Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018
SNOWPAC (Stochastic Nonlinear Optimization With Path-Augmented Constraints) is a method for stochastic nonlinear constrained derivative-free optimization. For such problems, it extends the path-augmented constraints framework introduced by the deterministic optimization method NOWPAC and uses a noise-adapted trust region approach and Gaussian processes for noise reduction. In recent developments, SNOWPAC is available in the DAKOTA framework which offers a highly flexible interface to couple the optimizer with different sampling strategies or surrogate models. In this paper we discuss details of SNOWPAC and demonstrate the coupling with DAKOTA. We showcase the approach by presenting design optimization results of a shape in a 2D supersonic duct. This simulation is supposed to imitate the behavior of the flow in a SCRAMJET simulation but at a much lower computational cost. Additionally different mesh or model fidelities can be tested. Thus, it serves as a convenient test case before moving to costly SCRAMJET computations. Here, we study deterministic results and results obtained by introducing uncertainty on inflow parameters. As sampling strategies we compare classical Monte Carlo sampling with multilevel Monte Carlo approaches for which we developed new error estimators. All approaches show a reasonable optimization of the design over the objective while maintaining or seeking feasibility. Furthermore, we achieve significant reductions in computational cost by using multilevel approaches that combine solutions from different grid resolutions.
This study explores the use of a Krylov iterative method (GMRES) as a smoother for an algebraic multigrid (AMG) preconditioned Newton–Krylov iterative solution approach for a fully-implicit variational multiscale (VMS) finite element (FE) resistive magnetohydrodynamics (MHD) formulation. The efficiency of this approach is critically dependent on the scalability and performance of the AMG preconditioner for the linear solutions and the performance of the smoothers play an essential role. Krylov smoothers are considered an attempt to reduce the time and memory requirements of existing robust smoothers based on additive Schwarz domain decomposition (DD) with incomplete LU factorization solves on each subdomain. This brief study presents three time dependent resistive MHD test cases to evaluate the method. The results demonstrate that the GMRES smoother can be faster due to a decrease in the preconditioner setup time and a reduction in outer GMRESR solver iterations, and requires less memory (typically 35% less memory for global GMRES smoother) than the DD ILU smoother.
Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018
As the mean time between failures on the future high-performance computing platforms is expected to decrease to just a few minutes, the development of “smart”, property-preserving checkpointing schemes becomes imperative to avoid dramatic decreases in application utilization. In this paper we formulate a generic optimization-based approach for fault-tolerant computations, which separates property preservation from the compression and recovery stages of the checkpointing processes. We then specialize the approach to obtain a fault recovery procedure for a model scalar transport equation, which preserves local solution bounds and total mass. Numerical examples showing solution recovery from a corrupted application state for three different failure modes illustrate the potential of the approach.
Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018
Wind energy is stochastic in nature; the prediction of aerodynamic quantities and loads relevant to wind energy applications involves modeling the interaction of a range of physics over many scales for many different cases. These predictions require a range of model fidelity, as predictive models that include the interaction of atmospheric and wind turbine wake physics can take weeks to solve on institutional high performance computing systems. In order to quantify the uncertainty in predictions of wind energy quantities with multiple models, researchers at Sandia National Laboratories have applied Multilevel-Multifidelity methods. A demonstration study was completed using simulations of a NREL 5MW rotor in an atmospheric boundary layer with wake interaction. The flow was simulated with two models of disparate fidelity; an actuator line wind plant large-eddy scale model, Nalu, using several mesh resolutions in combination with a lower fidelity model, OpenFAST. Uncertainties in the flow conditions and actuator forces were propagated through the model using Monte Carlo sampling to estimate the velocity defect in the wake and forces on the rotor. Coarse-mesh simulations were leveraged along with the lower-fidelity flow model to reduce the variance of the estimator, and the resulting Multilevel-Multifidelity strategy demonstrated a substantial improvement in estimator efficiency compared to the standard Monte Carlo method.
Stereolithography (SL) is a process that uses photosensitive polymer solutions to create 3D parts in a layer by layer approach. Sandia National Labs is interested in using SL for the printing of ceramic loaded resins, namely alumina, that we are formulating here at the labs. One of the most important aspects for SL printing of ceramics is the properties of the slurry itself. The work presented here will focus on the use of a novel commercially available low viscosity resin provided by Colorado Photopolymer Solutions, CPS 2030, and a Hypermer KD1 dispersant from Croda. Two types of a commercially available alumina powder, Almatis A16 SG and Almatis A15 SG, are compared to determine the effects that the size and the distribution of the powder have on the loading of the solution using rheology. The choice of a low viscosity resin allows for a high particle loading, which is necessary for the printing of high density parts using a commercial SL printer. The Krieger-Dougherty equation was used to evaluate the maximum particle loading for the system. This study found that a bimodal distribution of micron sized powder (A15 SG) reduced the shear thickening effects caused by hydroclusters, and allows for the highest alumina powder loading. A final sintered density of 90% of the theoretical density of alumina was achieved based on the optimized formulation and printing conditions.
Lecture Notes in Computational Science and Engineering
Maljaars, Jakob M.; Labeur, Robert J.; Trask, Nathaniel A.; Sulsky, Deborah L.
A particle-mesh strategy is presented for scalar transport problems which provides diffusion-free advection, conserves mass locally (i.e. cellwise) and exhibits optimal convergence on arbitrary polyhedral meshes. This is achieved by expressing the convective field naturally located on the Lagrangian particles as a mesh quantity by formulating a dedicated particle-mesh projection based via a PDE-constrained optimization problem. Optimal convergence and local conservation are demonstrated for a benchmark test, and the application of the scheme to mass conservative density tracking is illustrated for the Rayleigh–Taylor instability.
We propose a new algorithm for the fast solution of large, sparse, symmetric positive-definite linear systems, spaND (sparsified Nested Dissection). It is based on nested dissection, sparsification, and low-rank compression. After eliminating all interiors at a given level of the elimination tree, the algorithm sparsifies all separators corresponding to the interiors. This operation reduces the size of the separators by eliminating some degrees of freedom but without introducing any fill-in. This is done at the expense of a small and controllable approximation error. The result is an approximate factorization that can be used as an efficient preconditioner. We then perform several numerical experiments to evaluate this algorithm. We demonstrate that a version using orthogonal factorization and block-diagonal scaling takes fewer CG iterations to converge than previous similar algorithms on various kinds of problems. Furthermore, this algorithm is provably guaranteed to never break down and the matrix stays symmetric positive-definite throughout the process. We evaluate the algorithm on some large problems show it exhibits near-linear scaling. The factorization time is roughly \scrO (N), and the number of iterations grows slowly with N.
Understanding the dynamic response of a structure is critical to design. This is of extreme importance in high-consequence systems on which human life can depend. Historically, these structures have been modeled as linear, where response scales proportionally with excitation amplitude. However, most structures are nonlinear to the extent that linear models are no longer sufficient to adequately capture important dynamics. Sources of nonlinearity include, but are not limited to: large deflections (so called geometric nonlinearities), complex materials, and frictional interfaces/joints in assemblies between subcomponents. Joint nonlinearities usually cause the natural frequency to decrease and the effective damping ratio to increase with response amplitude due to microslip effects. These characteristics can drastically alter the dynamics of a structure and, if not well understood, could lead to unforeseen failure or unnecessarily over-designed features. Nonlinear structural dynamics has been a subject of study for many years, and provide a summary of recent developments and discoveries in this field. One topic discussed in these papers are nonlinear normal modes (NNMs) which are periodic solutions of the underlying conservative system. They provide a theoretical framework for describing the energy-dependence of natural frequencies and mode shapes of nonlinear systems, and lead to a promising method to validate nonlinear models. In and, a force appropriation testing technique was developed which allowed for the experimental tracking of undamped NNMs by achieving phase quadrature between the excitation and response. These studies considered damping to be small to moderate, and constant. Nonlinear damping of an NNM was studied in using power-based quantities for a structure with a discrete, single-bolt interface. In this work, the force appropriation technique where phase quadrature is achieved between force and response as described in is applied to a target mode of a structure with two bolted joints, one of which comprised a large, continuous interface. This is a preliminary investigation which includes a study of nonlinear natural frequency, mode shape, and damping trends extracted from the measured data.
In the past year, resonant plate tests designed to excite all three axes simultaneously have become increasingly popular at Sandia National Labs. Historically, only one axis was tested at a time, but unintended off axis responses were generated. In order to control the off-axis motion so that off-axis responses were created which satisfy appropriate test specifications, the test setup has to be iteratively modified so that the coupling between axes was desired. The iterative modifications were done with modeling and simulation. To model the resonant plate test, an accurate forcing function must be specified. For resonant plate shock experiments, the input force of the projectile impacting the plate is prohibitively difficult to measure in situ. To improve on current simulation results, a method to use contact forces from an explicit simulation as an input load was implemented. This work covers an overview and background of three axes resonant plate shock tests, their design, their value in experiments, and the difficulties faced in simulating them. The work also covers a summary of contact force implementation in an explicit dynamics code and how it is used to evaluate an input force for a three axes resonant plate simulation. The results from the work show 3D finite element projectile and impact block interactions as well as simulation shock response data compared to experimental shock response data.
The Sandia National Laboratories Human Factors team designed and executed an experiment to quantify the differences between 2D and 3D reference materials with respect to task performance and cognitive workload. A between-subjects design was used where 27 participants were randomly assigned either 2D or 3D reference material condition (14 and 13 participants, respectively). The experimental tasks required participants to interpret, locate, and report dimensions on their assigned reference material. Performance was measured by accuracy of task completion and time-to-complete. After all experimental tasks were completed, cognitive workload data were collected. Response times were longer in the 3D condition vice the 2D. However, no differences were found between conditions with respect to response accuracy and cognitive workload, which may indicate no negative cognitive impacts concerning the sole use of 3D reference materials in the work-place. This paper concludes with possible future efforts to address the limitations of this experiment and to explore the mechanisms behind the findings of this work.
Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Weir, Nathan A.; Alleyne, Andrew G.
Due to the unique structure of two-input single-output (TISO) feedback systems, several closed-loop properties can be characterized using the concepts of plant and controller "directions"and "alignment."Poor plant/controller alignment indicates significant limitations in terms of closed-loop performance. In general, it is desirable to design a controller that is well aligned with the plant in order to minimize the size of the closed-loop sensitivity functions and closed-loop interactions. Although the concept of alignment can be a useful analysis tool for a given plant/controller pair, it is not obvious how a controller should be designed to achieve good alignment. We present a new controller design approach, based on the PQ method (Schroeck et al., 2001, "On Compensator Design for Linear Time invariant Dual-Input Single-Output Systems,"IEEE/ASME Trans. Mechatronics, 6(1), pp. 50-57), which explicitly incorporates knowledge of alignment into the design process. This is accomplished by providing graphical information about the alignment angle on the Bode plot of the PQ frequency response. We show the utility of this approach through a design example.
At the molecular level, resonant coupling of infrared radiation with oscillations of the electric dipole moment determines the absorption cross section, σ. The parameter σ relates the bond density to the total integrated absorption. In this work, σ was measured for the Si-N asymmetric stretch mode in SiNx thin films of varying composition and thickness. Thin films were deposited by low pressure chemical vapor deposition at 850 °C from mixtures of dichlorosilane and ammonia. σ for each film was determined from Fourier transform infrared spectroscopy and ellipsometric measurements. Increasing the silicon content from 0% to 25% volume fraction amorphous silicon led to increased optical absorption and a corresponding systematic increase in σ from 4.77 × 10-20 to 6.95 × 10-20cm2, which is consistent with literature values. The authors believe that this trend is related to charge transfer induced structural changes in the basal SiNx tetrahedron as the volume fraction of amorphous silicon increases. Experimental σ values were used to calculate the effective dipole oscillating charge, q, for four films of varying composition. The authors find that q increases with increasing amorphous silicon content, indicating that compositional factors contribute to modulation of the Si-N dipole moment. Additionally, in the composition range investigated, the authors found that σ agrees favorably with trends observed in films deposited by plasma enhanced chemical vapor deposition.
Particle-in-cell (PIC) simulation methods are attractive for representing species distribution functions in plasmas. However, as a model, they introduce uncertain parameters, and for quantifying their prediction uncertainty it is useful to be able to assess the sensitivity of a quantity-of-interest (QoI) to these parameters. Such sensitivity information is likewise useful for optimization. However, computing sensitivity for PIC methods is challenging due to the chaotic particle dynamics, and sensitivity techniques remain underdeveloped compared to those for Eulerian discretizations. This challenge is examined from a dual particle–continuum perspective that motivates a new sensitivity discretization. Two routes to sensitivity computation are presented and compared: a direct fully-Lagrangian particle-exact approach provides sensitivities of each particle trajectory, and a new particle-pdf discretization, which is formulated from a continuum perspective but discretized by particles to take the advantages of the same type of Lagrangian particle description leveraged by PIC methods. Since the sensitivity particles in this approach are only indirectly linked to the plasma-PIC particles, they can be positioned and weighted independently for efficiency and accuracy. The corresponding numerical algorithms are presented in mathematical detail. The advantage of the particle-pdf approach in avoiding the spurious chaotic sensitivity of the particle-exact approach is demonstrated for Debye shielding and sheath configurations. In essence, the continuum perspective makes implicit the distinctness of the particles, which circumvents the Lyapunov instability of the N-body PIC system. The cost of the particle-pdf approach is comparable to the baseline PIC simulation.
Accurate predictions of device performance in 14-MeV neutron environments rely upon understanding the recoil cascades that may be produced. Recoils from 14-MeV neutrons impinging on both gallium nitride (GaN) and gallium arsenide (GaAs) devices were modeled and compared to the recoil spectra of devices exposed to 14-MeV neutrons. Recoil spectra were generated using nuclear reaction modeling programs and converted into an ionizing energy loss (IEL) spectrum. We measured the recoil IEL spectra by capturing the photocurrent pulses produced by single neutron interactions with the device. Good agreement, with a factor of two, was found between the model and the experiment under strongly depleted conditions. However, this range of agreement between the model and the experiment decreased significantly when the bias was removed, indicating partial energy deposition due to cascades that escape the active volume of the device not captured by the model. Consistent event rates across multiple detectors confirm the reliability of our neutron recoil detection method.
Renewed interest in the development of molten salt reactors has created the need for analytical tools that can perform safeguards assessments on these advanced reactors. This work outlines a flexible framework to perform safeguards analyses on a wide range of advanced reactor designs. The framework consists of two parts, a process model and a safeguards tool. The process model, developed in MATLAB Simulink, simulates the flow materials through a reactor facility. These models are linked to SCALE/TRITON and SCALE/ORIGEN to approximate depletion and decay of fuel salts but are flexible enough to accommodate higher fidelity tools if needed. The safeguards tool uses the process data to calculate common statistical quantities of interest such as material unaccounted for (MUF) and Page's trend test on the standardized independent transformed MUF (SITMUF). This paper documents the development of these tools.
Recent advancements in micro-scale additive manufacturing techniques have created opportunities for design of novel electrode geometries that improve battery performance by deviating from the traditional layered battery design. These 3D batteries typically exhibit interpenetrating anode and cathode materials throughout the design space, but the existing well-established porous electrode theory models assume only one type of electrode is present in each battery layer. We therefore develop and demonstrate a multielectrode volume-averaged electrochemical transport model to simulate transient discharge performance of these new interpenetrating electrode architectures. We implement the new reduced-order model in the PETSc framework and asses its accuracy by comparing predictions to corresponding mesoscale-resolved simulations that are orders of magnitude more computationally-intensive. For simple electrode designs such as alternating plates or cylinders, the volume-averaged model predicts performance within ∼2% for electrode feature sizes comparable to traditional particle sizes (5-10μm) at discharge rates up to 3C. When considering more complex geometries such as minimal surface designs (i.e. gyroid, Schwarz P), we show that using calibrated characteristic diffusion lengths for each design results in errors below 3% for discharge rates up to 3C. These comparisons verify that this novel model has made reliable cell-scale simulations of interpenetrating electrode designs possible.
Metal additive manufacturing (AM) allows for the freeform creation of complex parts. However, AM microstructures are highly sensitive to the process parameters used. Resulting microstructures vary significantly from typical metal alloys in grain morphology distributions, defect populations and crystallographic texture. AM microstructures are often anisotropic and possess three-dimensional features. These microstructural features determine the mechanical properties of AM parts. Here, we reproduce three “canonical” AM microstructures from the literature and investigate their mechanical responses. Stochastic volume elements are generated with a kinetic Monte Carlo process simulation. A crystal plasticity-finite element model is then used to simulate plastic deformation of the AM microstructures and a reference equiaxed microstructure. Results demonstrate that AM microstructures possess significant variability in strength and plastic anisotropy compared with conventional equiaxed microstructures.
Atomistic modeling of radiation damage through displacement cascades is deceptively non-trivial. Due to the high energy and stochastic nature of atomic collisions, individual primary knock-on atom (PKA) cascade simulations are computationally expensive and ill-suited for length and dose upscaling. Here, we propose a reduced-order atomistic cascade model capable of predicting and replicating radiation events in metals across a wide range of recoil energies. Our methodology approximates cascade and displacement damage production by modeling the cascade as a core-shell atomic structure composed of two damage production estimators, namely an athermal recombination corrected displacements per atom (arc-dpa) in the shell and a replacements per atom (rpa) representing atomic mixing in the core. These estimators are calibrated from explicit PKA simulations and a standard displacement damage model that incorporates cascade defect production efficiency and mixing effects. We illustrate the predictability and accuracy of our reduced-order atomistic cascade method for the cases of copper and niobium by comparing its results with those from full PKA simulations in terms of defect production as well as the resulting cascade evolution and structure. We provide examples for simulating high energy cascade fragmentation and large dose ion-bombardment to demonstrate its possible applicability. Finally, we discuss the various practical considerations and challenges associated with this methodology especially when simulating subcascade formation and dose effects.
In the resource-rich environment of data centers most failures can quickly failover to redundant resources. In contrast, failure in edge infrastructures with limited resources might require maintenance personnel to drive to the location in order to fix the problem. The operational cost of these“truck rolls” to locations at the edge infrastructure competes with the operational cost incurred by extra space and power needed for redundant resources at the edge. Computational storage devices with network interfaces can act as network-attached storage servers and offer a new design point for storage systems at the edge. In this paper we hypothesize that a system consisting of a larger number of such small “embedded” storage nodes provides higher availability due to a larger number of failure domains while also saving operational cost in terms of space and power. As evidence for our hypothesis, we compared the possibility of data loss between two different types of storage systems: one is constructed with general-purpose servers, and the other one is constructed with embedded storage nodes. Our results show that the storage system constructed with general-purpose servers has 7 to 20 times higher risk of losing data over the storage system constructed with embedded storage devices. We also compare the two alternatives in terms of power and space using the Media-Based Work Unit (MBWU) that we developed in an earlier paper as a reference point.
Experiments with the lower divertor of DIII-D during the Metal Rings Campaign (MRC) show that the fraction F of atomic D in the total recycling flux is material-dependent and varies through the ELM cycle, which may affect divertor fueling. Between ELMs, F C ∼ 10% and F W ∼ 40%, consistent with expectations if all atomic recycling is due to reflections. During ELMs, FC increases to 50% and F W to 60%. In contrast, the total D recycling coefficient including atoms and molecules R stays close to unity near the strike point where the surface is saturated with D. During ELMs, R can deviate from unity, increasing during high energy ELM-ion deposition (net D release) and decreasing at the end of the ELM which leads to ability of the target to trap the ELM-deposited D. The increase of R > 1 in response to an increase in ion impact energy E i has been studied with small divertor target samples using Divertor Materials Evaluation System (DiMES). An electrostatic bias was applied to DiMES to change E i by 90 eV. On all studied materials including C, Mo, uncoated and W-coated TZM (>99% Mo, Ti, and Zr alloy), W, and W fuzz, an increase of E i transiently increased the D yield (and R) by ∼10%. On C there was also an increase in the molecular D2 yield, probably due to ion-induced D2 desorption. Despite the measured increase in F on W compared to C, attached H-mode shots with OSP on W during MRC did not demonstrate a higher pedestal density. About 8% increase in the edge density could be seen only in attached L-mode scenarios. The difference can be explained by higher D trapping in the divertor and lower divertor fueling efficiency in H-versus L-mode.
We present a numerical method for synchronous and concurrent solution of transient elastodynamics problem where the computational domain is divided into subdomains that may reside on separate computational platforms. This work employs the variational multiscale discontinuous Galerkin (VMDG) method to develop interdomain transmission conditions for transient problems. The fine-scale modeling concept leads to variationally consistent coupling terms at the common interfaces. The method admits a large class of time discretization schemes, and decoupling of the solution for each subdomain is achieved by selecting any explicit algorithm. Numerical tests with a manufactured solution problem show optimal convergence rates. The energy history in a free vibration problem is in agreement with that of the solution from a monolithic computational domain.
In this work, we develop Gaussian process regression (GPR) models of isotropic hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this approach that embeds rotational invariance of the stress-stretch constitutive relation in the GPR representation. This approach requires fewer training examples and achieves higher accuracy while maintaining invariance to rotations exactly. Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential. Although the error of this model for predicting the stress tensor is higher, the strain-energy density is recovered with high accuracy from limited training data. The approaches presented here are examples of physics-informed machine learning. They go beyond purely data-driven approaches by embedding the physical system constraints directly into the Gaussian process representation of materials models.
Many applications benefit from the ability of an RFID tag to operate both on and off a conducting ground plane. This paper presents an electrically small loop antenna at 433 MHz that passively maintains its free-space tune and match when located a certain distance away from a large conducting ground plane. The design achieves this using a single radiation mechanism (that of a loop) in both environments without the use of a ground plane or EBG/AMC structure. An equivalent circuit model is developed that explains the dual-environment behavior and shows that the geometry balances inductive and capacitive parasitics introduced by the ground plane such that the free-space loop reactance, and thus resonant frequency, does not change. A design equation for balancing the inductive and capacitive parasitic effects is derived. Finally, experimental data showing the design eliminates ground plane detuning in practice is presented. The design is suitable for active, 'hard' RFID tag applications.
We investigate the spatial organization and temporal dynamics of large-scale, coherent structures in turbulent Rayleigh-Bénard convection via direct numerical simulation of a 6.3 aspect-ratio cylinder with Rayleigh and Prandtl numbers of and, respectively. Fourier modal decomposition is performed to investigate the structural organization of the coherent turbulent motions by analysing the length scales, time scales and the underlying dynamical processes that are ultimately responsible for the large-scale structure formation and evolution. We observe a high level of rotational symmetry in the large-scale structure in this study and that the structure is well described by the first four azimuthal Fourier modes. Two different large-scale organizations are observed during the duration of the simulation and these patterns are dominated spatially and energetically by azimuthal Fourier modes with frequencies of 2 and 3. Studies of the transition between these two large-scale patterns, radial and vertical variations in the azimuthal energy spectra, as well as the spatial and modal variations in the system's correlation time are conducted. Rotational dynamics are observed for individual Fourier modes and the global structure with strong similarities to the dynamics that have been reported for unit aspect-ratio domains in prior works. It is shown that the large-scale structures have very long correlation time scales, on the order of hundreds to thousands of free-fall time units, and that they are the primary source for a horizontal inhomogeneity within the system that can be observed during a finite, but a very long-time simulation or experiment.
Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells. These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic framework for identifying these parameters by finding a posterior parameter distribution that captures their uncer-tainty. Traditional computational methods for solving inference problems such as Markov chain Monte Carlo methods based on the classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the sequential tempered Markov chain Monte Carlo (ST-MCMC) sam-pler. This algorithm is built upon sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appro-priate fidelity, we introduce a novel information-theoretic criterion that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high-performance computing resources using biologically relevant problems.
TlBr can surpass CZT as the leading semiconductor for γ- A nd X-radiation detection. Unfortunately, the optimum properties of TlBr quickly decay when an operating electrical field is applied. Quantum mechanical studies indicated that if this property degradation comes from the conventional mechanism of ionic migration of vacancies, then an unrealistically high vacancy concentration is required to account for the rapid aging of TlBr seen in experiments. In this work, we have applied large scale molecular dynamics simulations to study the effects of dislocations on ionic migration of TlBr crystals under electrical fields. We found that electrical fields can drive the motion of edge dislocations in both slip and climb directions. These combined motions eject enormous vacancies in the dislocation trail. Both dislocation motion and a high vacancy concentration can account for the rapid aging of the TlBr detectors. These findings suggest that strengthening methods to pin dislocations should be explored to increase the lifetimes of TlBr crystals.
Ju, Zhaoyang; Xiao, Weihua; Yao, Xiaoqian; Tan, Xin; Simmons, Blake A.; Sale, Kenneth L.; Sun, Ning
Keggin-type polyoxometalate derived ionic liquids (POM-ILs) have recently been presented as effective solvent systems for biomass delignification. To investigate the mechanism of lignin dissolution in POM-ILs, the system involving POM-IL ([C4C1Im]3[PW12O40]) and guaiacyl glycerol-β-guaiacyl ether (GGE), which contains a β-O-4 bond (the most dominant bond moiety in lignin), was studied using quantum mechanical calculations and molecular dynamics simulations. These studies show that more stable POM-IL structures are formed when [C4C1Im]+ is anchored in the connecting four terminal oxygen region of the [PW12O40]3- surface. The cations in POM-ILs appear to stabilize the geometry by offering strong and positively charged sites, and the POM anion is a good H-bond acceptor. Calculations of POM-IL interacting with GGE show the POM anion interacts strongly with GGE through many H-bonds and π-π interactions which are the main interactions between the POM-IL anion and GGE and are strong enough to force GGE into highly bent conformations. These simulations provide fundamental models of the dissolution mechanism of lignin by POM-IL, which is promoted by strong interactions of the POM-IL anion with lignin.
One of the primary concerns with the long-term performance of storage systems for spent nuclear fuel (SNF) is the potential for corrosion due to deliquescence of salts deposited as aerosols on the surface of the canister, which is typically made of austentic stainless steel. In regions of high residual weld stresses, this may lead to localized stress-corrosion cracking (SCC). The ability to detect and image SCC at an early stage (long before the cracks are susceptible to propagate through the thickness of the canister wall and leaks of radioactive material may occur) is essential to the performance evaluation and licensing process of the storage systems. In this paper, we explore a number of nondestructive testing techniques to detect and image SCC in austenitic stainless steel. Our attention is focused on a small rectangular sample of 1 × 2 in2 with two cracks of mm-scale sizes. The techniques explored in this paper include nonlinear resonant ultrasound spectroscopy (NRUS) for detection, Linear Elastodynamic Gradient Imaging Technique (LEGIT), ultrasonic C-scan, vibrothermography, and synchrotron X-ray diffraction for imaging. Results obtained from these techniques are compared. Cracks of mm-scale sizes can be detected and imaged with all the techniques explored in this study.
Inverters are a leading source of hardware failures and contribute to significant energy losses at photovoltaic (PV) sites. An understanding of failure modes within inverters requires evaluation of a dataset that captures insights from multiple characterization techniques (including field diagnostics, production data analysis, and current-voltage curves). One readily available dataset that can be leveraged to support such an evaluation are maintenance records, which are used to log all site-related technician activities, but vary in structuring of information. Using machine learning, this analysis evaluated a database of 55,000 maintenance records across 800+ sites to identify inverter-related records and consistently categorize them to gain insight into common failure modes within this critical asset. Communications, ground faults, heat management systems, and insulated gate bipolar transistors emerge as the most frequently discussed inverter subsystems. Further evaluation of these failure modes identified distinct variations in failure frequencies over time and across inverter types, with communication failures occurring more frequently in early years. Increased understanding of these failure patterns can inform ongoing PV system reliability activities, including simulation analyses, spare parts inventory management, cost estimates for operations and maintenance, and development of standards for inverter testing. Advanced implementations of machine learning techniques coupled with standardization of asset labels and descriptions can extend these insights into actionable information that can support development of algorithms for condition-based maintenance, which could further reduce failures and associated energy losses at PV sites.
Harris, Oliver C.; Lin, Yuxiao; Qi, Yue; Leung, Kevin; Tang, Maureen H.
At high operating voltages, metals like Mn, Ni, and Co dissolve from Li-ion cathodes, deposit at the anode, and interfere with the performance of the solid-electrolyte interphase (SEI) to cause constant Li loss. The mechanism by which these metals disrupt SEI processes at the anode remains poorly understood. Experiments from Part I of this work demonstrate that Mn, Ni, and Co all affect the electronic properties of the SEI much more than the morphology, and that Mn is the most aggressively disruptive of the three metals. In this work we determine how a proposed electrocatalytic mechanism can explain why Mn contamination is uniquely detrimental to SEI passivation. We develop a microkinetic model of the redox cycling mechanism and apply it to experiments from Part I. The results show that the thermodynamic metal reduction potential does not explain why Mn is the most active of the three metals. Instead, kinetic differences between the three metals are more likely to govern their reactivity in the SEI. Our results emphasize the importance of local coordination environment and proximity to the anode within the SEI for controlling electron transfer and resulting capacity fade.
Ultrathin epitaxial films of ferromagnetic insulators (FMIs) with Curie temperatures near room temperature are critically needed for use in dissipationless quantum computation and spintronic devices. However, such materials are extremely rare. Here, a room-temperature FMI is achieved in ultrathin La0.9Ba0.1MnO3 films grown on SrTiO3 substrates via an interface proximity effect. Detailed scanning transmission electron microscopy images clearly demonstrate that MnO6 octahedral rotations in La0.9Ba0.1MnO3 close to the interface are strongly suppressed. As determined from in situ X-ray photoemission spectroscopy, O K-edge X-ray absorption spectroscopy, and density functional theory, the realization of the FMI state arises from a reduction of Mn eg bandwidth caused by the quenched MnO6 octahedral rotations. The emerging FMI state in La0.9Ba0.1MnO3 together with necessary coherent interface achieved with the perovskite substrate gives very high potential for future high performance electronic devices.
Griffin, Patrick J.; Trkov, A.; Simakov, S.P.; Greenwood, L.R.; Zolotarev, K.I.; Capote, R.; Destouches, C.; Kahler, A.C.; Konno, C.; Kostal, M.; Aldama, D.L.; Chechev, V.; Majerle, M.; Malambu, E.; Ohta, M.; Pronyaev, V.G.; Yashima, H.; White, M.; Wagemans, J.; Vavtar, I.; Simeckova, E.; Radulovic, V.; Sato, S.
High quality nuclear data is the most fundamental underpinning for all neutron metrology applications. This paper describes the release of version II of the International Reactor Dosimetry and Fusion File (IRDFF-II) that contains a consistent set of nuclear data for fission and fusion neutron metrology applications up to 60 MeV neutron energy. The library is intended to support: a) applications in research reactors; b) safety and regulatory applications in the nuclear power generation in commercial fission reactors; and c) material damage studies in support of the research and development of advanced fusion concepts. The paper describes the contents of the library, documents the thorough verification process used in its preparation, and provides an extensive set of validation data gathered from a wide range of neutron benchmark fields. The new IRDFF-II library includes 119 metrology reactions, four cover material reactions to support self-shielding corrections, five metrology metrics used by the dosimetry community, and cumulative fission products yields for seven fission products in three different neutron energy regions. In support of characterizing the measurement of the residual nuclei from the dosimetry reactions and the fission product decay modes, the present document lists the recommended decay data, particle emission energies and probabilities for 68 activation products. It also includes neutron spectral characterization data for 29 neutron benchmark fields for the validation of the library contents. Additional six reference fields were assessed (four from plutonium critical assemblies, two measured fields for thermal-neutron induced fission on 233U and 239Pu targets) but not used for validation due to systematic discrepancies in C/E reaction rate values or lack of reaction-rate experimental data. Another ten analytical functions are included that can be useful for calculating average cross sections, average energy, thermal spectrum average cross sections and resonance integrals. The IRDFF-II library and comprehensive documentation is available online at www-nds.iaea.org/IRDFF/. Evaluated cross sections can be compared with experimental data and other evaluations at www-nds.iaea.org/exfor/endf.htm. The new library is expected to become the international reference in neutron metrology for multiple applications.
Results from the new Department of Energy super-parameterized (SP) Energy Exascale Earth System Model (SP-E3SM) are analyzed and compared to the traditionally parameterized E3SMv1 and previous studies using SP models. SP-E3SM is unique in that it utilizes Graphics Processing Unit hardware acceleration, cloud resolving model mean-state acceleration, and reduced radiation to dramatically increase the model throughput and allow decadal experiments at 100-km external resolution. It also differs from other SP models by using a spectral element dynamical core on a cubed-sphere grid and a finer vertical grid with a higher model top. Despite these differences, SP-E3SM generally reproduces the behavior of other SP models. Tropical wave variability is improved relative to E3SM, including the emergence of a Madden-Julian Oscillation and a realistic slowdown of Moist Kelvin Waves. However, the distribution of precipitation exhibits indicates an overly frequent occurrence of rain rates less than 1 mm day-1, and while the timing of diurnal rainfall shows modest improvements the signal is not as coherent as observations. A notable grid imprinting bias is identified in the precipitation field and attributed to a unique feedback associated with the interactions between the explicit cloud resolving model convection and the spectral element grid structure. Spurious zonal mean column water tendencies due to grid imprinting are quantified—while negligible for the conventionally parameterized E3SM, they become large with super-parameterization, approaching 10% of the physical tendencies. The implication is that finding a remedy to grid imprinting will become especially important as spectral element dynamical cores begin to be combined with explicitly resolved convection.
Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome's plausibility. Information measures based on Shannon's concept of entropy include realization information, Kullback-Leibler divergence, Lindley's information in experiment, cross entropy, and mutual information. We derive a general theory of information from first principles that accounts for evolving belief and recovers all of these measures. Rather than simply gauging uncertainty, information is understood in this theory to measure change in belief. We may then regard entropy as the information we expect to gain upon realization of a discrete latent random variable. This theory of information is compatible with the Bayesian paradigm in which rational belief is updated as evidence becomes available. Furthermore, this theory admits novel measures of information with well-defined properties, which we explored in both analysis and experiment. This view of information illuminates the study of machine learning by allowing us to quantify information captured by a predictive model and distinguish it from residual information contained in training data. We gain related insights regarding feature selection, anomaly detection, and novel Bayesian approaches.
We invert infrasound signals for an equivalent seismoacoustic source function using different atmospheric models to produce the necessary Green’s functions. The infrasound signals were produced by a series of underground chemical explosions as part of the Source Physics Experiment (SPE). In a previous study, we inverted the infrasound data using so-called predictive atmospheric models, which were based on historic, regional-scaled, publicly available weather observations interpolated onto a 3D grid. For the work presented here, we invert the same infrasound data, but using atmospheric models based on weather data collected in a time window that includes the approximate time of the explosion experiments, which we term postdictive models. We build two versions of the postdictive models for each SPE event: one that is based solely on the regional scaled observations, and one that is based on both regional scaled observations combined with on-site observations obtained by a weather sonde released at the time of the SPE. We then invert the observed data set three times, once for each atmospheric model type. We find that the estimated seismoacoustic source functions are relatively similar in waveform shape regardless of which atmospheric model that we used to construct the Green’s functions. However, we find that the amplitude of the estimated source functions is systematically dependent on the atmospheric model type: using the predictive atmospheric models to invert the data generally yields estimated source functions that are larger in amplitude than those estimated using the postdictive models.
This paper presents an in-depth review of ongoing experimental research efforts to fundamentally understand the strong near-field enhancement of radiative heat transfer and make use of the underlying physics for various novel applications. Compared to theoretical studies on near-field radiative heat transfer (NFRHT), its experimental demonstration has not been explored as much until recently due to technical challenges in precision gap control and heat transfer measurement. However, recent advances in micro-/nanofabrication and nanoscale instrumentation/control techniques as well as unprecedented growth in materials science and engineering have created remarkable opportunities to overcome the existing challenges in the measurement and engineering of NFRHT. Beginning with the pioneering works in 1960s, this paper tracks the past and current experimental efforts of NFRHT in three different configurations (i.e., sphere-plane, plane-plane, and tip-plane). In addition, future remarks on how to address current challenges in the experimental research of NFRHT are briefly discussed.
Nonlocal models provide accurate representations of physical phenomena ranging from fracture mechanics to complex subsurface flows, where traditional partial differential equations fail to capture effects caused by long-range forces at the microscale and mesoscale. However, the application of nonlocal models to problems involving interfaces such as multimaterial simulations and fluid-structure interaction, is hampered by the lack of a rigorous nonlocal interface theory needed to support numerical developments. In this paper, we use an energy-based approach to develop a mathematically rigorous nonlocal interface theory which provides a physically consistent extension of the classical perfect interface PDE formulation. Numerical examples validate the proposed framework and demonstrate the scope of our theory.
The initial product specification' for the H12 Universal Cartridge Carrier (UUC) was released in October 1952 and is the twelfth piece of H-Gear (sequentially numbered) ever developed. It is the oldest piece of H-Gear currently in use. To gain perspective on the number of H-Gear since designed, the most currently developed and deployed H-Gear is the H1768, Inspection Stand. The UUC, (commonly referred to as just the "H12") has since been renamed to the H12 Adjustable Hand Truck. It was developed to support various maintenance operations for ordnance assembly and disassembly. This paper will provide evidence (where available) for the H12s current state of reliability, maintainability, and sustainability (RMA). Where documented evidence is not available, conclusions will be drawn based on its continued effective use over the past 67-years of service.
The Arroyo Seco Improvement Program is being carried out at Sandia National Laboratories, California in order to address erosion and other streambed instability issues in the Arroyo Seco as it crosses the Sandia National Laboratories, California. The work involves both repair of existing eroded areas, and habitat enhancement. This work is being carried out under the requirements of Army Corps of Engineers permit 2006-400195S and California Regional Water Quality Control Board, San Francisco Bay Region Water Quality Certification Site No. 02-01-00987.
Sandia National Laboratories worked with NERC staff to provide stakeholder guidance in responding to a May 2018 NERC alert regarding dynamic performance and modeling issues for utility-scale inverter-based resources. The NERC alert resulted from event analyses for grid disturbances that occurred in southern California in August 2016 and October 2017. Those disturbances revealed the use of momentary cessation of transmission connected inverter-based generation- a short time period when they ceased to inject current into the grid, counter to desired transmission operation. The event analyses concluded that, in many cases, the Western Interconnection system models used to determine planning and operating criteria do not reflect the actual behavior of solar plants, resulting in overly optimistic planning assessments and substandard operational responses. This technical report summarizes the gaps between the models and actual performance that were observed at those times, and the guidance that Sandia and NERC provided to owners of solar PV power plants, transmission planners, transmission operators and planning/reliability coordinators to modify existing models to reflect that actual performance
Ohta, Taisuke; Berg, Morgann; Liu, Fangze; Smith, Sean; Copeland, R.G.; Chan, Calvin K.; Mohite, Aditya D.; Beechem, Thomas E.
Imaging of fabricated nanostructures or nanomaterials covered by dielectrics is highly sought after for diagnostics of optoelectronics components. We show imaging of atomically thin MoS2 flakes grown on SiO2-covered Si substrates and buried beneath HfO2 overlayers up to 120 nm in thickness using photoemission electron microscopy with deep-UV photoexcitation. Comparison of photoemission yield (PEY) to modeled optical absorption evinced the formation of optical standing waves in the dielectric stacks (i.e., cavity resonances of HfO2 and SiO2 layers on Si). The presence of atomically thin MoS2 flakes modifies the optical properties of the dielectric stack locally. Accordingly, the cavity resonance condition varies between the sample locations over buried MoS2 and surrounding areas, resulting in image contrast with submicron lateral resolution. This subsurface sensitivity underscores the role of optical effects in photoemission imaging with low-energy photons. This approach can be extended to nondestructive imaging of buried interfaces and subsurface features needed for analysis of microelectronic circuits and nanomaterial integration into optoelectronic devices.
This report describes a structure to aid in evaluation of release mitigation strategies across a range of reactor technologies. The assessment performed for example reactor concepts utilizes previous studies of postulated accident sequences for each reactor concept. This simplified approach classifies release mitigation strategies based on a range of barriers, physical attenuation processes, and system performance. It is not, however, intended to develop quantitative estimates of radiological release magnitudes and compositions to the environment. Rather, this approach is intended to identify the characteristics of a reactor design concepts release mitigation strategies that are most important to different classes of accident scenarios. It uses a scoping methodology to provide an approximate, order-of-magnitude, estimate of the radiological release to the environment and associated off-site consequences. This scoping method is applied to different reactor concepts, considering the performance of barriers to fission product release for these concepts under sample accident scenarios. The accident scenarios and sensitivity evaluations are selected in this report to evaluate the role of different fission product barriers in ameliorating the source term to the environment and associated off-site consequences. This report applies this structure to characterize how release mitigation measures are integrated to define overall release mitigation strategies for High Temperature Gas Reactors (HTGRs), Sodium Fast Reactors (SFRs), and liquid fueled Molten Salt Reactors (MSRs). To support this evaluation framework, factors defining a chain of release attenuation stages, and thus an overall mitigation strategy, must be established through mechanistic source term calculations. This has typically required the application of an integral plant analysis code such as MELCOR. At present, there is insufficient evidence to support a priori evaluation of the effectiveness of a release mitigation strategy for advanced reactor concepts across the spectrum of events that could challenge the radiological containment function. While it is clear that these designs have significant margin to radiological release to the environment for the scenarios comprising the design basis, detailed studies have not yet been performed to assess the risk profile for these plants. Such studies would require extensive evaluation across a reasonably complete spectrum of accident scenarios that could lead to radiological release to the environment.
Multivariate multiple regression models are applied to simplified pyrotechnic igniters for the first time to understand how changes in manufactured parameters can affect the output gas dynamic response and the timing of ignition events. The statistical modeling technique is applied to demonstrate quantification of the effects of a set of independent variables measured in the as-fabricated igniters on a set of responses experimentally measured from the functioned igniters. Two independent process variables were intentionally varied following a full factorial experimental design while several other independent variables varied within their normal manufacturing variability range. The four igniter performance responses consisted of the timing of sequential events during igniter function and visual gas dynamic output in the form of shock wave strength observed with high-speed schlieren imaging. Linear regression models built using the measurements throughout the manufacturing processes and the output variance provide insight into the critical device parameters that dominate performance
Structural modeling and visualization of salt caverns requires three-dimensional representations. These representations are typically produced from sonar surveys conducted by companies that then produce a report of depths, distances, and volumes. There are multiple formats that are vendor dependent, and, as technology improves, there have been changes from only horizontal surveys to inclined shots for ceilings and floors to mid-cavern inclined shots. For geomechanical modeling, leaching predictions, and cavern stability visualizations, Sandia has previously written in-house software, called SONAR8, that created a consistent geometry format from the processed sonar reports. However, the increase in the need for mid-cavern inclined surveys led to the discovery of certain limitations in that code. This report describes methods used to process the multiple different formats to handle inclined shots in a consistent and accurate manner in our modeling efforts. A set of file formats and a database schema that was developed for this work is also documented in the appendices.
This document describes the requirements for a software tool that will enable FRMAC to simulate large sets of sample result data that is based realistically on simulated radionuclide deposition grids from NARAC. The user of this tool would be scientists involved in exercise and drill planning or part of the simulation cell of an exercise controller team. A key requirement is that this tool must be able to be run with a reasonable amount of training and job aids by any person within the Assessment, Laboratory Analysis, or Monitoring and Sampling divisions of the FRMAC to support any level of exercise from the small IPX to the national level full scale exercise. This tool should be relatively lean and stand-alone so that the user can run it in the field with limited IT resources. This document will describe the desired architecture, design characteristics, order of operations, and algorithms that can be given to a software development team to assist them in project scoping, costing, and eventually, development.
This report documents the results of analysis performed to investigate the impact of inverter-based resource (IBR) response to unbalanced faults on transmission system protective relay dependability and security. Electromagnetic transient (EMT) simulations were performed to simulate IBR response to these faults using existing manufacturer-developed EMT models for four separate IBRs. The study team was composed of IBR manufacturers, relay manufacturers, transmission providers, reliability coordinators and industry consultants with experience in EMT simulation and system protection. The results indicate that under certain conditions, IBR response can result in protective relay misoperations if current protection practices, which were developed based on conventional power sources, are not adapted to the characteristics of IBRs.
This Environmental Restoration Operations (ER) Consolidated Quarterly Report (ER Quarterly Report) provides the status of ongoing corrective action activities being implemented at Sandia National Laboratories, New Mexico (SNL/NM) during the July - September 2019 reporting period. Table I-1 lists the Solid Waste Management Units (SWMUs) and Areas of Concern (A0Cs) currently identified for corrective action at SNL/NM. This section of the ER Quarterly Report summarizes the work completed during this quarterly reporting period at sites undergoing corrective action. Corrective action activities were conducted during this reporting period at the three groundwater AOCs (Burn Site Groundwater [BSG] AOC, Technical Area-V [TA-V] Groundwater [TAVG] AOC, and Tijeras Arroyo Groundwater [TAG] AOC). Corrective action activities are deferred at the Long Sled Track (SWMU 83), the Gun Facilities (SWMU 84), and the Short Sled Track (SWMU 240) because these three sites are active mission facilities. These three active mission sites are located in Technical Area-III. There were no SWMUs or AOCs in the corrective action complete regulatory process during this quarterly reporting period.
Each year Wind Energy Technologies Dept. 08821 submits a memo through the Sandia National Labs Review and Approval (R&A) system to facilitate the release of the Scaled Wind Farm Technology (SWiFT) Facility raw logged data. This release of data explicitly does not cover specialized instruments, or guest researcher instruments (i.e. SpiDAR, SpinnerLidar), nor processed data.
This report will describe the one test conducted during phase III of the Pipe Overpack Container (POC) test campaign, present preliminary results from these tests, and discuss implications for the Criticality Control Overpack (CCO). The goal of this test was to see if aerosol surrogate material inside the Criticality Control Container (CCC) gets released when the drum lid of the CCO comes off during a thirty-minute long, fully-engulfing, fire test. As expected from POC tests conducted in Phase I and II of this test campaign, the CCO drum lid is ejected about one minute after the drum is exposed to fully-engulfing flames. The remaining pressure inside the drum is high enough to eject the top plywood dunnage a considerable distance from the drum. Subsequently, most of the bottom plywood dunnage supporting the CCC burns off during and after the fire. High pressure buildup inside the CCC and inside two primary containers holding the surrogate powder also results in damage to the filter media of the CCC and the filter-house, thread attachment of the primary canisters. No discernable release of surrogate powder material was detected from the two primary containers when pre- and post-test average mass were compared. However, when the average masses are corrected to account for possible uncertainties in mass measurements, error overlap does not preclude the possibility that some surrogate powder mass may have been lost from these primary canisters. Still, post-test conditions of the secondary canisters enclosing these two primary canisters suggest it is very unlikely this mass loss would have escaped into the CCC.
Across many industries and engineering disciplines, physical components and systems of components are designed and deployed into their environment of intended use. It is the desire of the design agency to be able to predict whether their component or system will survive its physical environment or if it will fail due to mechanical stresses. One method to determine if the component will survive the environment is to expose the component to a simulation of the environment in a laboratory. One difficulty in doing this is that the component may not have the same boundary condition in the laboratory as is in the field configuration. This paper presents a novel method of quantifying the error in the modal domain that occurs from the impedance difference between the laboratory test fixture and the next level of assembly in the field configuration. The error is calculated from the projection from one mode shape space to the other, and the error is in terms of each mode of the field configuration. This provides insight into the effectiveness of the test fixture with respect to the ability to recreate the mode shapes of the field configuration. A case study is presented to show that the error in the modal projection between two configurations is a lower limit for the error that can be achieved by a laboratory test.
This document provides a scanned version of a 1987 SAND report that was never formally published. However, this report was referenced within the MELCOR Reference Manual and, therefore, provides historical information and technical basis for the MELCOR code. This document is being made available to permit users of the MELCOR code access to the information. The title page has been edited to prevent any confusion with regards to the possible documentation identifiers, such as the SAND report number or the intended date of publication. Beyond these modifications, a cover, distribution, and back cover are prepended and appended to the document to conform to modern SAND report style guidelines. The first four chapters of this report were updated and released under the title "Fission Product Behavior During Severe LWR Accidents: Recommendations for the MELCOR Code System. Volume I" and were made available by the U.S. NRC through the Adams database under accession number ML19227A327. No prior release of the remaining content of this report has occurred.
The Navajo Nation covers about 27,000 square miles in the Southwestern United States with approximately 270 sunny days a year. Therefore, the Navajo Nation has the potential to develop utility-scale solar photovoltaic (PV) energy for the Navajo people and export electricity to major cities to generate revenues. In April 2019, the Navajo Nation issued a proclamation to increase residential and utility-scale renewable energy development on the Navajo Nation. In response, this research assesses the potential for utility-scale solar energy development on the Navajo Nation using criteria such as access to roads, transmission lines, slope/terrain data, aspect/direction, and culturally sensitive sites. These datasets are applied as layers using ArcGIS to identify regions that have good potential for utility-scale solar PV installations. Land availability on the Navajo Nation has been an issue for developing utility-scale solar PV, so this study proposes potential locations for solar PV and how much energy these potential sites could generate. Furthermore, two coal-fired power plants, the Navajo Generating Station and the San Juan Generating Station, will close soon and impact the Navajo Nation's energy supply and economy. This study seeks to answer two main questions: whether utility- scale solar energy could be used to replace the energy generated by both coal-fired powerplants, and what percentage of the Navajo Nation's energy demands can be met by utility-scale solar energy development? Economic development is a major concern; therefore, this study also examines what utility-scale solar development will mean for the Navajo Nation economy. The results of this study show that the Navajo Nation has a potential PV capacity of 45,729 MW to 91,459 MW. Even with the lowest calculated capacity, utility-scale solar PV has the potential to generate more than 11 times the power of the NGS and SJGS combined.
Wind energy can provide renewable and sustainable electricity to Native American reservations, including rural homes, and power schools and businesses on reservations. It can also provide tribes with a source of income and economic development. The purpose of this paper is to determine the potential for deploying community and utility-scale wind renewable technologies on the Turtle Mountain Band of Chippewa tribal lands. Ideal areas for wind technology development were investigated based on annual wind resources, terrain, land usage, and other factors such as culturally sensitive sites. The result is a preliminary assessment of wind energy potential on Turtle Mountain lands, which can be used to justify further investigation and investment into determining the feasibility of future wind technology projects.
The Navajo Nation consists of about 55,000 residential homes spread across 27,000 square miles of trust land in the Southwest region of the United States. The Navajo Tribal Utility Authority (NTUA) reports that approximately 15,000 homes on the reservation do not have electricity due to the high costs of connecting rural homes located miles from utility distribution lines. In order to get these rural homeowners access electricity, NTUA and other Native owned companies are examining small-scale renewable energy systems to provide power for necessary usage such as lighting and refrigeration. The goal of this study is to evaluate the current renewable deployment efforts and provide additional considerations for photovoltaic (PV) systems that will optimize performance and improve efficiency to reduce costs. There are three case studies presented in different locations on the Navajo Nation with varying solar resource and energy load requirements. For each location, an assessment is completed that includes environmental parameters of the site- specific landscape and a system performance analysis of an off-grid residential PV system. The technical process, repeated for each location, demonstrates how the variance and uniqueness of each household can impact the system requirements after optimizations are applied. Therefore, the household variabilities and difference in locations must be considered. The differing results of each case study suggests additional analysis is needed for designing small-scale PV systems that takes a home-land-family specific approach to allow for better efficiency and more flexibility for future solar innovations to be considered for overall cost reductions.
The use of the Monte Carlo N-Particle Transport Code (MCNP) to calculate detector sensitivity for Self-Powered Neutron Detectors (SPNDs) in the Annular Core Research Reactor (ACRR) could be a vital tool in the effort to optimize the design of next-generation SPNDs. Next-generation SPND designs, which consider specific materials and geometry, may provide experimenters with capabilities for advanced mixed field dosimetry. These detectors will need to be optimized for configuration, materials, and geometries and the ability to model and iterate must be available in order to decide on the ideal. SPND design. SPNDs were modeled in MCNP which closely resembled the dimensions and location of actual detectors used in the ACRR. Tallies were used to calculate detector sensitivity. Using metrics from a previous report, oscilloscope data from pulses were manipulated in a Matrix Laboratory computing environment (MATLAB) script to calculate experimental detector sensitivity. This report outlines the process in which experimental data from ACRR pulses verified results from tallies in an MCNP ACRR model. The sensitivity values from experiments and MCNP calculations agreed within one standard deviation. Parametric studies were also performed with MCNP to investigate the effects of materials and dimensions of different SPNDs.
Reactor pulse characterization at the Annular Core Research Reactor (ACRR) at Sandia Technical Area V (TA-V) is commonly done through photo conductive detection (PCD) and calorimeter detectors. Each of these offer a mode of analyzing a digital signal with different advantages/methods for determination of integrated dose or temporal based metrology. This report outlines a method and code that takes the millions of data points from such detectors and delivers a characteristic pulse trendline through two main methods: digital signal filtration and machine learning, in particular, Support Vector Machines (SVMs). Each method's endpoint is to deliver a characteristic curve for the many bucket environments of ACRR while considering other points of interest including delayed gamma removal for prompt dose metrics. This work draws and adds on previous work detailing the delayed gamma fraction contributions from CINDER simulations of the ACRR. Results from this project show a method to determine characteristic curves in a way that has previously been limited by data set size.
Additive manufacturing (AM) includes a diverse suite of innovative manufacturing processes for producing near-net shape components, typically from powder or wire feedstock. Reported mechanical properties of AM materials vary significantly depending on the details of the manufacturing process and the characteristics of the processing defects (namely, lack of fusion defects). However, an excellent combination of strength, ductility and fracture resistance can be achieved in AM type 304L and 316L austenitic stainless steels by minimizing processing defects. It is also important to recognize that localized solidification processing during AIVI produces microstructures more analogous to weld microstructures than wrought microstructures. Consequently, the mechanical behavior of AM austenitic stainless steels in harsh environments can diverge from the performance of wrought materials. This report gives an overview of the fracture and fatigue response of type 304L materials from both directed energy deposition (DED) and powder bed fusion (PBF) techniques. In particular, the mechanical performance of these materials is considered for high-pressure hydrogen applications by evaluating fatigue and fracture resistance after thermally precharging of test specimens in high-pressure gaseous hydrogen. The mechanical behaviors are considered with respect to previous reports on hydrogen-assisted fracture of austenitic stainless steel welds and the unique characteristics of the AM microstructures. Fatigue crack growth can be relatively insensitive to processing defects, displaying similar behavior as wrought materials. Fracture resistance of dense AM austenitic stainless steel, on the other hand, is more consistent with weld metal than with compositionally-similar wrought materials. Hydrogen effects in the AM materials are generally more severe than in wrought materials, but comparable to measurements on welded austenitic stainless steels in hydrogen environments. While hydrogenassisted fracture manifests differently in welded and AM austenitic stainless steel, the fracture process appears to have a common origin in the compositional microsegregation intrinsic to solidification processes.
Many problems in engineering and sciences require the solution of large scale optimization constrained by partial differential equations (PDEs). Though PDE-constrained optimization is itself challenging, most applications pose additional complexity, namely, uncertain parameters in the PDEs. Uncertainty quantification (UQ) is necessary to characterize, prioritize, and study the influence of these uncertain parameters. Sensitivity analysis, a classical tool in UQ, is frequently used to study the sensitivity of a model to uncertain parameters. In this article, we introduce "hyper-differential sensitivity analysis" which considers the sensitivity of the solution of a PDE-constrained optimization problem to uncertain parameters. Our approach is a goal-oriented analysis which may be viewed as a tool to complement other UQ methods in the service of decision making and robust design. We formally define hyper-differential sensitivity indices and highlight their relationship to the existing optimization and sensitivity analysis literatures. Assuming the presence of low rank structure in the parameter space, computational efficiency is achieved by leveraging a generalized singular value decomposition in conjunction with a randomized solver which converts the computational bottleneck of the algorithm into an embarrassingly parallel loop. Two multi-physics examples, consisting of nonlinear steady state control and transient linear inversion, demonstrate efficient identification of the uncertain parameters which have the greatest influence on the optimal solution.