Hybrid CMOS multi-frame imagers with exposure times down to ∼2 ns have made significant impacts in high energy density physics and inertial confinement fusion research. The detector thickness is a key parameter in both detector quantum efficiency and temporal response. The Icarus hybrid CMOS imager has been fabricated with Si detector thicknesses of 8, 25, and 100 μm. The temporal response of imaging sensors with exposure time down to 2 ns has been examined and compared to directly measured photodiode current. The 100-μm thick variant displays extended features related to charge carrier collection and is more susceptible to field collapse. We also demonstrate charge collection time effects on spatial response.
We investigated by arc-plasma heating the feasibility of attributing inherent lightning protection to 55-gallon DOT 7A, Type A, open head carbon steel drums made of 1.5 millimeter painted carbon steel, designed to protect Department of Energy transuranic nuclear waste. The Sandia Lightning Simulator transferred continuing current in 300 ampere (A), 400 A, and 500 A tests to achieve a 350 coulomb charge transfer and simulate cloud-to-ground lightning attachment to test coupons and 9 drums. A tungsten electrode was placed 0.75 inch from the drums. High-speed photography was recorded to observe the exterior containment breach, or "first light," seen on camera when burnthrough opened a hole in the containment. Sheet metal burnthrough occurred between 18 and 71 coulombs in lid and rolling hoop tests, but 12-gauge closure ring tests did not result in burnthrough, which suggests this feature may provide an inherent air terminal protective feature.
Objectives: Automate the labor-intensive process of generating Analytical Action Levels (AALs) in Turbo FRMAC to shorten the timeline for planning sampling campaigns and sample analysis during a response. Make the tool output results in a format that is easily imported to RadResponder as a Mixture for use in Analysis Request Forms. Deliver training to EPA on using this new tool in Turbo FRMAC (Delayed due to COVID.
Epitaxial single-domain LiNbO3 thin-films are realized using a novel nanocomposite seeding method. Full microstructure characterization and optical property measurement is conducted as a first step to demonstrate viability of this material for integrated photonics applications.
The Open Radiation Monitoring Project seeks to develop and demonstrate a modular radiation detection architecture designed specifically for use in arms control treaty verification (ACTV) applications that will facilitate rapid development of trusted systems to meet the needs of potential future treaties. A modular architecture can be used to reduce more complex systems to a series of single purpose building blocks, thereby facilitating equipment inspection and in turn building trust in the equipment by all treaty parties. Furthermore, a modular architecture can be used to control data flow within the measurement system, reducing the risk of "hidden switches" and constraining the amount of sensitive information that could potentially be inadvertently leaked. This report details the first revision of a prototype circuit that will convert analog pulses directly into a histogrammed data set for further processing. The circuit was designed with both spectroscopy and multiplicity analysis in mind but can, in principle, be used to reduce any raw data stream into a histogram. The number of output channels is limited, and the histogram bin ranges are user configurable to allow for non-uniform and discontinuous bins, which makes it possible to restrict the information being passed down stream if desired. Pulse processing relies entirely on analog circuitry and non- programmable logic, which enables operation without the need for a central processor or other programmable control unit. The circuit remains untested under the Open Radiation Monitoring project due to the closure of the sponsoring program. However, further development and testing is scheduled to take place in support of a purpose-built trusted verification system development effort known as COGNIZANT, which demonstrates the potential benefit of developing a suite of modular trusted system components.
Foliage penetration (FOPEN) radar at lower frequencies (VHF, UHF) is a well-studied area with many contributions. However, there is growing interest in using higher Ku-band frequencies (12-18 GHz) for FOPEN. Specifically, the reduced wavelength sizes provide some key saliencies for developing more optimized detection solutions. The disadvantage is that exploiting Ku-band for FOPEN is complicated because higher frequencies have pronounced scattering effects due to their smaller wavelengths. A methodology has been developed to model and simulate FOPEN problems that characterize the phenomenology of Ku-band EM wave transmissions through moderate foliage. The details of this research are documented in multiple reports. The main focus of this report is to describe the FOPEN model simulation scene setup, validation and results.
The U.S. Department of Energy/National Nuclear Security Administration (DOE/ NNSA) and National Technology & Engineering Solutions of Sandia, LLC (NTESS), the management and operating contractor for Sandia National Laboratories/California (SNL/CA), has prepared this soil sampling results report for closure of a portion of Solid Waste Management Unit (SWMU) #16. The entire network of SNL/CA sanitary sewer lines, including building laterals, was identified as SWMU #16 under a Resource Conservation and Recovery Act (RCRA) Facility Assessment conducted for SNL/CA in April 1991 (DOE 1992). Along with the previous SWMU #16 investigation results (SNL/CA 2019), the results of this investigation are intended to support closure decisions by the San Francisco Bay Regional Water Quality Control Board (RWQCB), as discussed below. SNL/CA personnel completed upgrading its sanitary sewer discharge network in 2019. These upgrades included installing new sections of underground lines and decommissioning certain sections of the old piping system by capping in place. To date, several sections of the sewer line have been abandoned-in-place by capping as new sewer lines were installed or flow was rerouted to other existing lines. To formally close these abandoned sections of the sewer line, the RWQCB required that SNL/CA personnel collect soil samples to be analyzed for contaminants potentially released from the sewer lines. SNL/CA personnel hired Weiss Associates (Weiss) of Emeryville, California to prepare a sampling and analysis plan, implement the sampling plan and report the results of the investigation under Purchase Order #2166257. The Sampling and Analysis Plan for Partial Closure of Solid Waste Management Unit #16 (SAP) was submitted to the RWQCB on August 14, 2020 by Weiss on behalf of SNL/CA. The RWQCB approved the SAP on September 30, 2020 after Weiss updated the method detection limit and reporting limits for total polychlorinated biphenyls (PCBs) and individual aroclors. Soil sampling was conducted in accordance with the SAP except that fewer locations were sampled due to site constraints, as discussed below. This report presents the results of the sampling effort and documents all associated field activities including borehole clearing, soil sample collection, storage and transportation to the analytical laboratories, borehole backfilling and surface restoration, and storage of investigation-derived waste (IDW) for future profiling and disposal by SNL/CA waste management personnel.
Sarnaik, Aditya; Mhatre, Apurv; Faisal, Muhammad; Smith, Dylan; Davis, Ryan W.; Varman, Arul M.
Ultra-low temperature (ULT) storage of microbial biomass is routinely practiced in biological laboratories. However, there is very little insight regarding the effects of biomass storage at ULT and the structure of the cell envelope, on cell viability. Eventually, these aspects influence bacterial cell lysis which is one of the critical steps for biomolecular extraction, especially protein extraction. Therefore, we studied the effects of ULT-storage (-80°C) on three different bacterial platforms: Escherichia coli, Bacillus subtilis and the cyanobacterium Synechocystis sp. PCC 6803. By using a propidium iodide assay and a modified MTT assay we determined the impact of ULT storage on cellular viability. Subsequently, the protein extraction efficiency was determined by analyzing the amount of protein released following the storage. The results successfully established that longer the ULT-storage time lower is the cell viability and larger is the protein extraction efficiency. Interestingly, E. coli and B. subtilis exhibited significant reduction in cell viability over Synechocystis 6803. This indicates that the cell membrane structure and composition may play a major role on cell viability in ULT storage. Interestingly, E. coli exhibited concomitant increase in cell lysis efficiency resulting in a 4.5-fold increase (from 109 μg/ml of protein on day 0 to 464 μg/ml of protein on day 2) in the extracted protein titer following ULT storage. Furthermore, our investigations confirmed that the protein function, tested through the extraction of fluorescent proteins from cells stored at ULT, remained unaltered. These results established the plausibility of using ULT storage to improve protein extraction efficiency. Towards this, the impact of shorter ULT storage time was investigated to make the strategy more time efficient to be adopted into protocols. Interestingly, E. coli transformants expressing mCherry yielded 2.7-fold increase (93 μg/mL to 254 μg/mL) after 10 mins, while 4-fold increase (380 μg/mL) after 120 mins of ULT storage in the extracted soluble protein. We thereby substantiate that: (1) the storage time of bacterial cells in-80°C affect cell viability and can alter protein extraction efficiency; and (2) exercising a simple ULT-storage prior to bacterial cell lysis can improve the desired protein yield without impacting its function.
Magnetoelectric systems could be used to develop magnetoelectric random access memory and microsensor devices. One promising system is the two-phase 3-1-type multiferroic nanocomposite in which a one-dimensional magnetic column is embedded in a three-dimensional ferroelectric matrix. However, it suffers from a number of limitations including unwanted leakage currents and the need for biasing with a magnetic field. Here we show that the addition of an antiferromagnet to a 3-1-type multiferroic nanocomposite can lead to a large, self-biased magnetoelectric effect at room temperature. Our three-phase system is composed of a ferroelectric Na0.5Bi0.5TiO3 matrix in which ferrimagnetic NiFe2O4 nanocolumns coated with antiferromagnetic p-type NiO are embedded. This system, which is self-assembled, exhibits a magnetoelectric coefficient of up to 1.38 × 10–9 s m–1, which is large enough to switch the magnetic anisotropy from the easy axis (Keff = 0.91 × 104 J m–3) to the easy plane (Keff = –1.65 × 104 J m–3).
This article is the first in a two-part series on the influence of inverter-based resources (IBRs) s on microgrid protection. In part one, the focus is on microgrids deployed on radial circuits. This article discusses some of the challenges related to the protection of IBR-based microgrids and presents some ongoing research and solutions in the area. The different controls for IBRs are discussed to present how their short current signatures and dynamic response under faults impact microgrid protection. Recently, microgrids have gained much attention in the electric power industry due to their capability for improving power system reliability and resiliency, their impact on increasing the use of renewable resources, the reduced cost of distributed energy resource (DER) equipment, and the continuing evolution of applicable codes and standards.
Van De Steeg, A.W.; Vialetto, L.; Silva, A.F.; Peeters, F.J.J.; van den Bekerom, Dirk C.; Gatti, N.; Diomede, P.; Van De Sanden, M.C.M.; Van Rooij, G.J.
In this Letter, the counterintuitive and largely unknown Raman activity of oxygen atoms is evaluated for its capacity to determine absolute densities in gases with significant O-density. The study involves CO2 microwave plasma to generate a self-calibrating mixture and establish accurate cross sections for the 3P2↔3P1 and 3P2↔3P0 transitions. The approach requires conservation of stoichiometry, confirmed within experimental uncertainty by a 1D fluid model. The measurements yield σJ =2→1 = 5.27 ±randsys:0.53:0.17 ×10−31 cm2/sr and σJ =2→0 = 2.11 ±randsys:0.21:0.06 ×10−31 cm2/sr, and the detection limit is estimated to be 1 × 1015 cm−3 for systems without other scattering species.
Dalbey, Keith R.; Eldred, Michael S.; Geraci, Gianluca; Jakeman, John D.; Maupin, Kathryn A.; Monschke, Jason A.; Seidl, Daniel T.; Tran, Anh; Menhorn, Friedrich; Zeng, Xiaoshu
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
X-pinches are a pulsed power wire-array configuration that produce nanosecond and micron-scale X-ray sources with numerous applications. The earliest embodiment of the X-pinch inspired its name, as it is typically composed of two or more fine (5-50 µm) wires crossed into the shape of an X (Fig. 1a). The ‘X’ ablates when subjected to large (101-104 kA), fast-rising (~1 kA/ns) currents, and extreme magnetic pressure at the cross-point constricts the ablated plasma, which develops instabilities and then pinches to near-zero radius, localized ‘hot spots’, emitting X-rays from a sub-nanosecond and 1 µm scale source characteristic of a hot (~1 keV), dense (10% solid density) plasma. A subsequent gap forms where the hot spot(s) occurred, across which substantial potential accelerates electron beams (e-beams) that generate larger, longer-lasting X-ray bursts composed of harder X-rays.
PAO is a Python-based package for Adversarial Optimization. The goal of this package is to provide a general modeling and analysis capability for bilevel, trilevel and other multilevel optimization forms that express adversarial dynamics. PAO integrates two different modeling abstractions: 1. Algebraic models extend the modeling concepts in the Pyomo algebraic modeling language to express problems with an intuitive algebraic syntax. Thus, we expect that this modeling abstraction will commonly be used by PAO end-users. 2. Compact models express objective and constraints in a manner that is typically used to express the mathematical form of these problems (e.g. using vector and matrix data types). PAO denes custom Multilevel Problem Representations (MPRs) that simplify the implementation of solvers for bilevel, trilevel and other multilevel optimization problems.
At the request of staff members from the Radiation Metrology Laboratory (RML), a series of Monte Carlo radiation transport calculations were performed using two different models of the detector geometry of the RMLs sulfur counting system. The fraction of electrons from each β-decay of 32P in the sulfur pellet that enter the window of the sulfur counting system was calculated with both MCNP and ITS. In addition, the differential energy distributions of the electrons entering the counting system window were computed. There was significant agreement between the integral and differential quantities calculated by the two transport codes. Summary tables are for the integral efficiency values are found in the body of the report.
Despite hitting major roadblocks in 2-D scaling, NAND flash continues to scale in the vertical direction and dominate the commercial nonvolatile memory market. However, several emerging nonvolatile technologies are under development by major commercial foundries or are already in small volume production, motivated by storage-class memory and embedded application drivers. These include spin-transfer torque magnetic random access memory (STT-MRAM), resistive random access memory (ReRAM), phase change random access memory (PCRAM), and conductive bridge random access memory (CBRAM). Emerging memories have improved resilience to radiation effects compared to flash, which is based on storing charge, and hence may offer an expanded selection from which radiation-tolerant system designers can choose from in the future. This review discusses the material and device physics, fabrication, operational principles, and commercial status of scaled 2-D flash, 3-D flash, and emerging memory technologies. Radiation effects relevant to each of these memories are described, including the physics of and errors caused by total ionizing dose, displacement damage, and single-event effects, with an eye toward the future role of emerging technologies in radiation environments.
Among Professor Arthur Gossard's many contributions to crystal growth are those resulting in important improvements in the quality and performance of quantum-well and quantum-dot semiconductor lasers. In celebration of his 85th birthday, we review the development of a semiconductor laser theory that is motivated and guided, in part, by those advances. This theory combines condensed matter theory and laser physics to provide understanding at a microscopic level, i.e., in terms of electrons and holes, and their interaction with the radiation field while influenced by the lattice.
Recorded speckle from a moving object hidden in a heavily scattering random medium is used to determine positions and coherently image at high resolution and through an amount of scatter limited only by detector noise.
We report experimental and numerical developments extending the operating range of vanadium dioxide based optical limiters into the short-wavelength infrared. Pixelated sensor elements have been fabricated which show optically-triggered limiting of a 2.7 µm probe.
Artificial muscles (AMs) traditionally rely on pneumatic sources of fluid power. The use of hydraulics can increase the power and force to weight and volume ratios of AM actuators. This paper develops a control-centric third-order single-input single-output (SISO) lumped-parameter dynamic model and sliding mode position controller based on Filippov's principle of equivalent dynamics for a braided hydraulic artificial muscle (HAM) actuator. The model predicts the nonlinear behavior of the HAM free contraction and captures the fluid and actuator nonlinear dynamic interactions in addition to the braid deformation. Model simulations are compared to experimental results for quasi-static pressurization, isometric pressurization, and open-loop square wave commands at 0.25, 0.5, and 1 Hz. Experiments of sine wave tracking at 0.25, 0.5, and 1 Hz and continuous square wave tracking at 0.067 Hz are conducted using a sliding mode controller (SMC) derived from the model. The SMC achieves a steady-state error of 6 lm at multiple setpoints within the actuator's 17 mm stroke. Compared to a proportional-integral-derivative (PID) controller, the SMC root-mean-square (RMS) error, mean error, and absolute maximum error are reduced on average by 53%, 61%, and 44%, respectively, demonstrating the benefit of model-based approaches for controlling HAMs.
There currently exist two methods for analysing an explosive mode introduced by chemical kinetics in a reacting process: the Computational Singular Perturbation (CSP) algorithm and the Chemical Explosive Mode Analysis (CEMA). CSP was introduced in 1989 and addressed both dissipative and explosive modes encountered in the multi-scale dynamics that characterize the process, while CEMA was introduced in 2009 and addressed only the explosive modes. It is shown that (i) the algorithmic tools incorporated in CEMA were developed previously on the basis of CSP and (ii) the examination of explosive modes has been the subject of CSP-based works, reported before the introduction of CEMA.
Metamaterials, otherwise known as architected or programmable materials, enable designers to tailor mesoscale topology and shape to achieve unique material properties that are not present in nature. Additionally, with the recent proliferation of additive manufacturing tools across industrial sectors, the ability to readily fabricate geometrically complex metamaterials is now possible. However, in many high-performance applications involving complex multi-physics interactions, design of novel lattice metamaterials is still difficult. Design is primarily guided by human intuition or gradient optimization for simple problems. In this work, we show how machine learning guides discovery of new unit cells that are Pareto optimal for multiple competing objectives; specifically, maximizing elastic stiffness during static loading and minimizing wave speed through the metamaterial during an impact event. Additionally, we show that our artificial intelligence approach works with relatively few (3500) simulation calls.
Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.
In the ideal case, plasma-enhanced atomic layer etching enables the ability to not only remove one monolayer of material but also leave adjacent layers undamaged. This dual mandate requires fine control over the flux of species to ensure efficacy, while maintaining an often arduously low ion energy. Electron beam-generated plasmas are well-suited for etching at low ion energies as they are generally characterized by highly charged particle densities (1010-1011 cm-3) and low electron temperatures (<1.0 eV), which provide the ability to deliver a large flux of ions whose energies are <5 eV. Raising the ion energy with substrate biasing thus enables process control over an energy range that extends down to values commensurate with the bond strength of most material systems. In this work, we discuss silicon nitride etching using pulsed, electron beam-generated plasmas produced in argon-SF6 backgrounds. We pay particular attention to the etch rates and selectivity versus oxidized silicon nitride and polycrystalline silicon as a function of ion energy from a few eV up to 50 eV. We find the blanket etch rate of Si3N4 to be in the range of 1 A/s, with selectivities (versus SiO2 and poly-Si) greater than 10:1 when ion energies are below 30 eV.
Remote Direct Memory Access (RDMA) capabilities have been provided by high-end networks for many years, but the network environments surrounding RDMA are evolving. RDMA performance has historically relied on using strict ordering guarantees to determine when data transfers complete, but modern adaptively-routed networks no longer provide those guarantees. RDMA also exposes low-level details about memory buffers: either all clients are required to coordinate access using a single shared buffer, or exclusive resources must be allocatable per-client for an unbounded amount of time. This makes RDMA unattractive for use in many-to-one communication models such as those found in public internet client-server situations.Remote Virtual Memory Access (RVMA) is a novel approach to data transfer which adapts and builds upon RDMA to provide better usability, resource management, and fault tolerance. RVMA provides a lightweight completion notification mechanism which addresses RDMA performance penalties imposed by adaptively-routed networks, enabling high-performance data transfer regardless of message ordering. RVMA also provides receiver-side resource management, abstracting away previously-exposed details from the sender-side and removing the RDMA requirement for exclusive/coordinated resources. RVMA requires only small hardware modifications from current designs, provides performance comparable or superior to traditional RDMA networks, and offers many new features.In this paper, we describe RVMA's receiver-managed resource approach and how it enables a variety of new data-transfer approaches on high-end networks. In particular, we demonstrate how an RVMA NIC could implement the first hardware-based fault tolerant RDMA-like solution. We present the design and validation of an RVMA simulation model in a popular simulation suite and use it to evaluate the advantages of RVMA at large scale. In addition to support for adaptive routing and easy programmability, RVMA can outperform RDMA on a 3D sweep application by 4.4X.
In this article, we have evaluated the Read-Retry (RR) functionality of the 3-D NAND chip of multilevel-cell (MLC) configuration after total ionization dose (TID) exposure. The RR function is typically offered in the high-density state-of-the-art NAND memory chips to recover data once the default memory read method fails to correct data with error correction codes (ECCs). In this work, we have applied the RR method on the irradiated 3-D NAND chip that was exposed with a Co-60 gamma-ray source for TID up to 50 krad (Si). Based on our experimental evaluation results, we have proposed an algorithm to efficiently implement the RR method to extend the radiation tolerance of the NAND memory chip. Our experimental evaluation shows that the RR method coupled with ECC can ensure data integrity of MLC 3-D NAND for TID up to 50 krad (Si).
Despite hitting major roadblocks in 2-D scaling, NAND flash continues to scale in the vertical direction and dominate the commercial nonvolatile memory market. However, several emerging nonvolatile technologies are under development by major commercial foundries or are already in small volume production, motivated by storage-class memory and embedded application drivers. These include spin-transfer torque magnetic random access memory (STT-MRAM), resistive random access memory (ReRAM), phase change random access memory (PCRAM), and conductive bridge random access memory (CBRAM). Emerging memories have improved resilience to radiation effects compared to flash, which is based on storing charge, and hence may offer an expanded selection from which radiation-tolerant system designers can choose from in the future. This review discusses the material and device physics, fabrication, operational principles, and commercial status of scaled 2-D flash, 3-D flash, and emerging memory technologies. Radiation effects relevant to each of these memories are described, including the physics of and errors caused by total ionizing dose, displacement damage, and single-event effects, with an eye toward the future role of emerging technologies in radiation environments.
Despite hitting major roadblocks in 2-D scaling, NAND flash continues to scale in the vertical direction and dominate the commercial nonvolatile memory market. However, several emerging nonvolatile technologies are under development by major commercial foundries or are already in small volume production, motivated by storage-class memory and embedded application drivers. These include spin-transfer torque magnetic random access memory (STT-MRAM), resistive random access memory (ReRAM), phase change random access memory (PCRAM), and conductive bridge random access memory (CBRAM). Emerging memories have improved resilience to radiation effects compared to flash, which is based on storing charge, and hence may offer an expanded selection from which radiation-tolerant system designers can choose from in the future. This review discusses the material and device physics, fabrication, operational principles, and commercial status of scaled 2-D flash, 3-D flash, and emerging memory technologies. Radiation effects relevant to each of these memories are described, including the physics of and errors caused by total ionizing dose, displacement damage, and single-event effects, with an eye toward the future role of emerging technologies in radiation environments.
We present a numerical framework for recovering unknown nonautonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the nonautonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimizing memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates ∼ 4 × speedup over performing format conversions in software.
The DIII-D small angle slot (SAS) divertor is designed for divertor physics studies with enhanced neutral confinement and special target geometries in a closed divertor. The closed nature of the SAS makes optical diagnostic measurements difficult, so a specially designed, multipurpose array of Langmuir probes has been implemented to study the plasma conditions in and around the slot. The probes are spaced to provide at least 2 mm resolution (shorter than the energy decay length) of the near scrape-off layer when mapped to the outer mid-plane. Due to space limitations at the bottom of the slot, a novel spring-loaded probe and tile design was developed to clamp several short rooftop probe tips and insulators to the cooled baseplate. Initial probe measurements revealed tile to tile edge shadowing, especially where magnetic field line surface angles were less than 1°. Additionally, it was found, using three Langmuir probes (at 90°, 180°, and 270°), that the strike point variation of ±5 mm radially around the torus was not well aligned with the circular slot geometry [Watkins et al., Nucl. Mater. Energy 18, 46 (2019)]. These issues were resolved by (1) designing tiles with all probes mounted near the tile center instead of near the edges and (2) aligning these new custom tiles to the measured strike point toroidal surface with a very accurate laser scanning alignment tool. Post-alignment Langmuir probe measurements and plasma behavior demonstrated close agreement at two separate toroidal locations that were 45° apart.
The Open Radiation Monitoring (ORM) Project seeks to develop and demonstrate a modular radiation detection architecture designed specifically for use in arms control treaty verification (ACTV) applications that will facilitate rapid development of trusted systems to meet the needs of potential future treaties. Development of trusted systems to support potential future treaties is a complex and costly endeavor that typically results in a purpose-built system designed to perform one specific task. The majority of prior trusted system development efforts have relied on the use of commercial embedded computers or microprocessors to control the system and process the acquired data. These processors are complex, making authentication and certification of measurement systems and collected data challenging and time consuming. We believe that a modular architecture can be used to reduce more complex systems to a series of single-purpose building blocks that could be used to implement a variety of detection modalities with shared functionalities. With proper design, the functionality of individual modules can be confirmed through simple input/output testing, thereby facilitating equipment inspection and in turn building trust in the equipment by all treaty parties. Furthermore, a modular architecture can be used to control data flow within the measurement system, reducing the risk of "hidden switches" and constraining the amount of sensitive information that could potentially be inadvertently leaked. This report documents a conceptual modular system architecture that is designed to facilitate inspection in an effort to reduce overall authentication and certification burden. As of publication, this architecture remains in a conceptual phase and additional funding is required to prove out the utility of a modular architecture and test the assumptions used to rationalize the design.
It has been previously demonstrated that thermal gas expansion might have a role in boundary layer flashback of premixed turbulent flames [Gruber et al., J Fluid Mech 2012], inducing local flow-reversal in the boundary layer's low-velocity streaks on the reactants’ side of the flame and facilitating its upstream propagation. We perform a two-dimensional numerical investigation of the interaction between a periodic shear flow and a laminar premixed flame. The periodic shear is a simplified model for the oncoming prolonged streamwise velocity streaks with alternating regions of high and low velocities found in turbulent boundary layers in the vicinity of the walls. The parametric study focuses on the amplitude and wavelength of the periodic shear flow and on the gas expansion ratio (unburnt-to-burnt density ratio). With the increase of the amplitudes of the periodic shear flow and of the gas expansion, the curved flame velocity increases monotonically. The flame velocity dependence on the periodic shear wavelength is non-monotonic, which is consistent with previous theoretical studies of curved premixed flame velocity. The flame shape that is initially formed by the oncoming periodic shear appears to be metastable. At a later stage of the flame propagation, the flame shape transforms into the stationary one dominated by the Darrieus-Landau instability.
Pit growth and repassivation are complex, with many interconnecting geometric and environmental parameters to consider. Experimentally, it is difficult to isolate these individual parameters to study their effect on the stability of pits. To enable these studies, a finite element modeling approach has been developed to allow systematic testing of parameters that impact a pit’s stability. The specific parameters studied were the cathode diameter, the pit diameter and shape, and the water layer thickness. Hemispherical and rectangular-based pits were studied to determine the impact of the overall pit shape. Pit stability results were compared with mathematical calculations based on the Maximum Pit Model, for both 50% saturation and 100% saturated salt film coverage. Further studies expanded the range of pit geometry to those relevant to additively manufactured surfaces.
Shock compression experiments on natural compositions are imperative to accurately model planetary accretion and the interior dynamics of planets. Combining shock compression experiments from the Sandia Z Machine and the OMEGA EP laser facility with density functional theory-based molecular dynamics calculations, we report the first pressure-density-temperature (P-ρ-T) relationship of natural iron (Fe)-bearing olivine ((Mg0.91Fe0.09)2SiO4) on the principal Hugoniot between 166 and 1,465 GPa. Additionally, we report the first reflectivities of natural olivine liquid in this pressure range. Compared to the magnesium-endmember forsterite (Mg2SiO4), the presence of Fe in typical mantle abundance (∼9 wt% FeO) alters the US-uP relation of olivine. On the other hand, the shock temperature and reflectivity of olivine are indistinguishable from forsterite where experimental conditions overlap. Both forsterite and olivine increase in reflectivity (and hence optical conductivity) with increasing temperature, with a maximum reflectivity of ∼31% at shock velocities greater than 22 km/s (∼800 GPa).
Ghosh, Chanchal; Singh, Manish K.; Parida, Shayani; Janish, Matthew T.; Dobley, Arthur; Dongare, Avinash M.; Carter, C.B.
Li-ion batteries function by Li intercalating into and through the layered electrode materials. Intercalation is a solid-state interaction resulting in the formation of new phases. The new observations presented here reveal that at the nanoscale the intercalation mechanism is fundamentally different from the existing models and is actually driven by nonuniform phase distributions rather than the localized Li concentration: the lithiation process is a ‘distribution-dependent’ phenomena. Direct structure imaging of 2H and 1T dual-phase microstructures in lithiated MoS2 and WS2 along with the localized chemical segregation has been demonstrated in the current study. Li, a perennial challenge for the TEM, is detected and imaged using a low-dose, direct-electron detection camera on an aberration-corrected TEM and confirmed by image simulation. This study shows the presence of fully lithiated nanoscale domains of 2D host matrix in the vicinity of Li-lean regions. This confirms the nanoscale phase formation followed by Oswald ripening, where the less-stable smaller domains dissolves at the expense of the larger and more stable phases.
We demonstrate an optical waveguide device, capable of supporting the high, invacuum, optical power necessary for trapping a single atom or a cold atom ensemble with evanescent fields. Our photonic integrated platform, with suspended membrane waveguides, successfully manages optical powers of 6 mW (500 μm span) to nearly 30 mW (125 μm span) over an un-tethered waveguide span. This platform is compatible with laser cooling and magnetooptical traps (MOTs) in the vicinity of the suspended waveguide, called the membrane MOT and the needle MOT, a key ingredient for efficient trap loading. We evaluate two novel designs that explore critical thermal management features that enable this large power handling. This work represents a significant step toward an integrated platform for coupling neutral atom quantum systems to photonic and electronic integrated circuits on silicon.
Random scattering and absorption of light by tiny particles in aerosols, like fog, reduce situational awareness and cause unacceptable down-time for critical systems or operations. Computationally efficient light transport models are desired for computational imaging to improve remote sensing capabilities in degraded optical environments. To this end, we have developed a model based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes, thereby covering the applicability domain of both the small angle and diffusion approximations. An analytic solution was derived and validated using experimental data acquired at the Sandia National Laboratory Fog Chamber facility. The evolution of the fog particle density and size distribution were measured and used to determine macroscopic absorption and scattering properties using Mie theory. A three-band (0.532, 1.55, and 9.68 μm) transmissometer with lock-in amplifiers enabled changes in fog density of over an order of magnitude to be measured due to the increased transmission at higher wavelengths, covering both the moderate and highly scattering regimes. The meteorological optical range parameter is shown to be about 0.6 times the transport mean free path length, suggesting an improved physical interpretation of this parameter.
Laccases are oxidative enzymes containing 4 conserved copper heteroatoms. Laccases catalyze cleavage of bonds in lignin using radical chemistry, yet their exact specificity for bonds (such as the β-O-4 or C-C) in lignin remains unknown and may vary with the diversity of laccases across fungi, plants and bacteria. Bond specificity may perhaps even vary for the same enzyme across different reaction conditions. Determining these differences has been difficult due to the fact that heterologous expression of soluble, active laccases has proven difficult. Here we describe the successful heterologous expression of functional laccases in two strains of Saccharomyces cerevisiae, including one we genetically modified with CRISPR. We phylogenically map the enzymes that we successfully expressed, compared to those that did not express. We also describe differences protein sequence differences and pH and temperature profiles and their ability to functionally express, leading to a potential future screening platform for directed evolution of laccases and other ligninolytic enzymes such as peroxidases.
We are working to generate fundamental understanding of enzymatic depolymerization of lignin and using this understanding to engineer mixtures of enzymes that catalyze the reactions necessary to efficiently depolymerize lignin into defined fragments. Over the years the enzymes involved in these processes have been difficult to study, because 1) the enzymes thought to be most important, fungal laccases and peroxidases, are very difficult to express in soluble, active form; 2) the full complement of required enzymes and whether or not they act synergistically is not known; 3) analysis of bond cleavage events is difficult due to the lack of analytical tools for measuring bond cleavage events in either polymeric lignin or model lignin-like compounds.
Esteves, Giovanni; Young, Travis R.; Tang, Zichen; Yen, Sean; Bauer, Todd M.; Henry, Michael D.; Olsson, Roy H.
Aluminum scandium nitride (Al1-xScxN/AlScN) (x = 0.32) Lamb wave resonators (LWR) have been fabricated and tested to demonstrate electromechanical coupling coefficients (kt2) in excess of 10%. The resonators exhibited an average kt2 and unloaded quality factor (Qu) of 10.28% and 711, respectively, when calculated from the measured data. Applying the Butterworth Van-Dyke (BVD) model to the measured data enabled the extraction of the resonator's lumped element parameters to calculate the motional quality factor (Qm), which neglects the contributions of the electrical traces. For the best measured resonator response, results from the BVD model showed a Qm of 1184 and a resulting figure-of-merit (FOM = K2·Qm) of 100. Comparing the response of similar AlScN and AlN resonators shows that the AlScN LWR has a significantly lower motional resistance (Rm), suggesting that AlScN has a strong potential for use in piezoelectric microelectromechanical oscillators.
NO planar laser induced fluorescence (PLIF) is used to obtain images of laser-induced breakdown plasma plumes in NO-seeded nitrogen and dry air at near atmospheric pressure. Single-shot PLIF-images show that the plume development 5–50 μs after the breakdown pulse is fairly reproducible shot-to-shot, although the plume becomes increasingly stochastic on longer timescales, 100–500 μs. The stochastic behavior of the plume is quantified using probability distributions of the loci of the plume boundary. Analysis of the single-shot images indicates that the mixing of the plume with ambient gas on sub-ms time scale is insignificant. The induced flow velocity in the plume is fairly low, up to 30 m s–1, suggesting that laser breakdowns are ineffective for mixing enhancement in high speed flows. The ensemble-averaged PLIF images indicate the evolution of the plume from an initially elongated shape to near-spherical to toroidal shape, with a subsequent radial expansion and formation of an axial jet in the center. Temperature distributions in the plume in air are obtained from the NO PLIF images, using two rotational transitions in the NO(X, v' = 0 → A, v'' = 0) band, J'' = 6.5 and 12.5 of the QR12 + Q2 branch. The results indicate that the temperature in the plume remains high, above 1000 K, for approximately 100 μs, after which it decays gradually, to below 500 K at 500 μs. The residual NO fraction in the plume is ~0.1%, indicating that repetitive laser-assisted ignition may result in significant NO-generation. Furthermore, these measured temperature and velocity distributions can be used for detailed validation of kinetic models of laser-induced breakdown and assessment of their predictive capability.
Luminescent lanthanide decanoate nanoparticles (LnC10NPs; Ln = Pr, Nd, Sm, Eu, Gd, Er) with spherical morphology (<100 nm) have been synthesizedviaa facile microwave (MWV) method using Ln(NO3)3·xH2O, ethanol/water, and decanoic acid. These hybrid nanomaterials adopt a lamellar structure consisting of inorganic Ln3+layers separated by a decanoate anion bilayer and exhibit liquid crystalline (LC) phases during melting. The particle size, crystalline structure, and LC behavior were characterized using transmission electron microscopy (TEM), differential scanning calorimetry (DSC), and powder X-ray diffraction (ambient and heated). Thermal analysis indicated the formation of Smectic A LC phases by LnC10nanoparticles, with the smaller lanthanides (Ln = Sm, Gd, Er) displaying additional solid intermediate and Smectic C phases. The formation of LC phases by the smaller Ln3+suggests that these nanoscale materials have vastly different thermal properties than their bulk counterparts, which do not exhibit LC behavior. Photoluminescence spectroscopy revealed the LnC10NPs to be highly optically active, producing strong visible emissions that corresponded to expected electronic transitions by the various Ln3+ions. Under long-wave UV irradiation (λ= 365 nm), bright visible luminescence was observed for colloidal suspensions of Nd, Sm, Eu, Gd, and ErC10NPs. To the best of the authors’ knowledge, this is the first reported synthesis of nanoscale metal alkanoates, the first report of liquid crystalline behavior by any decanoate of lanthanides smaller than Nd, and the first observation of strong visible luminescence by non-vitrified lanthanide alkanoates.
The surfaces of textured polycrystalline N-type bismuth telluride and P-type antimony telluride materials were investigated using ex situ photoelectron emission microscopy (PEEM). PEEM enabled imaging of the work function for different oxidation times due to exposure to air across sample surfaces. The spatially averaged work function was also tracked as a function of air exposure time. N-type bismuth telluride showed an increase in the work function around grain boundaries relative to grain interiors during the early stages of air exposure-driven oxidation. At longer time exposure to air, the surface became homogenous after a ∼5 nm-thick oxide formed. X-ray photoemission spectroscopy was used to correlate changes in PEEM imaging in real space and work function evolution to the progressive growth of an oxide layer. The observed work function contrast is consistent with the pinning of electronic surface states due to the defects at a grain boundary.
In this work, we report the presence of surface-densified phases (β-Ni5O8, γ-Ni3O4, and δ-Ni7O8) in LiNiO2 (LNO)- and LiNi0.8Al0.2O2 (LNA)-layered compounds by combined atomic level scanning transmission electron microscopy (STEM) and electron energy loss spectroscopy (EELS). These surface phases form upon electrochemical aging at high state of charge corresponding to a fully delithiated state. A unique feature of these phases is the periodic occupancy by Ni2+ in the Li layer. This periodic Ni occupancy gives rise to extra diffraction reflections, which are qualitatively similar to those of the LiNi2O4 spinel structure, but these surface phases have a lower Ni valence state and cation content than spinel. These experimental results confirm the presence of thermodynamically stable surface phases and provide new insights into the phenomena of surface phase formation in Ni-rich layered structures.
Single-photon detectors have typically consisted of macroscopic materials where both the photon absorption and transduction to an electrical signal happen. Newly proposed designs suggest that large arrays of nanoscale detectors could provide improved performance in addition to decoupling the absorption and transduction processes. Here we study the properties of such a detector consisting of a nanoscale superconducting (SC) transport channel functionalized by a photon absorber. We explore two detection mechanisms based on photoinduced electrostatic gating and magnetic effects. To this end we model the narrow channel as a one-dimensional atomic chain and use a self-consistent Keldysh-Nambu Green's function formalism to describe nonequilibrium effects and SC phenomena. We consider cases where the photon creates electrostatic and magnetic changes in the absorber, as well as devices with strong and weak coupling to the metal leads. Our results indicate that the most promising case is when the SC channel is weakly coupled to the leads and in the presence of a background magnetic field, where photoexcitation of a magnetic molecule can trigger a SC-to-normal transition in the channel that leads to a change in the device current several times larger than in the case of a normal-phase channel device.
Incentivizing bioenergy crop production in locations with marginal soils, where low-input perennial crops can provide net carbon sequestration and economic benefits, will be crucial to building a successful bioeconomy. We developed an integrated assessment framework to compare switchgrass cultivation with corn-soybean rotations on the basis of production costs, revenues, and soil organic carbon (SOC) sequestration at a 100 m spatial resolution. We calculated profits (or losses) when marginal lands are converted from a corn-soy rotation to switchgrass across a range of farm gate biomass prices and payments for SOC sequestration in the State of Illinois, United States. The annual net SOC sequestration and switchgrass yields are estimated to range from 0.1 to 0.4 Mg ha-1 and 7.3 to 15.5 Mg dry matter ha-1, respectively, across the state. Without payments for SOC sequestration, only a small fraction of marginal corn-soybean land would achieve a 20% profit margin if converted to switchgrass, but $40-80 Mg-1 CO2e compensation could increase the economically viable area by 140-414%. With the compensation, switchgrass cultivation for 10 years on 1.6 million ha of marginal land in Illinois will produce biomass worth $1.6-2.9 billion (0.95-1.8 million Mg dry biomass) and mitigate 5-22 million Mg CO2e.
Meshfree discretizations of state-based peridynamic models are attractive due to their ability to naturally describe fracture of general materials. However, two factors conspire to prevent meshfree discretizations of state-based peridynamics from converging to corresponding local solutions as resolution is increased: quadrature error prevents an accurate prediction of bulk mechanics, and the lack of an explicit boundary representation presents challenges when applying traction loads. In this paper, we develop a reformulation of the linear peridynamic solid (LPS) model to address these shortcomings, using improved meshfree quadrature, a reformulation of the nonlocal dilatation, and a consistent handling of the nonlocal traction condition to construct a model with rigorous accuracy guarantees. In particular, these improvements are designed to enforce discrete consistency in the presence of evolving fractures, whose a priori unknown location render consistent treatment difficult. In the absence of fracture, when a corresponding classical continuum mechanics model exists, our improvements provide asymptotically compatible convergence to corresponding local solutions, eliminating surface effects and issues with traction loading which have historically plagued peridynamic discretizations. When fracture occurs, our formulation automatically provides a sharp representation of the fracture surface by breaking bonds, avoiding the loss of mass. We provide rigorous error analysis and demonstrate convergence for a number of benchmarks, including manufactured solutions, free-surface, nonhomogeneous traction loading, and composite material problems. Finally, we validate simulations of brittle fracture against a recent experiment of dynamic crack branching in soda-lime glass, providing evidence that the scheme yields accurate predictions for practical engineering problems.
• Shows detailed methodology for applying building energy model fleets to institutional heat wave analysis. • Demonstrates uncertainty in heat wave analysis based on meter data. • Shows how detailed building energy models used for energy retrofit analysis can be used for heat wave analyses. • The proposed methodology is much more extensible than data-driven or low-order energy models to detailed cross analyses between energy efficiency and resilience for future institutional studies. • Cross benefits between resilience analysis and energy retrofit analyses are demonstrated. Heat waves increase electric demand from buildings which can cause power outages. Modeling can help planners quantify the risk of such events. This study shows how Building Energy Modeling (BEM), meter data, and climate projections can estimate heat wave effect on energy consumption and electric peak load. The methodology assumes that a partial representation of BEM for an entire site of buildings is sufficient to represent the entire site. Two linear regression models of the BEM results are produced: 1) Energy use as a function of heat wave heat content and 2) Peak load as a function of maximum daily temperature. The uncertainty conveyed in meter data is applied to these regressions providing slope and intercept 95% confidence intervals. The methodology was applied using 97 detailed BEM, site weather data, 242 building meters, and NEX-DCP30 down-scaled climate data for an entire institution in Albuquerque, New Mexico. A series of heat waves that vary from 2019 weather to a peak increase of 5.9 °C was derived. The results of the study provided institutional planners with information needed for a site that is presently growing very rapidly. The resulting regression models are also useful for resilience analyses involving probabilistic risk assessments.
Holland, Rayne; Khan, M.A.H.; Driscoll, Isabel; Chhantyal-Pun, Rabi; Derwent, Richard G.; Taatjes, Craig A.; Orr-Ewing, Andrew J.; Percival, Carl J.; Shallcross, Dudley E.
Trifluoroacetic acid (TFA), a highly soluble and stable organic acid, is photochemically produced by certain anthropogenically emitted halocarbons such as HFC-134a and HFO-1234yf. Both these halocarbons are used as refrigerants in the automobile industry, and the high global warming potential of HFC-134a has promoted regulation of its use. Industries are transitioning to the use of HFO-1234yf as a more environmentally friendly alternative. We investigated the environmental effects of this change and found a 33-fold increase in the global burden of TFA from an annual value of 65 tonnes formed from the 2015 emissions of HFC-134a to a value of 2220 tonnes formed from an equivalent emission of HFO-1234yf. The percentage increase in surface TFA concentrations resulting from the switch from HFC-134a to HFO-1234yf remains substantial with an increase of up to 250-fold across Europe. The increase in emissions greater than the current emission scenario of HFO-1234yf is likely to result in significant TFA burden as the atmosphere is not able to disperse and deposit relevant oxidation products. The Criegee intermediate initiated loss process of TFA reduces the surface level atmospheric lifetime of TFA by up to 5 days (from 7 days to 2 days) in tropical forested regions.
This initial gap analysis considers proposed accident tolerant fuel (ATF) options currently being irradiated in commercial reactors, since these are most likely for future batch implementation. Also, advanced fuel (AF) options that may be likely for use in advanced reactors are considered. The cladding technologies considered were chromium-coated zirconium-based alloys, FeCrAl, and both monolithic and matrix composite Silicide carbide (SiC). The fuel technologies considered were chromium-doped uranium dioxide fuel, uranium alloys, uranium nitride, and uranium silicide. Numerous national labs, industry, and countries are performing significant testing and modeling on these proposed technologies to establish performance, but at this time none of the prototypes being irradiated have achieved end-of-life (EOL) burnup. There are some testing results after one burnup cycle to verify in-reactor performance, but little data beyond that. As the ATF prototypes acquire more burnup, data will be produced that is relevant to storage and transportation. The DOE:NE Spent Fuel and Waste Science and Technology (SWFST) Storage and Transportation (ST) Control Account will evaluate the performance data as it becomes available for application to the identified gaps for ST.
The focus of this project is to improve the fidelity of the radiation parameterization used in earth system models to deliver increased accuracy with a cheaper computational approach.
Multivalent batteries represent an important beyond Li-ion energy storage concept. The prospect of calcium batteries, in particular, has emerged recently due to novel electrolyte demonstrations, especially that of a ground-breaking combination of the borohydride salt Ca(BH4)2 dissolved in tetrahydrofuran. Recent analysis of magnesium and calcium versions of this electrolyte led to the identification of divergent speciation pathways for Mg2+ and Ca2+ despite identical anions and solvents, owing to differences in cation size and attendant flexibility of coordination. To test these proposed speciation equilibria and develop a more quantitative understanding thereof, we have applied pulsed-field-gradient nuclear magnetic resonance and dielectric relaxation spectroscopy to study these electrolytes. Concentration-dependent variation in anion diffusivities and solution dipole relaxations, interpreted with the aid of molecular dynamics simulations, confirms these divergent Mg2+ and Ca2+ speciation pathways. These results provide a more quantitative description of the electroactive species populations. We find that these species are present in relatively small quantities, even in the highly active Ca(BH4)2/tetrahydrofuran electrolyte. This finding helps interpret previous characterizations of metal deposition efficiency and morphology control and thus provides important fundamental insight into the dynamic properties of multivalent electrolytes for next-generation batteries.
The feasibility of liquid temperature measurements using X-ray scattering is investigated for liquids with varying properties (water, ethanol, and n-dodecane) on beamline 7-BM at the Advanced Photon Source at Argonne National Laboratory. The temperature is inferred through the change in the scattering pattern from the liquid as a function of temperature using partial least squares regression. An accuracy of ∼98% or higher was achieved enabling measurements for a wide range of applications.
We carefully investigate three important effects including postgrowth activation annealing, delta (δ) dose and magnesium (Mg) buildup delay as well as experimentally demonstrate their influence on the electrical properties of GaN homojunction p–n diodes with a tunnel junction (TJ). The diodes were monolithically grown by metalorganic chemical vapor deposition (MOCVD) in a single growth step. By optimizing the annealing parameters for Mg activation, δ-dose for both donors and acceptors at TJ interfaces, and p+-GaN layer thickness, a significant improvement in tunneling properties is achieved. For the TJs embedded within the continuously-grown, all-MOCVD GaN diode structures, ultra-low voltage penalties of 158 mV and 490 mV are obtained at current densities of 20 A cm−2 and 100 A cm−2, respectively. The diodes with the engineered TJs show a record-low differential resistivity of 1.6 × 10−4 Ω cm2 at 5 kA cm−2.
Spurred by recent discoveries of high-temperature superconductivity in Fe-Se-based materials, the magnetic, electronic, and catalytic properties of iron chalcogenides have drawn significant attention. However, much remains to be understood about the sequence of phase formation in these systems. Here, we shed light on this issue by preparing a series of binary Fe-Se ultrathin diffusion couples via designed thin-film precursors and investigating their structural evolution as a function of composition and annealing temperature. Two previously unreported Fe-Se phases crystallized during the deposition process on a nominally room-temperature Si substrate in the 27-33 and 37-47% Fe (atomic percent) composition regimes. Both phases completely decompose after annealing to 200 °C in a nitrogen glovebox. At higher temperatures, the sequence of phase formation is governed by Se loss in the annealing process, consistent with what would be expected from the phase diagram. Films rich in Fe (53-59% Fe) crystalized during deposition as β-FeSe (P4/nmm) with preferred c-axis orientation to the amorphous SiO2 substrate surface, providing a means to nonepitaxial self-assembly of crystallographically aligned, iron-rich β-FeSe for future research. Our findings suggest that the crystallization of binary Fe-Se compounds at room temperature via near diffusionless transformations should be a significant consideration in future attempts to prepare metastable ternary and higher-order compounds containing Fe and Se.
The goal of this paper is to utilize machine learning (ML) techniques for estimating the distribution circuit topology in an adaptive protection system. In a reconfigurable distribution system with multiple tie lines, the adaptive protection system requires knowledge of the existing circuit topology to adapt the correct settings for the relay. Relays rely on the communication system to identify the latest status of remote breakers and tie lines. However, in the case of communication system failure, the performance of adaptive protection system can be significantly impacted. To tackle this challenge, the remote circuit breakers and tie lines' status are estimated locally at a relay to identify the circuit topology in a reconfigurable distribution system. This paper utilizes Support Vector Machine (SVM) to forecast the status of remote circuit breakers and identify the circuit topology. The effectiveness of proposed approach is verified on two sample test systems.
This paper investigates the design of low-level probing signals for accurate estimation of inertia and damping constants in microgrids. Increasing utilization of renewable energy sources and their different dynamics has created unknowns in time-varying system inertia and damping constants. Thus, it is difficult to know these parameters at any given time in converter-dominated microgrids. This paper describes the design characteristics, considerations, methodology, and accuracy level of different probing signals in determining unknown parameters of a system. The main goal of this paper is to find an effective probing signal with a simple implementation and minimal impacts on power system operation. The test-case model in this paper analyzes nonintrusive excitation signals to perturb a power system model (i.e., square wave, multisine wave, filtered white Gaussian noise, and pseudo-random binary sequence). A moving horizon estimation (MHE)-based approach is then implemented in an energy storage system (ESS) in MATLAB/Simulink for estimation of inertia and damping constants of a system based on frequency measurements from a local phase-locked-loop (PLL). The accuracy of parameter estimates alters depending on the chosen probing signal; when estimating inertia and damping constants using MHE with the different probing signals, square waves yielded the lowest error.
The integration of renewable and distributed energy resources to the electric power system is expected to increase, particularly at the distribution level. As a consequence, the grid will become more modular consisting of many interconnected microgrids. These microgrids will likely evolve from existing distribution feeders and hence be unbalanced in nature. As the world moves towards cleaner and distributed generation, microgrids that are 100% inverter sourced will become more commonplace. To increase resiliency and reliability, these microgrids will need to operate in both grid-connected and islanded modes. Protection and control of these microgrids needs to be studied in real-time to test and validate possible solutions with hardware-in-the-loop (HIL) and real communication delays. This paper describes the creation of a real-time microgrid test bed based on the IEEE 13-bus distribution system using the RTDS platform. The inverter models with grid-forming and grid-following control schemes are discussed. Results highlighting stable operation, power sharing, and fault response are shown.
Increasing installation of renewable energy resources makes the power system inertia a time-varying quantity. Furthermore, converter-dominated grids have different dynamics compared to conventional grids and therefore estimates of the inertia constant using existing dynamic power system models are unsuitable. In this paper, a novel inertia estimation technique based on convolutional neural networks that use local frequency measurements is proposed. The model uses a non-intrusive excitation signal to perturb the system and measure frequency using a phase-locked loop. The estimated inertia constants, within 10% of actual values, have an accuracy of 97.35% and root mean square error of 0.2309. Furthermore, the model evaluated on unknown frequency measurements during the testing phase estimated the inertia constant with a root mean square error of 0.1763. The proposed model-free approach can estimate the inertia constant with just local frequency measurements and can be applied over traditional inertia estimation methods that do not incorporate the dynamic impact of renewable energy sources.
This paper presents a preliminary investigation on controlling the existing high voltage dc (HVDC) links connecting the North American western interconnection (WI) to the other interconnections, to provide damping to inter-area oscillations. The control scheme is meant to damp inter-area modes of oscillation in the WI by using wide area synchrophasor feedback. A custom model is developed in General Electric's PSLF software for the wide area damping control scheme, and simulations are analyzed on a validated full 22,000 bus WI model. Results indicate that implementing the proposed control technique to the existing HVDC links in the WI can significantly improve the damping of the inter-area modes of the system.
The microstructures of 316 L stainless steel created by rapid solidification are investigated by comparing the similar microstructures of individual hatches of directed energy deposition additive manufacturing (DED-AM) and those of single, laser surface-melted tracks formed on a solid plate. High recoil pressure, which is exponentially dependent on the laser beam power density, induces convection of the melt pool, which causes formation of microstructural bands in the as-solidified microstructure. The microstructural bands are associated with changes in the chromium concentration and are a significant component of the inhomogeneous microstructure of DED-AM.
Liquid thermometry during primary and secondary breakup of liquid sprays is challenging due to the presence of highly dynamic, optically complex flow features. This work evaluates the use of x-ray scattering from a focused, monochromatic beam of the Advanced Photon Source at Argonne National Laboratory for the measurement of liquid temperatures within the mixing zone of an impinging jet spray. The measured scattering profiles are converted to temperature through a previously developed two-component partial least squares (PLS) regression model. Transmitive mixing during jet merging is inferred through spatial mapping of temperatures within the impingement region. The technique exhibits uncertainties of ±2 K in temperature and 2% in capturing the correct scattering profile, showing its potential utility for probing liquid temperature distributions in multiphase flows.
In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.
It is well known that ultraviolet photoexcitation of iron pentacarbonyl results in rapid loss of carbonyl ligands leading to the formation of coordinatively unsaturated iron carbonyl compounds. We employ ultrafast mid-infrared transient absorption spectroscopy to probe the photodissociation dynamics of gas-phase iron pentacarbonyl following ultraviolet excitation at 265 and 199 nm. After photoexcitation at 265 nm, our results show evidence for sequential dissociation of iron pentacarbonyl to form iron tricarbonyl via a short-lived iron tetracarbonyl intermediate. Photodissociation at 199 nm results in the prompt production of Fe(CO)3 within 0.25 ps via several energetically accessible pathways. An additional 15 ps time constant extracted from the data is tentatively assigned to intersystem crossing to the triplet manifold of iron tricarbonyl or iron dicarbonyl. Mechanisms for formation of iron tetracarbonyl, iron tricarbonyl, and iron dicarbonyl are proposed and theoretically validated with one-dimensional cuts through the potential energy surface as well as bond dissociation energies. Ground state calculations are computed at the CCSD(T) level of theory and excited states are computed with EOM-EE-CCSD(dT).
On April 6-8, 2021, Sandia National Laboratories hosted a virtual workshop to explore the potential for developing AI-Enhanced Co-Design for Next-Generation Microelectronics (AICoM). The workshop brought together two themes. The first theme was articulated in the 2018 Department of Energy Office of Science (DOE SC) “Basic Research Needs for Microelectronics” (BRN) report, which called for a “fundamental rethinking” of the traditional design approach to microelectronics, in which subject matter experts (SMEs) in each microelectronics discipline (materials, devices, circuits, algorithms, etc.) work near-independently. Instead, the BRN called for a non-hierarchical, egalitarian vision of co-design, wherein “each scientific discipline informs and engages the others” in “parallel but intimately networked efforts to create radically new capabilities.” The second theme was the recognition of the continuing breakthroughs in artificial intelligence (AI) that are currently enhancing and accelerating the solution of traditional design problems in materials science, circuit design, and electronic design automation (EDA).
Appropriate spray modeling in multidimensional simulations of diesel engines is well known to affect the overall accuracy of the results. More and more accurate models are being developed to deal with drop dynamics, breakup, collisions, and vaporization/multiphase processes; the latter ones being the most computationally demanding. In fact, in parallel calculations, the droplets occupy a physical region of the in-cylinder domain, which is generally very different than the topology-driven finite-volume mesh decomposition. This makes the CPU decomposition of the spray cloud severely uneven when many CPUs are employed, yielding poor parallel performance of the spray computation. Furthermore, mesh-independent models such as collision calculations require checking of each possible droplet pair, which leads to a practically intractable O(np2/2) computational cost, np being the total number of droplets in the spray cloud, and additional overhead for parallel communications. This problem is usually overcome by employing O°Rourke°s same-cell collision condition, which, however, introduces severe mesh dependency. In this work, we introduced two strategies to achieve optimal load balancing for fast spray calculations with mesh-independent models. Both methods were implemented in the FRESCO CFD code. For drop collisions, a mesh-independent collision detection algorithm with high parallel efficiency was developed. This method pre-sorts eligible collision pairs using a high-performance three-dimensional clustering algorithm similar to what is used for on-the-fly chemistry model reduction; these are then filtered again based on deterministic impact parameters and assembled in parallel into a global sparse adjacency structure. For the particle-in-cell vaporization/multiphase solver, we developed a solution-preserving load balancing algorithm. At each timestep, the parallel cell-ownership-based spray cloud structure is re-sorted into cell-owner bins, which are used to distribute the spray parcels across all CPUs along with their cell thermodynamic states; the distributed solution results are then sent back to the cell owners. The combination of both methods achieved more than one order of magnitude speed-up in spray solution for diesel engine simulations with a full and sector cylinder geometry.
Gaussian processes and other kernel-based methods are used extensively to construct approximations of multivariate data sets. The accuracy of these approximations is dependent on the data used. This paper presents a computationally efficient algorithm to greedily select training samples that minimize the weighted Lp error of kernel-based approximations for a given number of data. The method successively generates nested samples, with the goal of minimizing the error in high probability regions of densities specified by users. The algorithm presented is extremely simple and can be implemented using existing pivoted Cholesky factorization methods. Training samples are generated in batches which allows training data to be evaluated (labeled) in parallel. For smooth kernels, the algorithm performs comparably with the greedy integrated variance design but has significantly lower complexity. Numerical experiments demonstrate the efficacy of the approach for bounded, unbounded, multi-modal and non-tensor product densities. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data using Bayesian inference.
Shah, Niral P.; Marleau, Peter M.; Fessler, Jeffrey A.; Chichester, David L.; Wehe, David K.
To the first order, the localization precision and angular resolution of a cylindrical, time-encoded imaging (c-TEI) system is governed by the geometry of the system. Improving either measure requires increasing the mask radius or decreasing the detector diameter, both of which are undesirable. We propose an alternative option of repositioning the detector within the mask to increase the detector-to-mask distance in the direction of a source, thereby improving the localization precision and angular resolution in that direction. Since the detector-to-mask distance only increases for a small portion of the field of view (FOV), we propose implementing adaptive imaging where one leverages data collected during the measurement to optimize the system configuration. This article utilizes both simulations and experiments to set upper bounds on the potential gain from adaptive detector movements for one and two sources in the FOV. When only one source is present, adaptive detector movements can improve the localization precision and angular resolution by 20% for a source at 90 cm and by 32% for a far-field source. When two sources are present, adaptive detector movements can improve localization precision and angular resolution by up to 50% for sources that are 10° apart (90 cm from the system). We experimentally verify these results through maximum likelihood estimation of the source position(s) and image reconstruction of point sources that are close together. As a demonstration of an adaptive imaging algorithm, we image a complex arrangement of special nuclear material at the Zero Power Physics Reactor facility at Idaho National Laboratory.
One of the greatest barriers to geothermal energy expansion is the high cost of drilling during exploration, assessment, and monitoring. Microhole drilling technology—small-diameter 2–4 in. (~5.1–10.2 cm) boreholes—is one potential low-cost alternative for monitoring and evaluating bores. However, delivering high weight-on-bit (WOB), high torque rotational horsepower to a conventional drill bit does not scale down to the hole sizes needed to realize the cost savings. Coiled tube drilling technology is one solution, but these systems are limited by the torque resistance of the coil system, helical buckling in compression, and most of all, WOB management. The evaluation presented herein will: (i) evaluate the technical and economic feasibility of low WOB technologies (specifically, a percussive hammer and a laser-mechanical system), (ii) develop downhole rotational solutions for low WOB drilling, (iii) provide specifications for a low WOB microhole drilling system, (iv) implement WOB control for low WOB drilling, and (v) evaluate and test low WOB drilling technologies.
This project is part of a multi-lab consortium that leverages U.S. research expertise and facilities at national labs and universities to significantly advance electric drive power density and reliability, while simultaneously reducing cost. The final objective of the consortium is to develop a 100 kW traction drive system that achieves 33 kW/L, has an operational life of 300,000 miles, and a cost of less than 6 dollars/kW. One element of the system is a 100 kW inverter with a power density of 100 kW/L and a cost of 2.7 dollars/kW. New materials such as wide-bandgap semiconductors, soft magnetic materials, and ceramic dielectrics, integrated using multi-objective co-optimization design techniques, will be utilized to achieve these program goals. This project focuses on a subset of the power electronics work within the consortium, specifically the design, fabrication, and evaluation of vertical GaN power devices suitable for automotive applications.
The use of grid-edge sensing in distribution model calibration is a significant aid in reducing the time and cost associated with finding and correcting errors in the models. This work proposes a novel method for the phase identification task employing correlation coefficients on residential advanced metering infrastructure (AMI) combined with additional sensors on the medium-voltage distribution system to enable utilities to effectively calibrate the phase classification in distribution system models algorithmically. The proposed method was tested on a real utility feeder of ∼800 customers that includes 15-min voltage measurements on each phase from IntelliRupters® and 15-min AMI voltage measurements from all customers. The proposed method is compared with a standard phase identification method using voltage correlations with the substation and shows significantly improved results. The final phase predictions were verified to be correct in the field by the utility company.
Distribution system model accuracy is increasingly important and using advanced metering infrastructure (AMI) data to algorithmically identify and correct errors can dramatically reduce the time required to correct errors in the models. This work proposes a data-driven, physics-based approach for grouping residential meters downstream of the same service transformer. The proposed method involves a two-stage approach that first uses correlation coefficient analysis to identify transformers with errors in their customer grouping then applies a second stage, using a linear regression formulation, to correct the errors. This method achieved >99% accuracy in transformer groupings, demonstrated using EPRI's Ckt 5 model containing 1379 customers and 591 transformers.
The Optically Segmented Single Volume Scatter Camera (OS-SVSC) aims to image neutron sources for non-proliferation applications using the kinematic reconstruction of elastic double-scatter events. Our prototype system consists of 64 EJ-204 organic plastic scintillator bars, each measuring 5 mm × 5 mm × 200 mm and individually wrapped in Teflon tape. The scintillator array is optically coupled to two silicon photomultiplier ArrayJ-60035 64P-PCB arrays, each comprised of 64 individual 6 mm × 6 mm J-Series sensors arranged in an 8 × 8 array. We report on the design details, including component selections, mechanical design and assembly, and the electronics system. The described design leveraged existing off-the-shelf solutions to support the rapid development of a phase 1 prototype. Several valuable lessons were learned from component and system testing, including those related to the detector’s mechanical structure and electrical crosstalk that we conclude originates in the commercial photodetector arrays and the associated custom breakout cards. We detail our calibration efforts, beginning with calibrations for the electronics, based on the IRS3D application-specific integrated circuits, and their associated timing resolutions, ranging from 30 ps to 90 ps. With electronics calibrations applied, energy and position calibrations were performed for a set of edge bars using 22Na and 90Sr, respectively, reporting an average resolution of (12.07 ± 0.03) mm for energy depositions between 900 keVee and 1000 keVee. We further demonstrate a position calibration method for the internal bars of the matrix using cosmic-ray muons as an alternative to emission sources that cannot easily access these bars, with an average measured resolution of (14.86 ± 0.29) mm for depositions between 900 keVee and 1000 keVee. The coincident time resolution reported between pairs of bars measured up to 400 ps from muon acquisitions. Energy and position calibration values measured with muons are consistent with those obtained using particle emission sources.
We introduce an immersed meshfree formulation for modeling heterogeneous materials with flexible non-body-fitted discretizations, approximations, and quadrature rules. The interfacial compatibility condition is imposed by a volumetric constraint, which avoids a tedious contour integral for complex material geometry. The proposed immersed approach is formulated under a variational multiscale based formulation, termed the variational multiscale immersed method (VMIM). Under this framework, the solution approximation on either the foreground or the background can be decoupled into coarse-scale and fine-scale in the variational equations, where the fine-scale approximation represents a correction to the residual of the coarse-scale equations. The resulting fine-scale solution leads to a residual-based stabilization in the VMIM discrete equations. The employment of reproducing kernel (RK) approximation for the coarse- and fine-scale variables allows arbitrary order of continuity in the approximation, which is particularly advantageous for modeling heterogeneous materials. The effectiveness of VMIM is demonstrated with several numerical examples, showing accuracy, stability, and discretization efficiency of the proposed method.
Pore-scale finite-volume continuum models of electrokinetic processes are used to predict the Debye lengths, velocity, and potential profiles for two-dimensional arrays of circles, ellipses and squares with different orientations. The pore-scale continuum model solves the coupled Navier–Stokes, Poisson, and Nernst–Planck equations to characterize the electro-osmotic pressure and streaming potentials developed on the application of an external voltage and pressure difference, respectively. This model is used to predict the macroscale permeabilities of geomaterials via the widely used Carmen–Kozeny equation and through the electrokinetic coupling coefficients. The permeability results for a two-dimensional X-ray tomography-derived sand microstructure are within the same order of magnitude as the experimentally calculated values. The effect of the particle aspect ratio and orientation on the electrokinetic coupling coefficients and subsequently the electrical and hydraulic tortuosity of the porous media has been determined. These calculations suggest a highly tortuous geomaterial can be efficient for applications like decontamination and desalination.
The electric grid is rapidly being modernized with novel technologies, adaptive and automated grid-support functions, and added connectivity with internet-based communications and remote interfaces. These advancements render the grid increasingly 'smart' and cyber-physical, but also broaden the vulnerability landscape and potential for malicious, cascading disturbances. The grid must be properly defended with security mechanisms such as intrusion detection systems (IDSs), but these tools must account for power system behavior as well as network traffic to be effective. In this paper, we present a cyber-physical IDS, the proactive intrusion detection and mitigation system (PIDMS), that analyzes both cyber and physical data streams in parallel, detects intrusion, and deploys proactive response. We demonstrate the PIDMS with an exemplar case study exploring a packet replay attack scenario focused on photovoltaic inverter communications; the scenario is tested with an emulated, cyber-physical grid environment with hardware-in-the-loop inverters.
Traditional protective relay voting schemes utilize simple logic to achieve confidence in relay trip actions. However, the smart grid is rapidly evolving and there are new needs for a next-generation relay voting scheme. In such new schemes, aspects such as inter-relay relationships and out-of-band data can be included. In this work, we explore the use of consensus algorithms and how they can be utilized for groups of relays to vote on system protection actions and also reach consensus on the values of variables in the system. A proposed design is explored with a simple case study with two different scenarios, including simulation in PowerWorld Simulator, to demonstrate the consensus algorithm benefits and future directions are discussed.
The threat of a large-scale electromagnetic event having a negative impact on the electric grid is real. Whether human-caused, via the detonation of a nuclear device, or natural, via a high-intensity burst of solar radiation, our historic experience with these phenomena indicates that as a global community, we should be prepared for such events and know how to mitigate their impacts. Current studies and related discussions provide a wide range of damage assessments for these events. We recommend continuing current technical investigations and research as well as strengthening collaboration between stakeholders and experts. This would ensure future threats are addressed in a timely and effective manner.
This report compares the performance of three Circular Error Probable (CEP) estimators: the Grubbs-Patnaik estimator, a new, non-iterative, radial-integration estimator, and a median estimator. It also compares the performance of two Spherical Error Probable (SEP) estimators. The performance of each estimator is assessed in terms of bias, uncertainty, robustness, and computational complexity. Robustness is evaluated with respect to outliers, variations in the underlying statistical distribution characterizing munition impact positions, and impact-position measurement errors. The performance assessments indicate the radial-integration and Grubbs-Patnaik estimators perform nearly identically providing the statistical distribution of impact-position coordinates is jointly normal with zero means. In that case, both estimators outperform the median estimator by about 2% relative to the true CEP in terms of estimator uncertainty. The bias performance of the radial-integration and median estimators is close to zero for jointly normal impacts, however, the Grubbs-Patnaik estimator can be significantly biased for jointly normal impacts with non-zero means. When the statistical distribution characterizing impact positions is known, but not jointly normal, the radial-integration estimator is superior. In this case, the median estimator also outperforms the Grubbs-Patnaik estimator but is not quite as good as the radial-integration estimator. If the statistical distribution characterizing impacts is unknown and not jointly normal, or if distribution parameters are difficult or impractical to estimate, or if test data is corrupted with outliers, then the median estimator dramatically outperforms the other estimators, especially in terms of estimation bias. Unexpectedly, measurement noise did not significantly degrade the performance of any of the estimators, except for cases with signal to noise ratios less than five. Although the Grubbs-Patnaik estimator has remained the gold standard for CEP estimation for over half a century, the performance assessments indicate the new, non-iterative, radial-integration estimator and the median estimator offer significant advantages and, in most practical real-world conditions, are superior estimators. These estimators are also useful for SEP estimation whereas the Grubbs-Patnaik estimator does not extend to three dimensions.
The objective of this work is to create an accurate elastic-plastic J2 plasticity model calibration for the Inconel 718 material at room temperature for use in finite element models. This calibration was made using a power-law hardening model of form σ = σy + $Aε^{n}_{p}$ where A and n are empirically determined constants, and σy is the proportional limit.
This work evaluated the iX Cameras iSpeed 727, a commercial CMOS-based continuous- recording high-speed camera. Various parameters of importance in the scheme of accurate time-resolved measurements and photonic quantification have been measured under controlled conditions on the bench, using state-of-the-art instrumentation. We will detail the procedures and results of the tests laid out to measure sensor sensitivity, linearity, signal-to-noise ratio and image lag. We also looked into the electronic shutter performance and accuracy, as exposure time is of particular interest to high-speed imaging. The results of the tests show that this camera matches or exceeds the performance of competing units in most aspects, but that, as is the case for other high-speed camera systems, corrections are necessary to make full use of the image data from a quantitative perspective.
The Bayesian optimal experimental design (OED) problem seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. The goal of OED is to find an experiment that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge about expected data. Therefore, within the context of seismic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize the ability to locate seismic events from arrival time data of detected seismic phases. In order to do utilize Bayesian OED we must develop four elements:1. A likelihood function that describes the uncertainty of detection and travel times; 2. A Bayesian solver that takes a prior and likelihood to identify the posterior; 3. An algorithm to compute EIG; and, 4. An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we can explore many relevant questions to monitoring such as: how and what multiphenomenology data can be used to optimally reduce uncertainty, how to trade off sensor fidelity and earth model uncertainty, and how sensor types, number, and locations influence uncertainty
The objective of this work is to extend the thermal-mechanical, elastic-plastic calibrations for 304L stainless steel [1] and and 6061-T651 aluminum alloy [2] to the regime between room temperature and -40 °C. The basis to extend the calibration consisted of new uniaxial tension tests conducted at -40 °C using the same plate material stocks, circular cylindrical specimen geometries and testing apparatus as previously, followed by attempts to fit power-law hardening functions to replicate the response observed in the specimens and then extend the yield, hardening constant, hardening exponent and rate constant functions in the calibrations to cover the new temperature regime.
Aerosol Jet Printing (AJP) is one technique of additive manufacturing used in the printing of electronics components. AJP enables the patterning of features at the ∼10 μm-100 μm scale based on hardware and print parameters. Optimization of print conditions enables the printing of high-resolution features with linewidths approaching 10 μm. The aerosol jet printing of electronic parts can be limited by the conductivities which are achievable by Ag nanoparticle inks (typically 15%-25% of bulk Ag). For certain electronics applications, the increased conductivity produces unacceptable loses during operation and methods are needed to increase the conductance of the devices without sacrificing resolution. Here, we report on the AJP of inductor spirals conductor traces of linewidth 50 μm separated by 55 μm gaps. The conductivity of these features is enhanced by electrodeposition of Cu onto the Ag, resulting in a decrease in resistance of 35-45x. Impedance measurements demonstrate that the addition of Cu by electrodeposition to a 27-turn spiral inductor resulted in an inductance of 3.6 μH. Finally, we demonstrate the use of a lift-off process to produce free-standing, flexible, conductive films using AJP.
In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. DA plays an important role when tracking multiple targets using measurements of uncertain origin. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with false alarms and missed detections. We are interested in two performance metrics: tracking accuracy and computation time. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time.
Nuclear Power Plants (NPPs) are a complex system of coupled physics controlled by a network of Programmable Logic Controllers (PLCs). These PLCs communicate process data across the network to coordinate control actions with each other and inform the operators of process variables and control decisions. Networking the PLCs allows more effective process control and provides the operator more information which results in more efficient plant operation. This interconnectivity creates new security issues, as operators have more access to the plant controls, so will bad actors. As plant networks become more digitized and encompass more sophisticated controllers, the network surface exposed to cyber interference grows. Understanding the dynamics of these coupled systems of physics, control logic, and network communications is critical to their protection. The research into the cybersecurity of the Operational Technologies of NPPs is developing and requires a platform that can allow high fidelity physics simulations to interact with digital networks of controllers. This will require three main components: a network simulation environment, a physics simulator, and virtual PLCs (vPLC) that represent typical industry hardware. A platform that incorporates these three components to provide the most accurate representation of actual NPP networks and controllers is developed in this paper.
Topology identification in transmission systems has historically been accomplished using SCADA measurements. In distribution systems, however, SCADA measurements are insufficient to determine system topology. An accurate system topology is essential for distribution system monitoring and operation. Recently there has been a proliferation of Advanced Metering Infrastructure (AMI) by the electrical utilities, which improved the visibility into distribution systems. These measurements offer a unique capability for Distribution System Topology Identification (DSTI). A novel approach to DSTI is presented in this paper which utilizes the voltage magnitudes collected by distribution grid sensors to facilitate identification of the topology of the distribution network in real-time using Linear Discriminant Analysis (LDA) and Regularized Diagonal Quadratic Discriminant Analysis (RDQDA). The results show that this method can leverage noisy voltage magnitude readings from load buses to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.
As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.
Synapse Energy Economics has conducted structured interviews to better characterize the current landscape of resilience planning within and across jurisdictions. Synapse interviewed representatives of a diverse group of communities and their electric utilities. The resulting case studies span geographies and utility regulatory structures and represent a range of threats. They also vary in terms of population density and size. This report summarizes our approach and the findings gleaned from these conversations. All the communities and utilities we interviewed see increased interest in and commitment of resources for energy-related resilience. The risks and consequences these communities and utilities faced in the past, face now, and will face in the future drove them to improve engagement, advance processes, further decision-making, and in many cases invest in projects. While no process used by communities and utilities was the same, the different processes used by communities and utilities allowed each one to make progress in its own way. Several approaches are emerging that can provide good models for other communities and utilities with an interest in improving resilience.