Distributed Acoustic Sensing (DAS) is a rapidly developing technology that can record acoustic wavefields at high sampling rates and with dense spatial spacing difficult to achieve with seismometers. However, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. A better quantitative understanding of DAS array behavior prior to SPE Phase III acquisition can help improve the quality of the data recorded by guiding the DAS array design. Here we use array response functions as well as beamforming and backprojection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A seven-sided polygon DAS array demonstrated improved capabilities for recording regional sources over segmented linear arrays, with potential improvements in recording and locating local sources. These results help reveal DAS array performance as a function of geometry.
We demonstrate the discrimination of ground-state hyperfine manifolds of a cesium atom in an optical tweezer using a simple probe beam with Formula Presented% detection fidelity and 0.9(2)% detection-driven loss of bright-state atoms. Our detection infidelity of Formula Presented% is an order of magnitude better than previously published low-loss readout results for alkali-metal atoms in optical tweezers. We achieve these results by identifying and mitigating an extra depumping mechanism due to stimulated Raman transitions induced by trap light in the presence of probe light. In this work, complex optical systems and stringent vacuum pressures are not required, enabling straightforward adoption of our techniques on contemporary experiments.
Quantum point contacts (QPC) are the building blocks of quantum dot qubits and semiconducting quantum electrical metrology circuits. QPCs also make highly sensitive electrical amplifiers with the potential to operate in the quantum-limited regime. Though the inherent operational bandwidth of QPCs can eclipse the THz regime, the impedance mismatch with the external circuitry limits the operational frequency to a few kHz. Lumped-element impedance-matching circuits are successful only up to a few hundreds of MHz in frequency. QPCs are characterised by a complex impedance consisting of quantized resistance, capacitance, and inductance elements. Characterising the complex admittance at higher frequencies and understanding the coupling of QPC to other circuit elements and electromagnetic environments will provide valuable insight into its sensing and backaction properties. In this work, we couple a QPC galvanically to a superconducting stub tuner impedance matching circuit realised in a coplanar waveguide architecture to enhance the operation frequency into the GHz regime and investigate the electrical amplification and complex admittance characteristics. The device, operating at ~1.96 $GHz$ exhibits a conductance sensitivity of 2.92 X 10-5(e2/h)/$\sqrt{Hz}$ with a bandwidth of 13 $MHz$. Besides, the RF reflected power unambiguously reveals the complex admittance characteristics of the QPC, shining more light on the behaviour of quantum tunnel junctions at higher operational frequencies.
In this project, we experimented the focused ion beam (FIB) based fabrications of semiconductor quantum dots (QDs) by using metal nano particles (NPs) (e.g., Al) on semiconductor as a template and by means of the FIB induced direct metal-to-QD conversion. We have examined effect of the experimental conditions, including Ga+ ion energy and dose as well as substrate temperature. The results of experiments have shown AlGaSb QD formation on GaSb substrate can be achieved under certain conditions but there are many challenges about the techniques, including compositional nonuniformity of the QDs formed, partial conversion of the metal NP to QD, and high defect concentration in the QDs.
Fracture prognosis and characterization efforts require knowledge of crack tip position and the Stress Intensity Factors (SIFs) acting in the vicinity of the crack. Here, we present an efficient numerical approach to infer both of these characteristics under a consistent theoretical framework from noisy, unstructured displacement data. The novel approach utilizes the separability of the asymptotic linear elastic fracture mechanics fields to expedite the search for crack tip position and is particularly useful for noisy displacement data. The manuscript begins with an assessment of the importance of accurately locating crack tip position when quantifying the SIFs from displacement data. Next, the proposed separability approach for quickly inferring crack tip position is introduced. Comparing to the widely used displacement correlation approach, the performance of the separability approach is assessed. Cases involving both noisy data and systematic deviation from the asymptotic linear elastic fracture mechanics model are considered, e.g. inelastic material behavior and finite geometries. An open source python implementation of the proposed approach is available for use by those doing field and laboratory work involving digital image correlation and simulations, e.g. finite element, discrete element, molecular dynamics and peridynamics, where the crack tip position is not explicitly defined.
Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. A peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.
Global climate change has prompted many national plans for rapid emissions reductions. For example, the United States recently committed to transitioning to 100% carbon-free electricity by 2035 and net-zero emissions economy-wide by 2050. Parallel to conversations surrounding emissions reductions is the call for energy justice, or the demand for more equitable distribution of energy-related burdens and benefits among communities. To date, energy justice has evolved as a mostly academic conversation, which may limit its utility to praxis. In response, we offer an interdisciplinary framework that aims to organize existing knowledge and lessons learned from energy development. Specifically, we developed the Meaningful Marine Renewable Energy (MRE) Development Framework and conducted a literature review using MRE as a case study. MRE was chosen because it is a nascent renewable energy technology in the US with projects mostly in demonstration stages and no commercial deployment, making it a useful case study to apply lessons learned from other energy sectors and other countries. Discussion of current resources being developed among the MRE community and their implications for furthering energy justice priorities are also explored. We conclude the review with a compiled list of questions meant to support stakeholders in translating theoretical concepts of Meaningful MRE Development to practice. Although the Meaningful MRE framework was developed using MRE as a use case, our interdisciplinary theoretical framework can be applied beyond MRE to other sustainable and renewable energy projects.
Ringwood, John V.; Tom, Nathan; Ferri, Francesco; Yu, Yi H.; Coe, Ryan G.; Ruehl, Kelley M.; Bacelli, Giorgio; Shi, Shuo; Patton, Ron J.; Tona, Paolino; Sabiron, Guillaume; Merigaud, Alexis; Ling, Bradley A.; Faedo, Nicolas
The wave energy control competition established a benchmark problem which was offered as an open challenge to the wave energy system control community. The competition had two stages: In the first stage, competitors used a standard wave energy simulation platform (WEC-Sim) to evaluate their controllers while, in the second stage, competitors were invited to test their controllers in a real-time implementation on a prototype system in a wave tank. The performance function used was based on converted energy across a range of standard sea states, but also included aspects related to economic performance, such as peak/average power, peak force, etc. This paper compares simulated and experimental results and, in particular, examines if the results obtained in a linear system simulation are borne out in reality. Overall, within the scope of the device tested, the range of sea states employed, and the performance metric used, the conclusion is that high-performance WEC controllers work well in practice, with good carry-over from simulation to experimentation. However, the availability of a good WEC mathematical model is deemed to be crucial.
Distribution system model calibration is a key enabling task for incipient failure identification within the distribution system. This report summarizes the work and publications by Sandia National Laboratories on the GMLC project titled “Incipient Failure Identification for Common Grid Asset Classes”. This project was a joint effort between Sandia National Laboratories, Lawrence Livermore National Laboratory, National Energy Technology Laboratory, and Oak Ridge National Laboratory. The included work covers distribution system topology identification, transformer groupings, phase identification, regulator and tap position estimation, and the open-source release and implementation of the developed algorithms.
This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.
Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.
This report documents the generation of a mechanism to predict the inclusion of carbon soot particles in a high explosive flow. The mechanism includes gasification and oxidation reactions, formation, sublimation, radiation, and agglomeration. Each part of the mechanism is derived from properties in the literature. The influence of each part of the mechanism is explored using simple, example simulations consisting of a 12 mm diameter 2,4,6-Trinitrotoluene charge detonated in ambient air. The mechanism has not been quantitatively compared to experiments. Additional efforts will be required to tune and validate it, which will require continued advancements in experimental diagnostics and simulation techniques.
This report documents the generation of a skeletal chemical reaction mechanism for use with hemispherical pentaerythritol tetranitrate charges. Skeletal mechanisms can substantially reduce computation time while maintaining accuracy. The methodology within uses faster running sample simulations to build a representative thermodynamic state space. These thermodynamic states are used with a constant-volume reactor analysis and a reaction flow analysis to remove unimportant species and reactions from a full chemical reaction mechanism. For the given test case, this results in a 6x speedup in computation time for directly comparable simulations in 2D axisymmetric simulations. We see a 30x speedup in simulations in 3D Cartesian coordainates when compared to a prior full kinetics simulation. There is strong agreement between temperature and species mass fraction profiles between the full and skeletal chemical reaction mechanisms. These methodologies can be applied to any explosive, given the availability of sample simulations.
This report describes a Late-Start LDRD project on a compact neutron time-of-flight (nTOF) diagnostic, based on the Ultra-Fast Pixel Array Camera (UPAC) platform. By leveraging advancements in microelectronics, the UPAC nTOF is designed to address existing challenges of current nTOFs at the Z-machine that rely on large scintillators and photomultiplier tubes using bulky, discrete digitizing units hindering scalability with signal routing presenting an additional challenge. The report describes the UPAC diagnostic platform, outlines the calibration of the diagnostic and the latest results of fielding the UPAC as a ride-a-long diagnostic on the Magnetized liner inertial fusion (MagLIF) campaigns at the Z-machine supported by the Late-Start LDRD, and finally discusses conclusions and proposed future work.
Frontal polymerization involves the propagation of a thermally driven polymerization wave through a monomer solution to rapidly generate high-performance polymeric materials with little energy input. The balance between latent catalyst activation and sufficient reactivity to sustain a front can be difficult to achieve and often results in systems with poor storage lives. This is of particular concern for frontal ring-opening metathesis polymerization (FROMP) where gelation occurs within a single day of resin preparation due to the highly reactive nature of Grubbs-type catalysts. In this report we demonstrate the use of encapsulated catalysts to provide remarkable latency to frontal polymerization systems, specifically using the highly active dicyclopentadiene monomer system. Negligible differences were observed in the frontal velocities or thermomechanical properties of the resulting polymeric materials. FROMP systems with encapsulated catalyst particles are shown with storage lives exceeding 12 months and front rates that increase over a well-characterized 2 month period. Moreover, the modularity of this encapsulation method is demonstrated by encapsulating a platinum catalyst for the frontal polymerization of silicones by using hydrosilylation chemistry.
Developed in 2018, PathTrace is a software package built with the intention of making path analysis simple and intuitive. PathTrace is a top-down pathway analysis software where a user is able to explore vulnerable pathways into a facility. The intention of utilizing a software tool like PathTrace is to characterize an existing physical protection system (PPS) and to upgrade the system to achieve a high level of response interruption, or probability of interruption (PI) of the adversary. There are four steps for conducting path analysis using PathTrace. The first step is to identify an image to use to build the model and scale the model within PathTrace using a section of known distance (wall or fence perimeter, for example). The scaling process will produce a grid of cells through which the user is able to build a model. The second step is to fill out the grid of cells with four categories of materials: Barriers, Detection Areas, Jumps, and Targets. These materials apply associated delay and detection values to the cells in which they are applied. The third step is to represent the adversary and response forces. The adversaries are represented by their capabilities in interacting with the materials identified in step two, and the response is represented by how quickly they will be able to respond to an adversary attack. Finally, the user is able to take all of the information from the previous three steps and perform a Most Vulnerable Path (MVP) analysis. In this stage, the user is able to visualize vulnerable adversary pathways and reason about how to upgrade these pathways to provide a high level of PI.
We explore the use of reduced physics models for efficient kinetic particle simulations of space charge limited (SCL) emission in inner magnetically insulated transmission lines (inner MITLs), with application to Sandia National Laboratories' Z machine. We propose a drift kinetic (guiding center) model of electron motion in place of a fully kinetic model and electrostatic-magnetostatic fields in place of electromagnetic fields. The validity of these approximations is suggested by the operational parameters of the Z machine, namely, current pulse lengths of order 100 ns compared with Larmor periods typically smaller than 10-11 s, typical Larmor radii of a few (tens) of microns (magnetic fields of tens to hundreds of Tesla) compared with MITL dimensions of a few centimeters, and transient time of light waves along the inner MITL of order a fraction of a nanosecond. Guiding center orbits eliminate the fast electron gyromotion, which enables the use of tens to hundreds of times larger time steps in the numerical particle advance. Electrostatic-magnetostatic fields eliminate the Courant-Friedrichs-Lewy (CFL) numerical stability limit on the time step and allow the use of higher grid resolutions or, alternatively, larger time steps in the fields advance. Overall, potential computational cost savings of tens to hundreds of times exists. The applicability of the reduced physics models is examined on two problems. First, in the simulation of space charge limited emission of electrons from the cathode surface due to high electric fields in a radial inner MITL geometry with a short load. In particular, it is shown that a drift kinetic-based particle-in-cell (PIC) model with electrostatic-magnetostatic fields is able to accurately reproduce well-known physics of electron vortex formation, spatially and temporally. Second, deeper understanding is gained of the mechanism behind vortex formation in this MITL geometry by considering an exemplar problem of an electron block of charge. This simpler setup reveals that the main mechanism of vortex formation can be attributed to pure drift motion of the electrons, that is, the (fully kinetic) gyromotion of the electrons is inessential to the process. This exemplar problem also suggests a correlation of the spatial dimensions of vortices to the thickness of the electron layer, as observed in SCL simulations. It also confirms that the electromagnetic nature of the fields does not play an essential role. Finally, an improved hybrid fully kinetic and drift kinetic model for electron motion is proposed, as means of capturing finite Larmor radius (FLR) effects; the particular FLR physics that is missed by the drift kinetic model is the particle-wall interaction. By initializing SCL emitted electrons as fully kinetic and later transitioning them to drift kinetic, according to simple criteria, the accuracy of SCL simulations can be improved, while preserving the potential for computational efficiency.
We propose a novel statistical inference methodology for multiway count data that is corrupted by false zeros that are indistinguishable from true zero counts. Our approach consists of zero-truncating the Poisson distribution to neglect all zero values. This simple truncated approach dispenses with the need to distinguish between true and false zero counts and reduces the amount of data to be processed. Inference is accomplished via tensor completion that imposes low-rank tensor structure on the Poisson parameter space. Our main result shows that an N-way rank-R parametric tensor M ∈ (0, ∞)I×.....×I generating Poisson observations can be accurately estimated by zero-truncated Poisson regression from approximately IR2 log22(I) non-zero counts under the nonnegative canonical polyadic decomposition. Our result also quantifies the error made by zero-truncating the Poisson distribution when the parameter is uniformly bounded from below. Therefore, under a low-rank multiparameter model, we propose an implementable approach guaranteed to achieve accurate regression in under-determined scenarios with substantial corruption by false zeros. Several numerical experiments are presented to explore the theoretical results.
While much work has been done in analyzing margins and uncertainties at the component level, a gap exists in NNSA methodology relating component level reliabilities and uncertainties to system level reliability and uncertainty. This paper shows how component level reliability data can be combined via a bootstrap analysis to estimate system level reliabilities and uncertainties. The performance of the bootstrap for this problem is validated through simulation studies. This paper extends the original work of Crowder by including sensitivity analyses related to changes in samples sizes and component failure probabilities. The use of the bootstrap as a decision-making tool is thus developed by quantifying the effect of such changes on system reliability.
Human activities involving subsurface reservoirs—resource extraction, carbon and nuclear waste storage—alter thermal, mechanical, and chemical steady-state conditions in these systems. Because these systems exist at lithostatic pressures, even minor chemical changes can cause chemically assisted deformation. Therefore, understanding how chemical effects control geomechanical properties is critical to optimizing engineering activities. The grand challenge in predicting the effect of chemical processes on mechanical properties lays in the fact that these phenomena take place at molecular scales, while they manifest all the way to reservoir scales. To address this fundamental challenge, we investigated chemical effects on deformation in model and real systems spanning molecular- to centimeter scales. We used theory, experiment, molecular dynamics simulation, and statistical analysis to (1) identify the effect of simple reactions, such as hydrolysis, on molecular structures in interfacial regions of stressed geomaterials; (2) quantify chemical effects on the bulk mechanical properties, fracture and displacement for granular rocks and single crystals; (3) develop initial understanding of universal scaling for individual displacement events in layered geomaterials; and (4) develop analytic approximations for the single-chain mechanical response utilizing asymptotically correct statistical thermodynamic theory. Taken together, these findings advance the challenging field of chemo-mechanics.
A direct numerical simulation (DNS) campaign is deployed for a series of confined downward oriented, non-isothermal turbulent impinging jet configurations. A baseline Reynolds number of 9960 is obtained through a precursor DNS pipe flow simulation (Reτ=505). Three jet temperature configurations (confinement height to nozzle diameter of three) enter a cylindrical domain that share ambient and impingement plate temperatures (298.15K). The range of jet temperatures are crafted such that the ratio of inlet to ambient density varies from unity to 0.52, showcasing the effect of density disparity on flow characteristics such as core collapse, radial mixing of momentum and energy, near-wall stagnation behavior, wall-jet profiles, and large-scale vortical structures. Surface quantities provided include mean radial heat flux and wall-shear stress profiles, and heat flux histograms at select radial stations. Results showcase increased radial normal stresses for higher temperature jets that support increased mixing, resulting in large-scale recirculation structures that are smaller, while retaining similar normalized radial wall profiles for shear stress, heat flux and pressure. Radial plots for wall shear stress and Nusselt number showcase strong radial decay as compared to previous configurations that share similar jet and ambient temperatures. For the 373.15 K case, a Gaussian-like histogram for heat fluxes at the impingement plate transitions to a log-normal profile as radial distances increase. In contrast, the 573.15 K configuration displays a bi-modal heat flux characteristic at the impingement plate, and in similar manner to the moderate temperature counterpart, transitions to a log-normal profile at larger radial distances.
In this report, we document the process related to developing a regional geologic model of a 605 x 1334 km area centered around Utah and encompassing surrounding states. This model is developed to test the effect that composition of a model has on the generation of synthetic data with the intent of using this information to improve upon full waveform moment tensor inversions. We compare observed data from three seismic events and five stations to the synthetic data generated by a preliminary model derived from a geologic framework model (GFM) developed by the USGS. The synthetic data and observed data comparisons indicate that our preliminary model performs well at smaller offset distances in the northern and central sections of the model. However, the southern stations consistently display synthetic data P- and S-wave arrival times that do not match the observed data arrival times, indicating that the velocity structure of the southern part of the model especially is inaccurate.
Superconducting qubits have reached the point where system designers are worried about the heat that control wiring brings into the cryostat. To continue scaling cryogenic quantum systems, control solutions that work inside the cold space must be explored. One possibility is to use control electronics that is native to superconductivity, so called single-flux-quantum (SFQ) circuitry, to form an interface between qubits and whatever other electronics is needed to control eventual quantum systems. To begin exploring the utility of SFQ as control circuitry, we performed modeling and experiments on qubit readout using ballistic fluxons which are SFQ in the limit of ballistic fluxon transport. Our modeling results show that a flavor of qubit, the fluxonium, can be read out using ballistic fluxons. We designed test samples to prove some of the key concepts needed for such a readout but were ultimately unable to getting a working demonstration. The lack of testing success was due to challenges in fabrication and running short of time to perform testing rather than a fundamental problem with our analysis.
This paper explores the utility of organizational system modeling frameworks to provide valuable insight into information flows within organizations and subsequently the opportunities for increasing resilience against disinformation campaigns targeting the system's ability to utilize information within its decision making. Disinformation is a growing challenge for many organizations and in recent years has created delay in decision making. Here the paper has utilized the viable systems model (VSM) to characterize organizational systems and used this approach to outline potential subsystem requirements to promote resilience of the system. The results of this paper can support the development of simulations and models considering the human elements within the system as well as support the development of quantitative measures of resilience.
The US Department of Energy (DOE) is investigating the use of different materials that could be used to fill the void space inside a dual-purpose canister (DPC) loaded with spent nuclear fuel (SNF) just before it is emplaced in a deep geologic repository. The purpose of adding filler material is to maintain subcritical conditions in the repository during the postclosure period, which can span up to 1,000,000 years. Several types of materials have been proposed, including metals, cements, particulates, and glass. Part of this investigation addresses how the presence of filler material inside a DPC will affect the performance of the repository with respect to the repository features; the consequences of events that may occur; and the multiple thermal, hydrologic, chemical, and mechanical processes that may occur in a deep geologic repository over long timescales. This report describes some of the filler materials that have been proposed and studied; identifies 11 features, 6 events, and 25 processes that may be affected by the presence of filler materials; and discusses the effects that may require consideration for each feature, event, or process. The results of this study can be used to direct appropriate research and to develop suitable models if the DOE decides to use fillers to maintain subcritical conditions in DPCs used to dispose of SNF.
Jove-Colon, Carlos F.; Ho, Tuan A.; Lopez, Carlos M.; Rutqvist, Jonny; Guglielmi, Yves; Hu, Mengsu; Sasaki, Tsubasa; Yoon, Sangcheol; Steefel, Carl I.; Tournassat, Christophe; Mital, Utkarsh; Luu, Keurfon; Sauer, Kirsten B.; Caporuscio, Florie A.; Rock, Marlena J.; Zandanel, Amber E.; Zavarin, Mavrik; Wolery, Thomas J.; Chang, Elliot; Han, Sol-Chan; Wainwright, Haruko; Greathouse, Jeffery A.
This report represents the milestone deliverable M2SF-23SN010301072 “Evaluation of Nuclear Spent Fuel Disposal in Clay-Bearing Rock - Process Model Development and Experimental Studies” The report provides a status update of FY23 activities for the work package Argillite Disposal work packages for the DOE-NE Spent Fuel Waste Form Science and Technology (SFWST) Program. Clay-rich geological media (often referred as shale or argillite) are among the most abundant type of sedimentary rock near the Earth’s surface. Argillaceous rock formations have the following advantageous attributes for deep geological nuclear waste disposal: widespread geologic occurrence, found in stable geologic settings, low permeability, self-sealing properties, low effective diffusion coefficient, high sorption capacity, and have the appropriate depth and thickness to host nuclear waste repository concepts. The DOE R&D program under the Spent Fuel Waste Science Technology (SFWST) campaign has made key progress (through experiment, modeling, and testing) in the study of chemical and physical phenomena that could impact the long-term safety assessment of heat-generating nuclear waste disposition in clay/shale/argillaceous rock. International collaboration activities comprising field-scale heater tests, field data monitoring, and laboratory-scale experiments provide key information on changes to the engineered barrier system (EBS) material exposed high thermal loads. Moreover, consideration of direct disposal of large capacity dual-purpose canisters (DPCs) as part of the back-end SNF waste disposition strategy has generated interest in improving our understanding of the effects of elevated temperatures on the engineered barrier system (EBS) design concepts. Chemical and structural analyses of sampled bentonite material from laboratory tests at elevated temperatures are key to the characterization of thermal effects affecting bentonite clay barrier performance. The knowledge provided by these experiments is crucial to constrain the extent of sacrificial zones in the EBS design during the thermal period. Thermal, hydrologic, mechanical, and chemical (THMC) data collected from heater tests and laboratory experiments have been used in the development, validation, and calibration of THMC simulators to model near-field coupled processes. This information leads to the development of simulation approaches to assess issues on coupled processes involving porous media flow, transport, geomechanical phenomena, chemical interactions with barrier/geologic materials, and the development of EBS concepts. These lines of knowledge are central to the design of deep geological backfilled repository concepts where temperature plays a key role in the EBS behavior, potential interactions with host rock, and long-term performance in the safety assessment.
Pressure-shear plate impact experiments were performed to quantify flow strength of wrought, as-built additively manufactured (AM), and heat-treated and recrystallized AM 304 L stainless steel (SS304L) under combined loading. Impact velocities spanned between 0.03 and 0.24 mm/μs, resulting in corresponding pressures of 0.62–5.93 GPa. Flow strength measurements are comparable for the sample variants across the studied loading conditions; however, shear wave structures significantly differ between sample type. Microstructurally aware simulations indicate local strain differences attributed to anisotropic elastic constants of large grains (~1 mm) in the as-built and heat-treated AM may impede the ability to uniformly transmit a shear wave.
We combine crossed-beam velocity map imaging with high-level ab initio/transition state theory modeling of the reaction of S(3P) with 1,3-butadiene and isoprene under single collision conditions. For the butadiene reaction, we detect both H and H2 loss from the initial adduct, and from reaction with isoprene, we see both H loss and methyl loss. Theoretical calculations confirm these arise following intersystem crossing to the singlet surface forming long-lived intermediates. For the butadiene reaction, these lose H2 to form thiophene as the dominant channel, H to form the detected 2H-thiophenyl radical, or ethene, giving thioketene. For isoprene, additional reaction products are suggested by theory, including the observed H and methyl loss radicals, but also methyl thiophene, thioformaldehyde, and thioketene. The results for S(3P) + 1,3-butadiene, showing direct cyclization to the aromatic product and yielding few bimolecular product channels, are in striking contrast to those for the analogous O(3P) reaction.
Here, a method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.
This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete, adjoint-based approach for error estimation is considered. The traditional method to derive an error estimate in this context requires linearizing both the nonlinear variational form and the nonlinear functional of interest which introduces linearization errors into the error estimate. In this paper, we investigate these linearization errors. In particular, we develop a novel discrete goal-oriented error estimate that accounts for traditionally neglected nonlinear terms at the expense of greater computational cost. We demonstrate how this error estimate can be used to drive mesh adaptivity. Here, we show that accounting for linearization errors in the error estimate can improve its effectivity for several nonlinear model problems and quantities of interest. We also demonstrate that an adaptive strategy based on the newly proposed estimate can lead to more accurate approximations of the nonlinear functional with fewer degrees of freedom when compared to uniform refinement and traditional adjoint-based approaches.
The formation of a stress corrosion crack (SCC) in the canister wall of a dry cask storage system (DCSS) has been identified as a potential issue for the long-term storage of spent nuclear fuel. The presence of an SCC in a storage system could represent a through-wall flow path from the canister interior to the environment. Modern, vertical DCSSs are of particular interest due to the commercial practice of using higher backfill pressures in the canister, up to approximately 800 kPa, compared to their horizontal counterparts. This pressure differential offers a relatively high driving potential for blowdown of any particulates that might be present in the canister. In this study, the rates of gas flow and aerosol transmission of a spent fuel surrogate through an engineered microchannel with dimensions representative of an SCC were evaluated experimentally using coupled mass flow and aerosol analyzers. The microchannel was formed by mating two gage blocks with a linearly tapering slot orifice nominally 13 μm (0.005 in.) tall on the upstream side and 25 μm (0.0010 in.) tall on the downstream side. The orifice is 12.7 mm (0.500 in.) wide by 8.86 mm (0.349 in.) long (flow length). Surrogate aerosols of cerium oxide, CeO2, were seeded and mixed with either helium or air inside a pressurized tank. The aerosol characteristics were measured immediately upstream and downstream of the simulated SCC at elevated and ambient pressures, respectively. These data sets are intended to add to previous testing that characterized SCCs under well-controlled boundary conditions through the inclusion of testing improvements that establish initial conditions in a more consistent way. While the engineered microchannel has dimensions similar to actual SCCs, it does not reproduce the tortuous path the aerosol laden flow would have to traverse for eventual transmission. SCCs can be rapidly grown in a laboratory setting given the right conditions, and initial characterization and clean-flow testing has begun on lab grown crack samples provided to Sandia National Laboratories (SNL). Many such samples are required to produce statistically relevant transmission results, and SNL is developing a procedure to produce samples in welded steel plates. These ongoing testing efforts are focused on understanding the evolution in both size and quantity of a hypothetical release of aerosolized spent fuel particles from failed fuel to the canister interior and ultimately through an SCC.
Sierra/SolidMechanics (Sierra/SM) is a Lagrangian, three-dimensional code for finite element analysis of solids and structures. It provides capabilities for explicit dynamic, implicit quasistatic and dynamic analyses. The explicit dynamics capabilities allow for the efficient and robust solution of models with extensive contact subjected to large, suddenly applied loads. For implicit problems, Sierra/SM uses a multi-level iterative solver, which enables it to effectively solve problems with large deformations, nonlinear material behavior, and contact. Sierra/SM has a versatile library of continuum and structural elements, and a large library of material models. The code is written for parallel computing environments enabling scalable solutions of extremely large problems for both implicit and explicit analyses. It is built on the SIERRA Framework, which facilitates coupling with other SIERRA mechanics codes. This document describes the functionality and input syntax for Sierra/SM.
Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.
Presented in this document is a small portion of the tests that exist in the Sierra/SolidMechanics (Sierra/SM) verification test suite. Most of these tests are run nightly with the Sierra/SM code suite, and the results of the test are checked versus the correct analytical result. For each of the tests presented in this document, the test setup, a description of the analytic solution, and comparison of the Sierra/SM code results to the analytic solution is provided. Mesh convergence is also checked on a nightly basis for several of these tests. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems. Additional example problems are provided in the Sierra/SM Example Problems Manual. Note, many other verification tests exist in the Sierra/SM test suite, but have not yet been included in this manual.
Intimately intertwined atomic and electronic structures of point defects govern diffusion-limited corrosion and underpin the operation of optoelectronic devices. For some materials, complex energy landscapes containing metastable defect configurations challenge first-principles modeling efforts. Here, we thoroughly reevaluate native point defect geometries for the illustrative case of α-Al2O3 by comparing three methods for sampling candidate geometries in density functional theory calculations: displacing atoms near a naively placed defect, initializing interstitials at high-symmetry points of a Voronoi decomposition, and Bayesian optimization. We find symmetry-breaking distortions for oxygen vacancies in some charge states, and we identify several distinct oxygen split-interstitial geometries that help explain literature discrepancies involving this defect. We also report a surprising and, to our knowledge, previously unknown trigonal geometry favored by aluminum interstitials in some charge states. These new configurations may have transformative impacts on our understanding of defect migration pathways in aluminum-oxide scales protecting metal alloys from corrosion. Overall, the Voronoi scheme appears most effective for sampling candidate interstitial sites because it always succeeded in finding the lowest-energy geometry identified in this study, although no approach found every metastable configuration. Finally, we show that the position of defect levels within the band gap can depend strongly on the defect geometry, underscoring the need to conduct careful searches for ground-state geometries in defect calculations.
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.
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 5.16 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 the conforming reproducing kernel (CRK) method, numerical fracture and failure modeling aids such as the extended finite element method (XFEM) and J-integral, explicit time step control techniques, dynamic mesh rebalancing, as well as a variety of new material models and finite element formulations.