Publications

Grid support functionalities from advanced PV inverters are increasingly being utilized to help regulate grid conditions and enable high PV penetration levels. To ensure a high degree of reliability, it is paramount that protective devices respond properly to a variety of fault conditions. However, while the fault response of PV inverters operating at unity power factor has been well documented, less work has been done to characterize the fault contributions and impacts of advanced inverters with grid support enabled under conditions like voltage sags and phase angle jumps. To address this knowledge gap, this paper presents experimental results of a three-phase photovoltaic inverter's response during and after a fault to investigate how PV systems behave under fault conditions when operating with and without a grid support functionality (autonomous Volt-Var) enabled. Simulations were then conducted to quantify the potential impact of the experimental findings on protection systems. It was observed that fault current magnitudes across several protective devices were impacted by non-unity power factor operating conditions, suggesting that protection settings may need to be studied and updated whenever grid support functions are enabled or modified.

The advent of bifacial PV systems drives new requirements for irradiance measurement at PV projects for monitoring and assessment purposes. While there are several approaches, there is still no uniform guidance for what irradiance parameters to measure and for the optimal selection and placement of irradiance sensors at bifacial arrays. Standards are emerging to address these topics but are not yet available. In this paper we review approaches to bifacial irradiance monitoring which are being discussed in the research literature and pursued in early systems, to provide a preliminary guide and framework for developers planning bifacial projects.

The advent of bifacial PV systems drives new requirements for irradiance measurement at PV projects for monitoring and assessment purposes. While there are several approaches, there is still no uniform guidance for what irradiance parameters to measure and for the optimal selection and placement of irradiance sensors at bifacial arrays. Standards are emerging to address these topics but are not yet available. In this paper we review approaches to bifacial irradiance monitoring which are being discussed in the research literature and pursued in early systems, to provide a preliminary guide and framework for developers planning bifacial projects.

Network structural equation modeling has been used for degradation modeling of glass/backsheet (GB) and double glass (DG) PERC PV minimodules, made by CSI and CWRU. The encapsulants used were ethylene vinyl acetate (EVA) and polyolefin elastomer (POE). The exposures included modified damp heat (80°C and 85% relative humidity), with and without full spectrum light. Each exposure cycle consists of 2520 hours, 5 steps of 504 hours each. The data from I-V and Suns-Voc was used in the analysis. We observe that most DG minimodules exhibit stability in power with exposure time and GB minimodules by CWRU showed a power loss of 5-6% on average due to corrosion.

We present the application of FAIR principles to photovoltaic time series data to increase their reusability within the photovoltaic research community. The main requirements for a "FAIRified"dataset is to have a clearly defined data format, and to make accessible all metadata for this dataset to humans and machines. To achieve FAIRification, we implement a data model that separates the photovoltaic data and its metadata. The metadata and their descriptions are registered on a data repository in a human and machine readable format, using JSON-LD. Also, secure APIs are developed to access photovoltaic data. This approach has long term scalability and maintainability.

To assess the reliability of PERC cells compared to Al-BSF in a commercial setting minimodules with cell and encapsulant combinations are compared in accelerated exposure. In both modified damp heat and modified damp heat with full spectrum light exposures, white EVA samples showed a higher susceptibility for metallization corrosion degradation than all other encapsulants. Al-BSF cells in particular showed higher power loss than PERC cells with white EVA. It was observed that the degree of degradation had a strong significance on the manufacturer of the white EVA encapsulant. In both exposures the encapsulant was a much stronger predictor of degradation than cell type. For modules with the same encapsulant, PERC cells showed the higher performance or were comparable to Al-BSF cells for all but one case.

Cell cracking in PV modules can lead to a variety of changes in module operation, with vastly different performance degradation based on the type and severity of the cracks. In this work, we demonstrate automated measurement of cell crack properties from electroluminescence images, and correlate these properties with current-voltage curve features on 35 four-cell Al-BSF and PERC mini-modules showing a range of crack types and severity. Power loss in PERC modules was associated with more total crack length, resulting in electrical isolation of cell areas and mild shunting and recombination. Many of the Al-BSF modules suffered catastrophic power loss due to crack-related shunts. Mild power loss in Al-BSF modules was not as strongly correlated with total crack length; instead crack angles and branching were better indicators of module performance for this cell type.

Renewable energy has become a viable solution for reducing the harmful effects that fossil fuels have on our environment, prompting utilities to replace traditional synchronous generators (SG) with more inverter-based devices that can provide clean energy. One of the biggest challenges utilities are facing is that by replacing SG, there is a reduction in the systems' mechanical inertia, making them vulnerable to frequency instability. Grid-forming inverters (GFMI) have the ability to create and regulate their own voltage reference in a manner that helps stabilize system frequency. As an emerging technology, there is a need for understanding their dynamic behavior when subjected to abrupt changes. This paper evaluates the performance of a GFMI when subjected to voltage phase jump conditions. Experimental results are presented for the GFMI subjected to both balanced and unbalanced voltage phase jump events in both P/Q and V/f modes.

In this paper, the development of a mathematical model for islanding detection method based on the concept of a digital twin is presented. The model estimates the grid impedance seen by a distributed energy resource. The proposed algorithm has characteristics of passive and active islanding detection methods. Using a discrete state-space representation of a dq0 axis power system as equality constraints, a digital twin is optimized to match the power system of interest. The concept is to use the estimated grid impedance as the parameter to identify the difference between normal operation and islanding scenarios. Selecting arbitrary initial values, the digital twin approximates the response of the actual system and therefore a value for the system impedance. Results indicate that the proposed method has the potential to estimate the grid impedance at the point of common coupling.

Due to the increased penetration in Distributed Energy Resources (DERs), especially in Photovoltaic (PV) systems, voltage and frequency regulation has become a topic of interest. Utilities have been requesting DER voltage and frequency support for almost two decades. Their request was addressed by standards such as the IEEE Std 1547-2018. With the continuous improvements in inverters' ability to control their output voltage, power, and frequency, a group of advanced techniques to support the grid is now required by the interconnection standard. These techniques are known as Grid Support Functions (GSF), and they allow the inverter to provide voltage and frequency support to the grid as well as the ability to ride-through abnormal events. Understanding how a GSF behaves is challenging, especially when multiple GSFs are combined to help the utility to control the system voltage and frequency. This paper evaluates the effects of GSF's on the IEEE Std 1547.1-2020 Unintentional Islanding Test 5B by comparing simulation results from a developed PV inverter model and experimental results from a Power Hardware-in-the-Loop platform.

DC microgrids envisioned with high bandwidth communications may well expand their application range by considering autonomous strategies as resiliency contingencies. In most cases, these strategies are based on the droop control method, seeking low voltage regulation and proportional load sharing. Control challenges arise when coordinating the output of multiple DC microgrids composed of several Distributed Energy Resources. This paper proposes an autonomous control strategy for transactional converters when multiple DC microgrids are connected through a common bus. The control seeks to match the external bus voltage with the internal bus voltage balancing power. Three case scenarios are considered: standalone operation of each DC microgrid, excess generation, and generation deficit in one DC microgrid. Results using Sandia National Laboratories Secure Scalable Microgrid Simulink library, and models developed in MATLAB are compared.

Recent trends in PV economics and advanced inverter functionalities have contributed to the rapid growth in PV adoption; PV modules have gotten much cheaper and advanced inverters can deliver a range of services in support of grid operations. However, these phenomena also provide conditions for PV curtailment, where high penetrations of distributed PV often necessitate the use of advanced inverter functions with VAR priority to address abnormal grid conditions like over- and under-voltages. This paper presents a detailed energy loss analysis, using a combination of open-source PV modeling tools and high-resolution time-series simulations, to place the magnitude of clipped and curtailed PV energy in context with other operational sources of PV energy loss. The simulations were conducted on a realistic distribution circuit, modified to include utility load data and 341 modeled PV systems at 25% of the customer locations. The results revealed that the magnitude of clipping losses often overshadows that of curtailment but, on average, both were among the lowest contributors to total annual PV energy loss. However, combined clipping and curtailment loss are likely to become more prevalent as recent trends continue.

In this paper we present an alternative approach to the representation of simulation particles for unstructured electrostatic and electromagnetic PIC simulations. In our modified PIC algorithm we represent particles as having a smooth shape function limited by some specified finite radius, r0. A unique feature of our approach is the representation of this shape by surrounding simulation particles with a set of virtual particles with delta shape, with fixed offsets and weights derived from Gaussian quadrature rules and the value of r0. As the virtual particles are purely computational, they provide the additional benefit of increasing the arithmetic intensity of traditionally memory bound particle kernels. The modified algorithm is implemented within Sandia National Laboratories' unstructured EMPIRE-PIC code, for electrostatic and electromagnetic simulations, using periodic boundary conditions. We show results for a representative set of benchmark problems, including electron orbit, a transverse electromagnetic wave propagating through a plasma, numerical heating, and a plasma slab expansion. Good error reduction across all of the chosen problems is achieved as the particles are made progressively smoother, with the optimal particle radius appearing to be problem-dependent.

Thermal spray processes involve the repeated impact of millions of discrete particles, whose melting, deformation, and coating-formation dynamics occur at microsecond timescales. The accumulated coating that evolves over minutes is comprised of complex, multiphase microstructures, and the timescale difference between the individual particle solidification and the overall coating formation represents a significant challenge for analysts attempting to simulate microstructure evolution. In order to overcome the computational burden, researchers have created rule-based models (similar to cellular automata methods) that do not directly simulate the physics of the process. Instead, the simulation is governed by a set of predefined rules, which do not capture the fine-details of the evolution, but do provide a useful approximation for the simulation of coating microstructures. Here, we introduce a new rules-based process model for microstructure formation during thermal spray processes. The model is 3D, allows for an arbitrary number of material types, and includes multiple porosity-generation mechanisms. Example results of the model for tantalum coatings are presented along with sensitivity analyses of model parameters and validation against 3D experimental data. The model's computational efficiency allows for investigations into the stochastic variation of coating microstructures, in addition to the typical process-to-structure relationships.

We analyze experimentally and theoretically the transport spectra of a gated lateral GaAs double quantum dot containing two holes. The strong spin-orbit interaction present in the hole subband lifts the Pauli spin blockade and allows to map out the complete spectra of the two-hole system. By performing measurements in both source-drain voltage directions, at different detunings and magnetic fields, we carry out quantitative fitting to a Hubbard two-site model accounting for the tunnel coupling to the leads and the spin-flip relaxation process. We extract the singlet-triplet gap and the magnetic field corresponding to the singlet-triplet transition in the double-hole ground state. Additionally, at the singlet-triplet transition we find a resonant enhancement (in the blockaded direction) and suppression of current (in the conduction direction). The current enhancement stems from the multiple resonance of two-hole levels, opening several conduction channels at once. The current suppression arises from the quantum interference of spin-conserving and spin-flipping tunneling processes.

The reactions of boric acid and 4-fluorophenylboronic acid with H- and Cl-terminated Si(100) surfaces in solution were investigated. X-ray photoelectron spectroscopy (XPS) studies reveal that both molecules react preferentially with Cl-Si(100) and not with H-Si(100) at identical conditions. On Cl-Si(100), the reactions introduce boron onto the surface, forming a Si-O-B structure. The quantification of boron surface coverage demonstrates that the 4-fluorophenylboronic acid leads to ∼2.8 times higher boron coverage compared to that of boric acid on Cl-Si(100). Consistent with these observations, density functional theory studies show that the reaction of boric acid and 4-fluorophenylboronic acid is more favorable with the Cl- versus H-terminated surface and that on Cl-Si(100) the reaction with 4-fluorophenylboronic acid is ∼55.3 kJ/mol more thermodynamically favorable than the reaction with boric acid. The computational studies were also used to demonstrate the propensity of the overall approach to form high-coverage monolayers on these surfaces, with implications for selective-area boron-based monolayer doping.

Nanoindentation provides a convenient and high-throughput means for mapping mechanical properties and for measuring the strain rate sensitivity of a material. Here, nanoindentation was applied to the study of microcrystalline cellulose. Constant strain rate nanoindentation revealed a depth dependence of nanohardness and modulus, mostly attributed to material densification. Nanomechanical maps of storage modulus and hardness resolved the shape and size of voids present in larger particles. In smaller, denser particles, however, where storage modulus varied little spatially, there was still some spatial dependence of hardness, which can be explained by cellulose’s structural anisotropy. Additionally, hardness changed with the indentation strain rate in strain rate jump tests. The resulting strain rate sensitivity values were found to be in agreement with those obtained by other techniques in the literature. Graphic abstract: [Figure not available: see fulltext.]

This paper presents a new high gain, multilevel, bidirectional DC-DC converter for interfacing battery energy storage systems (BESS) with the distribution grid. The proposed topology employs a current-fed structure on the low-voltage (LV) BESS side to obtain high voltage gain during battery-to-grid mode of operation without requiring a large turns ratio isolation transformer. The high-voltage (HV) side of the converter is a voltage-doubler network comprising two half-bridge circuits with an intermediary bidirectional switch that re-configures the two bridges in series connection to enhance the boost ratio. A seamless commutation of the transformer leakage inductor current is ensured by the phase-shift modulation of HV side devices. The modulating duty cycle of the intermediary bidirectional devices generates a multilevel voltage of twice the switching frequency at the grid-side dc link, which significantly reduces the filter size. The presented modulation strategy ensures zero current switching (ZCS) of the LV devices and zero voltage switching (ZVS) of the HV devices to achieve a high power conversion efficiency. Design and operation of the proposed converter is explained with modal analysis, and further verified by detailed simulation results.

A review of a new vertically aligned nanocomposite (VAN) structure based on two-dimensional (2D) layered oxides has been designed and self-assembled on both LaAlO₃ (001) and SrTiO₃ (001) substrates. The new VAN structure consists of epitaxially grown Co₃O₄ nanopillars embedded in the Bi₂WO₆ matrix with a unique 2D layered structure, as evidenced by the microstructural analysis. Physical property measurements show that the new Bi₂WO₆-Co₃O₄ VAN structure exhibits strong ferromagnetic and piezoelectric response at room temperature as well as anisotropic permittivity response. This work demonstrates a new approach in processing multifunctional VANs structure based on the layered oxide systems towards future nonlinear optics, ferromagnets, and multiferroics.

Due to their impressive energy density, power density, lifetime, and cost, lithium-ion batteries have become the most important electrochemical storage system, with applications including consumer electronics, electric vehicles, and stationary energy storage. However, each application has unique, often conflicting product specifications, requiring a balanced overall assessment. The Ragone plot is a commonly-used plot to compare energy and power of lithium-ion battery chemistries. Important parameters including cost, lifetime, and temperature sensitivity are not considered. Overall, a standardized and balanced reporting and visualization of specifications would greatly help an informed cell selection process.

The dramatic 50% improvement in energy density that Li-metal anodes offer in comparison to graphite anodes in conventional lithium (Li)-ion batteries cannot be realized with current cell designs because of cell failure after a few cycles. Often, failure is caused by Li dendrites that grow through the separator, leading to short circuits. Here, we used a new characterization technique, cryogenic femtosecond laser cross sectioning and subsequent scanning electron microscopy, to observe the electroplated Li-metal morphology and the accompanying solid electrolyte interphase (SEI) into and through the intact coin cell battery's separator, gradually opening pathways for soft-short circuits that cause failure. We found that separator penetration by the SEI guided the growth of Li dendrites through the cell. A short-circuit mechanism via SEI growth at high current density within the separator is provided. These results will inform future efforts for separator and electrolyte design for Li-metal anodes.

The self-Assembly of binary polymer-grafted nanoparticles (NPs) in a selective solvent is investigated using coarse-grained simulations. Simulations are performed using theoretically informed Langevin dynamics (TILD), a particle-based method that employs a particle-To-mesh scheme to efficiently calculate the nonbonded interactions. The particles are densely grafted with two immiscible polymers, A and B, that are permanently bound to the NP either at random grafting sites (random-grafted) or with all the A chains on one hemisphere of the NP and all the B chains on the other hemisphere (Janus-grafted). For NPs with random grafting, the polymers phase-separate on the surface of the NP to form Janus-Type structures in dilute solution, even though some of the chains have to stretch around the particle to form the Janus structure. When the solvent quality is sufficiently poor for the solvophobic chains, the binary grafted NPs assemble into various structures, including double-walled vesicles. In particular, vesicles are formed when the solvophilic volume fraction is between 0.2 and 0.3, in a similar range to that required for vesicle formation in diblock copolymers in a selective solvent. For mixed-grafted NPs, there is considerable variation in the structure of each individual NP, but nevertheless, these NPs form ordered vesicles, similar to those formed by Janus-grafted NPs.

Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process uncertainty; which most functional alignment methods cannot handle. A pair of functions can also have multiple optimal alignment solutions, which is not addressed in current literature. In this paper, a flexible Bayesian approach to functional alignment is presented, which appropriately accounts for noise in the data without any pre-smoothing required. Additionally, by running parallel MCMC chains, the method can account for multiple optimal alignments via the multi-modal posterior distribution of the warping functions. To most efficiently sample the warping functions, the approach relies on a modification of the standard Hamiltonian Monte Carlo to be well-defined on the infinite-dimensional Hilbert space. In this work, this flexible Bayesian alignment method is applied to both simulated data and real data sets to show its efficiency in handling noisy functions and successfully accounting for multiple optimal alignments in the posterior; characterizing the uncertainty surrounding the warping functions.

Modeling the degradation of cement-based infrastructure due to aqueous environmental conditions continues to be a challenge. In order to develop a capability to predict concrete infrastructure failure due to chemical degradation, we created a chemomechanical model of the effects of long-term water exposure on cement paste. The model couples the mechanical static equilibrium balance with reactive–diffusive transport and incorporates fracture and failure via peridynamics (a meshless simulation method). The model includes fundamental aspects of degradation of ordinary Portland cement (OPC) paste, including the observed softening, reduced toughness, and shrinkage of the cement paste, and increased reactivity and transport with water induced degradation. This version of the model focuses on the first stage of cement paste decalcification, the dissolution of portlandite. Given unknowns in the cement paste degradation process and the cost of uncertainty quantification (UQ), we adopt a minimally complex model in two dimensions (2D) in order to perform sensitivity analysis and UQ. We calibrate the model to existing experimental data using simulations of common tests such as flexure, compression and diffusion. Then we calculate the global sensitivity and uncertainty of predicted failure times based on variation of eleven unique and fundamental material properties. We observed particularly strong sensitivities to the diffusion coefficient, the reaction rate, and the shrinkage with degradation. Also, the predicted time of first fracture is highly correlated with the time to total failure in compression, which implies fracture can indicate impending degradation induced failure; however, the distributions of the two events overlap so the lead time may be minimal. Extension of the model to include the multiple reactions that describe complete degradation, viscous relaxation, post-peak load mechanisms, and to three dimensions to explore the interactions of complex fracture patterns evoked by more realistic geometry is straightforward and ongoing.

Terry steam turbines are widely used in various industries because of their robust design. Within the nuclear power generation industry, they are used in the Reactor Core Isolation Cooling System to remove decay heat during reactor isolation events. During the Fukushima Daiichi nuclear power station disaster in Japan in 2011, the Reactor Core Isolation Cooling System and associated Terry turbine operated for over 70 hours in Unit 2; this runtime is well beyond the expected operating duration. Theories suggest the turbine was subjected to a two-phase inlet flow, which could degrade the turbine performance. In this work, an experimental test rig was constructed to test a full-scale Terry model GS-2 steam turbine under two-phase air/water flows. Steady-state efficiency and torque performance maps of the turbine were developed over a range of turbine inlet pressures (1.38–4.83 bar or 20–70 psia), air mass fractions (0.05–1.0) and rotational speeds up to 4000 RPM. Turbine performance followed expected trends with torque varying linearly and efficiency varying quadratically with rotational speed. In addition, high-speed images of the two-phase flow entering the turbine were also analyzed to understand how changes in inlet pressure and air mass fraction affect the flow regime and homogenization. The present tests with air–water two-phase mixtures are an important step towards providing an understanding of the full-scale Terry turbine's behavior and performance curves under two-phase conditions. The results of this work will be combined with air/water and steam/water data gathered using a small-scale Terry ZS-1 steam turbine in order to understand the scaling relationship between large and small size Terry turbines and fluid pairs. The combined data set will enable further development of analytical models over a wide range of conditions and may be used to provide technical justification for expanded use of the Terry turbines in nuclear power plant safety systems and other systems.

ParaView has been deployed as a visualization tool at Sandia for almost 20 years. ParaView of the talk title refers to a collection of software.

Single Image SICD-Based Automatic Object Processing (SIS-AOP) is an automatic object identification tool for SAR imagery. It ingests a SAR image in standard SICD format, and it will run a suite of algorithms to cue possible vehicle detections, cull those detections and then ultimately label them either as detections only or possible expound to give a class-level ID or a vehicle-type ID. The SIS-AOP results are given in an XML (Extensible Markup Language) output format. This document defines the elements in the SISAOPR XML output format.

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

The adsorption of AlCl3 on Si(100) and the effect of annealing the AlCl3-dosed substrate were studied to reveal key surface processes for the development of atomic-precision, acceptor-doping techniques. This investigation was performed via scanning tunneling microscopy (STM), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) calculations. At room temperature, AlCl3 readily adsorbed to the Si substrate dimers and dissociated to form a variety of species. Annealing the AlCl3-dosed substrate at temperatures below 450 °C produced unique chlorinated aluminum chains (CACs) elongated along the Si(100) dimer row direction. An atomic model for the chains is proposed with supporting DFT calculations. Al was incorporated into the Si substrate upon annealing at 450 °C and above, and Cl desorption was observed for temperatures beyond 450 °C. Al-incorporated samples were encapsulated in Si and characterized by secondary ion mass spectrometry (SIMS) depth profiling to quantify the Al atom concentration, which was found to be in excess of 1020 cm-3 across a ∼2.7 nm-thick δ-doped region. The Al concentration achieved here and the processing parameters utilized promote AlCl3 as a viable gaseous precursor for novel acceptor-doped Si materials and devices for quantum computing.

The Smoothed Particle Hydrodynamics (SPH) package within LAMMPS is explored as a possible tool for simulating the motion of bubbles in a vibrating liquid-filled container. As an initial test case, the unphysical but computationally less intense situation of a two-dimensional single bubble rising in a quiescent liquid under the influence of gravity is considered herein. Although physically plausible behavior was obtained under certain conditions, this behavior depends strongly on the system parameters. Moreover, the large density ratio between the liquid and bubble requires extremely small timesteps, which make the simulations undesirably computationally expensive. Ultimately, it was determined that this method is not feasible for providing quantitatively accurate results for the desired application.

With a longer-term goal of addressing the comparative behavior of the aqueous halides F-, Cl-, Br-, and I-on the basis of quasi-chemical theory (QCT), here we study structures and free energies of hydration clusters for those anions. We confirm that energetically optimal (H2O)nX clusters, with X = Cl-, Br-, and I-, exhibit surface hydration structures. Computed free energies, based on optimized surface hydration structures utilizing a harmonic approximation, typically (but not always) disagree with experimental free energies. To remedy the harmonic approximation, we utilize single-point electronic structure calculations on cluster geometries sampled from an AIMD (ab initio molecular dynamics) simulation stream. This rough-landscape procedure is broadly satisfactory and suggests unfavorable ligand crowding as the physical effect addressed. Nevertheless, this procedure can break down when n≳4, with the characteristic discrepancy resulting from a relaxed definition of clustering in the identification of (H2O)nX clusters, including ramified structures natural in physical cluster theories. With ramified structures, the central equation for the present rough-landscape approach can acquire some inconsistency. Extension of these physical cluster theories in the direction of QCT should remedy that issue, and should be the next step in this research direction.

We present a new method to discriminate between earthquakes and buried explosions using observed seismic data. The method is different from previous seismic discrimination algorithms in two main ways. First, we use seismic spatial gradients, as well as the wave attributes estimated from them (referred to as gradiometric attributes), rather than the conventional three-component seismograms recorded on a distributed array. The primary advantage of this is that a gradiometer is only a fraction of a wavelength in aperture com¬pared with a conventional seismic array or network. Second, we use the gradiometric attributes as input data into a machine learning algorithm. The resulting discrimination algorithm uses the norms of truncated principal components obtained from the gradio- metric data to distinguish the two classes of seismic events. Using high-fidelity synthetic data, we show that the data and gradiometric attributes recorded by a single seismic gra¬diometer performs as well as a conventional distributed array at the event type discrimi¬nation task.

The critical pitting temperature (CPT) of selective laser melted (SLM) 316 L stainless steel in 1.0 M NaCl was measured and compared with a commercial wrought alloy. Potentiostatic measurements determined a mean CPT value of 16 ± 0.7 °C, 27.5 ± 0.8 °C and 31 ± 1 °C for the wrought alloy, the SLM alloy normal to the build direction and parallel to the build direction, respectively. The lead-in pencil electrode technique was used to study the pit chemistry of the two alloys and to explain the higher CPT values observed for the SLM alloy. A lower critical current density required for passivation in a simulated pit solution was measured for the SLM alloy. Moreover, the ratio of the critical concentration to saturated concentration of dissolving metal cations was found to be higher for the SLM alloy, which was related to its different salt film properties, possibly as a result of the SLM's distinct microstructure.

The peridynamic theory of solid mechanics is applied to the continuum modeling of the impact of small, high-velocity silica spheres on multilayer graphene targets. The model treats the laminate as a brittle elastic membrane. The material model includes separate failure criteria for the initial rupture of the membrane and for propagating cracks. Material variability is incorporated by assigning random variations in elastic properties within Voronoi cells. The computational model is shown to reproduce the primary aspects of the response observed in experiments, including the growth of a family of radial cracks from the point of impact.

While a great deal of research has been performed to quantify and characterize the wave energy resource, there are still open questions about how a wave energy developer should use this wave resource information to design a wave energy converter device to suit a specific environment or, alternatively, to assess potential deployment locations. It is natural to focus first on the impressive magnitudes of power available from ocean waves, and to be drawn to locations where mean power levels are highest. However, a number of additional factors such as intermittency and capacity factor may be influential in determining economic viability of a wave energy converter, and should therefore be considered at the resource level, so that these factors can influence device design decisions. This study examines a set of wave resource metrics aimed towards this end of bettering accounting for variability in wave energy converter design. The results show distinct regional trends that may factor into project siting and wave energy converter design. Although a definitive solution for the optimal size of a wave energy converter is beyond the reaches of this study, the evidence presented does support the idea that smaller devices with lower power ratings may merit closer consideration.

The most revealing indicator for oxidative processes or state of degraded plastics is usually carbonyl formation, a key step in materials degradation as part of the carbon cycle for man-made materials. Hence, the identification and quantification of carbonyl species with infrared spectroscopy have been the method of choice for generations, thanks to their strong absorbance and being an essential intermediate in carbon oxidation pathways. Despite their importance, precise identification and quantification can be challenging and rigorous fully traceable data are surprisingly rare in the existing literature. An overview of the complexity of carbonyl quantification is presented by the screening of reference compounds in solution with transmission and polymer films with ATR IR spectroscopy, and systematic data analyses. Significant variances in existing data and their past use have been recognized. Guidance is offered how better measurements and data reporting could be accomplished. Experimental variances depend on the combination of uncertainty in exact carbonyl species, extinction coefficient, contributions from neighboring convoluting peaks, matrix interaction phenomena and instrumental variations in primary IR spectral acquisition (refractive index and penetration depth for ATR measurements). In addition, diverging sources for relevant extinction coefficients may exist, based on original spectral acquisition. For common polymer degradation challenges, a relative comparison of carbonyl yields for a material is easily accessible, but quantification for other purposes, such as degradation rates and spatially dependent interpretation, requires thorough experimental validation. All variables highlighted in this overview demonstrate the significant error margins in carbonyl quantification, with exact carbonyl species and extinction coefficients already being major contributors on their own.

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green's function of an underlying PDE. As such, each particle is a sample from the Green's function; therefore, each particle should be distributed according to the Green's function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green's function. When the Green's function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

Corrosion-resistant welded alloys are frequently used as a leak-tight boundary in critical applications that require confinement of hazardous and/or radioactive substances, including an increasing population of spent nuclear fuel (SNF) canisters. The behavior of residual stresses generated as a result of irregular elastic–plastic deformation during processes such as welding is one of today's key issues to a full understanding of the aging mechanisms that may compromise the confinement boundary. Whether such processes and any subsequent weld repairs, not subjected to post-weld heat treatment, would negatively affect the initial material by introducing through-thickness tensile stresses remains an open question. Here we report the first residual stress measurements using neutron diffraction on the welded joints of a SNF canister. We found significant tensile residual stresses in the as welded sample, indicating that initiation and through-thickness growth of cracks may be possible. Following repair, we observed a stress redistribution and introduction of beneficial compressive stresses. We anticipate our results will improve understanding of confinement susceptibility to aging and guide improvements in repair techniques.

This paper describes results from an optical-engine investigation of oxygenated fuel effects on ducted fuel injection (DFI) relative to conventional diesel combustion (CDC). Three fuels were tested: a baseline, non-oxygenated No. 2 emissions certification diesel (denoted CFB), and two blends containing potential renewable oxygenates. The first oxygenated blend contained 25 vol% methyl decanoate in CFB (denoted MD25), and the second contained 25 vol% tri-propylene glycol mono-methyl ether in CFB (denoted T25). Whereas DFI and fuel oxygenation primarily curtail soot emissions, intake-oxygen mole fractions of 21% and 16% were employed to explore the potential additional beneficial impact of dilution on engine-out emissions of nitrogen oxides (NOx). It was found that DFI with an oxygenated fuel can attenuate soot incandescence by ~100X (~10X from DFI and an additional ~10X from fuel oxygenation) relative to CDC with conventional diesel fuel, regardless of dilution level and without large effects on other emissions or efficiency. This breaks the soot/NOx trade-off with dilution, enabling simultaneous reductions in both soot and NOx emissions, even with conventional diesel fuel. Significant cyclic variability in soot incandescence for both CDC and DFI suggests that additional improvements in engine-out soot emissions may be possible via improved control of in-cylinder mixture formation and evolution.

The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of time and temperature margins associated with loss of assured safety (LOAS) for one weak link (WL)/two strong link (SL) systems is illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) SL/ WL failure time margins defined by (time at which SL failure potentially causes LOAS) - (time at which WL failure potentially prevents LOAS), (ii) SL/WL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) - (the temperature at which WL failure potentially prevents LOAS), and (iii) SL/SL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) - (the temperature of SL whose failure potentially causes LOAS at the time at which WL failure potentially prevents LOAS).

The supercritical carbon dioxide (s-CO2) Brayton cycle is currently being explored as a replacement for the steam Rankine cycle due to its potential for higher efficiency and lower cycle cost. 316 stainless steel is a candidate alloy for use in s-CO2 up to roughly 600 °C, but the mechanical effects of prolonged exposure of base and welded material in s-CO2 have not been analyzed. The potential for carburization makes this an important concern for the implementation of 316 and similar austenitic stainless steels in the s-CO2 environment. In this study, welded and base material of two types of 316–316L and 316H–were exposed in either s-CO2 or argon at 550 °C or 750 °C for 1000 h. 550 °C s-CO2 exposure yielded a thin (< 1 µm) Cr oxide with occasional nodules of duplex Fe oxide and Fe–Cr spinel that were up to 5 microns thick. However, tensile results from s-CO−2 exposure matched those of 550 °C thermal aging in Ar, indicating that no mechanically detrimental carburization occurred in either 316 variant after 1000 h exposure. Conversely, 750 °C s-CO2 exposure produced roughly 10 × the oxide thickness, with a more substantial Fe oxide (3–5 µm) on the majority of the surface and nodules of up to 40 µm thick. In comparison to aged samples, tensile testing of 750 °C CO2-exposed samples revealed ductility loss attributed to carburization. Projections of 316L performance in s-CO2 indicate that mechanically detrimental carburization—equal to that shown here for 750 °C, 1000 h—will likely be present after 7–14 years of service at 550 °C.

Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. However, computational science and engineering (CSE) problems require double precision accuracy in several domains. This conflict between hardware trends and application needs has resulted in a need for multiprecision strategies at the linear algebra algorithms level if we want to exploit the hardware to its full potential while meeting the accuracy requirements. In this paper, we focus on preconditioned sparse iterative linear solvers, a key kernel in several CSE applications. We present a study of multiprecision strategies for accelerating this kernel on GPUs. We seek the best methods for incorporating multiple precisions into the GMRES linear solver; these include iterative refinement and parallelizable preconditioners. Our work presents strategies to determine when multiprecision GMRES will be effective and to choose parameters for a multiprecision iterative refinement solver to achieve better performance. We use an implementation that is based on the Trilinos library and employs Kokkos Kernels for performance portability of linear algebra kernels. Performance results demonstrate the promise of multiprecision approaches and demonstrate even further improvements are possible by optimizing low-level kernels.

Optimally-shaped electromagnetic fields have the capacity to coherently control the dynamics of quantum systems and thus offer a promising means for controlling molecular transformations relevant to chemical, biological, and materials applications. Currently, advances in this area are hindered by the prohibitive cost of the quantum dynamics simulations needed to explore the principles and possibilities of molecular control. However, the emergence of nascent quantum-computing devices suggests that efficient simulations of quantum dynamics may be on the horizon. In this article, we study how quantum computers could be employed to design optimally-shaped fields to control molecular systems. We introduce a hybrid algorithm that utilizes a quantum computer for simulating the field-induced quantum dynamics of a molecular system in polynomial time, in combination with a classical optimization approach for updating the field. Qubit encoding methods relevant for molecular control problems are described, and procedures for simulating the quantum dynamics and obtaining the simulation results are discussed. Numerical illustrations are then presented that explicitly treat paradigmatic vibrational and rotational control problems, and also consider how optimally-shaped fields could be used to elucidate the mechanisms of energy transfer in light-harvesting complexes. Resource estimates, as well as a numerical assessment of the impact of hardware noise and the prospects of near-term hardware implementations, are provided for the latter task.

Electron emission from thick polished samples of polycrystalline molybdenum (Mo) and single crystalline 〈111〉 silver (Ag) was measured with hard x-ray photoemission spectroscopy. Six different excitation x-ray energies were used, nominally 8.0, 11.0, 13.0, 15.0, 18.0, and 21.5 keV. Survey spectra were recorded with each excitation to a kinetic energy of at most 15 keV, often capturing the entire emission range. The Mo 1s core peak was measured. Detailed LMM Auger spectra of Mo show marked increases in intensity and altered shape when x-ray energy exceeds the Mo 1s binding energy. The Mo and Ag L-shell photoelectron peaks are measured at four x-ray energies up to 18 keV showing the transition from 2p3/2 to 2s photoionization dominance.

Streaks in the buffer layer of wall-bounded turbulence are tracked in time to study their life cycle. Spatially and temporally resolved direct numerical simulation data are used to analyze the strong wall-parallel movements conditioned to low-speed streamwise flow. The analysis of the streaks shows that there is a clear distinction between wall-attached and detached streaks, and that the wall-attached streaks can be further categorized into streaks that are contained in the buffer layer and the ones that reach the outer region. The results reveal that streaks are born in the buffer layer, coalescing with each other to create larger streaks that are still attached to the wall. Once the streak becomes large enough, it starts to meander due to the large streamwise-to-wall-normal aspect ratio, and consequently the elongation in the streamwise direction, which makes it more difficult for the streak to be oriented strictly in the streamwise direction. While the continuous interaction of the streaks allows the superstructure to span extremely long temporal and length scales, individual streak components are relatively small and short-lived. Tall-attached streaks eventually split into wall-attached and wall-detached components. These wall-detached streaks have a strong wall-normal velocity away from the wall, similar to ejections or bursts observed in the literature. Conditionally averaging the flow fields to these split events show that the detached streak has not only a larger wall-normal velocity compared to the wall-attached counterpart, it also has a larger (less negative) streamwise velocity, similar to the velocity field at the tip of a vortex cluster.

Computing k-cores on graphs is an important graph mining target as it provides an efficient means of identifying a graph's dense and cohesive regions. Computing k-cores on hypergraphs has seen recent interest, as many datasets naturally produce hypergraphs. Maintaining k-cores as the underlying data changes is important as graphs are large, growing, and continuously modified. In many practical applications, the graph updates are bursty, both with periods of significant activity and periods of relative calm. Existing maintenance algorithms fail to handle large bursts, and prior parallel approaches on both graphs and hypergraphs fail to scale as available cores increase.We address these problems by presenting two parallel and scalable fully-dynamic batch algorithms for maintaining k-cores on both graphs and hypergraphs. Both algorithms take advantage of the connection between k-cores and h-indices. One algorithm is well suited for large batches and the other for small. We provide the first algorithms that experimentally demonstrate scalability as the number of threads increase while sustaining high change rates in graphs and hypergraphs.

Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. However, computational science and engineering (CSE) problems require double precision accuracy in several domains. This conflict between hardware trends and application needs has resulted in a need for multiprecision strategies at the linear algebra algorithms level if we want to exploit the hardware to its full potential while meeting the accuracy requirements. In this paper, we focus on preconditioned sparse iterative linear solvers, a key kernel in several CSE applications. We present a study of multiprecision strategies for accelerating this kernel on GPUs. We seek the best methods for incorporating multiple precisions into the GMRES linear solver; these include iterative refinement and parallelizable preconditioners. Our work presents strategies to determine when multiprecision GMRES will be effective and to choose parameters for a multiprecision iterative refinement solver to achieve better performance. We use an implementation that is based on the Trilinos library and employs Kokkos Kernels for performance portability of linear algebra kernels. Performance results demonstrate the promise of multiprecision approaches and demonstrate even further improvements are possible by optimizing low-level kernels.

Introduction: Empathy is critical for human interactions to become shared and meaningful, and it is facilitated by the expression and processing of facial emotions. Deficits in empathy and facial emotion recognition are associated with individuals with autism spectrum disorder (ASD), with specific concerns over inaccurate recognition of facial emotion expressions conveying a threat. Yet, the number of evidenced interventions for facial emotion recognition and processing (FERP), emotion, and empathy remains limited, particularly for adults with ASD. Transcranial direct current stimulation (tDCS), a noninvasive brain stimulation, may be a promising treatment modality to safely accelerate or enhance treatment interventions to increase their efficacy. Methods: This study investigates the effectiveness of FERP, emotion, and empathy treatment interventions paired with tDCS for adults with ASD. Verum or sham tDCS was randomly assigned in a within-subjects, double-blinded design with seven adults with ASD without intellectual disability. Outcomes were measured using scores from the Empathy Quotient (EQ) and a FERP test for both verum and sham tDCS. Results: Verum tDCS significantly improved EQ scores and FERP scores for emotions that conveyed threat. Conclusions: These results suggest the potential for increasing the efficacy of treatment interventions by pairing them with tDCS for individuals with ASD.

The reversible computation paradigm aims to provide a new foundation for general classical digital computing that is capable of circumventing the thermodynamic limits to the energy efficiency of the conventional, non-reversible digital paradigm. However, to date, the essential rationale for, and analysis of, classical reversible computing (RC) has not yet been expressed in terms that leverage the modern formal methods of non-equilibrium quantum thermodynamics (NEQT). In this paper, we begin developing an NEQT-based foundation for the physics of reversible computing. We use the framework of Gorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics and stochastic thermodynamics. Important conclusions include that, as expected: (1) Landauer’s Principle indeed sets a strict lower bound on entropy generation in traditional non-reversible architectures for deterministic computing machines when we account for the loss of correlations; and (2) implementations of the alternative reversible computation paradigm can potentially avoid such losses, and thereby circumvent the Landauer limit, potentially allowing the efficiency of future digital computing technologies to continue improving indefinitely. We also outline a research plan for identifying the fundamental minimum energy dissipation of reversible computing machines as a function of speed.