Coronal mass ejections (CMEs) occur when long-lived magnetic flux ropes (MFRs) anchored to the solar surface destabilize and erupt away from the Sun. This destabilization is often described in terms of an ideal magnetohydrodynamic instability called the torus instability. It occurs when the external magnetic field decreases sufficiently fast such that its decay index, n = -z θ(ln B) θz, is larger than a critical value, n > ncr, where ncr = 1.5 for a full, large aspect ratio torus. However, when this is applied to solar MFRs, a range of conflicting values for ncr is found in the literature. To investigate this discrepancy, we have conducted laboratory experiments on arched, line-tied flux ropes and applied a theoretical model of the torus instability. Our model describes an MFR as a partial torus with foot points anchored in a conducting surface and numerically calculates various magnetic forces on it. This calculation yields better predictions of ncr that take into account the specific parameters of the MFR. We describe a systematic methodology to properly translate laboratory results to their solar counterparts, provided that the MFRs have a sufficiently small edge safety factor or, equivalently, a large enough twist. After this translation, our model predicts that ncr in solar conditions falls near ncr ~ 0.9 solar and within a larger range of ncr ~ (0.7, 1.2) solar, depending on the parameters. The methodology of translating laboratory MFRs to their solar counterparts enables quantitative investigations of CME initiation through laboratory experiments. These experiments allow for new physics insights that are required for better predictions of space weather events but are difficult to obtain otherwise.
Dynamic interface instabilities such as Rayleigh–Taylor, Kelvin–Helmholtz, and Richtmyer–Meshkov are important in a number of physical phenomena. Besides meriting study because of their role in natural events and man-made applications, they can also be used to study constitutive properties of materials in extreme conditions. Both RTI and RMI configurations have been used to study the strength of solids at high strain rates, though RMI has largely been limited to zero or ambient pressure. Recently, advances in imaging have allowed tamped RMI experiments to be performed in which the pressure is maintained above ambient. In this study, we examine the tamped RMI for determining material strength. Through simulation, we explore the behavior of the jetting material and examine the sensitivity of jetting to material properties. We identify simple scaling laws that relate the key physical parameters controlling jetting, which are compared to previous results from the literature. We use these scaling law and other considerations to examine issues associated with tamped RMI experiments.
Fracture resistance of pipeline welds from a range of strength grades and welding techniques was measured in air and 21 MPa hydrogen gas, including electric resistance weld of X52, friction stir weld of X100 and gas metal arc welds (GMAW) of X52, X65 and X100. Welds exhibited a decrease in fracture resistance in hydrogen compared to complementary tests in air. A general trend was observed that fracture resistance in 21 MPa hydrogen gas decreased with increasing yield strength. To accommodate material constraints, two different fracture coupon geometries were used in this study, which were shown to yield similar fracture resistance values in air and 21 MPa hydrogen gas; values using different coupons resulted in less than 15% difference. In addition, fracture coupons were removed from controlled locations in select welds to examine the potential influence of orientation and residual stress. The two orientations examined in the X100 GMAW exhibited negligible differences in fracture resistance in air and, similarly, negligible differences in hydrogen. Residual stress exhibited a modest influence on fracture resistance; however, a consistent trend was not observed between tests in air and hydrogen, suggesting further studies are necessary to better understand the influence of residual stress. A comparison of welds and base metals tested in hydrogen gas showed similar susceptibility to hydrogen-assisted fracture. The overall dominant factor in determining the susceptibility to fracture resistance in hydrogen is the yield strength.
In this study, we report on the thermal conductivity of amorphous carbon generated in diamond via nitrogen ion implantation (N 3 + at 16.5 MeV). Transmission electron microscopy techniques demonstrate amorphous band formation about the longitudinal projected range, localized approximately 7 μm beneath the sample surface. While high-frequency time-domain thermoreflectance measurements provide insight into the thermal properties of the near-surface preceding the longitudinal projected range depth, a complimentary technique, steady-state thermoreflectance, is used to probe the thermal conductivity at depths which could not otherwise be resolved. Through measurements with a series of focusing objective lenses for the laser spot size, we find the thermal conductivity of the amorphous region to be approximately 1.4 W m-1 K-1, which is comparable to that measured for amorphous carbon films fabricated through other techniques.
The solid-electrolyte interphase (SEI) that forms on lithium ion battery (LIB) anodes prevents degradation-causing transfer of electrons to the electrolyte. Grain boundaries (GBs) between different SEI components, like LiF, have been suggested to accelerate Li+transport. However, using the non-equilibrium Green's function technique with density functional theory (NEGF-DFT), we find that GBs enhance electron tunneling in thin LiF films by 1-2 orders of magnitude, depending on the bias. Extrapolating to thicker films using the Wentzel-Kramers-Brillouin (WKB) method emphasizes that safer batteries require passivation of GBs in the SEI.
Objectives: Introduce a sensor selection approach to assist test engineers with MIMO test design. Demonstrate the capability of the approach. Approach: Define a desired response from a field model. Supply the sensor selection technique to two lab models, with different boundary conditions than the “field” model. Compare the laboratory response to the field response using sensors selected from the approach.
Summary: Sensor selection approach for MIMO testing was defined and demonstrated on a model of a practical system. Laboratory response obtained using the approach matched the field response well. Information about how many control sensors, and where to locate sensors can be gleaned from the approach.
The outer core of the Earth is composed primarily of liquid iron, and the inner core boundary is governed by the intersection of the melt line and the geotherm. While there are many studies on the thermodynamic equation of state for solid iron, the equation of state of liquid iron is relatively unexplored. In this work, we use dynamic compression to diagnose the high-pressure liquid equation of state of iron by utilizing the shock-ramp capability at Sandia National Laboratories’ Z-Machine. This technique enables measurements of material states off the Hugoniot by initially shocking samples and subsequently driving a further, shockless compression. Planetary studies benefit greatly from isentropic, off-Hugoniot experiments since they can cover pressure-temperature (P-T) conditions that are close to adiabatic profiles found in planetary interiors. We used this method to drive iron to P-T conditions similar to those of the Earth’s outer-inner core boundary, along an elevated-temperature isentrope in the liquid from 275 GPa to 400 GPa. We derive the equation of state using a hybrid backward integration – forward Lagrangian technique on particle velocity traces to determine the pressure-density history of the sample. Our results are in excellent agreement with SESAME 92141, a previously published equation of state table. With our data and previous experimental data on liquid iron we provide new information on the iron melting line and derive new parameters for a Vinet-based equation of state. The table and our parameterized equation of state are applied to provide an updated means of modeling the pressure, mass, and density of liquid iron cores in exoplanetary interiors.
Wang, Dixiong; Musavigharavi, Pariasadat; Zheng, Jeffrey; Esteves, Giovanni; Liu, Xiwen; Stach, Eric A.; Jariwala, Deep; Olsson III, Roy H.
In this work, the frequency-dependent ferroelectric properties of 45 nm (Al,Sc)N films sputter deposited on complementary metal–oxide–semiconductor (CMOS)-compatible Al metal electrodes are measured and compared. Low in-plane compressive stress (-10 ± 20 MPa) is observed in (Al,Sc)N thin films deposited on Al electrodes. The (Al,Sc)N films exhibit an imprint in the measured coercive fields (Ec) of -4.3/+5.3 MV cm-1 at 10 kHz. Using positive-up negative-down (PUND) measurements, ferroelectric switching is observed within ≈200 ns of an applied voltage pulse, which demonstrates the ability of ferroelectric (Al,Sc)N to achieve the fast read/write speeds desired in memory devices.
Each year there are millions of dollars spent on the research and production of high-resolution detectors. This research indicates that the pursuit of higher resolution detectors is not always necessary. The terminal resolution of a NaI detector, or highest detector resolution, at which identification algorithms fail to identify highly enriched uranium (HEU) was evaluated using GADRAS, Genie, and GammaVision. GADRAS employs a template matching algorithm, while Genie and GammaVision utilize a mathematical approach for peak search and identification. The NaI spectra utilized for evaluation were generated using the GADRAS Inject tab and source modeling functions. Each spectrum included terrestrial and cosmic background from Dallas, TX. The resolutions for each spectrum were increased from a default 8.92% to a point where each algorithm would fail to identify 235U from a HEU source. Six different source configurations were used in this research: bare HEU, 50% shielded HEU, 90% shielded HEU, bare HEU with an interference source of 99mTc, bare HEU with 99mTc both shielded 50%, and bare HEU with 99mTc both shielded 90%. T
The role of local molecular structure on calculated 13C and 19F NMR chemical shifts for graphite fluoride materials was explored by using gauge-including projector augmented wave (GIPAW) computational methods for different periodic crystal polymorphs and density functional theory (DFT) gauge-including atomic orbital (GIAO) computational methods for individual graphite fluoride platelets, i.e., fluorinated graphene (FG). The impact of stacking sequences, d-spacing, and ring conformations on fully fluorinated graphite fluoride structures was investigated. A range of different defects including Stone-Wales, F and C vacancies, void formation, and F inversion were also evaluated using FG structures. These calculations show that distinct chemical shift signatures exist for many of these polymorphs and defects, therefore providing a basis for spectral assignment and development of models describing the mean local CF structure in disordered graphite fluoride materials.
MLIR (Multi-Level Intermediate Representation), is an extensible compiler framework that supports high-level data structures and operation constructs. These higher-level code representations are particularly applicable to the artificial intelligence and machine learning (AI/ML) domain, allowing developers to more easily support upcoming heterogeneous AI/ML accelerators and develop flexible domain specific compilers/frameworks with higher-level intermediate representations (IRs) and advanced compiler optimizations. The result of using MLIR within the LLVM compiler framework is expected to yield significant improvement in the quality of generated machine code, which in turn will result in improved performance and hardware efficiency
Karve, Vaibhav; Foulk, James W.; Abolhelm, Marzieh; Work, Daniel B.; Sowers, Richard B.
This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.
Vemuganti, Shreya; Stormont, John C.; Pyrak-Nolte, Laura J.; Dewers, Thomas; Taha, M.M.R.
Cement is widely used in wellbores to stabilize the steel casing used in wellbore operations for oil and gas production, enhanced geothermal systems and carbon sequestration, and to limit fluid movement between sub-surface strata. Flaws such as microcracks in wellbore cement can lead to leakage along the wellbore compromising wellbore integrity. There is an increasing need for methods to monitor cement crack propagation in wellbore environments. In this study, we develop and report the first cementitious sensors capable of exhibiting high frequency acoustic bandgaps (ABGs) using carbon nanotubes (CNTs). Computational simulations of a sensor unit cell are used to design cement-multi walled carbon nanotubes (MWCNTs) sensors that show a wide bandgap. When the cement-MWCNTs sensors is embedded in cement specimens, bandgaps were measured experimentally under 300 kHz and under 600 kHz, consistent with the computationally predicted bandgaps in the range of 290–360 kHz, 410–460 kHz and 515–585 kHz. These bandgap features were absent in homogeneous cement specimens. X-ray tomographic reconstructions showed microscopic debonding at cement-MWCNTs sensor interface. Frequency response analysis of a three-dimensional computational model indicated a shift of frequency of minimum transmission due to the interface debonding, but no perturbation of bandgap response was observed. Here, the cement-MWCNTs sensors developed in this study show the potential of a packed CNT inclusion material in cementitious matrix to create ABGs in a cement matrix.
Hamm, Susan G.; X, Arlene A.; Blankenship, Douglas A.; Boyd, Lauren W.; X, Elizabeth B.; Frone, Zachary; X, Ian H.; X, Hannah H.; X, Matthew K.; X, Alethia M.; Mckittrick, Alexis M.W.; X, Lindsey M.; X, Elisabet M.; X, Angel N.; X, Jon P.; Porse, Sean L.; X, Alexandra P.; X, George S.; X, Coryne T.; X, William V.; X, Gerry W.; X, Michael W.; X, Jeffrey W.
Geothermal energy can provide answers to many of America’s essential energy questions. The United States has tremendous geothermal resources, as illustrated by the results of the DOE GeoVision analysis, but technical and non-technical barriers have historically stood in the way of widespread deployment of geothermal energy. The U.S. Department of Energy’s Geothermal Technologies Office within the Office of Energy Efficiency and Renewable Energy has invested more than $470 million in research and development (R&D) since 2015 to meet its three strategic goals: (1) unlock the potential of enhanced geothermal systems, (2) advance technologies to increase geothermal energy on the U.S. electricity grid, and (3) support R&D to expand geothermal energy opportunities throughout the United States. Here, we describe many of those R&D initiatives and outlines future directions in geothermal research.
Pyruvic acid, a representative alpha-keto carboxylic acid, is one of the few organic molecules destroyed in the troposphere by solar radiation rather than by reactions with free radicals. To date, only its stable final products were identified, often with contribution from secondary chemistry, making it difficult to elucidate photodissociation mechanisms following excitation to the lowest singlet excited-state (S1) and the role of the internal hydrogen bond in the most-stable Tc conformer. Using multiplexed photoionization mass spectrometry we report the first direct experimental evidence, via the observation of singlet methylhydroxycarbene (MHC) following 351 nm excitation, supporting the decarboxylation mechanism previously proposed. Decarboxylation to MHC + CO2 represents 97–100% of product branching at 351 nm. We observe vinyl alcohol and acetaldehyde, which we attribute to isomerization of MHC. We also observe a 3 ± 2% yield of the Norrish Type I photoproducts CH3CO + DOCO, but only from d1-pyruvic acid. At 4 Torr pressure, we measure a photodissociation quantum yield of $1.0^{+0}_{–0.4}$, consistent with IUPAC recommendations. However, our measured product branching fractions disagree with IUPAC. In light of previous calculations, these results support a mechanism in which hydrogen transfer on the S1 excited state occurs at least partially by tunneling, in competition with intersystem crossing to the T1 state. Here, we present the first evidence of a bimolecular reaction of MHC in the gas phase, where MHC reacts with pyruvic acid to produce a C4H8O2 product. This observation implies that some MHC produced from pyruvic acid in Earth's troposphere will be stabilized and participate in chemical reactions with O2 and H2O, and should be considered in atmospheric modeling.
Theocharides, Spyros; Theristis, Marios; Makrides, George; Kynigos, Marios; Spanias, Chrysovalantis; Georghiou, George E.
A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.
Rempe, Susan; Cetuk, Hannah; Anishkin, Andriy; Scott, Alison J.; Ernst, Robert K.
The outer membrane (OM) of Gram-negative (G-) bacteria presents a barrier for many classes of antibacterial agents. Lipopolysaccharide (LPS), present in the outer leaflet of the OM, is stabilized by divalent cations and is considered to be the major impediment for antibacterial agent permeation. However, the actual affinities of major antibiotic classes toward LPS have not yet been determined. In the present work, we use Langmuir monolayers formed from E. coli Re and Rd types of LPS to record pressure-area isotherms in the presence of antimicrobial agents. Our observations suggest three general types of interactions. First, some antimicrobials demonstrated no measurable interactions with LPS. This lack of interaction in the case of cefsulodin, a third-generation cephalosporin antibiotic, correlates with its low efficacy against G-bacteria. Ampicillin and ciprofloxacin also show no interactions with LPS, but in contrast to cefsulodin, both exhibit good efficacy against G-bacteria, indicating permeation through common porins. Second, we observe substantial intercalation of the more hydrophobic antibiotics, novobiocin, rifampicin, azithromycin, and telithromycin, into relaxed LPS monolayers. These largely repartition back to the subphase with monolayer compression. We find that the hydrophobic area, charge, and dipole all show correlations with both the mole fraction of antibiotic retained in the monolayer at the monolayer-bilayer equivalence pressure and the efficacies of these antibiotics against G-bacteria. Third, amine-rich gentamicin and the cationic antimicrobial peptides polymyxin B and colistin show no hydrophobic insertion but are instead strongly driven into the polar LPS layer by electrostatic interactions in a pressure-independent manner. Their intercalation stably increases the area per molecule (by up to 20%), which indicates massive formation of defects in the LPS layer. These defects support a self-promoted permeation mechanism of these antibiotics through the OM, which explains the high efficacy and specificity of these antimicrobials against G-bacteria.
This paper presents the first systematic comparison between south-facing monofacial and bifacial photovoltaic (PV) modules, as well as between south-facing bifacial and vertical east-west facing bifacial PV modules in Alaska. The state’s solar industry, driven by the high price of energy and dropping equipment costs, is quickly growing. The challenges posed by extreme sun angles in Alaska’s northern regions also present opportunities for unique system designs. Annual bifacial gains of 21% were observed between side by side south-facing monofacial and bifacial modules. Vertical east-west bifacial modules had virtually the same annual production as south-facing latitude tilt bifacial modules, but with different energy production profiles.
Su, Yang D.; Preger, Yuliya; Burroughs, Hannah; Sun, Chenhu; Ohodnicki, Paul R.
Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.
Herein we report the progress towards an organic glass scintillator with fast and thermal neutron sensitivity providing “triple” pulse shape discrimination (PSD) through the inclusion of a boron-incorporated aromatic molecule. The commercially available molecule 2-(p-tolyl)-1,3,2-dioxaborinane (TDB) can be readily synthesized in one step using inexpensive materials and incorporated into the organic glass scintillator at 20% by weight or 0.25% 10B by mass. In addition, we demonstrate that TDB can be easily scaled up and formulated into organic glass scintillator samples to produce a thermal neutron capture signal with a light yield equivalent to 120.4 ± 3.7 keVee, which is the highest value reported in the literature to date.
Over the past decade as Moore's Law has slowed, the need for new forms of computation that can provide sustainable performance improvements has risen. A new method, called in situ computing, has shown great potential to accelerate matrix vector multiplication (MVM), an important kernel for a diverse range of applications from neural networks to scientific computing. Existing in situ accelerators for scientific computing, however, have a significant limitation: These accelerators provide no acceleration for preconditioning-A key bottleneck in linear solvers and in scientific computing workflows. This paper enables in situ acceleration for state-of-The-Art linear solvers by demonstrating how to use a new in situ matrix inversion accelerator for analog preconditioning. As existing techniques that enable high precision and scalability for in situ MVM are inapplicable to in situ matrix inversion, new techniques to compensate for circuit non-idealities are proposed. Additionally, a new approach to bit slicing that enables splitting operands across multiple devices without external digital logic is proposed. For scalability, this paper demonstrates how in situ matrix inversion kernels can work in tandem with existing domain decomposition techniques to accelerate the solutions of arbitrarily large linear systems. The analog kernel can be directly integrated into existing preconditioning workflows, leveraging several well-optimized numerical linear algebra tools to improve the behavior of the circuit. The result is an analog preconditioner that is more effective (up to 50% fewer iterations) than the widely used incomplete LU factorization preconditioner, ILU(0), while also reducing the energy and execution time of each approximate solve operation by 1025x and 105x respectively.
Over the past decade as Moore's Law has slowed, the need for new forms of computation that can provide sustainable performance improvements has risen. A new method, called in situ computing, has shown great potential to accelerate matrix vector multiplication (MVM), an important kernel for a diverse range of applications from neural networks to scientific computing. Existing in situ accelerators for scientific computing, however, have a significant limitation: These accelerators provide no acceleration for preconditioning-A key bottleneck in linear solvers and in scientific computing workflows. This paper enables in situ acceleration for state-of-The-Art linear solvers by demonstrating how to use a new in situ matrix inversion accelerator for analog preconditioning. As existing techniques that enable high precision and scalability for in situ MVM are inapplicable to in situ matrix inversion, new techniques to compensate for circuit non-idealities are proposed. Additionally, a new approach to bit slicing that enables splitting operands across multiple devices without external digital logic is proposed. For scalability, this paper demonstrates how in situ matrix inversion kernels can work in tandem with existing domain decomposition techniques to accelerate the solutions of arbitrarily large linear systems. The analog kernel can be directly integrated into existing preconditioning workflows, leveraging several well-optimized numerical linear algebra tools to improve the behavior of the circuit. The result is an analog preconditioner that is more effective (up to 50% fewer iterations) than the widely used incomplete LU factorization preconditioner, ILU(0), while also reducing the energy and execution time of each approximate solve operation by 1025x and 105x respectively.
Millimeter-sized CD foils fielded close (order mm) to inertial confinement fusion (ICF) implosions have been proposed as a game-changer for improving energy resolution and allowing time-resolution in neutron spectrum measurements using the magnetic recoil technique. This paper presents results from initial experiments testing this concept for direct drive ICF at the OMEGA Laser Facility. While the foils are shown to produce reasonable signals, inferred spectral broadening is seen to be high (∼5 keV) and signal levels are low (by ∼20%) compared to expectation. Before this type of foil is used for precision experiments, the foil mount must be improved, oxygen uptake in the foils must be better characterized, and impact of uncontrolled foil motion prior to detection must be investigated.
Metamaterials derive their unusual properties from their architected structure, which generally consists of a repeating unit cell designed to perform a particular function. However, existing metamaterials are, with few exceptions, physically continuous throughout their volume, and thus cannot take advantage of multi-body behavior or contact interactions. Here we introduce the concept of multi-body interpenetrating lattices, where two or more lattices interlace through the same volume without any direct connection to each other. This new design freedom allows us to create architected interpenetrating structures where energy transfer is controlled by surface interactions. As a result, multifunctional or composite-like responses can be achieved even with only a single print material. While the geometry defining interpenetrating lattices has been studied since the days of Euclid, additive manufacturing allows us to turn these mathematical concepts into physical objects with programmable interface-dominated properties. In this first study on interpenetrating lattices, we reveal remarkable mechanical properties including improved toughness, multi-stable/negative stiffness behavior, and electromechanical coupling.
This work proposes a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.
Electron-temperature (Te) measurements in implosions provide valuable diagnostic information, as Te is unaffected by residual flows and other non-thermal effects unlike ion temperature inferred from a fusion product spectrum. In OMEGA cryogenic implosions, measurement of Te(t) can be used to investigate effects related to time-resolved hot-spot energy balance. The proposed diagnostic utilizes five fast-rise (15 ps) scintillator channels with distinct X-ray filtering. Titanium and stepped aluminum filtering were chosen to maximize detector sensitivity in the 10 keV-20 keV range, as it has been shown that these x rays have similar density and temperature weighting to the emitted deuterium-tritium fusion neutrons. Initial data collected using a prototype nosecone on the existing neutron temporal diagnostic demonstrate the validity of this diagnostic technique. The proposed system will be capable of measuring spatially integrated Te(t) with 20 ps time resolution and <10% uncertainty at peak emission in cryogenic DT implosions.
This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. A semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.
Background: Using a thin-walled tube torsion test to characterize a material’s shear response is a well-known technique; however, the thin walled specimen tends to buckle before reaching large shear deformation and failure. An alternative technique is the surface stress method (Nadai 1950; Wu et al. J Test Eval 20:396–402, 1992), which derives a shear stress-strain curve from the torque-angular displacement relationship of a solid cylindrical bar. The solid bar torsion test uniquely stabilizes the deformation which allows us to control and explore very large shear deformation up to failure. However, this method has rarely been considered in the literature, possibly due to the complexity of the analysis and experimental issues such as twist measurement and specimen uniformity. Objective: In this investigation, we develop a method to measure the large angular displacement in the solid bar torsion experiments to study the large shear deformation of two common engineering materials, Al6061-T6 and SS304L, which have distinctive hardening behaviors. Methods: Modern stereo-DIC methods were applied to make deformation measurements. The large angular displacement of the specimen posed challenges for the DIC analysis. An analysis method using multiple reference configurations and transformation of deformation gradient is developed to make the large shear deformation measurement successful. Results: We successfully applied the solid bar torsion experiment and the new analysis method to measure the large shear deformation of Al6061-T6 and SS304L till specimen failure. The engineering shear strains at failure are on the order of 2–3 for Al6061-T6 and 3–4 for SS304L. Shear stress-strain curves of Al6061-T6 and SS304L are also obtained. Conclusions: Solid bar torsion experiments coupled with 3D-DIC technique and the new analysis method of deformation gradient transformation enable measurement of very large shear deformation up to specimen failure.
Progress in Photovoltaics: Research and Applications
Livera, Andreas; Theristis, Marios; Koumpli, Elena; Theocharides, Spyros; Makrides, George; Sutterlueti, Juergen; Stein, Joshua; Georghiou, George E.
Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in-field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.
The complex postdetonation geologic structures that form after an underground nuclear explosion are hard to constrain because increased heterogeneity around the damage zone affects seismic waves that propagate through the explosion site. Generally, a vertical rub-ble-filled structure known as a chimney is formed after an underground nuclear explosion that is composed of debris that falls into the subsurface cavity generated by the explosion. Compared with chimneys that collapse fully, leaving a surface crater, partially collapsed chimneys can have remnant subsurface cavities left in place above collapsed rubble. The 1964 nuclear test HADDOCK, conducted at the Nevada test site (now the Nevada National Security Site), formed a partially collapsed chimney with no surface crater. Understanding the subsurface structure of these features has significant national security applications, such as aiding the study of suspected underground nuclear explosions under a treaty verification. In this study, we investigated the subsurface architecture of the HADDOCK legacy nuclear test using hybrid 2D–3D active source seismic reflection and refraction data. The seismic data were acquired using 275 survey shots from the Seismic Hammer (a 13,000 kg weight drop) and 65 survey shots from a smaller accelerated weight drop, both recorded by ∼ 1000 three-component 5 Hz geophones. First-arrival, P-wave tomographic modeling shows a low-velocity anomaly at ∼ 200 m depth, likely an air-filled cavity caused by partial collapse of the rock column into the temporary post-detonation cavity. A high-velocity anomaly between 20 and 60 m depth represents spall-related compaction of the shallow alluvium. Hints of low velocities are also present near the burial depth ( ∼ 364 m). The reflection seismic data show a prominent subhorizontal reflector at ∼ 300 m depth, a short-curved reflector at ∼ 200 m, and a high-amplitude reflector at ∼ 50 m depth. Comparisons of the reflection sections to synthetic data and borehole stratigraphy suggest that these features correspond to the alluvium–tuff contact, the partial collapse cavity, and the spalled layer, respectively.
This communication describes a novel series of linear and crosslinked polyurethanes (PUs) and their selective depolymerization under mild conditions. Two unique polyols are synthesized bearing unsaturated units in a configuration designed to favor ring-closing metathesis (RCM) to five- and six-membered cycloalkenes. These polyols are co-polymerized with toluene diisocyanate to generate linear PUs and trifunctional hexamethylene- and diphenylmethane-based isocyanates to generate crosslinked PUs. The polyol design is such that the RCM reaction cleaves the backbone of the polymer chain. Upon exposure to dilute solutions of Grubbs’ catalyst under ambient conditions, the PUs are rapidly depolymerized to low molecular weight, soluble products bearing vinyl and cycloalkene functionalities. These functionalities enable further re-polymerization by traditional strategies for polymerization of double bonds. It is anticipated that this general approach can be expanded to develop a range of chemically recyclable condensation polymers that are readily depolymerized by orthogonal metathesis chemistry.
Land-use conflicts created by the growth of solar photovoltaics (PV) can be mitigated by applying the concept of agrivoltaics, that is, the co-development of land for both PV and agricultural purposes, to commercial-scale solar installations. In this study, we present a conceptual design for a novel agrivoltaic system based on pasture-fed rabbit farming and provide the technical, environmental and economic analyses to demonstrate the viability of the concept. Included in our analysis are the economic advantages to the PV operator of grazing rabbits at a density sufficient to control vegetative growth, thus reducing the economic and environmental costs of mowing; the dual-revenue stream from the sale of both rabbits and electricity, contrasted with estimates of the capital-investment costs for rabbits co-located with, and also independent of, PV; and the economic value to the rabbit farmer of higher colony-growth rates (made possible by the shading and predator protection provided by the PV arrays and of reduced fencing costs, which are the largest capital cost, by being able to leverage the PV systems for rabbit fencing. We also provide an environmental analysis that suggests that rabbit-PV farming is a pathway to a measurable reduction in agriculturally-generated greenhouse-gas emissions. Our calculations indicate that the co-location of solar and rabbit farms is a viable form of agrivoltaics, increasing overall site revenue by 2.5%–24.0% above projected electricity revenue depending on location and rental/ownership of rabbits, while providing a high-value agricultural product that, on a per weight basis, has significantly less environmental impact than cattle.
1 The exponential growth of data has driven technology providers to develop new protocols, such as cache coherent interconnects and memory semantic fabrics, to help users and facilities leverage advances in memory technologies to satisfy these growing memory and storage demands. Using these new protocols, fabric-Attached memories (FAM) can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. One promising use-case for FAMs is in High-Performance Compute (HPC) systems, where the underutilization of memory is a major challenge. However, adopting FAMs in HPC systems brings new challenges. In addition to cost, flexibility, and efficiency, one particular problem that requires rethinking is virtual memory support for security and performance. To address these challenges, this paper presents decoupled access control and address translation (DeACT), a novel virtual memory implementation that supports HPC systems equipped with FAM. Compared to the state-of-The-Art two-level translation approach, DeACT achieves speedup of up to 4.59x (1.8x on average) without compromising security.1Part of this work was done when Vamsee was working under the supervision of Amro Awad at UCF. Amro Awad is now with the ECE Department at NC State.
A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. In this work we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity — it is particularly challenging to extract surrogates which are provably well-posed and numerically stable. Our scheme not only yields a convex optimization problem, but also allows extraction of nonlocal models whose kernels may be partially negative while maintaining well-posedness even in small-data regimes. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints to meet the requisite conditions of the nonlocal theory. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels.
The swelling of clay at high temperature and pressure is important for applications including nuclear waste storage but is not well understood. A molecular dynamics study of the swelling of Na montmorillonite in water at several temperatures (T = 298, 400, and 500 K) and water environment pressures (Pe = 5 and 100 MPa) is reported here. Adopting a rarely used setup that enables swelling pressure to be resolved with an accuracy of ~1 MPa, the swelling pressure was computed at basal spacings 1.6–2.6 nm (or 2–5 water layers between neighboring clay sheets), which has not been widely studied before. At T = 298 K and Pe = 5 MPa, swelling pressure Ps oscillates at d-spacing d smaller than 2.2 nm and decays monotonically as d increases. Increasing T to 500 K but keeping Pe at 5 MPa, Ps remains oscillatory at small d, but its repulsive peak at d = 2.2 nm shifts to ~2.0 nm and Ps at different d-spacings can grow more attractive or repulsive. At d > 2.0 nm, Ps is weakened greatly. Keeping T at 500 K and increasing Pe to 100 MPa, Ps recovers toward that at T = 298 K and Pe = 5 MPa, however, the repulsive peak at d = 2.0 nm remains the same. The opposite effects of increasing temperature and pressure on the density and dielectric screening of water, which control ion correlations and thus double layer repulsion, are essential for understanding the observed swelling pressure at elevated temperatures and its response to environment pressures.
Background:: Subcritical crack growth can occur in a brittle material when the stress intensity factor is smaller than the fracture toughness if an oxidizing agent (such as water) is present at the crack tip. Objective:: We present a novel bi-material beam specimen which can measure environmentally assisted crack growth rates. The specimen is “self-loaded” by residual stress and requires no external loading. Methods:: Two materials with different coefficient of thermal expansion are diffusion bonded at high temperature. After cooling to room temperature a subcritical crack is driven by thermal residual stresses. A finite element model is used to design the specimen geometry in terms of material properties in order to achieve the desired crack tip driving force. Results:: The specimen is designed so that the crack driving force decreases as the crack extends, thus enabling the measurement of the crack velocity versus driving force relationship with a single test. The method is demonstrated by measuring slow crack growth data in soda lime silicate glass and validated by comparison to previously published data. Conclusions:: The self-loaded nature of the specimen makes it ideal for measuring the very low crack velocities needed to predict brittle failure at long lifetimes.
Magneto-optical (MO) coupling incorporates photon-induced change of magnetic polarization that can be adopted in ultrafast switching, optical isolators, mode convertors, and optical data storage components for advanced optical integrated circuits. However, integrating plasmonic, magnetic, and dielectric properties in one single material system poses challenges since one natural material can hardly possess all these functionalities. Here, co-deposition of a three-phase heterostructure composed of a durable conductive nitride matrix with embedded core–shell vertically aligned nanopillars, is demonstrated. The unique coupling between ferromagnetic NiO core and atomically sharp plasmonic Au shell enables strong MO activity out-of-plane at room temperature. Further, a template growth process is applied, which significantly enhances the ordering of the nanopillar array. The ordered nanostructure offers two schemes of spin polarization which result in stronger antisymmetry of Kerr rotation. The presented complex hybrid metamaterial platform with strong magnetic and optical anisotropies is promising for tunable and modulated all-optical-based nanodevices.
Ground 304 stainless steel (SS) samples were exposed to sea salt particles at 35 °C and two relative humidity (RH) levels for durations ranging from 1 week to 2 years. For all exposure times, pit number density and total pit volume at 40% RH were observed to be considerably greater than those at 76% RH. Statistical analysis of distributions of pit populations for both RH conditions showed that pit number density and total pit volume increased rapidly at first but slowed as exposure time increased. Cross-hatched features were observed in the 40% RH pits while ellipsoidal, faceted pits were observed at 76% RH. Optical profilometry indicated that most pits were not hemispherical. X-ray tomography provided evidence of undercutting and fissures. Piecewise curve fitting modeled the 40% RH data closely, predicting that corrosion damage would eventually plateau. However, a similar treatment of the 76% RH data suggested that corrosion damage would continuously increase, which implied that the piecewise power-law fit was limited in its ability to model atmospheric corrosion generally. Based on these observations, the operative mechanisms determining long-term corrosion behavior were hypothesized to be different depending on the RH of exposure.
Ropero-Giralda, Pablo; Crespo, Alejandro J.C.; Coe, Ryan G.; Tagliafierro, Bonaventura; Dominguez, Jose M.; Bacelli, Giorgio; Gomez-Gesteira, Moncho
The present work addresses the need for an efficient, versatile, accurate and open-source numerical tool to be used during the design stage of wave energy converters (WECs). The device considered here is the heaving point-absorber developed and tested by Sandia National Laboratories. The smoothed particle hydrodynamics (SPH) method, as implemented in DualSPHysics, is proposed since its meshless approach presents some important advantages when simulating floating devices. The dynamics of the power take-off system are also modelled by coupling DualSPHysics with the multi-physics library Project Chrono. A satisfactory matching between experimental and numerical results is obtained for: (i) the heave response of the device when forced via its actuator; (ii) the vertical forces acting on the fixed device under regular waves and; (iii) the heave response of the WEC under the action of both regular waves and the actuator force. This proves the ability of the numerical approach proposed to simulate accurately the fluid–structure interaction along with the WEC’s closed-loop control system. In addition, radiation models built from the experimental and WAMIT results are compared with DualSPHysics by plotting the intrinsic impedance in the frequency domain, showing that the SPH method can be also employed for system identification.
Tunnel junction devices grown monolithically by metal organic chemical vapor deposition were optimized for minimization of the tunnel junction voltage drop. Two device structures were studied: an all-GaN homojunction tunnel junction and a graded InGaN heterojunction-based tunnel junction. This work reports a record-low voltage drop in the graded-InGaN heterojunction based tunnel junction device structure achieving a de-embedded tunnel junction voltage drop of 0.17 V at 100 A/cm2. The experimental data were compared with a theoretical model developed through technology computer-aided design (TCAD) simulations that offer a physics-based approach to understanding the key components of the design space, which lead to a more efficient tunnel junction.
MACCS (MELCOR Accident Consequence Code System), WinMACCS, and MelMACCS now facilitate a multi-unit consequence analysis. MACCS evaluates the consequences of an atmospheric release of radioactive gases and aerosols into the atmosphere and is most commonly used to perform probabilistic safety assessments (PSAs) and related consequence analyses for nuclear power plants (NPPs). WinMACCS is a user-friendly preprocessor for MACCS. MelMACCS extracts source-term information from a MELCOR plot file. The current development can combine an arbitrary number of source terms, representing simultaneous releases from a multi-unit facility, into a single consequence analysis. The development supports different release signatures, fission product inventories, and accident initiation times for each unit. The treatment is completely general except that the model is currently limited to collocated units. A major practical consideration for performing a multi-unit PSA is that a comprehensive treatment for more than two units may involve an intractable number of combinations of source terms. This paper proposes and evaluates an approach for reducing the number of calculations to be tractable, even for sites with eight or ten units. The approximation error introduced by the approach is acceptable and is considerably less than other errors and uncertainties inherent in a Level 3 PSA.
Gaussian processes and other kernel-based methods are used extensively to construct approximations of multivariate data sets. The accuracy of these approximations is dependent on the data used. This paper presents a computationally efficient algorithm to greedily select training samples that minimize the weighted Lp error of kernel-based approximations for a given number of data. The method successively generates nested samples, with the goal of minimizing the error in high probability regions of densities specified by users. The algorithm presented is extremely simple and can be implemented using existing pivoted Cholesky factorization methods. Training samples are generated in batches which allows training data to be evaluated (labeled) in parallel. For smooth kernels, the algorithm performs comparably with the greedy integrated variance design but has significantly lower complexity. Numerical experiments demonstrate the efficacy of the approach for bounded, unbounded, multi-modal and non-tensor product densities. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data using Bayesian inference.
This project has four main technical objectives: 1) Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. 2) Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. 3) Evaluate optimal bifacial system designs using simulations leveraging high-performance computing, and also using full sized and miniaturized experimental field deployments. 4) Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.
This analysis shows that when lower density crude oil is injected into the top of an underground salt storage cavern containing more dense crude, separate oil phases can form and coexist indefinitely. This has been observed at the U.S. Strategic Petroleum Reserve in spite of geothermal heating and natural convection, which tend to mix the contents of containers with significant vertical extent subjected to wall and bottom heating. Such persistent layering can create operational challenges for meeting delivery specifications if high-value, low-vapor pressure oil becomes trapped below incoming low-density, high-vapor pressure oil, effectively blocking access to the lower layers until the top layer is removed. Previous conceptual models assumed that the oil injection process mixed incoming oil with resident oil in a storage cavern, forming a single oil phase with relatively homogeneous properties. Here, a review of historical data from the Strategic Petroleum Reserve revealed that several caverns contain multiple oil layers. As a result, oil layering needs to be another variable considered when planning oil movements at SPR in order to optimize low-vapor pressure oil availability to assist in oil delivery blending.
The objectives of this project are as follows: 1) Reduce uncertainty in PV performance models by developing and validating new and improved models and submodes. 2) Create and manage an open source repository of modeling functions and data. 3) Build and grow the PV Performance Modeling Collaborative 4) Represent the US in the IEA PVPS Task 13 Working group.
Elucidating the mechanisms responsible for sub-microsecond desorption of water and other impurities from electrode surfaces at high heating rates is crucial for understanding pulsed power behavior. Ionization of desorbed impurities in the vacuum regions causes power or current loss; devising methods to limit such desorption during the short time scale of pulsed power is needed to improve corresponding applications. Previous molecular modeling studies have strongly suggested that, under high vacuum conditions, the amount of water impurity desorbing from oxide surfaces on metal electrodes is at a sub-monolayer level at room temperature, which appears insufficient to explain observed pulsed power energy losses at high current densities. In this work, we apply Density Functional Theory (DFT) techniques to show that hydrogen trapped inside iron metal can diffuse into hematite (α-Fe2O3) on the metal surface, ultimately reacting with the oxide to form Fe(II) and H2O. The latter desorbs at elevated temperature and may explain the anomalous amount of desorbed impurity inferred from pulsed-power experiments. We also apply a suite of characterization techniques to demonstrate that when iron metal is heated to 650 °C, the dominant surface oxide component becomes α-Fe2O3. The oxide facets exposed are found to be a mixture of (0001), (10-10), and others, in agreement with the DFT models used.
The effect of nitrogen on the surfaces of polycrystalline ITER-grade tungsten and a tungsten single crystal were studied with low energy ion scattering (LEIS) and direct recoil spectroscopy (DRS). LEIS and DRS measurements on both tungsten surfaces were performed in an ultra-high vacuum system as various quantities of N2 were introduced into the chamber through a variable leak valve. The obtained ion energy spectra reveal that nitrogen was readily adsorbed onto the surface, in turn limiting the amount of hydrogen that could be adsorbed onto the surface. These results not only provide insight into how the presence of nitrogen on tungsten surfaces may play a role in hydrogen adsorption and retention, but also serve to benchmark models being developed to describe the H-N-W system.