, also considered the integration of the hereditary integrals and the impact on the problem of a pressurized thick-walled cylinder and developed an adaptive scheme to bound the error. Chambers later developed three-point Gauss and composite integration schemes for the hereditary integrals and noted improved accuracy. With respect to ISV-based schemes, formulations for the non-linear Schapery model have been proposed. However, in those efforts greater attention was paid to convergence of the non-linear solution scheme than impact of numerical integration. Various authors (e.g. Holzapfel and Simo and Hughes) have also studied the use of convolution integrals with differential forms of ISVs for temperature-independent formulations. Regardless, while the "potential energy clock" (PEC) and "simplified potential energy clock"(SPEC) models have been used to study a variety of non-linear responses (e.g.), limited attention has been paid to the numerical performance. As will be discussed later, the "clock" at the center of the formulations includes temperature and complex history dependence making the numerical integration of such a model even more challenging. Thus, in the current work an initial effort towards characterizing the numerical integration of the constitutive model through simplified problems is performed. To that end, in Section 2 the theory of the model is briefly presented while the numerical integration is discussed in Section 3. Results of various studies characterizing the numerical behavior and performance are then given in Section 4. Finally, some concluding remarks and thoughts for follow on works are provided in Section 5.
This project seeks to leverage various hyperspectral tensor products for the purposes of target classification/detection/prediction. In addition to hyperspectral, these products may be images, time series, geometries, or other modalities. The scenarios in which the targets of interest must be identified are typically from remote sensing platforms such as satellites. As such, there are numerous real-world constraints that drive algorithmic formulation. Cost, complexity, and feasibility of the algorithm should all be considered. Targets of interest are exceedingly rare, and collecting many data samples is prohibitively expensive. Furthermore, model interpretability is paramount due to the application space. The goal of this project is to develop a constrained supervised tensor factorization framework for use on hyperspectral data products. Supervised tensor factorizations already exist in the literature, although they have not seen widespread adoption in the remote sensing domain. The novelty of this project will be the formulation and inclusion of constraints that take into account mission considerations and physics based limits to learn a factorization that is both physically interpretable and mission deployable. This will represent a new contribution to the field of remote sensing for performing supervised learning tasks with hyperspectral data.
A transition to sustainable energy is among society’s greatest challenges. With this change, the development of more efficient, safe, and cost-effective batteries is also necessary. Batteries are becoming ever more integral to today’s technology rich world. 3 Intermittent power sources, such as wind and solar, require storage devices to deliver a consistent energy supply. More ecofriendly, electric vehicles, have limited travel distances due to low energy density batteries. Phones and other personal electronics are also reliant on battery performance. An encouraging improvement in specific energy from current, lithium (Li) ion batteries, which are already nearing the bounds of their performance potential, are lithium sulfur (Li-S) batteries.
This project investigates the behavior of a venting feature on a hermetically sealed volume under abnormal thermal environments. A pressure profile is used to simulate internal pressure build up due to thermal decomposition of foam caused by temperatures approaching 800K. For the purposes of his scoping study a small can composed of 304L stainless steel is used to emulate the problem at hand and simplify the model. The venting feature can be described as an elliptical thinned area with X-like scoring marks on the circumferential surface of the can where the highest stresses are located without the feature. The feature variables include elliptical size, depth, and quantity to optimize the venting feature geometry to vent at a desired pressure range. Cubit, SIERRA: Solid Mechanics, and Ensight modeling programs are used to simulate and analyze several iterations of this feature to determine the most optimal geometry. The model indicates through the simulations that the feature does in fact vent in a predictable manner and can be used in a variety of pressure vessel applications where uncontrolled over pressurizations are undesirable. Future work includes model validation tests, looking at different geometries, and using a more accurate failure criterion.
Researchers at Sandia National Laboratories have developed a high-fidelity virtual model of the human head, neck, and torso to investigate the details of life-threatening injury to the central nervous, respiratory, and cardiovascular systems as a result of blast exposure and behind-armor blunt trauma. This model set is comprised of separate head-neck and torso models that can be used independently or combined to investigate comprehensive injury to life-critical organs as a result of blast, blunt impact, and/or projectile penetration. The Sandia head-neck-torso model represents a 60th percentile human male from the waist up possessing anatomically correct distributions of bone, white and gray brain matter, falx & tentorium membranes, spinal cord, intervertebral disks, cartilage, vasculature, blood, airways, lungs, heart, liver, stomach, kidneys, spleen, muscle, and fat/skin.
As new memory technologies appear on the market, there is a growing push to incorporate them into future architectures. Compared to traditional DDR DRAM, these technologies provide appealing advantages such as increased bandwidth or non-volatility. However, the technologies have significant downsides as well including higher cost, manufacturing complexity, and for non-volatile memories, higher latency and wear-out limitations. As such, no technology has emerged as a clear technological and economic winner. As a result, systems are turning to the concept of multi-level memory, or mixing multiple memory technologies in a single system to balance cost, performance, and reliability.
Lithium batteries provide high energy density storage with applications ranging from consumer electronics to electric vehicles. However, they have a limited lifespan and experience capacity loss with aging. Multiple mechanisms contribute to battery aging. The battery binder plays two important roles in the electrodes, and the damage it sustains during cycling may play a role in the degradation of the overall battery performance. Mechanical stress during battery operations occurs as a result of the swelling and shrinking of the electrodes because of the movement of lithium with cycling. The yield stress of the swollen polyvinylidene fluoride carbon black (PVDFCB) binder was measured at approximately 4MPa for PVDF with carbon black CB weight fractions between 10-30% swollen in propylene carbonate. This is far less stress than is typically experienced in an electrode during cycling. The effects of this permanent damage to the binder were explored by measuring the conductivity loss with strains in excess of the binder yield.
This document outlines the gradient-based digital image correlation (DIC) formulation used in DICe, the Digital Image Correlation Engine (Sandia’s open source DIC code). The gradient-based algorithm implemented in DICe directly reflects the formulation presented here. Every effort is made to point out any simplifications or assumptions involved in the implementation. The focus of this document is on determination of the motion parameters. Computing strain is not discussed herein.
The ethical, legal, and social issues (ELSI) surrounding Artificial Intelligence (AI) can have as great of an impact on the technologies’ success as technical issues such as safety, reliability, and security. Addressing these risks can counter potential program failures, legal and ethical battles, constraints to scientific research, and product vulnerabilities. This paper presents a surety engineering framework and process that can be applied to AI to identify and address technical, ethical, legal and societal risks. Extending sound engineering practices to incorporate a method to “engineer” ELSI can offer the scientific rigor required to significantly reduce the risk of AI vulnerabilities. Modeling the specification, design, evaluation and quality/risk indicators for AI provides a foundation for a risk-informed decision process that can benefit researchers and stakeholders alike as they use it to critically examine both substantial and intangible risks.
The review was conducted on May 9-10, 2016 at the University of Utah. Overall the review team was impressed with the work presented and found that the CCMSC had met or exceeded the Year 2 milestones. Specific details, comments and recommendations are included in this document.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling (DOE 2011, Table 6). These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) work package, which is charged with developing a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media.
The concept of total-internal-reflection elastic metasurface (TIR-MS)was recently proposed [1]and employed within flexible planar waveguides in order to create highly subwavelength sound-hard barriers impenetrable to low frequency elastic waves. The underlying physical mechanism relies on the design of engineered interfaces exhibiting extreme phase gradients such that any incoming wave at, approximately, any incidence will experience total-internal-reflection conditions. At the design frequency, the metasurface exhibits a large phase gradient such that, in accordance with the generalized Snell's law, the first critical angle is virtually always exceeded. It is worth noting that in practical realizations, the actual total reflection performance might vary depending on the angle of incidence. This dependence is due to the discrete implementation of the metasurface which results in diffraction effects. This paper presents the results of an experimental study that explores the vibration isolation performance of TIR-MS when applied to structures made of complex combinations of different elastic waveguides (e.g. bolted assemblies of beams, plates, and shells). Such system can be seen as a prototypical structure emulating mechanical assemblies of practical interest for many engineering applications. Experimental results confirm that, when the TIR-MS is embedded in the host waveguide, significant vibration isolation capabilities are achieved under quasi-omnidirectional incidence and highly subwavelength excitation conditions (i.e. the ratio of the operating wavelength to the width of the TIR-MS is approximately 5.25). These experimental results suggest new interesting directions to achieve vibration isolation and mechanical energy filtering for practical engineering systems.
Silicone elastomer filled with glass micro balloons (GMB) is an elastomeric syntactic foam used in electronics and component packaging for encapsulation, potting, stress-relief layer, and electrical insulation purposes. Under mechanical loading, the reinforcing phase, namely the GMBs embedded in the elastomer matrix, may break or delaminate, leading to internal damage and macroscale stiffness degradation, which can alter the material's protective capacity against mechanical shock and vibration. The degree of damage is controlled by the loading history, delamination, and failure behavior of the GMBs. We investigate the GMB failure behavior in this work wherein we present an indentation experiment to measure the force required to fail individual GMBs that are either embedded in the elastomer matrix or adhered to the surface of an elastomer layer. The indentation apparatus is augmented with an inverted optical microscope to enable in situ imaging of the GMB. Failure modes for the embedded or non-embedded GMBs are discussed based on the morphology of the broken GMBs and the measured failure forces. We also measure the adhesion energy between the glass balloon and the elastomer, based on which the possibility of delamination between the GMB and the surrounding elastomer matrix during the failure process is evaluated. Our results can facilitate the development of a failure criterion of GMBs which is necessary for establishing a physics-based constitutive model to describe the macroscopic damage mechanics of elastomeric syntactic foams.
Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online.
Metallic alloys are extensively utilized in applications where extreme loading and environmental conditions occur and engineering reliability of components or structures made of such materials is a significant concern in applications. Adiabatic heating in these materials during high-rate deformation is of great interest to analysts, experimentalists, and modelers due to a reduction in strength that is produced. Capturing the thermosoftening caused by adiabatic heating is critical in material model development to precisely predict the dynamic response of materials and structures at high rates of loading. In addition to strain rate effect, the Johnson–Cook (JC) model includes a term to describe the effect of either environmental or adiabatic temperature rise. The standard expression of the JC model requires quantitative knowledge of temperature rise, but it can be challenging to obtain in situ temperature measurements, especially in dynamic experiments. The temperature rise can be calculated from plastic work with a predetermined Taylor-Quinney (TQ) coefficient. However, the TQ coefficient is difficult to determine since it may be strain and strain-rate dependent. In this study, we modified the JC model with a power-law strain rate effect and an explicit form of strain- and strain-rate-dependent thermosoftening due to adiabatic temperature rise to describe the strain-rate-dependent tensile stress–strain response, prior to the onset of necking, for 304L stainless steel, A572, and 4140 steels. The modified JC model was also used to describe the true stress–strain response during necking for A572 and 4140 steels at various strain rates. The results predicted with the modified JC model agreed with the tensile experimental data reasonably well.
Gate length dependent (80 nm–5000 mm) radio frequency measurements to extract saturation velocity are reported for Al0.85Ga0.15N/Al0.7Ga0.3N high electron mobility transistors fabricated into radio frequency devices using electron beam lithography. Direct current characterization revealed the threshold voltage shifting positively with increasing gate length, with devices changing from depletion mode to enhancement mode when the gate length was greater than or equal to 450 nm. Transconductance varied from 10 mS/mm to 25 mS/mm, with the 450 nm device having the highest values. Maximum drain current density was 268 mA/mm at 10 V gate bias. Scattering-parameter characterization revealed a maximum unity gain bandwidth (fT) of 28 GHz, achieved by the 80 nm gate length device. A saturation velocity value of 3.8 × 106 cm/s, or 35% of the maximum saturation velocity reported for GaN, was extracted from the fT measurements.
The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) ribonucleoprotein (RNP) complex is an RNA-guided DNA-nuclease that is part of the bacterial adaptive immune system. CRISPR/Cas9 RNP has been adapted for targeted genome editing within cells and whole organisms with new applications vastly outpacing detection and quantification of gene-editing reagents. Detection of the CRISPR/Cas9 RNP within biological samples is critical for assessing gene-editing reagent delivery efficiency, retention, persistence, and distribution within living organisms. Conventional detection methods are effective, yet the expense and lack of scalability for antibody-based affinity reagents limit these techniques for clinical and/or field settings. This necessitates the development of low cost, scalable CRISPR/Cas9 RNP affinity reagents as alternatives or augments to antibodies. Herein, we report the development of the Streptococcus pyogenes anti-CRISPR/Cas9 protein, AcrIIA4, as a novel affinity reagent. An engineered cysteine linker enables covalent immobilization of AcrIIA4 onto glassy carbon electrodes functionalized via aryl diazonium chemistry for detection of CRISPR/Cas9 RNP by electrochemical, fluorescent, and colorimetric methods. Electrochemical measurements achieve a detection of 280 pM RNP in reaction buffer and 8 nM RNP in biologically representative conditions. Our results demonstrate the ability of anti-CRISPR proteins to serve as robust, specific, flexible, and economical recognition elements in biosensing/quantification devices for CRISPR/Cas9 RNP.
A coarse-grained model previously used to simulate Nafion using dissipative particle dynamics (DPD) is modified to describe sulfonated Diels-Alder poly(phenylene) (SDAPP) polymers. The model includes a proton-hopping mechanism similar to the Grotthuss mechanism. The intramolecular parameters for SDAPP are derived from atomistic molecular dynamics (MD) simulation using the iterative Boltzmann inversion. The polymer radii of gyration, domain morphologies, and cluster distributions obtained from our DPD model are in good agreement with previous atomistic MD simulations. As found in the atomistic simulations, the DPD simulations predict that the SDAPP nanophase separates into hydrophobic polymer domains and hydrophilic domains that percolate through the system at sufficiently high sulfonation and hydration levels. Increasing sulfonation and/or hydration leads to larger proton and water diffusion constants, in agreement with experimental measurements in SDAPP. In the DPD simulations, the proton hopping (Grotthuss) mechanism becomes important as sulfonation and hydration increase, in qualitative agreement with experiment. The turning on of the hopping mechanism also roughly correlates with the point at which the DPD simulations exhibit clear percolated, hydrophilic domains, demonstrating the important effects of morphology on proton transport.
Sandia has a legacy of leadership in the advancement of high performance computing (HPC) at extreme scales. First-of-a-kind scalable distributed-memory parallel platforms such as the Intel Paragon, ASCI Red (the world’s first teraflops computer), and Red Storm (co-developed with Cray) helped form the basis for one of the most successful supercomputer product lines ever: the Cray XT series. Sandia also has pioneered system software elements—including lightweight operating systems, the Portals network programming interface, advanced interconnection network designs, and scalable I/O— that are critical to achieving scalability on large computing systems.
To achieve exascale computing, fundamental hardware architectures must change. The most significant consequence of this assertion is the impact on the scientific and engineering applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to exascale architectures, developers must be able to reason about new hardware and determine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware architects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of models that can help software developers prepare for exascale. In addition, through the use of proxy architectures, we can enable a more concrete exploration of how well new and evolving application codes map onto future architectures. This second version of the document addresses system scale considerations and provides a system-level abstract machine model with proxy architecture information.
A number of construction and demolition projects are planned for the 10-year period evaluated in this Sandia National Laboratories, New Mexico (SNL/NM) site-wide environmental impact statement (SWEIS). Construction and decommissioning and demolition (D&D) activities are continually being accomplished at SNL/NM as new facilities are brought on line to replace older, less efficient facilities. The construction and D&D projects presented in this appendix are projected to take place, assuming funding and administrative/regulatory approvals are obtained, under both the No Action Alternative and the Expanded Operations Alternative. Under the Reduced Operations Alternative, no new construction projects were assumed to take place, and D&D activities were assumed to be limited to those needed to maintain a safe operating environment on the site.
The Center for Integrated Nanotechnologies (CINT) is a Department of Energy/Office of Science Nanoscale Science Research Center (NSRC), operating as a national user facility devoted to establishing the scientific principles that govern nanoscale integration. Nanoscale integration is defined as assembling diverse nanoscale materials across length scales to design and achieve new properties and functionality. The CINT Theory and Simulation of Nanoscale Phenomena thrust is the component of CINT dedicated to developing and applying theory to enable nanoscale integration. Our focus is on understanding and simulating the unique behavior of integrated materials and systems with nanoscale structure. This mission is achieved through collaborations with CINT Users, between thrust scientists, and with CINT scientists from other thrusts. Our research is focused on three science directions that together form the basis for integration at the nanoscale, namely (i) Hierarchical structure and dynamics in soft matter, (ii) Excitation and Transport in Nanostructured Systems, and (iii) Emergent phenomena at surfaces and interfaces. A broad spectrum of techniques is developed and applied including continuum fluid theory, atomistic and coarse-grained molecular dynamics simulations, static and dynamic electronic structure calculations, multiscale modeling, low-energy effective Hamiltonian methods, and perturbative and exact quantum many-body approaches. These tools are applied to physical systems of interest to CINT Users, the other CINT thrusts, and the general scientific community with the goals of understanding and controlling the interactions between nanoscale building blocks to assemble specific integrated structures, controlling energy transfer and other interactions over multiple length scales, and designing and exploiting the interactions within assembled structures to achieve new materials functionality.
The opportunities presented by nanomaterials are exciting and broad, with revolutionary implications spanning energy technologies, electronics, computing, sensing capabilities and biomedical diagnostics. Deriving the ultimate benefit from these materials will require the controlled assembly of diverse nanoscale materials across multiple length scales to design and achieve new properties and functionality, in other words, nanomaterials integration.
Positive thermal expansion can cause significant stress or even catastrophic device failure in applications where materials are placed in confined environments. At material interfaces such as coatings, thermal expansion effects can also lead to cracking and peeling behavior. The ability to impart controlled thermal expansion properties in an array of designs via additive manufacturing technologies would mitigate such problems and bring significant value to various materials science and engineering challenges. Negative thermal expansion materials are of interest for composite material applications whereby they can compensate for the behavior of a positive thermal expansion matrix. This Truman Fellowship LDRD research project presents complimentary experimental and molecular modeling results towards the fundamental understanding and development of metal-organic framework (MOF) materials with controlled thermal expansion properties. Design strategies for imparting precisely tailored negative, zero, and positive thermal expansion regimes in MOFs are studied and the implications of these design strategies for the use of MOFs as an emergent negative thermal expansion material class are examined. Challenges towards exploiting this nanoscale behavior at length scales relevant to composite material systems are introduced. ACKNOWLEDGEMENTS I will be forever grateful to the Truman Fellowship Selection Committee for providing me with the opportunity to pursue this research project. The unique opportunity to carry out this exciting scientific research with all of the resources and support that Sandia has to offer has been a truly rewarding experience. I would like to especially thank Yolanda Moreno for her endless assistance throughout my time as a Truman Fellow. It is hard to imagine a more supportive and stimulating scientific environment to carry out this research. I would also like to thank all of my colleagues and the individuals that have collaborated with me throughout this Fellowship, both internal and external to Sandia. Many of you appear as co-authors on the publications resulting from this LDRD, including especially fruitful collaborations with researchers at the Georgia Institute of Technology and the University of Amsterdam. I cannot express enough my gratitude for the significant role that you all played in shaping my educational experience and the success during my time at Sandia as a Truman Fellow.
We will develop Malliavin estimators for Monte Carlo radiation transport by formulating the governing jump stochastic differential equation and deriving the applicable estimators that produce sensitivities for our equations. Efficient and effective sensitivity can be used for design optimization and uncertainty quantification with broad utilization for radiation environments. The technology demonstration will lower development risk for other particle-based simulation methods.
To modify existing Bruker connectors for the BCU-II cooler system to allow use with older style MAS probes where the cooling gas is the bearing gas. The use of the BCU will allow lower temperature MAS experiments without the need to use N2 liquid exchangers.
This document presents a Tribal Telecom/Internet Service Provider (ISP) internet protocol, version 6 (IPv6) business planning advisory. It includes examples of business areas, services, challenges, benefits, and a vision for the future.
Although its demise has been frequently predicted, the Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on high-performance computing (HPC) systems. MPI specifies powerful semantics for interprocess communication that have enabled scientists to write applications for simulating important physical phenomena. However, these semantics have also presented several significant challenges. For example, the existence of wildcard values has made the efficient enforcement of MPI message matching semantics challenging. Significant research has been dedicated to accelerating MPI message matching. One common approach has been to offload matching to dedicated hardware. One of the challenges that hardware designers have faced is knowing how to size hardware structures to accommodate outstanding match requests. Applications that exceed the capacity of specialized hardware typically must fall back to storing match requests in bulk memory, e.g. DRAM on the host processor. In this paper, we examine the implications of hardware matching and develop guidance on sizing hardware matching structure to strike a balance between minimizing expensive dedicated hardware resources and overall matching performance. By examining the message matching behavior of several important HPC workloads, we show that when specialized hardware matching is not dramatically faster than matching in memory the offload hardware's match queue capacity can be reduced without significantly increasing match time. On the other hand, effectively exploiting the benefits of very fast specialized matching hardware requires sufficient storage resources to ensure that every search completes in the specialized hardware. The data and analysis in this paper provide important guidance for designers of MPI message matching hardware.
Most experimental setups and environment specifications define acceleration loads on the component. However, Sierra Structural Dynamics cannot apply acceleration boundary conditions in modal transient analysis. Modal analysis of these systems and environments must be done through the application of a huge artificial force to a large fictitious point mass. Introducing a large mass into the analysis is a common source of numerical error. In this report we detail a mathematical procedure to directly apply acceleration boundary conditions in modal analyses without the requirement of adding a non-physical mass to the system. We prototype and demonstrate this procedure in Matlab and scope the work required to integrate this procedure into Sierra Structural Dynamics.
This manuscript comprises the final report for the 1-year, FY19 LDRD project "Rigorous Data Fusion for Computationally Expensive Simulations," wherein an alternative approach to Bayesian calibration was developed based a new sampling technique called VoroSpokes. Vorospokes is a novel quadrature and sampling framework defined with respect to Voronoi tessellations of bounded domains in $R^d$ developed within this project. In this work, we first establish local quadrature and sampling results on convex polytopes using randomly directed rays, or spokes, to approximate the quantities of interest for a specified target function. A theoretical justification for both procedures is provided along with empirical results demonstrating the unbiased convergence in the resulting estimates/samples. The local quadrature and sampling procedures are then extended to global procedures defined on more general domains by applying the local results to the cells of a Voronoi tessellation covering the domain in consideration. We then demonstrate how the proposed global sampling procedure can be used to define a natural framework for adaptively constructing Voronoi Piecewise Surrogate (VPS) approximations based on local error estimates. Finally, we show that the adaptive VPS procedure can be used to form a surrogate model approximation to a specified, potentially unnormalized, density function, and that the global sampling procedure can be used to efficiently draw independent samples from the surrogate density in parallel. The performance of the resulting VoroSpokes sampling framework is assessed on a collection of Bayesian inference problems and is shown to provide highly accurate posterior predictions which align with the results obtained using traditional methods such as Gibbs sampling and random-walk Markov Chain Monte Carlo (MCMC). Importantly, the proposed framework provides a foundation for performing Bayesian inference tasks which is entirely independent from the theory of Markov chains.
As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation. The increasing complexity of quantum dot devices necessitates the advancement of automated control software and image recognition techniques for rapidly evaluating charge stability diagrams. We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention. Moreover, we show that straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion. Finally, we adopt the use of a "tunnel coupling lever arm" to model the interdot barrier gate response and discuss how it can be used to more rapidly tune interdot tunnel couplings to the gigahertz values that are compatible with exchange gates.
This milestone was created to ensure the Sandia FOUS program has the needed levels of project direction, programmatic information and an escalation path (if needed) for their role within the deployment and operations of the Astra cluster.
The current standard Bayesian approach to model calibration, which assigns a Gaussian process prior to the discrepancy term, often suffers from issues of unidentifiability and computational complexity and instability. When the goal is to quantify uncertainty in physical parameters for extrapolative prediction, then there is no need to perform inference on the discrepancy term. With this in mind, we introduce Gibbs posteriors as an alternative Bayesian method for model calibration, which updates the prior with a loss function connecting the data to the parameter. The target of inference is the physical parameter value which minimizes the expected loss. We propose to tune the loss scale of the Gibbs posterior to maintain nominal frequentist coverage under assumptions of the form of model discrepancy, and present a bootstrap implementation for approximating coverage rates. Our approach is highly modular, allowing an analyst to easily encode a wide variety of such assumptions. Furthermore, we provide a principled method of combining posteriors calculated from data subsets. We apply our methods to data from an experiment measuring the material properties of tantalum.
To identify the critical issues that affect the evolution of microstructure during additive manufacturing, we investigated the influence of process parameters on the evolution of the dimensional and surface quality, microstructure, internal defects, and mechanical properties in 316L stainless steel (SS) components fabricated using laser engineered net shaping (LENS®), a directed energy deposition (DED) additive manufacturing (AM) technique. The results show that the accumulation of un-melted powder particles on the side walls of deposited sections can be avoided by selecting a laser under-focused condition. Moreover, we report that the variation of melt pool width is more sensitive to laser power than to the depth of the melt pool. The formation of a so-called “hierarchical” microstructure with cellular morphology is attributable to a combination of layer deposition and rapid solidification, which are characteristics of AM. Finally, we discuss microstructure evolution and defect formation, particularly the formation of multiple interfaces and the presence of un-melted powder particles and pores, in light of the dynamic convective fluid flow and rapid solidification that occur in the melt pool. X-ray computed tomography (X-CT) was used to precisely map the spatial distribution of pores in the DED components. The evolution of microstructure during DED is discussed in the context of related thermal phenomena in an effort to provide fundamental insight into the mechanisms that govern defect formation.
Background: The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Results: Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. Conclusions: The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.
This report documents the completion of milestone STPM12-17 Kokkos Training Bootcamp. The goal of this milestone was to hold a combined tutorial and hackathon bootcamp event for the Kokkos community and prospective users. The Kokkos Bootcamp event was held at Argonne National Laboratories from August 27 — August 29, 2019. Attendance being lower than expected (we believe largely due to bad timing), the team focused with a select set of ECP partners on early work in preparation for Aurora. In particular we evaluated issues posed by exposing SYCL and OpenMP target offload to applications via the Kokkos Pro Model.
Here we report molecular level details regarding the adsorption of sarin (GB) gas in a prototypical zirconium-based metal-organic framework (MOF, UiO-66). By combining predictive modeling and experimental spectroscopic techniques, we unambiguously identify several unique bindings sites within the MOF, using the P=O stretch frequency of GB as a probe. Remarkable agreement between predicted and experimental IR spectrum is demonstrated. As previously hypothesized, the undercoordinated Lewis acid metal site is the most favorable binding site. Yet multiple sites participate in the adsorption process; specifically, the Zr-chelated hydroxyl groups form hydrogen bonds with the GB molecule, and GB weakly interacts with fully coordinated metals. Importantly, this work highlights that subtle orientational effects of bound GB are observable via shifts in characteristic vibrational modes; this finding has large implications for degradation rates and opens a new route for future materials design.
Trask, Nathaniel; Patel, Ravi; Gross, Ben; Atzberger, Paul
Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a nonparametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications.
Watson, Jean-Paul; Goujard, Guillaume; Woodruff, David L.
We describe algorithms for creating probabilistic scenarios for the situation when the underlying forecast methodology is modeled as being more (or less) accurate than it has been historically. Such scenarios can be used in studies that extend into the future and may need to consider the possibility that forecast technology will improve. Our approach can also be used to generate alternative realizations of renewable energy production that are consistent with historical forecast accuracy, in effect serving as a method for creating families of realistic alternatives — which are often critical in simulation-based analysis methodologies.
Porwitzky, Andrew; Hutsel, Brian T.; Seagle, Christopher T.; Ao, Tommy; Grant, Sean; Bernstein, Aaron; Lin, Jung F.; Ditmire, Todd
Interest in studying power flow dynamics has grown in recent years, with new power flow diagnostics being developed at Sandia National Laboratories for the Z Pulsed Power Facility. Presently, the only power flow loads that have been studied are cylindrical static or imploding loads that are driven by synchronous short pulse (100 ns rise time). Presented is a design that utilizes the dynamic materials properties program's stripline geometry in a high voltage pulsed shaped (asymmetric asynchronous) driving mode. This design has exhibited repeatable current loss with a large time-varying inductance that is well matched to the machine at pulse initialization but which triples to high inductance in 800 ns. Evidence is presented that plasma not captured in the magnetohydrodynamic approximation and ill represented by any of our existing predictive pulsed power codes is adversely affecting load current delivery. The authors believe this design could be of great interest to the experimental and modeling communities for studying power flow dynamics.
Davis, Ryan W.; Liu, Fang; Derose, Katherine; Simmons, Blake A.; Quinn, Jason C.
Distiller's grains are a byproduct of corn ethanol production and provide an opportunity for increasing the economic viability and sustainability of the overall grain-to-fuels process. Typically, these grains are dried and sold as a ruminant feed adjunct. This study considers utilization of the residuals in a novel supplementary fermentation process to produce two products, enriched protein and fusel alcohols. The value-added proposition and environmental impact of this second fermentation step for distiller's grains are evaluated by considering three different processing scenarios. Techno-economic results show the minimum protein selling price, assuming fusel alcohol products are valued at $0.79 per liter gasoline equivalent, ranges between $1.65-$2.48 kg protein-1 for the different cases. Environmental impacts of the systems were evaluated through life cycle assessment. Results show a baseline emission results of 17 g CO2-eq (MJ fuel)-1 for the fuel product and 10.3 kg CO2-eq kg protein-1 for the protein product. Sensitivity to allocation methods show a dramatic impact with results ranging between -8 to 140 g CO2-eq (MJ fuel)-1 for the fuel product and -0.3 to 6.4 kg CO2-eq kg protein-1 for the protein product. The discussion is focused on the potential impact of the technology on corn ethanol production economics and sustainability.
The Department of Energy(DOE), Office of Nuclear Energy (NE), Spent Fuel and Waste Science and Technology (SFWST) program is performing research and development in the area of commercial spent nuclear fuel (SNF) long term storage and transportation. This program is being conducted under the provisions of the Nuclear Waste Policy Act (NWPA) of 1982 and its amendments that require the DOE to take title to and manage SNF after storage at the utility reactor site. This report is a condensed version of previous gap reports (Hanson 2012 and Hanson 2019) with up-dated gap priority assessments. The gap priorities have been updated from Hanson 2019 because 2019 is based on R&D performed through 2017. Much important work has been done since 2017 that requires a change in a few of the priority rankings to better focus the near-term R&D program. Background material, regulatory positions, operational and inventory status, and prioritization schemes are discussed in detail in Hanson 2012/2019, and are not repeated in this report. One exception is an overview of the prioritization criteria for reference. This is meant to give the reader an appreciation of the framework for prioritization of the identified gaps. A complete discussion of the prioritization scheme is provided in Hanson 2019.
Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of massive amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.
Inertial confinement fusion facilities generate implosions at speeds greater than 100 km/s, and measuring the material velocities is important and challenging. We have developed a new velocimetry technique that uses time-stretched spectral interferometry to increase the measurable velocity range normally limited by the detector bandwidth. In this approach, the signal is encoded on a chirped laser pulse that is stretched in time to reduce the beat frequency before detection. We demonstrate the technique on an imploding liner experiment at the Sandia National Laboratories’ Z machine, where beat frequencies in excess of 50 GHz were measured with 20 GHz bandwidth detection.
The authors exposed a radiatively cooled, Li-filled tantalum (Ta) heat pipe (HP) to a H plasma in Magnum PSI continuously for ˜2 h. We kept the overall heat load on the inclined HP constant and varied the tilt to give peak heat fluxes of ˜7.5–13 MW/m2. The peak temperature reached ˜1250 °C. This paper describes the post-test analysis and discusses Li HPs with materials other than Ta for fusion. A companion paper describes the experiment.
Extensive deployment of interoperable distributed energy resources (DER) is increasing the power system cyber security attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid-support functions, which can drastically influence power quality, voltage, and bulk system frequency. Here, the authors investigate the impact to the cyber-physical power system in scenarios where communications and operations of DER are controlled by an adversary. The findings show that each grid-support function exposes the power system to distinct types and magnitudes of risk. The physical impact from cyber actions was analysed in cases of DER providing distribution system voltage regulation and transmission system support. Finally, recommendations are presented for minimising the risk using engineered parameter limits and segmenting the control network to minimise common-mode vulnerabilities.
We consider a joint-chance constraint (JCC) as a union of sets, and approximate this union using bounds from classical probability theory. When these bounds are used in an optimization model constrained by the JCC, we obtain corresponding upper and lower bounds on the optimal objective function value. We compare the strength of these bounds against each other under two different sampling schemes, and observe that a larger correlation between the uncertainties tends to result in more computationally challenging optimization models. We also observe the same set of inequalities to provide the tightest upper and lower bounds in our computational experiments.
The non-classical linear Boltzmann equation (NCLBE) is a recently developed framework based on non-classical transport theory for modeling the expected value of particle flux in an arbitrary stochastic medium. Provided with a non-classical cross-section for a given statistical description of a medium, any transport problem in that medium may be solved. Previous work has been limited in the types of material variability considered and has not explicitly introduced finite boundaries and sources. In this work the solution approach for the NCLBE in multidimensional media with finite boundaries is outlined. The discrete ordinates method with an implicit discretization of the pathlength variable is used to leverage sweeping methods for the transport operator. In addition, several convenient approximations for non-classical cross-sections are introduced based on existing theories of stochastic media. The solution approach is verified against random realizations of a Gaussian process medium in a square enclosure.
Variability in the predicted cost of energy of an ocean energy converter array is more substantial than for other forms of energy generation, due to the combined stochastic action of weather conditions and failures. If the variability is great enough, then this may influence future financial decisions. This paper provides the unique contribution of quantifying variability in the predicted cost of energy and introduces a framework for investigating reduction of variability through investment in components. Following review of existing methodologies for parametric analysis of ocean energy array design, the development of the DTOcean software tool is presented. DTOcean can quantify variability by simulating the design, deployment and operation of arrays with higher complexity than previous models, designing sub-systems at component level. A case study of a theoretical floating wave energy converter array is used to demonstrate that the variability in levelised cost of energy (LCOE) can be greatest for the smallest arrays and that investment in improved component reliability can reduce both the variability and most likely value of LCOE. A hypothetical study of improved electrical cables and connectors shows reductions in LCOE up to 2.51% and reductions in the variability of LCOE of over 50%; these minima occur for different combinations of components.
Applications at the intersection of quantum and EM physics are becoming more prevalent in the engineering community. Interestingly, many of these applications require solving purely classical EM problems to characterize the most important dynamics of the system. As a result, computational electromagnetics (CEM) can play a vital role in this new area. However, the classical problems that typically need to be solved are the broadband analysis of near-field scattering problems in complicated regions with multiscale and/or subwavelength features. Recently, potential-based time domain integral equations (TDIEs) have been investigated to solve these traditionally challenging CEM problems [1], [2]. However, for these methods to be applicable, they must be robustly stable when analyzing complicated geometries over broad bandwidths.
In this work we present a performance exploration on Eager K-truss, a linear-algebraic formulation of the K-truss graph algorithm. We address performance issues related to load imbalance of parallel tasks in symmetric, triangular graphs by presenting a fine-grained parallel approach to executing the support computation. This approach also increases available parallelism, making it amenable to GPU execution. We demonstrate our fine-grained parallel approach using implementations in Kokkos and evaluate them on an Intel Skylake CPU and an Nvidia Tesla V100 GPU. Overall, we observe between a 1.261. 48x improvement on the CPU and a 9.97-16.92x improvement on the GPU due to our fine-grained parallel formulation.
Over the last decade, hardware advances have led to the feasibility of training and inference for very large deep neural networks. Sparsified deep neural networks (DNNs) can greatly reduce memory costs and increase throughput of standard DNNs, if loss of accuracy can be controlled. The IEEE HPEC Sparse Deep Neural Network Graph Challenge serves as a testbed for algorithmic and implementation advances to maximize computational performance of sparse deep neural networks. We base our sparse network for DNNs, KK-SpDNN, on the sparse linear algebra kernels within the Kokkos Kernels library. Using the sparse matrix-matrix multiplication in Kokkos Kernels allows us to reuse a highly optimized kernel. We focus on reducing the single node and multi-node runtimes for 12 sparse networks. We test KK-SpDNN on Intel Skylake and Knights Landing architectures and see 120-500x improvement on single node performance over the serial reference implementation. We run in data-parallel mode with MPI to further speed up network inference, ultimately obtaining an edge processing rate of 1.16e+12 on 20 Skylake nodes. This translates to a 13x speed up on 20 nodes compared to our highly optimized multithreaded implementation on a single Skylake node.
We present the detector response comparison between a 10x10 pixellated array of scintillator read out with Anger logic using four 2" Hamamatsu R7724-100 super bialkali photomultiplier tubes (PMT) and a custom Silicon photomultiplier (SiPM) board consisting of 100 C-series 6x6 mm SiPMs from SensL. An array of these pixellated detectors are currently used in the Neutron Coded Aperture (NCA) imaging system. The energy, timing and pulse shape discrimination response using both readout schemes are presented, along with an analysis of multiple scatter events occurring within the block. An evaluation of the impact of photodetector readout on the overall detection efficiency and imaging accuracy is presented.
Network security researchers often rely on EmulyticsTM to provide a way to evaluate the safety and security of real world systems. This work involves running a large number of virtual machines on a distributed platform to observe how software and hardware will respond to different types of attacks. While EmulyticsTM software such as minimega provide a scalable system for conducting experiments, the sheer volume of network traffic produced in an experiment can easily exceed the rate at which data can be recorded for offline analysis. As such, researchers must perform live analytics, narrow their monitoring scope or accept that they must run an experiment multiple times to capture all the information they require. In support of Sandia's commitment to EmulyticsTM, we are developing new storage components for the Carlin cluster that will enable researchers to capture significantly more network traffic from their experiments. This report provides a summary of Haoda Wang's initial investigation of how new AMD Epyc storage nodes can be adapted to perform packet capture at 100Gbps speeds with minimal loss. This work found that the NVMe storage capabilities of the Epyc architecture are suitable for capturing 100Gbps Ethernet traffic. While capturing traffic with existing libraries was surprisingly challenging, we were able to develop a DPDK-based software tool that recorded network traffic to disk with minimal packet loss.
Under high-rate loading in tension, metals can sustain much larger tensile stresses for sub-microsecond time periods than would be possible under quasi-static conditions. This type of failure, known as spall, is not adequately reproduced by hydrocodes with commonly used failure models. The Spall Kinetics Model treats spall by incorporating a time scale into the process of failure. Under sufficiently strong tensile states of stress, damage accumulates over this time scale, which can be thought of as an incubation time. The time scale depends on the previous loading history of the material, reflecting possible damage by a shock wave. The model acts by modifying the hydrostatic pressure that is predicted by any equation of state and is therefore simple to implement. Examples illustrate the ability of the model to reproduce the spall stress and resulting release waves in plate impact experiments on stainless steel.
Methods are proposed to measure the sensitivity of utility or value function to the variations of attribute values for Multi-Criteria Decision Analyses that are based on functions that cannot be expressed as a weighted sum of the attribute values. These measures, called factor Influence Metrics, can be used to examine the characteristics of the option scoring algorithm and help verify the algorithm is consistent with decision makers value structure and processes.
Advances in HEDP research and limitations in currently established theories and models challenge us to better understand how high-intensity (>1014 W/cm2) laser light couples to plasmas. Considering both energy transfer processes and the resulting particle-beam and radiation generation, we find one overarching question: How do we understand, predict, and control laser-plasma interactions and laser-driven particle beams?
Models and experiments are developed to investigate how a small amount of gas can cause large rectified motion of a piston in a vibrated liquid-filled housing when piston drag depends on piston position so that damping is nonlinear even for viscous flow. Two bellows serve as surrogates for the upper and lower gas regions maintained by Bjerknes forces. Without the bellows, piston motion is highly damped. With the bellows, the piston, the liquid, and the two bellows move together so that almost no liquid is forced through the gaps between the piston and the housing. This Couette mode has low damping and a strong resonance: the piston and the liquid vibrate against the spring formed by the two bellows (like the pneumatic spring formed by the gas regions). Near this resonance, the piston motion becomes large, and the nonlinear damping produces a large rectified force that pushes the piston downward against its spring suspension. A recently developed model based on quasi-steady Stokes flow is applied to this system. A drift model is developed from the full model and used to determine the equilibrium piston position as a function of vibration amplitude and frequency. Corresponding experiments are performed for two different systems. In the two-spring system, the piston is suspended against gravity between upper and lower springs. In the spring-stop system, the piston is pushed up against a stop by a lower spring. Model and experimental results agree closely for both systems and for different bellows properties.
This report analyzes data from multi-arm caliper (MAC) surveys taken at the Bayou Choctaw 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, several 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.
Many geologic materials and minerals are seismically anisotropic, with the most general anisotropic material having up to 21 independent elastic coefficients. This report outlines the development of a 3-D, generally anisotropic, linear elastic full waveform finite-difference solver. First, a mathematical description of the solution equations will be described. The finite-difference implementation of these equations will then be shown. Finally, a comparison of results from this new solver to other solutions will be provided as verification that the new algorithm can accurately replicate these solutions.
ParelastiFWl is a python-based frontend to the seismic full waveform inversion process using Sandia Geophysics Department's 3-D isotropic elastic full waveform simulation code, Parelasti. The arguments one provides to ParelastiFWl guide the full waveform inversion process, including resolution of the inversion grid and basic regularization. This report outlines the user flags and ParelastiFWI usage to control the full waveform inversion procedure.
Sandia National Laboratories, California (SNL/CA) is a Department of Energy (DOE) facility. The management and operations of the facility are under a contract with the DOE's National Nuclear Security Administration (NNSA). On May 1, 2017, the name of the management and operating contractor changed from Sandia Corporation to National Technology & Engineering Solutions of Sandia, LLC (NTESS). The DOE, NNSA, Sandia Field Office administers the contract and oversees contractor operations at the site. DOE and its management and operating contractor for Sandia are committed to safeguarding environmental protection, compliance, and sustainability and to ensuring the validity and accuracy of the monitoring data presented in this Annual Site Environmental Report. This Site Environmental Report for 2018 was prepared in accordance with DOE Order 231.1 B, Environment, Safety and Health Reporting (DOE 2012). The report provides a summary of environmental monitoring information and compliance activities that occurred at SNL/CA during calendar year 2018, unless noted otherwise. General site and environmental program information is also included.