Two blocks of alluvium were extensively tested at the Sandia National Laboratories Geomechanics laboratory. The alluvium blocks are intended to serve as surrogate material for mechanical property determinations to support the SPE DAG experimental series. From constant mean stress triaxial testing, strength failure envelopes were parameterized and are presented for each block. Modulus and stress relationships are given including bulk modulus versus mean stress, shear modulus versus shear stress, Young's modulus versus axial stress and Poisson's ratio versus axial stress. In addition, P-&S-wave velocities, and porosity, determined using helium porosimetry, were obtained on each block. Generally, both Young's modulus and Poisson's ratio increase with increasing axial stress, bulk modulus increases with increasing pressure, and increases more dramatically upon pore crush, shear modulus decreases with increasing shear stress and then appears to plateau. The Unconfined Compressive Strength for the BM is in the range of 0.5-0.6, and for SM in the range of 2.0-2.6 MPa. The confined compressive strength increases with increasing confining pressure, and the BM alluvium is significantly weaker compared to SM alluvium for mean stress levels above 8 MPa.
Motivated by the need for improved forward modeling and inversion capabilities of geophysical response in geologic settings whose fine--scale features demand accountability, this project describes two novel approaches which advance the current state of the art. First is a hierarchical material properties representation for finite element analysis whereby material properties can be prescribed on volumetric elements, in addition to their facets and edges. Hence, thin or fine--scaled features can be economically represented by small numbers of connected edges or facets, rather than 10's of millions of very small volumetric elements. Examples of this approach are drawn from oilfield and near--surface geophysics where, for example, electrostatic response of metallic infastructure or fracture swarms is easily calculable on a laptop computer with an estimated reduction in resource allocation by 4 orders of magnitude over traditional methods. Second is a first-ever solution method for the space--fractional Helmholtz equation in geophysical electromagnetics, accompanied by newly--found magnetotelluric evidence supporting a fractional calculus representation of multi-scale geomaterials. Whereas these two achievements are significant in themselves, a clear understanding the intermediate length scale where these two endmember viewpoints must converge remains unresolved and is a natural direction for future research. Additionally, an explicit mapping from a known multi-scale geomaterial model to its equivalent fractional calculus representation proved beyond the scope of the present research and, similarly, remains fertile ground for future exploration.
The research has 2 thrusts: 1. new data architectures and tech-transfer-ready prototype tools that use write optimized data structures (WODS) to track real-world events, provide value to analysts, and support our cyber missions; and 2. algorithm research to address infinite streams of data, including expiration and sustainability.
This user’s guide documents capabilities in Sierra/SolidMechanics which remain “in-development” and thus are not tested and hardened to the standards of capabilities listed in Sierra/SM 4.54 User’s Guide. Capabilities documented herein are available in Sierra/SM for experimental use only until their official release. These capabilities include, but are not limited to, novel discretization approaches such as peridynamics and the reproducing kernel particle method (RKPM), numerical fracture and failure modeling aids such as the extended finite element method (XFEM) and /-integral, explicit time step control techniques, dynamic mesh rebalancing, as well as a variety of new material models and finite element formulations.
The objective of the Optimized Carbon Fiber project is to assess the commercial viability to develop cost-competitive wind-specific carbon fiber composites to enable larger rotors for increased energy capture. Although glass fiber reinforcement is the primary structural material in wind blade manufacturing, utilization of carbon fiber has been identified as a key enabler for achieving larger rotors because of its higher specific stiffness (stiffness per unit mass), specific strength (strength per unit mass), and fatigue resistance in comparison to glass. This report contains the testing process and results from the mechanical characterization portion of the project. Low-cost textile carbon fiber materials are tested along with a baseline, commercial carbon fiber system common to the wind industry. Material comparisons are made across coupons of similar manufacturing and quality to assess the properties of the novel carbon fibers.
ATS platforms are some of the largest, most complex, and most expensive computer systems installed in the United States at just a few major national laboratories. This milestone describes our recent efforts to procure, install, and test a machine called Vortex at Sandia National Laboratories that is compatible with the larger ATS platform Sierra at LLNL. In this milestone, we have 1) configured and procured a machine with similar hardware characteristics as Sierra ATS, 2) installed the machine, verified its physical hardware, and measured its baseline performance, and 3) demonstrated the machine's compatibility with Sierra ATS, and capacity for useful development and testing of Sandia computer codes (such as SPARC), including uses such as nightly regression testing workloads.
Additive manufacturing (AM) of metal parts can save time, energy, and produce parts that cannot otherwise be made with traditional machining methods. Near final part geometry is the goal for AM, but material microstructures are inherently different from those of wrought materials as they arise from a complex temperature history associated with the additive process. It is well known that strength and other properties of interest in engineering design follow from microstructure and temperature history. Because of complex microstructure morphologies and spatial heterogeneities, properties are heterogeneous and reflect underlying microstructure. This report describes a method for distributing properties across a finite element mesh so that effects of complex heterogeneous microstructures arising from additive manufacturing can be systematically incorporated into engineering scale calculations without the need for conducting a nearly impossible and time consuming effort of meshing material details. Furthermore, the method reflects the inherent variability in AM materials by making use of kinetic Monte Carlo calculations to model the AM process associated with a build.
Low frequency sound ≤ 20 Hz, known as infrasound, is generated by a variety of natural and anthropogenic sources. Following an event, infrasonic waves travel through a dynamic atmosphere that can change on the order of minutes. This makes infrasound event classification a difficult problem as waveforms from the same source type can look drastically different. Event classification usually requires ground truth information from seismic or other methods. This is time consuming, inefficient, and does not allow for a classification if the event locates somewhere other than a known source, the location accuracy is poor, or ground truth from seismic data is lacking. Here we compare the performance of the state of the art for infrasound event classification, support vector machine (SVM), to the performance of a convolutional neural network (CNN), a method that has been proven in tangential fields such as seismology. For a 1-class catalog consisting of only volcanic activity and earthquake events, the 4-fold average SVM classification accuracy is 75%, while it is 74% when using a CNN. Classification accuracies from the 4-class catalog consisting of the most common infrasound events detected at the global scale are 55% and 56% for the SVM and CNN architectures, respectively. These results demonstrate that using a CNN does not increase performance for infrasound event classification. This suggests that SVM should be the preferred classification method as it is a simpler and more trustworthy architecture and can be tied to the physical properties of the waveforms. The SVM and CNN algorithms described in this paper are not yet generalizable to other infrasound event catalogs. We anticipate this study to be a starting point for the development of large and comprehensive, systematically labeled, infrasound event catalogs as such catalogs will be necessary to provide an increase in the value of deep learning on event classification.
This report includes energy storage policy analysis from six states: Arizona, California, Massachusetts, Nevada, New Mexico, and New York. These summaries offer prototypes for summaries that will subsequently be prepared for all 50 states (and territories). There is presently a shortage of comprehensive energy storage policy analysis that public utility regulators can call upon to inform policymaking in their own jurisdictions. The state policy summaries that will be offered publicly on the Global Energy Storage Database (GESDB) will include analysis on the executive directives, legislation, regulations pertaining to energy storage that have been adopted by an individual state, along with perspective on the remaining policy issues pertaining to storage that a state will be likely to address in the future. It is anticipated that public utility regulators in particular will find the database to be a useful resource in benchmarking policy approaches critical to the continued development of an energy storage marketplace in the U.S., including policy approaches specific to storage and renewables procurement targets, interconnection standards, valuation of energy storage, rate reform and tariff design specific to energy storage, consideration of multiple uses for storage at the distribution level, and potential revisions to existing state net metering programs to accommodate an expected growth of energy storage technologies.
Sandia National Laboratories has tested and evaluated a new digitizer, the Affinity, manufactured by Guralp Systems. This digitizer is used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as sensitivity, self-noise, dynamic range, system noise, relative transfer function, analog bandwidth, harmonic distortion, common mode, cross talk, timing tag accuracy and timing drift. The Affinity provides eight, rather the typical six, channels of 24 bit high sample rate digitization. Moreover, it also provides 16 single-ended, 24 bit resolution, low sample rate auxiliary channels. The Affinity digitizer is undergoing these tests prior to installation in the FACT site infrasound test bed.
The Scanning Ultrafast Electron Microscope (SUEM) 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. 8942. 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.
We reformulate fundamental numerical problems to run on novel hardware inspired by the brain. Such "neuromorphie hardware consumes less energy per computation, promising a means to augment next-generation exascale computers. However, their programming model is radically different from floating-point machines, with fewer guarantees about precision and communication. The approach is to pass each given problem through a sequence of transformations (algorithmic "reductions") which change it from conventional form into a dynamical system, then ultimately into a spiking neural network. Results for the eigenvalue problem are presented, showing that the dynamical system formulation is feasible.
This report summarizes the work performed under a three year LDRD project aiming to develop mathematical and software foundations for compatible meshfree and particle discretizations. We review major technical accomplishments and project metrics such as publications, conference and colloquia presentations and organization of special sessions and minisimposia. The report concludes with a brief summary of ongoing projects and collaborations that utilize the products of this work.
ATS platforms are some of the largest, most complex, and most expensive computer systems installed in the United States at just a few major national laboratories. This milestone describes our recent efforts to procure, install, and test a machine called Vortex at Sandia National Laboratories that is compatible with the larger ATS platform Sierra at LLNL. In this milestone, we have 1) configured and procured a machine with similar hardware characteristics as Sierra ATS, 2) installed the machine, verified its physical hardware, and measured its baseline performance, and 3) demonstrated the machine’s compatibility with Sierra ATS, and capacity for useful development and testing of Sandia computer codes (such as SPARC), including uses such as nightly regression testing workloads.
This project has developed models of variability of performance to enable robust design and certification. Material variability originating from microstructure has significant effects on component behavior and creates uncertainty in material response. The outcomes of this project are uncertainty quantification (UQ) enabled analysis of material variability effects on performance and methods to evaluate the consequences of microstructural variability on material response in general. Material variability originating from heterogeneous microstructural features, such as grain and pore morphologies, has significant effects on component behavior and creates uncertainty around performance. Current engineering material models typically do not incorporate microstructural variability explicitly, rather functional forms are chosen based on intuition and parameters are selected to reflect mean behavior. Conversely, mesoscale models that capture the microstructural physics, and inherent variability, are impractical to utilize at the engineering scale. Therefore, current efforts ignore physical characteristics of systems that may be the predominant factors for quantifying system reliability. To address this gap we have developed explicit connections between models of microstructural variability and component/system performance. Our focus on variability of mechanical response due to grain and pore distributions enabled us to fully probe these influences on performance and develop a methodology to propagate input variability to output performance. This project is at the forefront of data-science and material modeling. We adapted and innovated from progressive techniques in machine learning and uncertainty quantification to develop a new, physically-based methodology to address the core issues of the Engineering Materials Reliability (EMR) research challenge in modeling constitutive response of materials with significant inherent variability and length-scales.
Sandia National Laboratories has tested and evaluated various infrasound shrouds, produced Doug Seastrand and Gary Walker, DOE staff in Las Vegas, Nevada, intended for a Hyperion 5201W digital infrasound sensor. The purpose of these shrouds is to improve the infrasound sensor’s attenuation of incoherent signals, specifically those caused by wind passing by the sensor. The purpose of the shroud evaluation was to measure amplitude and phase of the sensors with a variety of shroud designs attached and determine whether there is any appreciable changes in amplitude and/or phase response over the IMS passband for infrasound applications, 0.02 Hz to 4.0 Hz. These shrouds utilize a tubule design that directs and mixes airflow from ports radially distributed at approximately 90 degree offsets in an attempt to minimize wind-generated signal due to Bernoulli effect caused by airflow passing by ports perpendicular to the wind.