Publications

Natural and anthropogenic infrasound may travel vast distances, making it an invaluable resource for monitoring phenomena such as nuclear explosions, volcanic eruptions, severe storms, and many others. Typically, these waves are captured using pressure sensors, which cannot encode the direction of arrival—critical information when the source location is not known beforehand. Obtaining this information therefore requires arrays of sensors with apertures ranging from tens of meters to kilometers depending on the wavelengths of interest. This is often impractical in locations that lack the necessary real estate (urban areas, rugged regions, or remote islands); in any case, it requires multiple power, digitizer, and telemetry deployments. Here, the theoretical basis behind a compact infrasound direction of arrival sensor based on the acoustic metamaterials is presented. This sensor occupies a footprint that is orders of magnitude smaller than the span of a typical infrasound array. The diminutive size of the unit greatly expands the locations where it can be deployed. The sensor design is described, its ability to determine the direction of arrival is evaluated, and further avenues of study are suggested.

A materials synthesis method that we call atomic-precision advanced manufacturing (APAM), which is the only known route to tailor silicon nanoelectronics with full 3D atomic precision, is making an impact as a powerful prototyping tool for quantum computing. Quantum computing schemes using atomic (31P) spin qubits are compelling for future scale-up owing to long dephasing times, one- and two-qubit gates nearing high-fidelity thresholds for fault-tolerant quantum error correction, and emerging routes to manufacturing via proven Si foundry techniques. Multiqubit devices are challenging to fabricate by conventional means owing to tight interqubit pitches forced by short-range spin interactions, and APAM offers the required (Å-scale) precision to systematically investigate solutions. However, applying APAM to fabricate circuitry with increasing numbers of qubits will require significant technique development. Here, we provide a tutorial on APAM techniques and materials and highlight its impacts in quantum computing research. Finally, we describe challenges on the path to multiqubit architectures and opportunities for APAM technique development. Graphic Abstract: [Figure not available: see fulltext.]

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

A function of global monitoring of nuclear explosions is the development of Earth models for predicting seismic travel times for more accurate calculation of event locations. Most monitoring agencies rely on fast, distance-dependent one-dimensional (1D) Earth models to calculate seismic event locations quickly and in near real-time. RSTT (Regional Seismic Travel Time) is a seismic velocity model and computer software package that captures the major effects of three-dimensional crust and upper mantle structure on regional seismic travel times, while still allowing for fast prediction speed (milliseconds). We describe updates to the RSTT model using a refined data set of regional phases (i.e., Pn, Pg, Sn, Lg) using the Bayesloc relative relocation algorithm. The tomographic inversion shown here acts to refine the previous RSTT public model (rstt201404um) and displays significant features related to areas of global tectonic complexity as well as further reduction in arrival residual values. Validation of the updated RSTT model demonstrates significant reduction in median epicenter mislocation (15.3 km) using all regional phases compared to the iasp91 1D model (22.1 km) as well as to the current station correction approach used at the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (18.9 km).

The electric discharge across a varistor granule filled air gap under a fast-rising voltage pulse was investigated for surge protection applications. The effects of temperature and pressure on the arc and the electrical conduction were analyzed by the characteristic changes in voltage waveforms triggered by a fast-rising high voltage pulse. In addition to the gap size, experimental results show that competing mechanisms among arc conduction, conduction through the varistor granule network, thermionic emission from Joule heating at granule-to-granule contact points, and the magnitude of the switching voltage dictate the maximum surge protection voltage for the filled air gap. Experimental evidence indicated that accumulated degradation was created at small contact points between varistor granules by repetitive assaults from longer duration, high voltage pulses. The uniqueness of using varistor over other dielectric granules in an air gap for surge protection is identified and discussed.

Austenitic stainless steels are the standard materials for containment of hydrogen and tritium because of their resistance to mechanical property degradation in those environments. The mechanical performance of the primary containment material is critical for tritium handling, processing, and storage, thus comprehensive understanding of the processes of tritium embrittlement is an enabling capability for fusion energy. This work describes the investigation of the effects of low levels of tritium-decay-helium ingrowth on 304 L tubes. Long-term aging with tritium leads to high helium contents in austenitic stainless steels and can reduce fracture toughness by 95 %, but the details of behavior at low helium contents are not as well characterized. Here, we present results from tensile testing of tritium pre-charged 304 L tube specimens with a variety of starting microstructures that all contain a low level of helium. The results of the tritium exposed-and-aged materials are compared to previously reported results on similar specimens tested in an unexposed condition as well as the hydrogen precharged condition. Tritium precharging and aging for a short duration resulted in increased yield strengths, ultimate tensile strengths and slightly increased elongation to failure, comparable to higher concentrations of hydrogen precharging.

The reliable design of magnetically insulated transmission lines (MITLs) for very high current pulsed power machines must be accomplished in the future by utilizing a variety of sophisticated modeling tools. The complexity of the models required is high and the number of sub-models and approximations large. The potential for significant analyst error using a single tool is large, with possible reliability issues associated with the plasma modeling tools themselves or the chosen approach by the analyst to solve a given problem. We report on a software infrastructure design that provides a workable framework for building self-consistent models and constraining feedback to limit analyst error. The framework and associated tools aid the development of physical intuition, the development of increasingly sophisticated models, and the comparison of performance results. The work lays the computational foundation for designing state-of-the-art pulsed-power experiments. The design and useful features of this environment are described. We discuss the utility of the Git source code management system and a GitLab interface for use in project management that extends beyond software development tasks.

This paper describes how performance problems can be “masked,” or not readily evident by several causes: by photovoltaic (PV) system configuration (such as the size of the PV array capacity relative to the size of the inverter and the resultant clipped operating mode); by instrumentation design, installation, and maintenance (such as a misaligned or dirty pyranometer); by contract clauses (when operational availability is transformed to contractual availability, which excludes many factors); and by identified management and operational practices (such as reporting on a portfolio of plants rather than individually). A simple method based on a duration curve is introduced to overcome shortcomings of Performance Ratio based on nameplate capacity and Performance Index based on hourly simulation when quantifying masking effects, and inverter clipping and pyranometer soiling are presented as two examples of the new method. With a better understanding of the non-transparency of masking issues, stakeholders can better interpret performance data and deliver improved AC and DC plant conditions through PV system operation and maintenance (O&M) for improved performance, reduced O&M costs, and a more consistently delivered, and reduced, levelized cost of energy (LCOE).

The prevalent use of organic materials in manufacturing is a fire safety concern, and motivates the need for predictive thermal decomposition models. A critical component of predictive modeling is numerical inference of kinetic parameters from bench scale data. Currently, an active area of computational pyrolysis research focuses on identifying efficient, robust methods for optimization. This paper demonstrates that kinetic parameter calibration problems can successfully be solved using classical gradient-based optimization. We explore calibration examples that exhibit characteristics of concern: high nonlinearity, high dimensionality, complicated schemes, overlapping reactions, noisy data, and poor initial guesses. The examples demonstrate that a simple, non-invasive change to the problem formulation can simultaneously avoid local minima, avoid computation of derivative matrices, achieve a computational efficiency speedup of 10x, and make optimization robust to perturbations of parameter components. Techniques from the mathematical optimization and inverse problem communities are employed. By re-examining gradient-based algorithms, we highlight opportunities to develop kinetic parameter calibration methods that should outperform current methods.

Previous studies have shown that atmospheric models with a spectral element grid can benefit from putting physics calculations on a relatively coarse finite volume grid. Here we demonstrate an alternative high-order, element-based mapping approach used to implement a quasi-equal-area, finite volume physics grid in E3SM. Unlike similar methods, the new method in E3SM requires topology data purely local to each spectral element, which trivially allows for regional mesh refinement. Simulations with physics grids defined by 2 × 2, 3 × 3, and 4 × 4 divisions of each element are shown to verify that the alternative physics grid does not qualitatively alter the model solution. The model performance is substantially affected by the reduction of physics columns when using the 2 × 2 grid, which can increase the throughput of physics calculations by roughly 60%–120% depending on whether the computational resources are configured to maximize throughput or efficiency. A pair of regionally refined cases are also shown to highlight the refinement capability.

Background: Organic scintillators are widely used for neutron detection in both basic nuclear physics and applications. While the proton light yield of organic scintillators has been extensively studied, measurements of the light yield from neutron interactions with carbon nuclei are scarce. Purpose: Demonstrate a new approach for the simultaneous measurement of the proton and carbon light yield of organic scintillators. Provide new carbon light yield data for the EJ-309 liquid and EJ-204 plastic organic scintillators. Method: A 33-MeV H+2 beam from the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory was impinged upon a 3-mm-thick Be target to produce a high-flux, broad-spectrum neutron beam. The double time-of-flight technique was extended to simultaneously measure the proton and carbon light yields of the organic scintillators, wherein the light output associated with the recoil particle was determined using np and nC elastic scattering kinematics. Results: The proton and carbon light yield relations of the EJ-309 liquid and EJ-204 plastic organic scintillators were measured over a recoil energy range of approximately 0.3 to 1 MeV and 2 to 5 MeV, respectively, for EJ-309, and 0.2 to 0.5 MeV and 1 to 4 MeV, respectively, for EJ-204. Conclusions: These data provide new insight into the ionization quenching effect in organic scintillators and key input for simulation of the response of organic scintillators for both basic science and a broad range of applications.

The final version of the above article was posted prematurely on 16 July 2021, owing to a technical error. The final, corrected version of record will be made fully available at a later date.

This study relates structure, properties and thermodynamics, through synthesis, characterization and heat of formation measurements of rare earth iridate pyrochlore (RE2Ir2O7; RE ​= ​Y, Eu, Pr) crystalline powders. The RE2Ir2O7 phases are synthesized by high temperature solid-state synthesis methods. X-ray diffraction and elemental analysis techniques are utilized to validate the synthesis and enable structural comparisons. Trends in the bond angles indicate deviations from the Y and Eu analogs for the Pr2Ir2O7 phase. High temperature oxide melt solution calorimetry is used to determine the heats of formation of each phase. Breaking the trend expected across the rare earth series, the enthalpy of formation for Pr2Ir2O7 is more exothermic than the anticipated from the Y and Eu analogs.

In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-To-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline-the similarity of the sample that MCS+ generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.

Th geomechanical response of a porous reservoir due to injection of fluid can result from a complex interplay between the changes in porepressure and temperature near the wellbore. As a result, predictions are usually made using either simplified analytical models, which may apply unrealistic assumptions in order to produce a tractable model, or detailed numerical simulations that can be computationally expensive. LaForce et al. (2014a, 2014b) developed a semi-analytical model for the geomechanical response of a reservoir to nonisothermal, multi-phase fluid injection, which has been used in studies of CO2 sequestration. We demonstrate that a numerical solution using the MOOSE software precisely matches the analytical formulae. We then include various effects in the numerical model that relax the simplifying assumptions made in the analytical derivation. We find the analytic and numerical solutions for the fluid and temperature fronts still agree reasonably, while only qualitative agreement is observed for other quantities such as stress and displacement. We conclude the LaForce et al. (2014a,b) solutions are useful for rapid investigation of projects involving injection of cold fluid into warm aquifers. However, the enhancements afforded by MOOSE, such as high-precision fluid equations of state and the ability to more accurately capture geological complexity, along with its computational scalability which greatly reduces runtimes, means that MOOSE should be preferred for more sophisticated analyses. Because validating complex coupled codes is challenging, we propose that the model contained herein can be used as a benchmark for other coupled codes.

Partial fuel stratification (PFS) is a promising fuel injection strategy to improve the stability of lean combustion by applying a small amount of pilot injection right before spark timing. Mixed-mode combustion, which makes use of end-gas autoignition following conventional deflagration-based combustion, can be further utilized to speed up the overall combustion. In this study, PFS-assisted mixed-mode combustion in a lean-burn direct injection sparkignition (DISI) engine is numerically investigated using multi-cycle large eddy simulation (LES). A previously developed hybrid G-equation/well-stirred reactor combustion model for the well-mixed operation is extended to the PFS-assisted operation. The experimental spray morphology is employed to derive spray model parameters for the pilot injection. The LES-based model is validated against experimental data and is further compared with the Reynolds-averaged Navier-Stokes (RANS)-based model. Overall, both RANS and LES predict the mean pressure and heat release rate traces well, while LES outperforms RANS in capturing the cycle-to-cycle variation (CCV) and the combustion phasing in the mass burned space. Liquid and vapor penetrations obtained from the simulations agree reasonably well with the experiment. Detailed flame structures predicted from the simulations reveal the transition from a sooting diffusion flame to a lean premixed flame, which is consistent with experimental findings. LES captures more wrinkled and stretched flames than RANS. Finally, the LES model is employed to investigate the impacts of fuel properties, including heat of vaporization (HoV) and laminar burning speed (SL). Combustion phasing is found more sensitive to SL than to HoV, with a larger fuel property sensitivity of the heat release rate from autoignition than that from deflagration. Moreover, the combustion phasing in the PFS-assisted operation is shown to be less sensitive to SL compared with the well-mixed operation.

The Lasserre Hierarchy, [18, 19], is a set of semidefinite programs which yield increasingly tight bounds on optimal solutions to many NP-hard optimization problems. The hierarchy is parameterized by levels, with a higher level corresponding to a more accurate relaxation. High level programs have proven to be invaluable components of approximation algorithms for many NP-hard optimization problems [3, 7, 26]. There is a natural analogous quantum hierarchy [5, 8, 24], which is also parameterized by level and provides a relaxation of many (QMA-hard) quantum problems of interest [5, 6, 9]. In contrast to the classical case, however, there is only one approximation algorithm which makes use of higher levels of the hierarchy [5]. Here we provide the first ever use of the level-2 hierarchy in an approximation algorithm for a particular QMA-complete problem, so-called Quantum Max Cut [2, 9]. We obtain modest improvements on state-of-the-art approximation factors for this problem, as well as demonstrate that the level-2 hierarchy satisfies many physically-motivated constraints that the level-1 does not satisfy. Indeed, this observation is at the heart of our analysis and indicates that higher levels of the quantum Lasserre Hierarchy may be very useful tools in the design of approximation algorithms for QMA-complete problems.

Abstract not provided.

This report represents the milestone deliverable M4SF-21SN010309021 “Modeling Activities Related to Waste Form Degradation: Progress Report” that describes the progress of R&D activities of ongoing modeling investigations specifically on nuclear waste glass degradation, Density Functional Theory (DFT) studies on clarkeite structure and stability, and electrochemical modeling of spent nuclear fuel (SNF). These activities are part of the newly-created Waste form Testing, Modeling, and Performance work package at Sandia National Laboratories (SNL). This work package is part of the “Inventory and Waste Form Characteristics and Performance” control account that includes various experimental and modeling activities on nuclear waste degradation conducted at Oak Ridge National Laboratory (ORNL), SNL, Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL).

The Sandia National Laboratories (SNL) staff is meeting the requirements of the Nuclear Fuel Cycle and Supply Chain (NFCSC) Quality Assurance Program Document (QAPD). Each of the 5 NFCSC FY 20 packages for SNL were reviewed. None of the 5 packages had incorrect QRL categories. No major corrective actions are assigned. Additional and minor PICS:NE checkbox errors may exist, but none were identified in this assessment. Training is now also geared to encourage WPMs to take credit for optional technical reviews by considering setting the QRL level to 3. This is because when the level is set to 4, the PICS:NE system does not provide fields for review information. Finally, training in the NFCSC QAPD has been delivered as always to WPMs.

Currently, spent nuclear fuel (SNF) is stored in on-site independent spent-fuel storage installations (ISFSIs) at seventythree (73) nuclear power plants (NPPs) in the US. Because a site for geologic repository for permanent disposal of SNF has not been constructed, the SNF will remain in dry storage significantly longer than planned. During this time, the ISFSIs, and potentially consolidated storage facilities, will experience earthquakes of different magnitudes. The dry storage systems are designed and licensed to withstand large seismic loads. When dry storage systems experience seismic loads, there are little data on the response of SNF assemblies contained within them. The Spent Fuel Waste Disposition (SFWD) program is planning to conduct a full-scale seismic shake table test to close the gap related to the seismic loads on the fuel assemblies in dry storage systems. This test will allow for quantifying the strains and accelerations on surrogate fuel assembly hardware and cladding during earthquakes of different magnitudes and frequency content. The main component of the test unit will be the full-scale NUHOMS 32 PTH2 dry storage canister. The canister will be loaded with three surrogate fuel assemblies and twenty-nine dummy assemblies. Two dry storage configurations will be tested – horizontal and vertical above-ground concrete overpacks. These configurations cover 91% of the current dry storage configurations. The major input into the shake table test are the seismic excitations or the earthquake ground motions – acceleration time histories in two horizontal and one vertical direction that will be applied to the shake table surface during the tests. The shake table surface represents the top of the concrete pad on which a dry storage system is placed. The goal of the ground motion task is to develop the ground motions that would be representative of the range of seismotectonic and other conditions that any site in the Western US (WUS) or Central Eastern US (CEUS) might entail. This task is challenging because of the large number of the ISFSI sites, variety of seismotectonic and site conditions, and effects that soil amplification, soil-structure interaction, and pad flexibility may have on the ground motions.

High penetration of solar photovoltaics can have a significant impact on the power flows and voltages in distribution systems. In order to support distribution grid planning, control and optimization, it is imperative for utilities to maintain an accurate database of the locations and sizes of PV systems. This paper extends previous work on methods to estimate the location of PV systems based on knowledge of the distribution network model and availability of voltage magnitude measurement streams. The proposed method leverages the expected impact of solar injection variations on the circuit voltage and takes into account the operation and impact of changes in voltage due to discrete voltage regulation equipment (VRE). The estimation model enables determining the most likely location of PV systems, as well as voltage regulator tap and switching capacitors state changes. The method has been tested for individual and multiple PV system, using the Chi-Square test as a metric to evaluate the goodness of fit. Simulations on the IEEE 13-bus and IEEE 123-bus distribution feeders demonstrate the ability of the method to provide consistent estimations of PV locations as well as VRE actions.

Ramp-compression experiments have been performed on the “Z” pulsed-power facility to investigate the strengths of Be and lead-antimony alloy. Yield strength and shear stress near peak pressure were obtained from measurements of the sound speed on release and using the Asay self-consistent method. Two S-65 grade Be samples, from batches that showed a significant difference in yield strength at ambient conditions, were found to have near identical yield strengths, which were also in agreement with similar earlier measurements on S-200 grade Be. Yield strength of the Pb4Sb alloy at ∼120 GPa was 1.35 GPa, while a National Ignition Facility experiment by Krygier et al. [Phys. Rev. Lett. 123, 205701 (2020)] found 3.8 GPa at ∼400 GPa pressure. Our result is intermediate between the ambient value and the one by Krygier et al., but the significantly increased strength is probably not associated with the transition to the high-pressure bcc phase of lead.

A two-step solar thermochemical cycle was considered for air separation to produce N2 based on (Ba,La)_xSr_1-xFeO_3-δ perovskite reduction/oxidation (redox) reactions for A-site fractions of 0 ≤ x ≤ 0.2. The cycle steps encompassed (1) thermal reduction and O₂ release via concentrated solar input and (2) re-oxidation with air to uptake O₂ and produce high-purity N₂. Thermogravimetry at temperatures between 400 and 1100 °C in atmospheres of 0.005 to 90% O₂/Ar at 1 bar was performed to measure equilibrium nonstoichiometries. The compound energy formalism was applied to model redox thermodynamics for both Ba²⁺ and La³⁺ substitution. Non-linear regression was used to determine the empirical parameters based on the thermogravimetric measurements. The model was used to define partial molar reaction enthalpies and entropies and predicted equilibrium oxygen nonstoichiometry as functions of oxide stoichiometry, site fraction, temperature, and O₂ partial pressure. The thermodynamic analysis showed the materials are appealing for air separation at temperatures below 800 °C.

Abstract not provided.

These points are covered in this presentation: Distributed GPU stencil, non-contiguous data; Equivalence of strided datatypes and minimal representation; GPU communication methods; Deploying on managed systems; Large messages and MPI datatypes; Translation and canonicalization; Automatic model-driven transfer method selection; and Interposed library implementation.

The efficient condition assessment of engineered systems requires the coupling of high fidelity models with data extracted from the state of the system ‘as-is’. In enabling this task, this paper implements a parametric Model Order Reduction (pMOR) scheme for nonlinear structural dynamics, and the particular case of material nonlinearity. A physics-based parametric representation is developed, incorporating dependencies on system properties and/or excitation characteristics. The pMOR formulation relies on use of a Proper Orthogonal Decomposition applied to a series of snapshots of the nonlinear dynamic response. A new approach to manifold interpolation is proposed, with interpolation taking place on the reduced coefficient matrix mapping local bases to a global one. We demonstrate the performance of this approach firstly on the simple example of a shear-frame structure, and secondly on the more complex 3D numerical case study of an wind turbine tower under a ground motion excitation. Parametric dependence pertains to structural properties, as well as the temporal and spectral characteristics of the applied excitation. The developed parametric Reduced Order Model (pROM) can be exploited for a number of tasks including monitoring and diagnostics, control of vibrating structures, and residual life estimation of critical components.

Mastery of order-disorder processes in highly nonequilibrium nanostructured oxides has significant implications for the development of emerging energy technologies. However, we are presently limited in our ability to quantify and harness these processes at high spatial, chemical, and temporal resolution, particularly in extreme environments. Here, we describe the percolation of disorder at the model oxide interface LaMnO3/SrTiO3, which we visualize during in situ ion irradiation in the transmission electron microscope. We observe the formation of a network of disorder during the initial stages of ion irradiation and track the global progression of the system to full disorder. We couple these measurements with detailed structural and chemical probes, examining possible underlying defect mechanisms responsible for this unique percolative behavior.

The high theoretical lithium storage capacity of Sn makes it an enticing anode material for Li-ion batteries (LIBs); however, its large volumetric expansion during Li–Sn alloying must be addressed. Combining Sn with metals that are electrochemically inactive to lithium leads to intermetallics that can alleviate volumetric expansion issues and still enable high capacity. Here, we present the cycling behavior of a nanostructured MnSn₂ intermetallic used in LIBs. Nanostructured MnSn₂ is synthesized by reducing Sn and Mn salts using a hot injection method. The resulting MnSn₂ is characterized by x-ray diffraction and transmission electron microscopy and then is investigated as an anode for LIBs. The MnSn₂ electrode delivers a stable capacity of 514 mAh g^-1 after 100 cycles at a C/10 current rate with a Coulombic efficiency >99%. Unlike other Sn-intermetallic anodes, an activation overpotential peak near 0.9 V versus Li is present from the second lithiation and in subsequent cycles. We hypothesize that this effect is likely due to electrolyte reactions with segregated Mn from MnSn₂. To prevent these undesirable Mn reactions with the electrolyte, a 5 nm TiO₂ protection layer is applied onto the MnSn₂ electrode surface via atomic layer deposition. The TiO₂-coated MnSn₂ electrodes do not exhibit the activation overpotential peak. The protection layer also increases the capacity to 612 mAh g^-1 after 100 cycles at a C/10 current rate with a Coulombic efficiency >99%. This higher capacity is achieved by suppressing the parasitic reaction of Mn with the electrolyte, as is supported by x-ray photoelectron spectroscopy analysis.

We consider the development of multifluid models for partially ionized multispecies plasmas. The models are composed of a standard set of five-moment fluid equations for each species plus a description of electromagnetics. The most general model considered utilizes a full set of fluid equations for each charge state of each atomic species, plus a set of fluid equations for electrons. The fluid equations are coupled through source terms describing electromagnetic coupling, ionization, recombination, charge exchange, and elastic scattering collisions in the low-density coronal limit. The form of each of these source terms is described in detail, and references for required rate coefficients are identified for a diverse range of atomic species. Initial efforts have been made to extend these models to incorporate some higher-density collisional effects, including ionization potential depression and three- body recombination. Some reductions of the general multifluid model are considered. First, a reduced multifluid model is derived which averages over all of the charge states (including neutrals) of each atomic species in the general multifluid model. The resulting model maintains full consistency with the general multifluid model from which it is derived by leveraging a quasi-steady-state collisional ionization equilibrium assumption to recover the ionization fractions required to make use of the general collision models. Further reductions are briefly considered to derive certain components of a single-fluid magnetohydrodynamics (MHD) model. In this case, a generalized Ohm's law is obtained, and the standard MHD resistivity is expressed in terms of the collisional models used in the general multifluid model. A number of numerical considerations required to obtain robust implementations of these multifluid models are discussed. First, an algebraic flux correction (AFC) stabilization approach for a continuous Galerkin finite element discretization of the multifluid system is described in which the characteristic speeds used in the stabilization of the fluid systems are synchronized across all species in the model. It is demonstrated that this synchronization is crucial in order to obtain a robust discretization of the multifluid system. Additionally, several different formulations are considered for describing the electromagnetics portion of the multifluid system using nodal continuous Galerkin finite element discretizations. The formulations considered include a parabolic divergence cleaning method and an implicit projection method for the traditional curl formulation of Maxwell's equations, a purely- hyperbolic potential-based formulation of Maxwell's equations, and a mixed hyperbolic-elliptic potential-based formulation of Maxwell's equations. Some advantages and disadvantages of each formulation are explored to compare solution robustness and the ease of use of each formulation. Numerical results are presented to demonstrate the accuracy and robustness of various components of our implementation. Analytic solutions for a spatially homogeneous damped plasma oscillation are derived in order to verify the implementation of the source terms for electromagnetic coupling and elastic collisions between fluid species. Ionization balance as a function of electron temperature is evaluated for several atomic species of interest by comparing to steady-state calculations using various sets of ionization and recombination rate coefficients. Several test problems in one and two spatial dimensions are used to demonstrate the accuracy and robustness of the discretization and stabilization approach for the fluid components of the multifluid system. This includes standard test problems for electrostatic and electromagnetic shock tubes in the two-fluid and ideal shock-MHD limits, a cylindrical diocotron instability, and the GEM challenge magnetic reconnection problem. A one-dimensional simplified prototype of an argon gas puff configuration as deployed on Sandia's Z-machine is used as a demonstration to exercise the full range of capabilities associated with the general multifluid model.

Recent experimental and simulation studies have shown that polymer-nanoparticle (NP) composites (PNCs) with ultra-high NP loading (>50%) exhibit remarkable mechanical properties and dramatic increases in polymer glass-transition temperature, viscosity, and thermal stability compared to the bulk polymer. These deviations in macroscopic properties suggest a slowdown in both segmental and chain-scale polymer dynamics due to confinement. In this work, we examine the polymer conformations and dynamics in these PNCs using molecular dynamics simulations of both unentangled and entangled coarse-grained polymers in random-close-packed NP packings with varying polymer fill fractions. We find that the changes in the polymer dynamics depend on the number of NPs in contact with a polymer segment. Using the number of polymer-NP contacts and different polymer chain conformations as criteria for categorization, we further examine the polymer dynamics at multiple length scales to show the high level of dynamic heterogeneity in PNCs with ultra-high NP loading.

We present progress on the synthesis of semimetal Cd₃As₂ by metal–organic chemical-vapor deposition (MOCVD). Specifically, we have optimized the growth conditions needed to obtain technologically useful growth rates and acceptable thin-film microstructures, with our studies evaluating the effects of varying the temperature, pressure, and carrier-gas type for MOCVD of Cd₃As₂ when performed using dimethylcadmium and tertiary-butylarsine precursors. In the course of the optimization studies, exploratory Cd₃As₂ growths are attempted on GaSb substrates, strain-relaxed InAs buffer layers grown on GaSb substrates, and InAs substrates. Notably, only the InAs-terminated substrate surfaces yield desirable results. Extensive microstructural studies of Cd₃As₂ thin films on InAs are performed by using multiple advanced imaging microscopies and x-ray diffraction modalities. The studied films are 5–75 nm in thickness and consist of oriented, coalesced polycrystals with lateral domain widths of 30–80 nm. The most optimized films are smooth and specular, exhibiting a surface roughness as low as 1.0 nm rms. Under cross-sectional imaging, the Cd₃As₂-InAs heterointerface appears smooth and abrupt at a lower film thickness, ~30 nm, but becomes quite irregular as the average thickness increases to ~55 nm. The films are strain-relaxed with a residual biaxial tensile strain (ϵ_xx = +0.0010) that opposes the initially compressive lattice-mismatch strain of Cd₃As₂ coherent on InAs (ϵ_xx = - 0.042). Importantly, phase-identification studies find a thin-film crystal structure consistent with the P4₂/nbc space group, placing MOCVD-grown Cd₃As₂ among the Dirac semimetals of substantial interest for topological quantum materials studies.

A general problem when designing functional nanomaterials for energy storage is the lack of control over the stability and reactivity of metastable phases. Using the high-capacity hydrogen storage candidate LiAlH4 as an exemplar, we demonstrate an alternative approach to the thermodynamic stabilization of metastable metal hydrides by coordination to nitrogen binding sites within the nanopores of N-doped CMK-3 carbon (NCMK-3). The resulting LiAlH4@NCMK-3 material releases H2 at temperatures as low as 126 °C with full decomposition below 240 °C, bypassing the usual Li3AlH6 intermediate observed in bulk. Moreover, >80% of LiAlH4 can be regenerated under 100 MPa H2, a feat previously thought to be impossible. Nitrogen sites are critical to these improvements, as no reversibility is observed with undoped CMK-3. Density functional theory predicts a drastically reduced Al-H bond dissociation energy and supports the observed change in the reaction pathway. The calculations also provide a rationale for the solid-state reversibility, which derives from the combined effects of nanoconfinement, Li adatom formation, and charge redistribution between the metal hydride and the host.

We consider an optimal control synthesis problem for a class of control-affine nonlinear systems. We propose Sum-of-Square based computational framework for optimal control synthesis. The proposed computation framework relies on the convex formulation of the optimal control problem in the dual space of densities. The convex formulation to the optimal control problem is based on the duality results in dynamical systems' stability theory. We used the Sum-of-Square based computational framework for the finite-dimensional approximation of the convex optimization problem. The efficacy of the developed framework is demonstrated using simulation results.

In many network applications, it may be desirable to conceal certain target nodes from detection by a data collector, who is using a crawling algorithm to explore a network. For example, in a computer network, the network administrator may wish to protect those computers (target nodes) with sensitive information from discovery by a hacker who has exploited vulnerable machines and entered the network. These networks are often protected by hiding the machines (nodes) from external access, and allow only fixed entry points into the system (protection against external attacks). However, in this protection scheme, once one of the entry points is breached, the safety of all internal machines is jeopardized (i.e., the external attack turns into an internal attack). In this paper, we view this problem from the perspective of the data protector. We propose the Node Protection Problem: given a network with known entry points, which edges should be removed/added so as to protect as many target nodes from the data collector as possible? A trivial way to solve this problem would be to simply disconnect either the entry points or the target nodes - but that would make the network non-functional. Accordingly, we impose certain constraints: for each node, only (1 - r) fraction of its edges can be removed, and the resulting network must not be disconnected. We propose two novel scoring mechanisms - the Frequent Path Score and the Shortest Path Score. Using these scores, we propose NetProtect, an algorithm that selects edges to be removed or added so as to best impede the progress of the data collector. We show experimentally that NetProtect outperforms baseline node protection algorithms across several real-world networks. In some datasets, With 1% of the edges removed by NetProtect, we found that the data collector requires up to 6 (4) times the budget compared to the next best baseline in order to discover 5 (50) nodes.

Large-scale, high-throughput computational science faces an accelerating convergence of software and hardware. Software container-based solutions have become common in cloud-based datacenter environments, and are considered promising tools for addressing heterogeneity and portability concerns. However, container solutions reflect a set of assumptions which complicate their adoption by developers and users of scientific workflow applications. Nor are containers a universal solution for deployment in high-performance computing (HPC) environments which have specialized and vertically integrated scheduling and runtime software stacks. In this paper, we present a container design and deployment approach which uses modular layering to ease the deployment of containers into existing HPC environments. This layered approach allows operating system integrations, support for different communication and performance monitoring libraries, and application code to be defined and interchanged in isolation. We describe in this paper the details of our approach, including specifics about container deployment and orchestration for different HPC scheduling systems. We also describe how this layering method can be used to build containers for two separate applications, each deployed on clusters with different batch schedulers, MPI networking support, and performance monitoring requirements. Our experience indicates that the layered approach is a viable strategy for building applications intended to provide similar behavior across widely varying deployment targets.

Energy storage systems (ESS) can provide multiple services to the electric grid, each with a unique charge/discharge profile. One category of such services comprises power quality applications, where ESS is deployed to protect downstream customers from events or disturbances that might result in poor power quality. This paper analyzes ESS usage to simultaneously mitigate two power quality issues: harmonic distortion and low power factor. Techniques for solving each one of these issues are already known by utilities; however, the main contribution of this paper is the utilization of a single asset to mitigate both power quality issues simultaneously. An optimization model was developed to determine the ESS dispatch that would satisfy the requirements for these stacked applications. Through case studies of a medium-size commercial customer, it was demonstrated that ESS can, indeed, correct and/or mitigate poor power quality issues.

This work presents a 3-Port acoustoelectric switch design for surface acoustic wave signal processing. Using a multistrip coupler, the input acoustic wave at Port 1 is split into two parallel and electrically cross-linked acoustoelectric delay lines where an applied voltage can alter the gain and attenuation in each delay line based on the voltage polarity. The switch is demonstrated using a 270 MHz Leaky SAW mode on an InGaAs on 41° Y-cut lithium niobate heterostructure. Applying a +40 V voltage pulse results in an IL of -12.5 dB and -57.5 dB in the gain and isolation switch paths, respectively. This leads to a 45 dB difference in signal strength at the output ports.

In this work, a model predictive dispatch framework is proposed to utilize Energy Storage Systems (ESSs) for voltage regulation in distribution systems. The objective is to utilize ESS resources to assist with voltage regulation while reducing the utilization of legacy devices such as on-load tap changers (OLTCs), capacitor banks, etc. The proposed framework is part of a two-stage solution where a secondary layer computes the ESS dispatch every 5-min based on 1-hr generation and load forecasts while a primary layer would handle the real-time uncertainties. In this paper, the secondary layer to dispatch the ESS is formulated. Simulation results show that dispatching ESSs by providing active and reactive support can minimize the OLTC movement in distribution networks thus increasing the lifetime of legacy mechanical devices.

In this work, a model predictive dispatch framework is proposed to utilize Energy Storage Systems (ESSs) for voltage regulation in distribution systems. The objective is to utilize ESS resources to assist with voltage regulation while reducing the utilization of legacy devices such as on-load tap changers (OLTCs), capacitor banks, etc. The proposed framework is part of a two-stage solution where a secondary layer computes the ESS dispatch every 5-min based on 1-hr generation and load forecasts while a primary layer would handle the real-time uncertainties. In this paper, the secondary layer to dispatch the ESS is formulated. Simulation results show that dispatching ESSs by providing active and reactive support can minimize the OLTC movement in distribution networks thus increasing the lifetime of legacy mechanical devices.

PV system reliability analyses often depend on production data to evaluate the system state. However, using this information alone leads to incomplete assessments, since contextual information about potential sources of data quality issues is lacking (e.g., missing data from offline communications vs. offline production). This paper introduces a new Python-based software capability (called pvOps) for fusing production data with readily available text-based maintenance information to improve reliability assessments. In addition to details about the package development process, the general capabilities to gain actionable insights using field data are presented through a case study. These findings highlight the significant potential for continued advancements in operational assessments.

Corrective maintenance strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to failures. In this work, a complete operation and maintenance (OM) decision support system (DSS) was developed for corrective maintenance. The DSS operates entirely on field measurements and incorporates technical asset and financial management features. It was validated experimentally on a large-scale PV system installed in Greece and the results demonstrated the financial benefits of performing corrective actions in case of failures and reversible loss mechanisms. Reduced response and resolution times of corrective actions could improve the PV power production of the test PV plant by up to 2.41%. Even for 1% energy yield improvement by performing corrective actions, a DSS is recommended for large-scale PV plants (with a peak capacity of at least 250 kWp).

PV system reliability analyses often depend on production data to evaluate the system state. However, using this information alone leads to incomplete assessments, since contextual information about potential sources of data quality issues is lacking (e.g., missing data from offline communications vs. offline production). This paper introduces a new Python-based software capability (called pvOps) for fusing production data with readily available text-based maintenance information to improve reliability assessments. In addition to details about the package development process, the general capabilities to gain actionable insights using field data are presented through a case study. These findings highlight the significant potential for continued advancements in operational assessments.

Dynamic operations of electric power switches in microgrid mode allows for distributed photovoltaic (PV) systems to support a critical load and enable the transfer of electrical power to non-critical loads. Instead of relying on an expensive system that includes a constant generation source (e.g. fossil fuel based generators), this work assess the potential balance of load and PV generation to properly charge a critical load battery while also supporting non-critical loads during the day. This work assumes that the battery is sized to only support the critical load and that the PV at the critical load is undersized. To compensate for the limited power capacity, a battery charging algorithm predicts and defines battery demand throughout the day; a particle swarm optimization (PSO) scheme connects and disconnects switch sections inside a distribution system with the objective of minimizing the difference between load and generation. The PSO reconfiguration scheme allows for continuous operations of a critical load as well as inclusion of non-critical loads.

High penetration of distributed energy resources presents challenges for monitoring and control of power distribution systems. Some of these problems might be solved through accurate monitoring of distribution systems, such as what can be achieved with distribution system state estimation (DSSE). With the recent large-scale deployment of advanced metering infrastructure associated with existing SCADA measurements, DSSE may become a reality in many utilities. In this paper, we present a sensitivity analysis of DSSE with respect to phase mislabeling of single-phase service transformers, another class of errors distribution system operators are faced with regularly. The results show DSSE is more robust to phase label errors than a power flow-based technique, which would allow distribution engineers to more accurately capture the impacts and benefits of distributed PV.

Detailed finite element models of a 60-cell crystalline silicon photovoltaic module undergoing a ±1.0 and ±2.4 kPa pressure load were simulated to compare differences created by a constrained frame boundary condition versus replicating manufacturer recommended rack mounting. Module deflection, interconnect strain, and first principal stresses on cell volumes were used as comparison metrics to assess how internal module damage was affected. Average results across all loads scenarios showed that constraining the frame of the module to its initial unloaded plane reduced peak deflections by approximately 13%, interconnect strains by 11%, and first principal stress by 11% when compared to a module with correctly modeled racking. Analysis of results based on damage metrics indicated that the constrained boundary condition reduced interconnect stress at most locations and increased fatigue life by an average of 34%, and likewise reduced the average probability of cell fracture by 82%, though individual results were highly variable. Nonetheless, location-specific trends were generally consistent across constraint methodologies, indicating that the constraint simplification can be applied successfully if corrected for with increased load, additional test cycles, or an informed interpretation of results. The goal of this work was to exercise a methodology for quantifying differences created by a simplified test constraint setup, since expedient experimental simplifications are often used or considered to reduce the complexity of exploratory mechanical tests not related to standards qualification.

Detailed finite element models of a 60-cell crystalline silicon photovoltaic module undergoing a ±1.0 and ±2.4 kPa pressure load were simulated to compare differences created by a constrained frame boundary condition versus replicating manufacturer recommended rack mounting. Module deflection, interconnect strain, and first principal stresses on cell volumes were used as comparison metrics to assess how internal module damage was affected. Average results across all loads scenarios showed that constraining the frame of the module to its initial unloaded plane reduced peak deflections by approximately 13%, interconnect strains by 11%, and first principal stress by 11% when compared to a module with correctly modeled racking. Analysis of results based on damage metrics indicated that the constrained boundary condition reduced interconnect stress at most locations and increased fatigue life by an average of 34%, and likewise reduced the average probability of cell fracture by 82%, though individual results were highly variable. Nonetheless, location-specific trends were generally consistent across constraint methodologies, indicating that the constraint simplification can be applied successfully if corrected for with increased load, additional test cycles, or an informed interpretation of results. The goal of this work was to exercise a methodology for quantifying differences created by a simplified test constraint setup, since expedient experimental simplifications are often used or considered to reduce the complexity of exploratory mechanical tests not related to standards qualification.

Smoke from wildfires results in air pollution that can impact the performance of solar photovoltaic plants. Production is impacted by factors including the proximity of the fire to a site of interest, the extent of the wildfire, wind direction, and ambient weather conditions. We construct a model that quantifies the relationships among weather, wildfire-induced pollution, and PV production for utility-scale and distributed generation sites located in the western USA. The regression model identified a 9.4%-37.8% reduction in solar PV production on smokey days. This model can be used to determine expected production losses at impacted sites. We also present an analysis of factors that contribute to solar photovoltaic energy production impacts from wildfires. This work will inform anticipated production changes for more accurate grid planning and operational considerations.

In order to address the recent inclement weather-related energy events, electricity production is experiencing an important transition from conventional fossil fuel based resources to the use of Distributed Energy Resources (DER), providing clean and renewable energy. These DERs make use of power electronic based devices that perform the energy conversion process required to interface with the utility grids. For the particular cases where DC/AC conversion is required, grid-forming inverters (GFMI) are gaining popularity over their grid-following (GFLI) counterpart. This is due to the fact that GFMI do not require a dedicated Phase Locked Loop (PLL) to synchronize with the grid. The absence of a PLL allows GFMI to operate in stand-alone (off-grid) mode when needed. Nowadays, inverter manufacturers are already offering several products with grid-forming capabilities. However, modeling the dynamics of commercially available GFMI under heavy loads or faults scenarios has become a critical task not only for stability studies, but also for coordination and protection schemes in power grids (or microgrids) that are experiencing a steady growth in their levels of DERs. Based upon experimental low-impedance fault results performed on a commercially available GFMI, this paper presents a modeling effort to replicate the dynamics of such inverters under these abnormal scenarios. The proposed modeling approach relies on modifying previously developed GFMI models, by adding the proper dynamics, to match the current and voltage transient behavior under low-impedance fault scenarios. For the first inverter tested, a modified CERTS GFMI model provides matching transient dynamics under faults scenarios with respect to the experimental results from the commercially available inverter.

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.