Publications

The paper proposes an implementation of Graph Neural Networks (GNNs) for distribution power system Traveling Wave (TW) - based protection schemes. Simulated faults on the IEEE 34 system are processed by using the Karrenbauer Transform and the Stationary Wavelet Transform (SWT), and the energy of the resulting signals is calculated using the Parseval's Energy Theorem. This data is used to train Graph Convolutional Networks (GCNs) to perform fault zone location. Several levels of measurement noise are considered for comparison. The results show outstanding performance, more than 90% for the most developed models, and outline a fast, reliable, asynchronous and distributed protection scheme for distribution level networks.

We investigate the space complexity of two graph streaming problems: MAX-CUT and its quantum analogue, QUANTUM MAX-CUT. Previous work by Kapralov and Krachun [STOC 19] resolved the classical complexity of the classical problem, showing that any (2 - ?)-approximation requires O(n) space (a 2-approximation is trivial with O(log n) space). We generalize both of these qualifiers, demonstrating O(n) space lower bounds for (2 - ?)-approximating MAX-CUT and QUANTUM MAX-CUT, even if the algorithm is allowed to maintain a quantum state. As the trivial approximation algorithm for QUANTUM MAX-CUT only gives a 4-approximation, we show tightness with an algorithm that returns a (2 + ?)-approximation to the QUANTUM MAX-CUT value of a graph in O(log n) space. Our work resolves the quantum and classical approximability of quantum and classical Max-Cut using o(n) space.We prove our lower bounds through the techniques of Boolean Fourier analysis. We give the first application of these methods to sequential one-way quantum communication, in which each player receives a quantum message from the previous player, and can then perform arbitrary quantum operations on it before sending it to the next. To this end, we show how Fourier-analytic techniques may be used to understand the application of a quantum channel.

As renewable energy penetration increases and system inertia levels drop, primary frequency control is becoming a critical concern in relatively small interconnections such as the Electric Reliability Council of Texas (ERCOT). To address this problem ERCOT is implementing a number of market rule changes including the introduction of a new Fast Frequency Response (FFR) reserve type to the electricity market. This FFR reserve type aims to help the traditional Primary Frequency Response (PFR) reserve type in arresting frequency decline in the event of a large generator outage. This paper derives reserve requirements to ensure sufficient reserve to arrest frequency decline before reaching the critical frequency threshold while coupling PFR reserve, FFR reserve, and system inertia. The general reserve requirement places limits on the amount of PFR reserve that can be provided by each unit based on its ramping capabilities. Two such limits are derived from first principles and another is proposed that is capable of accommodating the equivalency ratio introduced in previous work. These PFR reserve limits also provide first principles insight into equivalency ratios, which have only been studied empirically in the past. High-level insights are provided on a large Texas test case.

A forward analytic model is required to rapidly simulate the neutron time-of-flight (nToF) signals that result from magnetized liner inertial fusion (MagLIF) experiments at Sandia’s Z Pulsed Power Facility. Various experimental parameters, such as the burn-weighted fuel-ion temperature and liner areal density, determine the shape of the nToF signal and are important for characterizing any given MagLIF experiment. Extracting these parameters from measured nToF signals requires an appropriate analytic model that includes the primary deuterium-deuterium neutron peak, once-scattered neutrons in the beryllium liner of the MagLIF target, and direct beamline attenuation. Mathematical expressions for this model were derived from the general-geometry time- and energy-dependent neutron transport equation with anisotropic scattering. Assumptions consistent with the time-of-flight technique were used to simplify this linear Boltzmann transport equation into a more tractable form. Models of the uncollided and once-collided neutron scalar fluxes were developed for one of the five nToF detector locations at the Z-Machine. Numerical results from these models were produced for a representative MagLIF problem and found to be in good agreement with similar neutron transport simulations. Twenty experimental MagLIF data sets were analyzed using the forward models, which were determined to only be significantly sensitive to the ion temperature. The results of this work were also found to agree with values obtained separately using a zero scatter analytic model and a high-fidelity Monte Carlo simulation. Inherent difficulties in this and similar techniques are identified, and a new approach forward is suggested.

Performance assessment is an important tool to estimate the long-term safety for a nuclear waste repository. Performance assessment simulations are subject to multiple kinds of uncertainty including stochastic uncertainty, state of knowledge uncertainty, and model uncertainty. Task F1 of the DECOVALEX project involves comparison of the models and methods used in post-closure performance assessment of deep geologic repositories in fractured crystalline rock, providing an opportunity to compare the effects of different sources of uncertainty. A generic reference case for a mined repository in fractured crystalline rock was put together by participating teams, where each team was responsible for determining how best to represent and implement the model. This work presents the preliminary crystalline reference case results for the Department of Energy (DOE) team.

Abstract not provided.

This work explores deriving transmissibility functions for a missile from a measured location at the base of the fairing to a desired location within the payload. A pressure on the outside of the fairing and the rocket motor’s excitation creates an acceleration at a measured location and a desired location. Typically, the desired location is not measured. In fact, it is typical that the payload may change, but measured acceleration at the base of the fairing is generally similar to previous test flights. Given this knowledge, it is desired to use a finite-element model to create a transmissibility function which relates acceleration from the previous test flight’s measured location at the base of the fairing to acceleration at a location in the new payload. Four methods are explored for deriving this transmissibility, with the goal of finding an appropriate transmissibility when both the pressure and rocket motor excitation are equally present. These methods are assessed using transient results from a simple example problem, and it is found that one of the methods gives good agreement with the transient results for the full range of loads considered.

Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi-disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this and analogous resources. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data, in conjunction with new and preceding geoscientific measurements and observations, are being integrated and evaluated in this study for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings (i.e., whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs) will also be evaluated. This paper provides details and current updates on the course of this study in-progress.

We present an autodifferentiable spectral modeling of exoplanets and brown dwarfs. This model enables a fully Bayesian inference of the high-dispersion data to fit the ab initio line-by-line spectral computation to the observed spectrum by combining it with the Hamiltonian Monte Carlo in recent probabilistic programming languages. An open-source code, ExoJAX (https://github.com/HajimeKawahara/exojax), developed in this study, was written in Python using the GPU/TPU compatible package for automatic differentiation and accelerated linear algebra, JAX. We validated the model by comparing it with existing opacity calculators and a radiative transfer code and found reasonable agreements for the output. As a demonstration, we analyzed the high-dispersion spectrum of a nearby brown dwarf, Luhman 16 A, and found that a model including water, carbon monoxide, and H2/He collision-induced absorption was well fitted to the observed spectrum (R = 105 and 2.28-2.30 μm). As a result, we found that T0=1295-32+35 K at 1 bar and C/O = 0.62 ± 0.03, which is slightly higher than the solar value. This work demonstrates the potential of a full Bayesian analysis of brown dwarfs and exoplanets as observed by high-dispersion spectrographs and also directly imaged exoplanets as observed by high-dispersion coronagraphy.

Dynamical systems subject to intermittent contact are often modeled with piecewise-smooth contact forces. However, the discontinuous nature of the contact can cause inaccuracies in numerical results or failure in numerical solvers. Representing the piecewise contact force with a continuous and smooth function can mitigate these problems, but not all continuous representations may be appropriate for this use. In this work, five representations used by previous researchers (polynomial, rational polynomial, hyperbolic tangent, arctangent, and logarithm-arctangent functions) are studied to determine which ones most accurately capture nonlinear behaviors including super- and subharmonic resonances, multiple solutions, and chaos. The test case is a single-DOF forced Duffing oscillator with freeplay nonlinearity, solved using direct time integration. This work intends to expand on past studies by determining the limits of applicability for each representation and what numerical problems may occur.

There is a need to perform offline anomaly detection in count data streams to simultaneously identify both systemic changes and outliers, simultaneously. We propose a new algorithmic method, called the Anomaly Detection Pipeline, which leverages common statistical process control procedures in a novel way to accomplish this. The method we propose does not require user-defined control or phase I training data, automatically identifying regions of stability for improved parameter estimation to support change point detection. The method does not require data to be normally distributed, and it detects outliers relative to the regimes in which they occur. Our proposed method performs comparably to state-of-the-art change point detection methods, provides additional capabilities, and is extendable to a larger set of possible data streams than known methods.

Abstract not provided.

Nonlinear force appropriation is an extension of its linear counterpart where sinusoidal excitation is applied to a structure with a modal shaker and phase quadrature is achieved between the excitation and response. While a standard practice in modal testing, modal shaker excitation has the potential to alter the dynamics of the structure under test. Previous studies have been conducted to address several concerns, but this work specifically focuses on a shaker-structure interaction phenomenon which arises during the force appropriation testing of a nonlinear structure. Under pure-tone sinusoidal forcing, a nonlinear structure may respond not only at the fundamental harmonic but also potentially at sub- or superharmonics, or it can even produce aperiodic and chaotic motion in certain cases. Shaker-structure interaction occurs when the response physically pushes back against the shaker attachment, producing non-fundamental harmonic content in the force measured by the load cell, even for pure tone voltage input to the shaker. This work develops a model to replicate these physics and investigates their influence on the response of a nonlinear normal mode of the structure. Experimental evidence is first provided that demonstrates the generation of harmonic content in the measured load cell force during a force appropriation test. This interaction is replicated by developing an electromechanical model of a modal shaker attached to a nonlinear, three-mass dynamical system. Several simulated experiments are conducted both with and without the shaker model in order to identify which effects are specifically due to the presence of the shaker. The results of these simulations are then compared to the undamped nonlinear normal modes of the structure under test to evaluate the influence of shaker-structure interaction on the identified system's dynamics.

We evaluate the use of reference modules for monitoring effective irradiance in PV power plants, as compared with traditional plane-of-array (POA) irradiance sensors, for PV monitoring and capacity tests. Common POA sensors such as pyranometers and reference cells are unable to capture module-level irradiance nonuniformity and require several correction factors to accurately represent the conditions for fielded modules. These problems are compounded for bifacial systems, where the power loss due to rear side shading and rear-side plane-of-array (RPOA) irradiance gradients are greater and more difficult to quantify. The resulting inaccuracy can have costly real-world consequences, particularly when the data are used to perform power ratings and capacity tests. Here we analyze data from a bifacial single-axis tracking PV power plant, (175.6 MWdc) using 5 meteorological (MET) stations, located on corresponding inverter blocks with capacities over 4 MWdc. Each MET station consists of bifacial reference modules as well pyranometers mounted in traditional POA and RPOA installations across the PV power plant. Short circuit current measurements of the reference modules are converted to effective irradiance with temperature correction and scaling based on flash test or nameplate short circuit values. Our work shows that bifacial effective irradiance measured by pyranometers averages 3.6% higher than the effective irradiance measured by bifacial reference modules, even when accounting for spectral, angle of incidence, and irradiance nonuniformity. We also performed capacity tests using effective irradiance measured by pyranometers and reference modules for each of the 5 bifacial single-axis tracking inverter blocks mentioned above. These capacity tests evaluated bifacial plant performance at ∼3.9% lower when using bifacial effective irradiance from pyranometers as compared to the same calculation performed with reference modules.

This paper presents a set of tests on a bolted benchmark structure called the S4 beam with a focus on evaluating coupling between the first two modes due to nonlinearity. Bolted joints are of interest in dynamically loaded structures because frictional slipping at the contact interface can introduce amplitude-dependent nonlinearities into the system, where the frequency of the structure decreases, and the damping increases. The challenge to model this phenomenon is even more difficult if the modes of the structure become coupled, violating a common assumption of mode orthogonality. This work presents a detailed set of measurements in which the nonlinearities of a bolted structure are highly coupled for the first two modes. Two nominally identical bolted structures are excited using an impact hammer test. The nonlinear damping curves for each beam are calculated using the Hilbert transform. Although the two structures have different frequency and damping characteristics, the mode coupling relationship between the first two modes of the structures is shown to be consistent and significant. The data is intended as a challenge problem for interested researchers; all data from these tests are available upon request.

Accurate quantification of photovoltaic (PV) system degradation rate (RD) is essential for lifetime yield predictions. Although RD is a critical parameter, its estimation lacks a standardized methodology that can be applied on outdoor field data. The purpose of this paper is to investigate the impact of time period duration and missing data on RD by analyzing the performance of different techniques applied to synthetic PV system data at different linear RD patterns and known noise conditions. The analysis includes the application of different techniques to a 10-year synthetic dataset of a crystalline Silicon PV system, with emulated degradation levels and imputed missing data. The analysis demonstrated that the accuracy of ordinary least squares (OLS), year-on-year (YOY), autoregressive integrated moving average (ARIMA) and robust principal component analysis (RPCA) techniques is affected by the evaluation duration with all techniques converging to lower RD deviations over the 10-year evaluation, apart from RPCA at high degradation levels. Moreover, the estimated RD is strongly affected by the amount of missing data. Filtering out the corrupted data yielded more accurate RD results for all techniques. It is proven that the application of a change-point detection stage is necessary and guidelines for accurate RD estimation are provided.

Many methods have been suggested to choose between distributions. There has been relatively less study to examine whether these methods accurately recover the distributions being studied. Hence, this research compares several popular distribution selection methods through a Monte Carlo simulation study and identifies which are robust for several types of discrete probability distributions. In addition, we study whether it matters that the distribution selection method does not accurately pick the correct probability distribution by calculating the expected distance, which is the amount of information lost for each distribution selection method compared to the generating probability distribution.

Powders under compression form mesostructures of particle agglomerations in response to both inter- and intra-particle forces. The ability to computationally predict the resulting mesostructures with reasonable accuracy requires models that capture the distributions associated with particle size and shape, contact forces, and mechanical response during deformation and fracture. The following report presents experimental data obtained for the purpose of validating emerging mesostructures simulated by discrete element method and peridynamic approaches. A custom compression apparatus, suitable for integration with our micro-computed tomography (micro-CT) system, was used to collect 3-D scans of a bulk powder at discrete steps of increasing compression. Details of the apparatus and the microcrystalline cellulose particles, with a nearly spherical shape and mean particle size, are presented. Comparative simulations were performed with an initial arrangement of particles and particle shapes directly extracted from the validation experiment. The experimental volumetric reconstruction was segmented to extract the relative positions and shapes of individual particles in the ensemble, including internal voids in the case of the microcrystalline cellulose particles. These computationally determined particles were then compressed within the computational domain and the evolving mesostructures compared directly to those in the validation experiment. The ability of the computational models to simulate the experimental mesostructures and particle behavior at increasing compression is discussed.

Residual stress is a contributor to stress corrosion cracking (SCC) and a common byproduct of additive manufacturing (AM). Here the relationship between residual stress and SCC susceptibility in laser powder bed fusion AM 316L stainless steel was studied through immersion in saturated boiling magnesium chloride per ASTM G36-94. The residual stress was varied by changing the sample height for the as-built condition and additionally by heat treatments at 600°C, 800°C, and 1,200°C to control, and in some cases reduce, residual stress. In general, all samples in the as-built condition showed susceptibility to SCC with the thinner, lower residual stress samples showing shallower cracks and crack propagation occurring perpendicular to melt tracks due to local residual stress fields. The heat-treated samples showed a reduction in residual stress for the 800°C and 1,200°C samples. Both were free of cracks after >300 h of immersion in MgCl2, while the 600°C sample showed similar cracking to their as-built counterpart. Geometrically necessary dislocation (GND) density analysis indicates that the dislocation density may play a major role in the SCC susceptibility.

In transit visualization offers a desirable approach to performing in situ visualization by decoupling the simulation and visualization components. This decoupling requires that the data be transferred from the simulation to the visualization, which is typically done using some form of aggregation and redistribution. As the data distribution is adjusted to match the visualization’s parallelism during redistribution, the data transport layer must have knowledge of the input data structures to partition or merge them. In this chapter, we will discuss an alternative approach suitable for quickly integrating in transit visualization into simulations without incurring significant overhead or aggregation cost. Our approach adopts an abstract view of the input simulation data and works only on regions of space owned by the simulation ranks, which are sent to visualization clients on demand.

Grid technologies connected via power electronic converter (PEC) interfaces increasingly include the grid support functions for voltage and frequency support defined by the IEEE 1547-2018 standard. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. In this paper, a method for assessing photovoltaic (PV) inverter dynamics using a data-driven technique with power hardware-in-the-loop is presented. The data-driven modeling technique uses various probing signals to estimate commercial off-the-shelf (COTS) inverter dynamics. The MATLAB system identification toolbox is used to develop a dynamic COTS inverter model from the perturbed grid voltage (i.e., probing signal) and measured current injected to the grid by the inverter. The goodness-of-fit of COTS inverter dynamics in Volt-VAr support mode under each probing signal is compared. The results show that the logarithmic square-chirp probing signal adequately excites the COTS inverter in Volt-VAr mode to fit a data-driven dynamic model.

We present a field-deployable microfluidic immunoassay device in response to the need for sensitive, quantitative, and high-throughput protein detection at point-of-need. The portable microfluidic system facilitates eight magnetic bead-based sandwich immunoassays from raw samples in 45 minutes. An innovative bead actuation strategy was incorporated into the system to automate multiple sample process steps with minimal user intervention. The device is capable of quantitative and sensitive protein analysis with a 10 pg/ml detection limit from interleukin 6-spiked human serum samples. We envision the reported device offering ultrasensitive point-of-care immunoassay tests for timely and accurate clinical diagnosis.

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous modes, which will produce new stresses on the grid-interfacing power electronics components, such as DC/AC inverters. In previous work, multiple DER devices were instrumented to evaluate additional component stress under multiple reactive power setpoints. We utilize quasi-static time-series simulations to determine voltage-reactive power mode (volt-var) mission profile of inverters in an active power system. Mission profiles and loss estimates are then combined to estimate the reduction of the useful life of inverters from different reactive power profiles. It was found that the average lifetime reduction was approximately 0.15% for an inverter between standard unity power factor operation and the IEEE 1547 default volt-var curve based on thermal damage due to switching in the power transistors. For an inverter with an expected 20-year lifetime, the 1547 volt-var curve would reduce the expected life of the device by 12 days. This framework for determining an inverter's useful life from experimental and modeling data can be applied to any failure mechanism and advanced inverter operation.

Wind turbine wakes are characterized by helical trailing tip vortices that are highly stable initially and act as a shield against mixing with the ambient flow and thereby delay wake recovery until destructive mutual interference of the vortices begins. Delayed wake recovery in turn reduces the power production of downstream turbines that are positioned in the wakes of upstream turbines. The long natural decay length forces wind farms to have large distances between turbines to yield sufficient wake recovery. Herein, we tested a new concept aimed at accelerating the breakdown of wind turbine tip vortices by causing the vortices to interact with one another almost immediately behind the rotor. By adding a spire behind the rotor, essentially a blockage to perturb the paths of the tip vortices, we hypothesized that the altered paths of the tip vortices would cause their destructive interference process to begin sooner. The concept of a nacelle-mounted spire was tested in high-fidelity large-eddy simulations using Nalu-Wind. Four different spires were modeled with wall-resolved meshes behind the rotor of a wind turbine with another turbine five diameters downstream. We compared power and wake data against baseline results to determine whether the spires accelerated wake recovery of the upstream turbine and thereby increased the power of the downstream turbine. The results showed no change in the total power of the two turbines for any spire compared to its respective baseline. These results were further explored by testing at higher spatial resolution and without turbulence in the inflow. The increased spatial resolution increased the apparent stability of the tip vortices while the lack of turbulence did not. We conclude that the spires’ geometry and size were inadequate to alter the helical paths of the trailing tip vortices and that modeling of the formation and decay of tip vortices may be highly sensitive to model parameters.

The application of traveling wave principles for fault detection in distribution systems is challenging because of multiple reflections from the laterals and other lumped elements, particularly when we consider communication-free applications. We propose and explore the use of Shapelets to characterize fault signatures and a data-driven machine learning model to accurately classify the faults based on their distance. Studies of a simple 5-bus system suggest that the use of Shapelets for detecting faults is promising. The application to practical three-phase distribution feeders is the subject of continuing research.

A 0.2-2 GHz digitally programmable RF delay element based on a time-interleaved multi-stage switched-capacitor (TIMS-SC) approach is presented. The proposed approach enables hundreds of ns of broadband RF delay by employing sample time expansion in multiple stages of switched-capacitor storage elements. The delay element was implemented in a 45 nm SOI CMOS process and achieves a 2.55-448.6 ns programmable delay range with < 0.12% delay variation across 1.8 GHz of bandwidth at maximum delay, 2.42 ns programmable delay steps, and 330 ns/mm2 area efficiency. The device achieves 24 dB gain, 7.1 dB noise figure, and consumes 80 mW from a 1 V supply with an active area of 1.36 mm2.

The possibility of estimating the effective resistance at contact points along a seam in a cylindrical vessel is investigated. The vessel is formed from two top-hat structures bolted together at a flange. Aluminum shims at the bolt locations ensure a nearly constant 5-mil gap or slot between the flanges. Cavity modes are excited with a short monopole antenna inside the structure, and external near fields 5 mm away from the slot are probed around the vessel circumference. Comparison of CST and FDTD simulations with measurements reveals that the shape of the field-vs-angle curve is strongly dependent on the contact resistance, indicating that meaningful estimates can be extracted.

A high-speed, two-color pyrometer was developed and employed to characterize the temperature of the ejecta from pyrotechnic igniters. The pyrometer used a single objective lens, beamsplitter, and two high-speed cameras to maximize the spatial and temporal resolutions. The pyrometer used the integrated intensity of under-resolved particles to maintain a large region of interest to capture more particles. The spectral response of the pyrometer was determined based on the response of each optical component and the total system was calibrated using a black body source to ensure accurate intensity ratios over the range of interest.

At Sandia National Laboratories, QSCOUT (the Quantum Scientific Computing Open User Testbed) is an ion-trap based quantum computer built for the purpose of allowing users low-level access to quantum hardware. Commands are executed on the hardware using Jaqal (Just Another Quantum Assembly Language), a programming language designed in-house to support the unique capabilities of QSCOUT. In this work, we describe a batching implementation of our custom software that speeds the experimental run-time through the reduction of communication and upload times. Reducing the code upload time during experimental runs improves system performance by mitigating the effects of drift. We demonstrate this implementation through a set of quantum chemistry experiments using a variational quantum eigensolver (VQE). While developed specifically for this testbed, this idea finds application across many similar experimental platforms that seek greater hardware control or reduced overhead.

The s-step Conjugate Gradient (CG) algorithm has the potential to reduce the communication cost of standard CG by a factor of s. However, though mathematically equivalent, s-step CG may be numerically less stable compared to standard CG in finite precision, exhibiting slower convergence and decreased attainable accuracy. This limits the use of s-step CG in practice. To improve the numerical behavior of s-step CG and overcome this potential limitation, we incorporate two techniques. First, we improve convergence behavior through the use of higher precision at critical parts of the s-step iteration and second, we integrate a residual replacement strategy into the resulting mixed precision s-step CG to improve attainable accuracy. Our experimental results on the Summit Supercomputer demonstrate that when the higher precision is implemented in hardware, these techniques have virtually no overhead on the iteration time while improving both the convergence rate and the attainable accuracy of s-step CG. Even when the higher precision is implemented in software, these techniques may still reduce the time-to-solution (speedups of up to 1.8times in our experiments), especially when s-step CG suffers from numerical instability with a small step size and the latency cost becomes a significant part of its iteration time.

Coherent anti-Stokes Raman scattering of the N2 molecule is performed at rates up to 100 kHz for thermometry in the Sandia free-piston, high-temperature shock-tube facility (HST) for reflected-shock conditions in excess of T = 4000 K at pressures up to P = 10 atm. A pulse-burst laser architecture delivers picosecond-duration pulses to provide both the CARS pump and probe photons, and to pump a solid-state optical parametric generator (OPG)/optical parametric amplifier (OPA) source, which provides frequency tunable Stokes pulses with a bandwidth of 100-120 cm-1 . Single-laser-shot and averaged CARS spectra obtained in both the incident (P = 1.1 atm, T = 2090 K) and reflected (P ~ 8-10.5 atm, T > 4000 K) shock regions of HST are presented. The results indicate that burst-mode CARS is capable of resolving impulsive, high-temperature events in HST.

A critical parameter for the well integrity in geothermal storage and production wells subjected to frequent thermal cycling is the interface between the steel and cement. In geothermal energy storage and energy production wells an insulating cement sheath is necessary to minimize heat losses through the heat uptake by cooler rock formations with high thermal conductivity. Also critical parameters for the well integrity in geothermal storage and production wells subjected to frequent thermal cycling is the interface between metal casing and cement composite. A team from Sandia and Brookhaven National Labs is evaluating special cement formulations to facilitate use during severe and repeated thermal cycling in geothermal wells; this paper reports on recent finding using these more recently developed cements. For this portion of the laboratory study we report on preliminary results from subjecting this cement to high temperature (T> 200°C), at a confining pressure of 13.8 MPa, and pore water pressure of 10.4 MPa. Building on previous work, we studied two sample types; solid cement and a steel cylinder sheathed with cement. In the first sample type we measured fluid flow at increasing elevated temperatures and pressure. In the second sample type, we flowed water through the inside of the steel cylinder rapidly to develop an inner to outer thermal gradient using this specialized test geometry. In the paper we report on water permeability estimates at elevated temperatures and the results of rapid thermal cycling of a steel/cement interface. Posttest observations of the steel-cement interface reveal insight into the nature of the steel/cement bond.

Soft-magnetic alloys exhibit exceptional functional properties that are beneficial for a variety of electromagnetic applications. These alloys are conventionally manufactured into sheet or bar forms using well-established insgot metallurgy practices that involve hot- and cold-working steps. However, recent developments in process metallurgy have unlocked opportunities to directly produce bulk soft-magnetic alloys with improved, and often tailorable, structure–property relationships that are unachievable conventionally. The emergence of unconventional manufacturing routes for soft-magnetic alloys is largely motivated by the need to improve the energy efficiency of electromagnetic devices. In this review, literature that details emerging manufacturing approaches for soft-magnetic alloys is overviewed. This review covers (1) severe plastic deformation, (2) recent advances in melt spinning, (3) powder-based methods, and (4) additive manufacturing. These methods are discussed in comparison with conventional rolling and bar processing. Perspectives and recommended future research directions are also discussed.

This paper presents a run-to-run (R2R) controller for mechanical serial sectioning (MSS). MSS is a destructive material analysis process which repeatedly removes a thin layer of material and images the exposed surface. The images are then used to gain insight into the material properties and often to construct a 3-dimensional reconstruction of the material sample. Currently, an experience human operator selects the parameters of the MSS to achieve the desired thickness. The proposed R2R controller will automate this process while improving the precision of the material removal. The proposed R2R controller solves an optimization problem designed to minimize the variance of the material removal subject to achieving the expected target removal. This optimization problem was embedded in an R2R framework to provide iterative feedback for disturbance rejection and convergence to the target removal amount. Since an analytic model of the MSS system is unavailable, we adopted a data-driven approach to synthesize our R2R controller from historical data. The proposed R2R controller is demonstrated through simulations. Future work will empirically demonstrate the proposed R2R through experiments with a real MSS system.

Dynamic injection shift factor (DISF) is the linear sensitivity factor that estimates the incremental line flows in a transmission network subject to load disturbances. The DISF provides fast computation of post-disturbance line flows without solving nonlinear equations of power-system dynamics for a given pre-disturbance operating condition. Furthermore, DISF can be utilized to derive other critical sensitivity factors used for fast contingency screening and generation dispatch in real-time markets. However, deriving the DISF analytically is difficult due to nonlinearity of power-system models. In this paper, we propose an approach based on a linear Koopman operator and a data-driven algorithm to construct a representative linear model for generator and network dynamics. The linear model constructed by the proposed approach is utilized to find an analytic expression of the DISF. Then, the DISF provides numerical tools to estimate line flows accurately subject to power injection changes in the network at any instant in time without solving nonlinear power-system equations.

OpenMP 5.0 added support for reductions over explicit tasks. This expands the previous reduction support that was limited primarily to worksharing and parallel constructs. While the scope of a reduction operation in a worksharing construct is the scope of the construct itself, the scope of a task reduction can vary. This difference requires syntactical means to define the scope of reductions, e.g., the task_reduction clause, and to associate participating tasks, e.g., the in_reduction clause. Furthermore, the disassociation of the number of threads and the number of tasks creates space for different implementations in the OpenMP runtime. In this work, we provide insights into the behavior and performance of task reduction implementations in GCC/g++ and LLVM/Clang. Our results indicate that task reductions are well supported by both compilers, but their performance differs in some cases and is often determined by the efficiency of the underlying task management.

Scalable coherent control hardware for quantum information platforms is rapidly growing in priority as their number of available qubits continues to increase. As these systems scale, more calibration steps are needed, leading to challenges with system instability as calibrated parameters drift. Moreover, the sheer amount of data required to run circuits with large depth tends to balloon, especially when implementing state-of-the-art dynamical-decoupling gates which require advanced modulation techniques. We present a control system that addresses these challenges for trapped-ion systems, through a combination of novel features that eliminate the need for manual bookkeeping, reduction in data transfer bandwidth requirements via gate compression schemes, and other automated error handling techniques. Moreover, we describe an embedded pulse compiler that applies staged optimization, including compressed intermediate representations of parsed output products, performs in-situ mutation of compressed gate data to support high-level algorithmic feedback to account for drift, and can be run entirely on chip.

Abstract not provided.

Integrating recent advancements in resilient algorithms and techniques into existing codes is a singular challenge in fault tolerance - in part due to the underlying complexity of implementing resilience in the first place, but also due to the difficulty introduced when integrating the functionality of a standalone new strategy with the preexisting resilience layers of an application. We propose that the answer is not to build integrated solutions for users, but runtimes designed to integrate into a larger comprehensive resilience system and thereby enable the necessary jump to multi-layered recovery. Our work designs, implements, and verifies one such comprehensive system of runtimes. Utilizing Fenix, a process resilience tool with integration into preexisting resilience systems as a design priority, we update Kokkos Resilience and the use pattern of VeloC to support application-level integration of resilience runtimes. Our work shows that designing integrable systems rather than integrated systems allows for user-designed optimization and upgrading of resilience techniques while maintaining the simplicity and performance of all-in-one resilience solutions. More application-specific choice in resilience strategies allows for better long-term flexibility, performance, and - importantly - simplicity.

This paper proposes a framework to explain and quantify how a Traveling Wave (TW)-based fault location classifier, a Random Forest, is affected by different TW propagation factors. The classifier's goal is to determine the faulty Protection Zone. In order to work with a simplified, yet realistic, distribution system, this work considers a use case with different configurations that are obtained by optionally including several common distribution elements such as voltage regulators, capacitor banks, laterals, and extra loads. Simulated faults are decomposed in frequency bands using the Stationary Wavelet Transform, and the classifier is trained with such signals' energy. SHapley Additive exPlanations (SHAP) are used to identify the most important features, and the effect of different fault configurations is quantified using the Jensen-Shannon Divergence. Results show that distance, the presence of voltage regulators and the fault type are the main factors that affect the classifier's behavior.

Physical systems that are subject to intermittent contact/impact are often studied using piecewise-smooth models. Freeplay is a common type of piecewise-smooth system and has been studied extensively for gear systems (backlash) and aeroelastic systems (control surfaces like ailerons and rudders). These systems can experience complex nonlinear behavior including isolated resonance, chaos, and discontinuity-induced bifurcations. This behavior can lead to undesired damaging responses in the system. In this work, bifurcation analysis is performed for a forced Duffing oscillator with freeplay. The freeplay nonlinearity in this system is dependent on the contact stiffness, the size of the freeplay region, and the symmetry/asymmetry of the freeplay region with respect to the system’s equilibrium. Past work on this system has shown that a rich variety of nonlinear behaviors is present. Modern methods of nonlinear dynamics are used to characterize the transitions in system response including phase portraits, frequency spectra, and Poincaré maps. Different freeplay contact stiffnesses are studied including soft, medium, and hard in order to determine how the system response changes as the freeplay transitions from soft contact to near-impact. Particular focus is given to the effects of different initial conditions on the activation of secondary- and isolated-resonance responses. Preliminary results show isolated resonances to occur only for softer-contact cases, regions of superharmonic resonances are more prevalent for harder-contact cases, and more nonlinear behavior occurs for higher initial conditions.

Abstract not provided.

This study presents a method that can be used to gain information relevant to determining the corrosion risk for spent nuclear fuel (SNF) canisters during extended dry storage. Currently, it is known that stainless steel canisters are susceptible to chloride-induced stress corrosion cracking (CISCC). However, the rate of CISCC degradation and the likelihood that it could lead to a through-wall crack is unknown. This study uses well-developed computational fluid dynamics and particle-tracking tools and applies them to SNF storage to determine the rate of deposition on canisters. The deposition rate is determined for a vertical canister system and a horizontal canister system, at various decay heat rates with a uniform particle size distribution, ranging from 0.25 to 25 µm, used as an input. In all cases, most of the dust entering the overpack passed through without depositing. Most of what was retained in the overpack was deposited on overpack surfaces (e.g., inlet and outlet vents); only a small fraction was deposited on the canister itself. These results are provided for generalized canister systems with a generalized input; as such, this technical note is intended to demonstrate the technique. This study is a part of an ongoing effort funded by the U.S. Department of Energy, Nuclear Energy Office of Spent Fuel Waste Science and Technology, which is tasked with doing research relevant to developing a sound technical basis for ensuring the safe extended storage and subsequent transport of SNF. This work is being presented to demonstrate a potentially useful technique for SNF canister vendors, utilities, regulators, and stakeholders to utilize and further develop for their own designs and site-specific studies.

The Spectral Physics Environment for Advanced Remote Sensing (SPEARS) application programming interface (API) is a Python-based, line-by-line, local thermal equilibrium (LTE) spectral modeling code which is optimized for simultaneously synthesizing optical spectra from any combination of fundamental spectroscopic databases. In this article, we contribute two novel spectral modeling techniques to the scientific literature. First we describe how SPEARS integrates a physics-based collisional model for calculating pressure broadening in the absence of available broadening coefficients. With this collisional model implementation, a generalized approach to fundamental spectroscopic databases can be achieved across multiple databases. We also detail our adaptive grid mesh algorithm developed to make the code scalable for simulating large spectral bandwidths at high spectral fidelity using intuitive grid parameters. We present comparisons to other modeling tools, experiments, and provide a discussion on the SPEARS user interface.

The growing x-ray detection burden for vehicles at Ports of Entry in the US requires the development of efficient and reliable algorithms to assist human operator in detecting contraband. Developing algorithms for large-scale non-intrusive inspection (NII) that both meet operational performance requirements and are extensible for use in an evolving environment requires large volumes and varieties of training data, yet collecting and labeling data for these enivornments is prohibitively costly and time consuming. Given these, generating synthetic data to augment algorithm training has been a focus of recent research. Here we discuss the use of synthetic imagery in an object detection framework, and describe a simulation based approach to determining domain-informed threat image projection (TIP) augmentation.

Abstract not provided.

Abstract not provided.

Using neural networks to solve variational problems, and other scientific machine learning tasks, has been limited by a lack of consistency and an inability to exactly integrate expressions involving neural network architectures. We address these limitations by formulating a polynomial-spline network, a novel shallow multilinear perceptron (MLP) architecture incorporating free knot B-spline basis functions into a polynomial mixture-of-experts model. Effectively, our architecture performs piecewise polynomial approximation on each cell of a trainable partition of unity while ensuring the MLP and its derivatives can be integrated exactly, obviating a reliance on sampling or quadrature and enabling error-free computation of variational forms. We demonstrate hp-convergence for regression problems at convergence rates expected from approximation theory and solve elliptic problems in one and two dimensions, with a favorable comparison to adaptive finite elements.

Unpredictable disturbances with dynamic trajectories such as extreme weather events and cyber attacks require adaptive, cyber-physical special protection schemes to mitigate cascading impact in the electric grid. A harmonized automatic relay mitigation of nefarious intentional events (HARMONIE) special protection scheme (SPS) is being developed to address that need. However, for evaluating the HARMONIE-SPS performance in classifying system disturbances and mitigating consequences, a cyber-physical testbed is required to further development and validate the methodology. In this paper, we present a design for a co-simulation testbed leveraging the SCEPTRE™ platform and the real-time digital simulator (RTDS). The integration of these two platforms is detailed, as well as the unique, specific needs for testing HARMONIE-SPS within the environment. Results are presented from tests involving a WSCC 9-bus system with different load shedding scenarios with varying cyber-physical impact.

Due to the increasing complexity of energy systems and consequent increase in attack vectors, protecting the power grid from unknown disturbances and attacks using special protection schemes is crucial. In this paper, we discuss the machine learning component of the HARMONIE special protection scheme which relies on a novel combination of graph neural networks and Transformer models to jointly process cyber (network) and physical data. Our approach shows promise in detecting cyber and physical disturbances and includes the capability to identify relevant portions of the input sequence that contribute to the model's prediction. With this in place, the end goal of developing automated mitigation strategies is within reach.

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180^{\circ } arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N-dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. These profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.

This paper presents a simulation and respective analysis of traveling waves from a 5-bus distribution system connected to a grid-forming inverter (GFMI). The goal is to analyze the numerical differences in traveling waves if a GFMI is used in place of a traditional generator. The paper introduces the topic of traveling waves and their use in distribution systems for fault clearing. Then it introduces a Simulink design of said 5-bus system around which this paper is centered. The system is subject to various simulation tests of which the results and design are explained further in the paper to discuss if and how exactly inverters affect traveling waves and how different design choices for the system can impact these waves. Finally, a consideration is made for what these traveling waves represent in a practical environment and how to properly address them using the information derived in this study.

An RF switch technique applying differential signal cancellation is presented. The proposed approach enables high isolation and extremely small size by employing cascode current steering within a differential amplifier. Unlike series RF switches, isolation is limited by device mismatch, not switch parasitic capacitance, enabling high frequency operation. Since the switch is within the already present cascode devices, there is no additional insertion loss from the switch. The switch was implemented in a 180 nm CMOS process within an amplifier as part of an on-chip receiver and achieves 36-43 dB isolation across 0.5-2 GHz, while occupying an area of only 0.0006 mm2.

Pre-chamber ignition has demonstrated capability to increase internal combustion engine in-cylinder burn rates and enable the use of low engine-out pollutant emission combustion strategies. In the present study, newly designed passive pre-chambers with different nozzle-hole patterns - that featured combinations of radial and axial nozzles - were experimentally investigated in an optically accessible, single-cylinder research engine. The pre-chambers analyzed had a narrow throat geometry to increase the velocity of the ejected jets. In addition to a conventional inductive spark igniter, a nanosecond spark ignition system that promotes faster early burn rates was also investigated. Time-resolved visualization of ignition and combustion processes was accomplished through high-speed hydroxyl radical (OH*) chemiluminescence imaging. Pressure was measured during the engine cycle in both the main chamber and pre-chamber to monitor respective combustion progress. Experimental heat release rates (HRR) calculated from the measured pressure profiles were used as inputs for two different GT-Power 1D simulations to evaluate the pre-chamber jet-exit momentum and penetration distance. The first simulation used both the calculated main-chamber and pre-chamber HRR, while the second used only the main chamber HRR with the pre-chamber HRR modeled. Results show discrepancies between the models mainly in the pressurization of the pre-chamber which in turn affected jet penetration rate and highlights the sensitivity of the simulation results to proper input selection. Experimental results further show increased pressurization, with an associated acceleration of jet penetration, when operating with nanosecond spark ignition systems regardless of the pre-chamber tip geometry used.

Abstract not provided.

We have extended the computational singular perturbation (CSP) method to differential algebraic equation (DAE) systems and demonstrated its application in a heterogeneous-catalysis problem. The extended method obtains the CSP basis vectors for DAEs from a reduced Jacobian matrix that takes the algebraic constraints into account. We use a canonical problem in heterogeneous catalysis, the transient continuous stirred tank reactor (T-CSTR), for illustration. The T-CSTR problem is modelled fundamentally as an ordinary differential equation (ODE) system, but it can be transformed to a DAE system if one approximates typically fast surface processes using algebraic constraints for the surface species. We demonstrate the application of CSP analysis for both ODE and DAE constructions of a T-CSTR problem, illustrating the dynamical response of the system in each case. We also highlight the utility of the analysis in commenting on the quality of any particular DAE approximation built using the quasi-steady state approximation (QSSA), relative to the ODE reference case.

We present a procedure for randomly generating realistic steady-state contingency scenarios based on the historical outage data from a particular event. First, we divide generation into classes and fit a probability distribution of outage magnitude for each class. Second, we provide a method for randomly synthesizing generator resilience levels in a way that preserves the data-driven probability distributions of outage magnitude. Finally, we devise a simple method of scaling the storm effects based on a single global parameter. We apply our methods using data from historical Winter Storm Uri to simulate contingency events for the ACTIVSg2000 synthetic grid on the footprint of Texas.

Abstract not provided.

Simple but mission-critical internet-based applications that require extremely high reliability, availability, and verifiability (e.g., auditability) could benefit from running on robust public programmable blockchain platforms such as Ethereum. Unfortunately, program code running on such blockchains is normally publicly viewable, rendering these platforms unsuitable for applications requiring strict privacy of application code, data, and results. In this work, we investigate using MPC techniques to protect the privacy of a blockchain computation. While our main goal is to hide both the data and the computed function itself, we also consider the standard MPC setting where the function is public. We describe GABLE (Garbled Autonomous Bots Leveraging Ethereum), a blockchain MPC architecture and system. The GABLE architecture specifies the roles and capabilities of the players. GABLE includes two approaches for implementing MPC over blockchain: Garbled Circuits (GC), evaluating universal circuits, and Garbled Finite State Automata (GFSA). We formally model and prove the security of GABLE implemented over garbling schemes, a popular abstraction of GC and GFSA from (Bellare et al., CCS 2012). We analyze in detail the performance (including Ethereum gas costs) of both approaches and discuss the trade-offs. We implement a simple prototype of GABLE and report on the implementation issues and experience.

In high temperature (HT) environments often encountered in geothermal wells, data rate transfers for downhole instrumentation are relatively limited due to transmission line bandwidth and insertion loss and the processing speed of HT microcontrollers. In previous research, Sandia National Laboratory Geothermal Department obtained 3.8 Mbps data rates over 1524 m (5000 ft) for single conductor wireline cable with less than a 1x10-8 bit error rate utilizing low temperature NITM hardware (formerly National InstrumentsTM). Our protocol technique was a combination of orthogonal frequency-division multiplexing and quadrature amplitude modulation across the bandwidth of the single conductor wireline. This showed it is possible to obtain high data rates in low bandwidth wirelines. This paper focuses on commercial HT microcontrollers (µC), rather than low temperature NITM modules, to enable high-speed communication in an HT environment. As part of this effort, four devices were evaluated, and an optimal device (SM320F28335-HT) was selected for its high clock rates, floating-point unit, and on-board analog-to-digital converter. A printed circuit board was assembled with the HT µC, an HT resistor digital-to-analog converter, and an HT line driver. The board was tested at the microcontroller's rated maximum temperature (210°C) for a week while transmitting through a 1524 m (5000 ft) wireline. A final test was conducted to the point of failure at elevated temperatures. This paper will discuss communication methods, achieved data rates, and hardware selection. This effort contributes to the enhancement of HT instrumentation by enabling greater sensor counts and improving data accuracy and transfer rates.

This paper studies the differences in a synthetic inertia controller of using two different feedback measurements: (i) an estimate of the rate of change of frequency from local voltage measurements, and (ii) a remote machine acceleration from a generator nearby to the actuator. The device that provides the synthetic inertia action is a converter interfaced generator (CIG). The paper carries out analysis in the frequency domain, using Bode plots, to show that synthetic inertia control using frequency estimates is more prone to instabilities than for the case where a machine speed is used. The paper then proposes a controller (or a filter) to mitigate these effects. In addition, the paper shows the effects that a delay of the machine speed signal of the nearby generator has on the synthetic inertia control of the system and how a controller is also needed in this case. Finally, the paper shows the difference in performance of a synthetic inertia controller when using these different measurement signals with simulations in time domain a electromagnetic transient program platform.

A method for measuring the velocity of a passing object by recording the voltage induced by a magnet fixed to that object is explored in this article. Both theory and applications are discussed along with the strengths and limitations of the technique.

A proper understanding of the complex physics associated with nonlinear dynamics can improve the accuracy of predictive engineering models and provide a foundation for understanding nonlinear response during environmental testing. Several researchers and studies have previously shown how localized nonlinearities can influence the global vibration modes of a system. This current work builds upon the study of a demonstration aluminum aircraft with a mock pylon with an intentionally designed, localized nonlinearity. In an effort to simplify the identification of the localized nonlinearity, previous work has developed a simplified experimental setup to collect experimental data for the isolated pylon mounted to a stiff fixture. This study builds on these test results by correlating a multi-degree-of-freedom model of the pylon to identify the appropriate model form and parameters of the nonlinear element. The experimentally measured backbone curves are correlated with a nonlinear Hurty/Craig-Bampton (HCB) reduced order model (ROM) using the calculated nonlinear normal modes (NNMs). Following the calibration, the nonlinear HCB ROM of the pylon is attached to a linear HCB ROM of the wing to predict the NNMs of the next-level wing-pylon assembly as a pre-test analysis to better understand the significance of the localized nonlinearity on the global modes of the wing structure.

Effects of gamma and proton irradiation, and of forward bias minority carrier injection, on minority carrier diffusion and photoresponse were investigated for long-wave (LW) and mid-wave (MW) infrared detectors with engineered majoritycarrier barriers. The LWIR detector was a type-II GaSb/InAs strained-layer superlattice pBiBn structure. The MWIR detector was a InAsSb/AlAsSb nBp structure without superlattices. Room temperature gamma irradiations degraded the minority carrier diffusion length of the LWIR structure, and minority carrier injections caused dramatic improvements, though there was little effect from either treatment on photoresponse. For the MWIR detector, effects of room temperature gamma irradiation and injection on minority carrier diffusion and photoresponse were negligible. Subsequently, both types of detectors were subjected to gamma irradiation at 77 K. In-situ photoresponse was unchanged for the LWIR detectors, while that for the MWIR ones decreased 19% after cumulative dose of ~500 krad(Si). Minority carrier injection had no effect on photoresponse for either. The LWIR detector was then subjected to 4 Mrad(Si) of 30 MeV proton irradiation at 77 K, and showed a 35% decrease in photoresponse, but again no effect from forward bias injection. These results suggest that photoresponse of the LWIR detectors is not limited by minority carrier diffusion.

Any program tasked with the evaluation and acquisition of algorithms for use in deployed scenarios must have an impartial, repeatable, and auditable means of benchmarking both candidate and fielded algorithms. Success in this endeavor requires a body of representative sensor data, data labels indicating the proper algorithmic response to the data as adjudicated by subject matter experts, a means of executing algorithms under review against the data, and the ability to automatically score and report algorithm performance. Each of these capabilities should be constructed in support of program and mission goals. By curating and maintaining data, labels, tests, and scoring methodology, a program can understand and continually improve the relationship between benchmarked and fielded performance of acquired algorithms. A system supporting these program needs, deployed in an environment with sufficient computational power and necessary security controls is a powerful tool for ensuring due diligence in evaluation and acquisition of mission critical algorithms. This paper describes the Seascape system and its place in such a process.

Many teams struggle to adapt and right-size software engineering best practices for quality assurance to fit their context. Introducing software quality is not usually framed in a way that motivates teams to take action, thus resulting in it becoming a "check the box for compliance"activity instead of a cultural practice that values software quality and the effort to achieve it. When and how can we provide effective incentives for software teams to adopt and integrate meaningful and enduring software quality practices? We explored this question through a persona-based ideation exercise at the 2021 Collegeville Workshop on Scientific Software in which we created three unique personas that represent different scientific software developer perspectives.

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are violated, the existing system security margins will be largely unknown. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of Reinforcement-based Machine Learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. The approach requires the training of a Machine Learning Agent to accomplish this task using modeled data and employs it as a decision support tool under severe, near-blackout conditions.

Sandia National Laboratories has developed technology enabling novel downhole electrochemical assessment in extreme downhole environments. High-temperature high-pressure (HTHP) electrodes selectively sensitive to hydrogen (H+), chloride (Cl-), iodide (I-) and overall ionic strength (Reference Electrode+-) have been demonstrated in representative geothermal environments (225°C and 103 bar in surrogate geothermal brine). This 2-year program is a collaboration effort between Sandia and Thermochem, Inc. with the goal of taking the prototype sensors and developing them into a commercial product that is operable up to 300°C and 345 bar. The Sandia-developed prototype HTHP chemical sensor package creates a capability that has never been possible to date. This technology is desired by the geothermal industry to fill a gap in available downhole real-time measurements. Only limited sensors are available that operate at the extreme temperatures and pressures found in geothermal wells. For the purpose of this paper, high temperature is defined as temperatures exceeding 200°C and high pressure is defined as pressures exceeding 35 bar. Chemical sensors exceeding these parameters and sized appropriately for downhole applications do not exist. The current Thermochem two-phase downhole sampling tool (rated to 350 °C) will be re-configured to accept the sensors. A downhole tool with an integrated pH real-time sensor capable of operation at 300°C and 345 bar does not exist and as such, the developed technology will provide the geothermal industry with data that would otherwise not be possible such as vertical in-situ pH-profiling of geothermal wells. The pH measurement was chosen as the first chemical sensor focus since it is one of the fundamental measurements required to understand downhole chemistry, scaling and corrosion processes.

Demonstration of broadband nanosecond output from a burst-mode-pumped noncolinear optical parametric oscillator (NOPO) has been achieved at 40 kHz. The NOPO is pumped by 355-nm output at 50 mJ/pulse for 45 pulses. A bandwidth of 540 cm-1 was achieved from the OPO with a conversion efficiency of 10% for 5 mJ/pulse. Higher bandwidths up to 750 cm-1 were readily achievable at reduced performance and beam quality. The broadband NOPO output was used for a planar BOXCARS phase matching scheme for N2 CARS measurements in a near adiabatic H2/air flame. Single-shot CARS measurements were taken for equivalence ratios of φ=0.52-0.86 for temperatures up to 2200 K.

Modern distribution systems can accommodate different topologies through controllable tie lines for increasing the reliability of the system. Estimating the prevailing circuit topology or configuration is of particular importance at the substation for different applications to properly operate and control the distribution system. One of the applications of circuit configuration estimation is adaptive protection. An adaptive protection system relies on the communication system infrastructure to identify the latest status of power. However, when the communication links to some of the equipment are outaged, the adaptive protection system may lose its awareness over the status of the system. Therefore, it is necessary to estimate the circuit status using the available healthy communicated data. This paper proposes the use of machine learning algorithms at the substation to estimate circuit configuration when the communication to the tie breakers is compromised. Doing so, the adaptive protection system can identify the correct protection settings corresponding to the estimated circuit topology. The effectiveness of the proposed approach is verified on IEEE 123 bus test system.

We present a distributed Brillouin fiber sensor that operates by exciting a series of discrete lasing modes. This approach provides inherently wide dynamic range (5m) while the narrow linewidth lasing modes enable low noise (8n/Hz)

Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state variables. While proposed methods have assumed noise only in the dependent variables, experimental and numerical data for operator learning typically exhibit noise in the independent variables as well, since both variables represent signals that are subject to measurement error. In regression on scalar data, failure to account for noisy independent variables can lead to biased parameter estimates. With noisy independent variables, linear models fitted via ordinary least squares (OLS) will show attenuation bias, wherein the slope will be underestimated. In this work, we derive an analogue of attenuation bias for linear operator regression with white noise in both the independent and dependent variables, showing that the norm upper bound of the operator learned via OLS decreases with increasing noise in the independent variable. In the nonlinear setting, we computationally demonstrate underprediction of the action of the Burgers operator in the presence of noise in the independent variable. We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy independent variables for a variety of operator learning problems. Considering the Burgers operator in 1D and 2D, we demonstrate that EiV operator learning robustly recovers operators in high-noise regimes that defeat OLS operator learning. We also introduce an EiV model for time-evolving PDE discovery and show that OLS and EiV perform similarly in learning the Kuramoto-Sivashinsky evolution operator from corrupted data, suggesting that the effect of bias in OLS operator learning depends on the regularity of the target operator.

This work presents an experimental investigation of the deformation and breakup of water drops behind conical shock waves. A conical shock is generated by firing a bullet at Mach 4.5 past a vertical column of drops with a mean initial diameter of 192 µm. The time-resolved drop position and maximum transverse dimension are characterized using backlit stereo videos taken at 500 kHz. A Reynolds-Averaged Navier Stokes (RANS) simulation of the bullet is used to estimate the gas density and velocity fields experienced by the drops. Classical correlations for breakup times derived from planar-shock/drop interactions are evaluated. Predicted drop breakup times are found to be in error by a factor of three or more, indicating that existing correlations are inadequate for predicting the response to the three-dimensional relaxation of the velocity and thermodynamic properties downstream of the conical shock. Next, the Taylor Analogy Breakup (TAB) model, which solves a transient equation for drop deformation, is evaluated. TAB predictions for drop diameter calculated using a dimensionless constant of C2 = 2, as compared to the accepted value of C2 = 2/3, are found to agree within the confidence bounds of the ensemble averaged experimental values for all drops studied. These results suggest the three-dimensional relaxation effects behind conical shock waves alter the drop response in comparison to a step change across a planar shock, and that future models describing the interaction between a drop and a non-planar shock wave should account for flow field variations.

The latest high temperature (HT) microcontrollers and memory technology have been investigated for the purpose of enhancing downhole instrumentation capabilities at temperatures above 210°C. As part of the effort, five microcontrollers (Honeywell HT83C51, RelChip RC10001, Texas Instruments SM470R1B1M-HT, SM320F2812-HT, SM320F28335-HT) and one memory chip (RelChip RC2110836) have been evaluated to its rated temperature for a period of one month to determine life expectancy and performance. Pulse rate of the integrated circuit and internal memory scan were performed during testing by remotely located axillary components. This paper will describe challenges encountered in the operation and HT testing of these components. Long-term HT tests results show the variation in power consumption and packaging degradation. The work described in this paper improves downhole instrumentation by enabling greater sensor counts and improving data accuracy and transfer rates at temperatures between 210°C and 300°C.

This work presents a high-speed laser-absorption-spectroscopy diagnostic capable of measuring temperature, pressure, and nitric oxide (NO) mole fraction in shock-heated air at a measurement rate of 500 kHz. This diagnostic was demonstrated in the High-Temperature Shock Tube (HST) facility at Sandia National Laboratories. The diagnostic utilizes a quantum-cascade laser to measure the absorbance spectra of two rovibrational transitions near 5.06 µm in the fundamental vibration bands (v" = 0 and 1) of NO in its ground electronic state (X2 Π1/2 ). Gas properties were determined using scanned-wavelength direct absorption and a recently established fitting method that utilizes a modified form of the time-domain molecular free-induction-decay signal (m-FID). This diagnostic was applied to acquire measurements in shock-heated air in the HST at temperatures ranging from approximately 2500 to 5500 K and pressures of 3 to 12 atm behind both incident and reflected shocks. The measurements agree well with the temperature predicted by NASA CEA and the pressure measured simultaneously using PCB pressure sensors. The measurements presented demonstrate that this diagnostic is capable of resolving the formation of NO in shock-heated air and the associated temperature change at the conditions studied.

Hardware design in high-performance computing (HPC) is often highly experimental. Exploring new designs is difficult and time-consuming, requiring lengthy vendor cooperation. RISC-V is an open-source processor ISA that improves the accessibility of chip design, including the ability to do hardware/software co-design using open-source hardware and tools. Co-design allows design decisions to easily flow across the hardware/software boundary and influence future design ideas. However, new hardware designs require corresponding software to drive and test them. Conventional operating systems like Linux are massively complex and modification is time-prohibitive. In this paper, we describe our port of the Kitten lightweight kernel operating system to RISC-V in order to provide an alternative to Linux for conducting co-design research. Kitten's small code base and simple resource management policies are well matched for quickly exploring new hardware ideas that may require radical operating system modifications and restructuring. Our evaluation shows that Kitten on RISC-V is functional and provides similar performance to Linux for single-core benchmarks. This provides a solid foundation for using Kitten in future co-design research involving RISC-V.

A self-synchronizing underwater acoustic network, designed for remote monitoring of mooring loads in Wave Energy Converters (WEC), has been developed and tested. This network uses Time Division Multiple Access and operates self-contained with the ability for users to remotely transmit commands to the network as needed. Each node is a self-contained unit, consisting of a protocol adaptor board, an underwater acoustic modem and a battery pack. A node can be connected to a load cell, to a topside user or to the WEC. Every node is swapable. The protocol adaptor board, named Protocol Adaptor for Digital LOad Cell (PADLOC) supports a variety of digital load cell message formats (CAN, MODBUS, custom ASCII) and underwater acoustic modem serial formats. PADLOC enables topside users to connect to separate load cells through a user-specific command.

A 0.2-2 GHz digitally programmable RF delay element based on a time-interleaved multi-stage switched-capacitor (TIMS-SC) approach is presented. The proposed approach enables hundreds of ns of broadband RF delay by employing sample time expansion in multiple stages of switched-capacitor storage elements. The delay element was implemented in a 45 nm SOI CMOS process and achieves a 2.55-448.6 ns programmable delay range with < 0.12% delay variation across 1.8 GHz of bandwidth at maximum delay, 2.42 ns programmable delay steps, and 330 ns/mm2 area efficiency. The device achieves 24 dB gain, 7.1 dB noise figure, and consumes 80 mW from a 1 V supply with an active area of 1.36 mm2.

Rock salt is being considered as a medium for energy storage and radioactive waste disposal. A Disturbed Rock Zone (DRZ) develops in the immediate vicinity of excavations in rock salt, with an increase in permeability, which alters the migration of gases and liquids around the excavation. When creep occurs adjacent to a stiff inclusion such as a concrete plug, it is expected that the stress state near the inclusion will become more hydrostatic and less deviatoric, promoting healing (permeability reduction) of the DRZ. In this scoping study, we measured the permeability of DRZ rock salt with time adjacent to inclusions (plugs) of varying stiffness to determine how the healing of rock salt, as reflected in the permeability changes, is a function of the stress and time. Samples were created with three different inclusion materials in a central hole along the axis of a salt core: (i) very soft silicone sealant, (ii) sorel cement, and (iii) carbon steel. The measured permeabilities are corrected for the gas slippage effect. We observed that the permeability change is a function of the inclusion material. The stiffer the inclusion, the more rapidly the permeability reduces with time.

Decarbonizing natural gas networks is a challenging enterprise. Replacing natural gas with renewable hydrogen is one option under global consideration to decarbonize heating, power and residential uses of natural gas. Hydrogen is known to degrade fatigue and fracture properties of structural steels, including pipeline steels. In this study, we describe environmental testing strategies aimed at generating baseline fatigue and fracture trends with efficient use of testing resources. For example, by controlling the stress intensity factor (K) in both K-increasing and K-decreasing modes, fatigue crack growth can be measured for multiple load ratios with a single specimen. Additionally, tests can be designed such that fracture tests can be performed at the conclusion of the fatigue crack growth test, further reducing the resources needed to evaluate the fracture mechanics parameters utilized in design. These testing strategies are employed to establish the fatigue crack growth behavior and fracture resistance of API grade steels in gaseous hydrogen environments. In particular, we explore the effects of load ratio and hydrogen partial pressure on the baseline fatigue and fracture trends of line pipe steels in gaseous hydrogen. These data are then used to test the applicability of a simple, universal fatigue crack growth model that accounts for both load ratio and hydrogen partial pressure. The appropriateness of this model for use as an upper bound fatigue crack growth is discussed.

Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neuromorphic systems should exploit the biomimetic behavior of emerging nanodevices. In particular, exceptional opportunities are provided by the non-volatility and analog capabilities of spintronic devices. While spintronic devices that emulate neurons have been previously proposed, they require complementary metal-oxide semiconductor (CMOS) technology to function. In turn, this significantly increases the power consumption, fabrication complexity, and device area of a single neuron. This work reviews three previously proposed CMOS-free spintronic neurons designed to resolve this issue.

Interest in the application of DC Microgrids to distribution systems have been spurred by the continued rise of renewable energy resources and the dependence on DC loads. However, in comparison to AC systems, the lack of natural zero crossing in DC Microgrids makes the interruption of fault currents with fuses and circuit breakers more difficult. DC faults can cause severe damage to voltage-source converters within few milliseconds, hence, the need to quickly detect and isolate the fault. In this paper, the potential for five different Machine Learning (ML) classifiers to identify fault type and fault resistance in a DC Microgrid is explored. The ML algorithms are trained using simulated fault data recorded from a 750 VDC Microgrid modeled in PSCAD/EMTDC. The performance of the trained algorithms are tested using real fault data gathered from an operational DC Microgrid located on the Kirtland Air Force Base. Of the five ML algorithms, three could detect the fault and determine the fault type with at least 99% accuracy, and only one could estimate the fault resistance with at least 99% accuracy. By performing a self-learning monitoring and decision making analysis, protection relays equipped with ML algorithms can quickly detect and isolate faults to improve the protection operations on DC Microgrids.

The latest high temperature (HT) microcontrollers and memory technology have been investigated for the purpose of enhancing downhole instrumentation capabilities at temperatures above 210°C. As part of the effort, five microcontrollers (Honeywell HT83C51, RelChip RC10001, Texas Instruments SM470R1B1M-HT, SM320F2812-HT, SM320F28335-HT) and one memory chip (RelChip RC2110836) have been evaluated to its rated temperature for a period of one month to determine life expectancy and performance. Pulse rate of the integrated circuit and internal memory scan were performed during testing by remotely located axillary components. This paper will describe challenges encountered in the operation and HT testing of these components. Long-term HT tests results show the variation in power consumption and packaging degradation. The work described in this paper improves downhole instrumentation by enabling greater sensor counts and improving data accuracy and transfer rates at temperatures between 210°C and 300°C.

We report an analysis quantifying the contribution to uncertainty in annual energy projections from uncertainty in ground-measured irradiance. Uncertainty in measured irradiance is quantified for eight instruments by the difference from a well-maintained, secondary standard pyranometer which is regarded as truthful. We construct a statistical model of irradiance uncertainty and apply the model to generate a sample of 100 annual time series of irradiance for each instrument. The sample is propagated through a common performance model for a reference photovoltaic system to quantify variation in annual energy. Although the measured irradiance varies from the reference by a few percent (standard deviation of 1-2%) the uncertainty in annual energy is on the order of a fraction of one percent. We propose a model for a factor that represents uncertainty in modeled annual energy that arises from uncertainty in ground-measured irradiance.

Quantifying gas-surface interactions for hypersonic reentry applications remains a challenging and complex problem where credible models are needed to design and analyze thermal protection systems. A flexible sensitivity analysis approach is demonstrated to analyze finite-rate ablation models to identify reaction parameters and mechanisms of influence on predicted quantities of interest. Simulations of hypersonic flow over a sphere-cone are presented using parameterized Park, Zhluktov and Abe (ZA), and MURI finite-rate models that describe the oxidation and sublimation of carbon. The results presented in this study emphasize the importance of characterizing model inputs that are shown to have a high impact on predicted quantities and build evidence to assess credibility of these models.

Large-scale deployment of energy storage systems is a pivotal step toward achieving the clean energy goals of the future. An accurate and publicly accessible database on energy storage projects can help accelerate deployment by providing valuable information and characteristic data to different stakeholders. The U.S. Department of Energy's Global Energy Storage Database (GESDB) aims at providing high-quality and accurate data on energy storage projects around the globe. This paper first provides an overview of the GESDB, briefly describing its features and overall usage. This is followed by a detailed description of the procedure used to validate the database. In doing so, the paper aims at improving the usability of the website while enhancing its value to the community. Furthermore, the presented validation procedure makes the underlying assumptions transparent to the public so that data misinterpretation can be minimized/avoided.

Non-volatile memory arrays require select devices to ensure accurate programming. The one-selector one-resistor (1S1R) array where a two-terminal nonlinear select device is placed in series with a resistive memory element is attractive due to its high-density data storage; however, the effect of the nonlinear select device on the accuracy of analog in-memory computing has not been explored. This work evaluates the impact of select and memory device properties on the results of analog matrix-vector multiplications. We integrate nonlinear circuit simulations into CrossSim and perform end-to-end neural network inference simulations to study how the select device affects the accuracy of neural network inference. We propose an adjustment to the input voltage that can effectively compensate for the electrical load of the select device. Our results show that for deep residual networks trained on CIFAR-10, a compensation that is uniform across all devices in the system can mitigate these effects over a wide range of values for the select device I-V steepness and memory device On/Off ratio. A realistic I-V curve steepness of 60 mV/dec can yield an accuracy on CIFAR-10 that is within 0.44% of the floating-point accuracy.

This paper presents a novel approach for fault location and classification based on combining mathematical morphology (MM) with Random Forests (RF). The MM stage of the method is used to pre-process voltage and current data. Signal vector norms on the output signals of the MM stage are then used as the input features for a RF machine learning classifier and regressor. The data used as input for the proposed approach comprises only a window of 50 µs before and after the fault is detected. The proposed method is tested with noisy data from a small simulated system. These results show 100% accuracy for the classification task and prediction errors with an average of ~13 m in the fault location task.

A new set of critical experiments exploring the temperature-dependence of the reactivity in a critical assembly is described. In the experiments, the temperature of the critical assembly will be varied to determine the temperature that produces the highest reactivity in the assembly. This temperature is the inversion point of the isothermal reactivity coefficient of the assembly. An analysis of relevant configurations is presented. Existing measurements are described and an analysis of these experiments presented. The overall experimental approach is described as are the modifications to the critical assembly needed to perform the experiments.

U.S. national research laboratories and agencies play an integral role in advancing science and technology for the public good. The authors of this article, as research software engineers (RSEs) and allies from eight unique national R&D organizations, came together to explore RSE needs from the perspective of national institutions. We identified three key areas of improvement for future RSEs to pursue science in the national interest: community establishment, hiring and retention, and recognition. To retain and cultivate this essential talent, U.S. national institutions must evolve to support appropriate career pathways for RSEs, and to recognize and reward RSEs’ work.

The Cramér-Rao Lower Bound (CRLB) is used as a classical benchmark to assess estimators. Online algorithms for estimating modal properties from ambient data, i.e., mode meters, can benefit from accurate estimates of forced oscillations. The CRLB provides insight into how well forced oscillation parameters, e.g., frequency and amplitude, can be estimated. Previous works have solved the lower bound under a single-channel PMU measurement; thus, this paper extends works further to study CRLB under two-channel PMU measurements. The goal is to study how correlated/uncorrelated noise can affect estimation accuracy. Interestingly, these studies shows that correlated noise can decrease the CRLB in some cases. This paper derives the CRLB for the two-channel case and discusses factors that affect the bound.

This paper presents the formulation, implementation, and demonstration of a new, largely phenomenological, model for the damage-free (micro-crack-free) thermomechanical behavior of rock salt. Unlike most salt constitutive models, the new model includes both drag stress (isotropic) and back stress (kinematic) hardening. The implementation utilizes a semi-implicit scheme and a fall-back fully-implicit scheme to numerically integrate the model's differential equations. Particular attention was paid to the initial guesses for the fully-implicit scheme. Of the four guesses investigated, an initial guess that interpolated between the previous converged state and the fully saturated hardening state had the best performance. The numerical implementation was then used in simulations that highlighted the difference between drag stress hardening versus combined drag and back stress hardening. Simulations of multi-stage constant stress tests showed that only combined hardening could qualitatively represent reverse (inverse transient) creep, as well as the large transient strains experimentally observed upon switching from axisymmetric compression to axisymmetric extension. Simulations of a gas storage cavern subjected to high and low gas pressure cycles showed that combined hardening led to substantially greater volume loss over time than drag stress hardening alone.

OpenMP 5.0 added support for reductions over explicit tasks. This expands the previous reduction support that was limited primarily to worksharing and parallel constructs. While the scope of a reduction operation in a worksharing construct is the scope of the construct itself, the scope of a task reduction can vary. This difference requires syntactical means to define the scope of reductions, e.g., the task_reduction clause, and to associate participating tasks, e.g., the in_reduction clause. Furthermore, the disassociation of the number of threads and the number of tasks creates space for different implementations in the OpenMP runtime. In this work, we provide insights into the behavior and performance of task reduction implementations in GCC/g++ and LLVM/Clang. Our results indicate that task reductions are well supported by both compilers, but their performance differs in some cases and is often determined by the efficiency of the underlying task management.

The emerging use of the physics-based athermal recombination-corrected displacement per atom (arc-dpa) model for the displacement damage efficiency has motivated a re-evaluation of the historical empirically-derived GaAs damage response function with the purpose of highlighting needs for future analytical and experimental work. The 1-MeV neutron damage equivalence methodology used in the ASTM E-722 standard for GaAs has been re-evaluated using updated nuclear data. This yielded a higher fidelity representation of the GaAs displacement kerma and, through the use of the refined PKA recoil energy-dependent damage efficiency model, an updated 1-MeV(GaAs) displacement damage function. This re-evaluation included use of the Norgett-Robinson-Torrens (NRT) model for an updated threshold treatment, rather than the sharp-threshold Kinchin-Pease model used in the current ASTM standard. The underlying nuclear data evaluations have been updated to use the ENDF/VIII.0 75As and TENDL-2019 71Ga/69Ga evaluations. The displacement kerma and 1-MeV-equivalent damage responses were calculated using a modified NJOY-2016 code which allowed for refinements in some of the damage models. This paper shows that an updated displacement damage function, based upon the latest nuclear data, is consistent with the experimental data used to develop the current ASTM E-722 GaAs standard. Using a double ratio approach to compare the available experimental data with the calculated response, the average legacy double ratio was found to be 0.97±0.05 and the average updated double ratio was found to be 0.94 ±0.05.

We present a field-deployable microfluidic immunoassay device in response to the need for sensitive, quantitative, and high-throughput protein detection at point-of-need. The portable microfluidic system facilitates eight magnetic bead-based sandwich immunoassays from raw samples in 45 minutes. An innovative bead actuation strategy was incorporated into the system to automate multiple sample process steps with minimal user intervention. The device is capable of quantitative and sensitive protein analysis with a 10 pg/ml detection limit from interleukin 6-spiked human serum samples. We envision the reported device offering ultrasensitive point-of-care immunoassay tests for timely and accurate clinical diagnosis.

The precise estimation of performance loss rate (PLR) of photovoltaic (PV) systems is vital for reducing investment risks and increasing the bankability of the technology. Until recently, the PLR of fielded PV systems was mainly estimated through the extraction of a linear trend from a time series of performance indicators. However, operating PV systems exhibit failures and performance losses that cause variability in the performance and may bias the PLR results obtained from linear trend techniques. Change-point (CP) methods were thus introduced to identify nonlinear trend changes and behaviour. The aim of this work is to perform a comparative analysis among different CP techniques for estimating the annual PLR of eleven grid-connected PV systems installed in Cyprus. Outdoor field measurements over an 8-year period (June 2006-June 2014) were used for the analysis. The obtained results when applying different CP algorithms to the performance ratio time series (aggregated into monthly blocks) demonstrated that the extracted trend may not always be linear but sometimes can exhibit nonlinearities. The application of different CP methods resulted to PLR values that differ by up to 0.85% per year (for the same number of CPs/segments).

Abstract not provided.

Abstract not provided.

For the model-based control of low-voltage microgrids, state and parameter information are required. Different optimal estimation techniques can be employed for this purpose. However, these estimation techniques require knowledge of noise covariances (process and measurement noise). Incorrect values of noise covariances can deteriorate the estimator performance, which in turn can reduce the overall controller performance. This paper presents a method to identify noise covariances for voltage dynamics estimation in a microgrid. The method is based on the autocovariance least squares technique. A simulation study of a simplified 100 kVA, 208 V microgrid system in MATLAB/Simulink validates the method. Results show that estimation accuracy is close to the actual value for Gaussian noise, and non-Gaussian noise has a slightly larger error.

Abstract not provided.

Creation of streaming video stimuli that allow for strict experimental control while providing ease of scene manipulation is difficult to achieve but desired by researchers seeking to approach ecological validity in contexts that involve processing streaming visual information. To that end, we propose leveraging video game modding tools as a method of creating research quality stimuli. As a pilot effort, we used a video game sandbox tool (Garry’s Mod) to create three steaming video scenarios designed to mimic video feeds that physical security personnel might observe. All scenarios required participants to identify the presences of a threat appearing during the video feed. Each scenario differed in level of complexity, in that one scenario required only location monitoring, one required location and action monitoring, and one required location, action, and conjunction monitoring in that when an action was performed it was only considered a threat when performed by a certain character model. While there was no behavioral effect of scenario in terms of accuracy or response times, in all scenarios we found evidence of a P300 when comparing response to threatening stimuli to that of standard stimuli. Results therefore indicate that sufficient levels of experimental control may be achieved to allow for the precise timing required for ERP analysis. Thus, we demonstrate the feasibility of using existing modding tools to create video scenarios amenable to neuroimaging analysis.

Area efficient self-correcting flip-flops for use with triple modular redundant (TMR) soft-error hardened logic are implemented in a 12-nm finFET process technology. The TMR flip-flop slave latches self-correct in the clock low phase using Muller C-elements in the latch feedback. These C-elements are driven by the two redundant stored values and not by the slave latch itself, saving area over a similar implementation using majority gate feedback. These flip-flops are implemented as large shift-register arrays on a test chip and have been experimentally tested for their soft-error mitigation in static and dynamic modes of operation using heavy ions and protons. We show how high clock skew can result in susceptibility to soft-errors in the dynamic mode, and explain the potential failure mechanism.

In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and H∞-norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large-scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian variational inference, reduces this burden through the variational approximation to the covariance usually in the form of a diagonal covariance approximation. The challenge is to retain convergence and correct for biases introduced by the sequential VIF steps. We desire a frame-work that improves feasibility while still maintaining reasonable proximity to the optimal Kalman filter as data is assimilated. To accomplish this goal, a H∞-norm based optimization perturbs the VIF covariance matrix to improve robustness. This yields a novel VIF-H∞ recursion that employs consecutive variational inference and H∞ based optimization steps. We explore the development of this method and investigate a numerical example to illustrate the effectiveness of the proposed filter.

This work describes the development and testing of a carbon dioxide seeding system for the Sandia Hypersonic Wind Tunnel. The seeder injects liquid carbon dioxide into the tunnel, which evaporates in the nitrogen supply line and then condenses during the nozzle expansion into a fog of particles that scatter light via Rayleigh scattering. A planar laser scattering (PLS) experiment is conducted in the boundary layer and wake of a cone at Mach 8 to evaluate the success of the seeder. Second-mode waves and turbulence transition were well-visualized by the PLS in the boundary layer and wake. PLS in the wake also captured the expansion wave over the base and wake recompression shock. No carbon dioxide appears to survive and condense in the boundary layer or wake, meaning alternative seeding methods must be explored to extract measurements within these regions. The seeding system offers planar flow visualization opportunities and can enable quantitative velocimetry measurements in the future, including filtered Rayleigh scattering.

Incorrect modeling of control characteristics for inverter-based resources (IBRs) can affect the accuracy of electric power system studies. In many distribution system contexts, the control settings for behind-the-meter (BTM) IBRs are unknown. This paper presents an efficient method for selecting a small number of time series samples from net load meter data that can be used for reconstructing or classifying the control settings of BTM IBRs. Sparse approximation techniques are used to select the time series samples that cause the inversion of a matrix of candidate responses to be as well-conditioned as possible. We verify these methods on 451 actual advanced metering infrastructure (AMI) datasets from loads with BTM IBRs. Selecting 60 15-minute granularity time series samples, we recover BTM control characteristics with a mean error less than 0.2 kVAR.

In order to evaluate the time evolution of avalanche breakdown in wide and ultra-wide bandgap devices, we have developed a cable pulser experimental setup that can evaluate the time-evolution of the terminating impedance for a semiconductor device with a time resolution of 130 ps. We have utilized this pulser setup to evaluate the time-to-breakdown of vertical Gallium Nitride and Silicon Carbide diodes for possible use as protection elements in the electrical grid against fast transient voltage pulses (such as those induced by an electromagnetic pulse event). We have found that the Gallium Nitride device demonstrated faster dynamics compared to the Silicon Carbide device, achieving 90% conduction within 1.37 ns compared to the SiC device response time of 2.98 ns. While the Gallium Nitride device did not demonstrate significant dependence of breakdown time with applied voltage, the Silicon Carbide device breakdown time was strongly dependent on applied voltage, ranging from a value of 2.97 ns at 1.33 kV to 0.78 ns at 2.6 kV. The fast response time (< 5 ns) of both the Gallium Nitride and Silicon Carbide devices indicate that both materials systems could meet the stringent response time requirements and may be appropriate for implementation as protection elements against electromagnetic pulse transients.

Variable energy resources (VERs) like wind and solar are the future of electricity generation as we gradually phase out fossil fuel due to environmental concerns. Nations across the globe are also making significant strides in integrating VERs into their power grids as we strive toward a greener future. However, integration of VERs leads to several challenges due to their variable nature and low inertia characteristics. In this paper, we discuss the hurdles faced by the power grid due to high penetration of wind power generation and how energy storage system (ESSs) can be used at the grid-level to overcome these hurdles. We propose a new planning strategy using which ESSs can be sized appropriately to provide inertial support as well as aid in variability mitigation, thus minimizing load curtailment. A probabilistic framework is developed for this purpose, which takes into consideration the outage of generators and the replacement of conventional units with wind farms. Wind speed is modeled using an autoregressive moving average technique. The efficacy of the proposed methodology is demonstrated on the WSCC 9-bus test system.

Femtosecond laser electronic excitation tagging (FLEET) is a powerful unseeded velocimetry technique typically used to measure one component of velocity along a line, or two or three components from a dot. In this Letter, we demonstrate a dotted-line FLEET technique which combines the dense profile capability of a line with the ability to perform two-component velocimetry with a single camera on a dot. Our set-up uses a single beam path to create multiple simultaneous spots, more than previously achieved in other FLEET spot configurations. We perform dotted-line FLEET measurements downstream of a highly turbulent, supersonic nitrogen free jet. Dotted-line FLEET is created by focusing light transmitted by a periodic mask with rectangular slits of 1.6 × 40 mm2 and an edge-to-edge spacing of 0.5 mm, then focusing the imaged light at the measurement region. Up to seven symmetric dots spaced approximately 0.9 mm apart, with mean full-width at half maximum diameters between 150 and 350 µm, are simultaneously imaged. Both streamwise and radial velocities are computed and presented in this Letter.

An RF switch technique applying differential signal cancellation is presented. The proposed approach enables high isolation and extremely small size by employing cascode current steering within a differential amplifier. Unlike series RF switches, isolation is limited by device mismatch, not switch parasitic capacitance, enabling high frequency operation. Since the switch is within the already present cascode devices, there is no additional insertion loss from the switch. The switch was implemented in a 180 nm CMOS process within an amplifier as part of an on-chip receiver and achieves 36-43 dB isolation across 0.5-2 GHz, while occupying an area of only 0.0006 mm2.

Abstract not provided.

We evaluate the use of reference modules for monitoring effective irradiance in PV power plants, as compared with traditional plane-of-array (POA) irradiance sensors, for PV monitoring and capacity tests. Common POA sensors such as pyranometers and reference cells are unable to capture module-level irradiance nonuniformity and require several correction factors to accurately represent the conditions for fielded modules. These problems are compounded for bifacial systems, where the power loss due to rear side shading and rear-side plane-of-array (RPOA) irradiance gradients are greater and more difficult to quantify. The resulting inaccuracy can have costly real-world consequences, particularly when the data are used to perform power ratings and capacity tests. Here we analyze data from a bifacial single-axis tracking PV power plant, (175.6 MWdc) using 5 meteorological (MET) stations, located on corresponding inverter blocks with capacities over 4 MWdc. Each MET station consists of bifacial reference modules as well pyranometers mounted in traditional POA and RPOA installations across the PV power plant. Short circuit current measurements of the reference modules are converted to effective irradiance with temperature correction and scaling based on flash test or nameplate short circuit values. Our work shows that bifacial effective irradiance measured by pyranometers averages 3.6% higher than the effective irradiance measured by bifacial reference modules, even when accounting for spectral, angle of incidence, and irradiance nonuniformity. We also performed capacity tests using effective irradiance measured by pyranometers and reference modules for each of the 5 bifacial single-axis tracking inverter blocks mentioned above. These capacity tests evaluated bifacial plant performance at ∼3.9% lower when using bifacial effective irradiance from pyranometers as compared to the same calculation performed with reference modules.

We propose a two-stage scenario-based stochastic optimization problem to determine investments that enhance power system resilience. The proposed optimization problem minimizes the Conditional Value at Risk (CVaR) of load loss to target low-probability high-impact events. We provide results in the context of generator winterization investments in Texas using winter storm scenarios generated from historical data collected from Winter Storm Uri. Results illustrate how the CVaR metric can be used to minimize the tail of the distribution of load loss and illustrate how risk-Aversity impacts investment decisions.

Intracellular transport by kinesin motors moving along their associated cytoskeletal filaments, microtubules, is essential to many biological processes. This active transport system can be reconstituted in vitro with the surface-adhered motors transporting the microtubules across a planar surface. In this geometry, the kinesin-microtubule system has been used to study active self-assembly, to power microdevices, and to perform analyte detection. Fundamental to these applications is the ability to characterize the interactions between the surface tethered motors and microtubules. Fluorescence Interference Contrast (FLIC) microscopy can illuminate the height of the microtubule above a surface, which, at sufficiently low surface densities of kinesin, also reveals the number, locations, and dynamics of the bound motors.

We computationally explore the optical and elastic modes necessary for acoustoelectrically enhanced Brillouin interactions. The large simulated piezoelectric (k2 ≈ 6%) and optome-chanical (|g0| ≈ 8000 (rad/s)√m) coupling theoretically predicts a performance enhancement of several orders of magnitude in Brillouin-based photonic technologies.

Metasurface lenses are fabricated using membrane projection lithography following a CMOS-compatible process flow. The lenses are 10-mm in diameter and employ 3-dimensional unit cells designed to function in the mid-infrared spectral range.

Incorrect modeling of control characteristics for inverter-based resources (IBRs) can affect the accuracy of electric power system studies. In many distribution system contexts, the control settings for behind-the-meter (BTM) IBRs are unknown. This paper presents an efficient method for selecting a small number of time series samples from net load meter data that can be used for reconstructing or classifying the control settings of BTM IBRs. Sparse approximation techniques are used to select the time series samples that cause the inversion of a matrix of candidate responses to be as well-conditioned as possible. We verify these methods on 451 actual advanced metering infrastructure (AMI) datasets from loads with BTM IBRs. Selecting 60 15-minute granularity time series samples, we recover BTM control characteristics with a mean error less than 0.2 kVAR.

Abstract not provided.

With the increase in penetration of inverter-based resources (IBRs) in the electrical power system, the ability of these devices to provide grid support to the system has become a necessity. With standards previously developed for the interconnection requirements of grid-following inverters (GFLI) (most commonly photovoltaic inverters), it has been well documented how these inverters 'should' respond to changes in voltage and frequency. However, with other IBRs such as grid-forming inverters (GFMIs) (used for energy storage systems, standalone systems, and as uninterruptable power supplies) these requirements are either: not yet documented, or require a more in deep analysis. With the increased interest in microgrids, GFMIs that can be paralleled onto a distribution system have become desired. With the proper control schemes, a GFMI can help maintain grid stability through fast response compared to rotating machines. This paper will present an experimental comparison of commercially available GFMIand GFLI ' responses to voltage and frequency deviation, as well as the GFMIoperating as a standalone system and subjected to various changes in loads.

Morphing wings have great potential to dramatically improve the efficiency of future generations of aircraft and to reduce noise and emissions. Among many camber morphing wing concepts, shape changing fingerlike mechanisms consist of components, such as torsion bars, bushings, bearings, and joints, all of which exhibit damping and stiffness nonlinearities that are dependent on excitation amplitude. These nonlinearities make the dynamic response difficult to model accurately with traditional simulation approaches. As a result, at high excitation levels, linear finite element models may be inaccurate, and a nonlinear modeling approach is required to capture the necessary physics. This work seeks to better understand the influence of nonlinearity on the effective damping and natural frequency of the morphing wing through the use of quasi-static modal analysis and model reduction techniques that employ multipoint constraints (i.e., spider elements). With over 500,000 elements and 39 frictional contact surfaces, this represents one of the most complicated models to which these methods have been applied to date. The results to date are summarized and lessons learned are highlighted.

The National Academy of Sciences, Engineering, and Medicine (NASEM) defines reproducibility as 'obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis,' and replicability as 'obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data' [1]. Due to an increasing number of applications of artificial intelligence and machine learning (AI/ML) to fields such as healthcare and digital medicine, there is a growing need for verifiable AI/ML results, and therefore reproducible research and replicable experiments. This paper establishes examples of irreproducible AI/ML applications to medical sciences and quantifies the variance of common AI/ML models (Artificial Neural Network, Naive Bayes classifier, and Random Forest classifiers) for tasks on medical data sets.

Abstract not provided.

Growing interest in renewable energy sources has led to an increased installation rate of distributed energy resources (DERs) such as solar photovoltaics (PVs) and wind turbine generators (WTGs). The variable nature of DERs has created several challenges for utilities and system operators related to maintaining voltage and frequency. New grid standards are requiring DERs to provide voltage regulation across distribution networks. Volt-Var Curve (VVC) control is an autonomous grid-support function that provides voltage regulation based on the relationship between voltage and reactive power. This paper evaluates the performance of a WTG operating with VVC control. The evaluation of the model involves a MATLAB/Simulink simulation of a distribution system. For this simulation the model considers three WTGs and a variable load that creates a voltage event.

Numerous projects are looking into distributing blends of natural gas and different amounts of gaseous hydrogen through the existing natural gas distribution system, which is widely composed of medium density polyethylene (MDPE) line pipes. The mechanical behavior of MDPE with hydrogen is not well understood; therefore, the effect of gaseous H2 on the mechanical properties of MDPE needs to be examined. In the current study, we investigate the effects of gaseous H2 on fatigue life and fracture resistance of MDPE in the presence of 3.4 MPa gaseous H2. Fatigue life tests were also conducted at a pressure of 21 MPa to investigate the effect of gas pressure on the fatigue behavior of MDPE. Results showed that the presence of gaseous H2 did not degrade the fatigue life nor the fracture resistance of MDPE. Additionally, based on the value of fracture resistance calculated, a failure assessment diagram was constructed to determine the applicability of using MDPE pipeline for distribution of gaseous H2. Even in the presence of a large internal crack, the failure assessment evaluation indicated that the MDPE pipes lie within the safe region under typical service conditions of natural gas distribution pipeline system.

Fault tolerance is a key challenge as high performance computing systems continue to increase component counts, individual component reliability decreases, and hardware and software complexity increases. To better understand the potential impacts of failures on next-generation systems, significant effort has been devoted to collecting, characterizing and analyzing failures on current systems. These studies require large volumes of data and complex analysis in an attempt to identify statistical properties of the failure data. In this paper, we examine the lifetime of failures on the Cielo supercomputer that was located at Los Alamos National Laboratory, looking specifically at the time between faults on this system. Through this analysis, we show that the time between uncorrectable faults for this system obeys Benford’s law, This law applies to a number of naturally occurring collections of numbers and states that the leading digit is more likely to be small, for example a leading digit of 1 is more likely than 9. We also show that a number of common distributions used to model failures also follow this law. This work provides critical analysis on the distribution of times between failures for extreme-scale systems. Specifically, the analysis in this work could be used as a simple form of failure prediction or used for modeling realistic failures.

We propose a new benchmark for high-performance (HP) computers. Similar to High Performance Conjugate Gradient (HPCG), the new benchmark is designed to rank computers based on how fast they can solve a sparse linear system of equations, exhibiting computational and communication requirements typical in many scientific applications. The main novelty of the new benchmark is that it is now based on Generalized Minimum Residual method (GMRES) (combined with Geometric Multi-Grid preconditioner and Gauss-Seidel smoother) and provides the flexibility to utilize lower precision arithmetic. This is motivated by new hardware architectures that deliver lower-precision arithmetic at higher performance. There are other machines that do not follow this trend. However, using a lower-precision arithmetic reduces the required amount of data transfer, which alone could improve solver performance. Considering these trends, an HP benchmark that allows the use of different precisions for solving important scientific problems will be valuable for many different disciplines, and we also hope to promote the design of future HP computers that can utilize mixed-precision arithmetic for achieving high application performance. We present our initial design of the new benchmark, its reference implementation, and the performance of the reference mixed (double and single) precision Geometric Multi-Grid solvers on current top-ranked architectures. We also discuss challenges of designing such a benchmark, along with our preliminary numerical results using 16-bit numerical values (half and bfloat precisions) for solving a sparse linear system of equations.

This paper presents a literature review on current practices and trends on cyberphysical security of grid-connected battery energy storage systems (BESSs). Energy storage is critical to the operation of Smart Grids powered by intermittent renewable energy resources. To achieve this goal, utility-scale and consumer-scale BESS will have to be fully integrated into power systems operations, providing ancillary services and performing functions to improve grid reliability, balance power and demand, among others. This vision of the future power grid will only become a reality if BESS are able to operate in a coordinated way with other grid entities, thus requiring significant communication capabilities. The pervasive networking infrastructure necessary to fully leverage the potential of storage increases the attack surface for cyberthreats, and the unique characteristics of battery systems pose challenges for cyberphysical security. This paper discusses a number of such threats, their associated attack vectors, detection methods, protective measures, research gaps in the literature and future research trends.

Hyperspectral Computed Tomography (HCT) Data is often visualized using dimension reduction algorithms. However, these methods often fail to adequately differentiate between materials with similar spectral signatures. Previous work showed that a combination of image preprocessing, clustering, and dimension reduction techniques can be used to colorize simulated HCT data and enhance the contrast between similar materials. In this work, we evaluate the efficacy of these existing methods on experimental HCT data and propose new improvements to the robustness of these methods. We introduce an automated channel selection method and compare the Feldkamp, Davis, and Kress filtered back-projection (FBP) algorithm with the maximum-likelihood estimation-maximization (MLEM) algorithm in terms of HCT reconstruction image quality and its effect on different colorization methods. Additionally, we propose adaptations to the colorization process that eliminate the need for a priori knowledge of the number distinct materials for material classification. Our results show that these methods generalize to materials in real-world experimental HCT data for both colorization and classification tasks; both tasks have applications in industry, medicine, and security, wherever rapid visualization and identification is needed.

This paper presents a type-IV wind turbine generator (WTG) model developed in MATLAB/Simulink. An aerodynamic model is used to improve an electromagnetic transient model. This model is further developed by incorporating a single-mass model of the turbine and including generator torque control from an aerodynamic model. The model is validated using field data collected from an actual WTG located in the Scaled Wind Farm Technology (SWiFT) facility. The model takes the nacelle wind speed as an estimate. To ensure the model and the SWiFT WTG field data is compared accurately, the wind speed is estimated using a Kalman filter. Simulation results shows that using a single-mass model instead of a two-mass model for aerodynamic torque, including the generator torque control from SWiFT, estimating wind speed via the Kalman filter and tunning the synchronous generator, accurately represent the generator torque, speed, and power, compared to the SWiFT WTG field data.

Abstract not provided.

This paper provides a study of the potential impacts of climate change on intermittent renewable energy resources, battery storage, and resource adequacy in Public Service Company of New Mexico's Integrated Resource Plan for 2020 - 2040. Climate change models and available data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed in NM in the coming decades. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources in NM. Results for the extreme climate-change scenario show that the projected wind power may decrease by ~13% due to projected decreases in wind speed. Projected solar power may decrease by ~4% due to decreases in irradiance and increases in temperature in NM. Uncertainty in these climateinduced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in roundtrip efficiency. The hourly energy balance was determined throughout the year given uncertainties in the renewable energy resources and energy storage. The loss of load expectation (LOLE) was evaluated for the 2040 No New Combustion portfolio and found to increase from 0 days/year to a median value of ~2 days/year due to potential reductions in renewable energy resources and battery storage performance and capacity. A rank-regression analyses revealed that battery round-trip efficiency was the most significant parameter that impacted LOLE, followed by solar resource, wind resource, and battery fade. An increase in battery storage capacity to ~30,000 MWh from a baseline value of ~14,000 MWh was required to reduce the median value of LOLE to ~0.2 days/year with consideration of potential climate impacts and battery degradation.

As transistors have been scaled over the past decade, modern systems have become increasingly susceptible to faults. Increased transistor densities and lower capacitances make a particle strike more likely to cause an upset. At the same time, complex computer systems are increasingly integrated into safety-critical systems such as autonomous vehicles. These two trends make the study of system reliability and fault tolerance essential for modern systems. To analyze and improve system reliability early in the design process, new tools are needed for RTL fault analysis.This paper proposes Eris, a novel framework to identify vulnerable components in hardware designs through fault-injection and fault propagation tracking. Eris builds on ESSENT - a fast C/C++ RTL simulation framework - to provide fault injection, fault tracking, and control-flow deviation detection capabilities for RTL designs. To demonstrate Eris' capabilities, we analyze the reliability of the open source Rocket Chip SoC by randomly injecting faults during thousands of runs on four microbenchmarks. As part of this analysis we measure the sensitivity of different hardware structures to faults based on the likelihood of a random fault causing silent data corruption, unrecoverable data errors, program crashes, and program hangs. We detect control flow deviations and determine whether or not they are benign. Additionally, using Eris' novel fault-tracking capabilities we are able to find 78% more vulnerable components in the same number of simulations compared to RTL-based fault injection techniques without these capabilities. We will release Eris as an open-source tool to aid future research into processor reliability and hardening.

Adaptive protection is defined as a real-time system that can modify the protective actions according to the changes in the system condition. An adaptive protection system (APS) is conventionally coordinated through a central management system located at the distribution system substation. An APS depends significantly on the communication infrastructure to monitor the latest status of the electric power grid and send appropriate settings to all of the protection relays existing in the grid. This makes an APS highly vulnerable to communication system failures (e.g., broken communication links due to natural disasters as well as wide-range cyber-attacks). To this end, this paper presents the addition of local adaptive modular protection (LAMP) units to the protection system to guarantee its reliable operation under extreme events when the operation of the APS is compromised. LAMP units operate in parallel with the conventional APS. As a backup, if APS fails to operate because of an issue in the communication system, LAMP units can accommodate a reliable fault detection and location on behalf of the protection relay. The performance of the proposed APS is verified using IEEE 123 node test system.

The use of containerization technology in high performance computing (HPC) workflows has substantially increased recently because it makes workflows much easier to develop and deploy. Although many HPC workflows include multiple data and multiple applications, they have traditionally all been bundled together into one monolithic container. This hinders the ability to trace the thread of execution, thus preventing scientists from establishing data provenance, or having workflow reproducibility. To provide a solution to this problem we extend the functionality of a popular HPC container runtime, Singularity. We implement both the ability to compose fine-grained containerized workflows and execute these workflows within the Singularity runtime with automatic metadata collection. Specifically, the new functionality collects a record trail of execution and creates data provenance. The use of our augmented Singularity is demonstrated with an earth science workflow, SOMOSPIE. The workflow is composed via our augmented Singularity which creates fine-grained containers and collects the metadata to trace, explain, and reproduce the prediction of soil moisture at a fine resolution.

Numerical simulations of Greenland and Antarctic ice sheets involve the solution of large-scale highly nonlinear systems of equations on complex shallow geometries. This work is concerned with the construction of Schwarz preconditioners for the solution of the associated tangent problems, which are challenging for solvers mainly because of the strong anisotropy of the meshes and wildly changing boundary conditions that can lead to poorly constrained problems on large portions of the domain. Here, two-level generalized Dryja-Smith-Widlund (GDSW)-type Schwarz preconditioners are applied to different land ice problems, i.e., a velocity problem, a temperature problem, as well as the coupling of the former two problems. We employ the message passing interface (MPI)- parallel implementation of multilevel Schwarz preconditioners provided by the package FROSch (fast and robust Schwarz) from the Trilinos library. The strength of the proposed preconditioner is that it yields out-of-the-box scalable and robust preconditioners for the single physics problems. To the best of our knowledge, this is the first time two-level Schwarz preconditioners have been applied to the ice sheet problem and a scalable preconditioner has been used for the coupled problem. The preconditioner for the coupled problem differs from previous monolithic GDSW preconditioners in the sense that decoupled extension operators are used to compute the values in the interior of the subdomains. Several approaches for improving the performance, such as reuse strategies and shared memory OpenMP parallelization, are explored as well. In our numerical study we target both uniform meshes of varying resolution for the Antarctic ice sheet as well as nonuniform meshes for the Greenland ice sheet. We present several weak and strong scaling studies confirming the robustness of the approach and the parallel scalability of the FROSch implementation. Among the highlights of the numerical results are a weak scaling study for up to 32 K processor cores (8 K MPI ranks and 4 OpenMP threads) and 566 M degrees of freedom for the velocity problem as well as a strong scaling study for up to 4 K processor cores (and MPI ranks) and 68 M degrees of freedom for the coupled problem.

An array of Wave Energy Converters (WEC) is required to supply a significant power level to the grid. However, the control and optimization of such an array is still an open research question. This paper analyzes two aspects that have a significant impact on the power production. First the spacing of the buoys in a WEC array will be analyzed to determine the optimal shift between the buoys in an array. Then the wave force interacting with the buoys will be angled to create additional sequencing between the electrical signals. A cost function is proposed to minimize the power variation and energy storage while maximizing the delivered energy to the onshore point of common coupling to the electrical grid.

This paper focuses on the development and testing of spoofing detection and localization techniques that rely only on clock deviations to identify threat signals. Detection methods that rely on dynamic receiver geometries to triangulate threat locations or signal geometry to identify spoofing are not considered here. Instead this paper focuses on single antenna receivers and assumes the receiver tracks only the inauthentic signal. The quality of the receiver clock has a significant impact on the performance of the receiver tracking loops. Low quality clocks have frequency instabilities that inherently limit the sensitivity of the receiver to slow growing errors. Some clocks provide better frequency stabilities but have a higher white frequency noise that can induce false detections. Because of these trends, various detection methods are tested with four types of receiver and transmitter clocks of varying quality.

To meet the challenges oflow-carbon power generation, distributed energy resources (DERs) such as solar and wind power generators are now widely integrated into the power grid. Because of the autonomous nature of DERs, ensuring properly regulated output voltages of the individual sources to the grid distribution system poses a technical challenge to grid operators. Stochastic, model-free voltage regulations methods such as deep reinforcement learning (DRL) have proven effective in the regulation of DER output voltages; however, deriving an optimal voltage control policy using DRL over a large state space has a large computational time complexity. In this paper we illustrate a computationally efficient method for deriving an optimal voltage control policy using a parallelized DRL ensemble. Additionally, we illustrate the resiliency of the control ensemble when random noise is introduced by a cyber adversary.

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous modes, which will produce new stresses on the grid-interfacing power electronics components, such as DC/AC inverters. In previous work, multiple DER devices were instrumented to evaluate additional component stress under multiple reactive power setpoints. We utilize quasi-static time-series simulations to determine voltage-reactive power mode (volt-var) mission profile of inverters in an active power system. Mission profiles and loss estimates are then combined to estimate the reduction of the useful life of inverters from different reactive power profiles. It was found that the average lifetime reduction was approximately 0.15% for an inverter between standard unity power factor operation and the IEEE 1547 default volt-var curve based on thermal damage due to switching in the power transistors. For an inverter with an expected 20-year lifetime, the 1547 volt-var curve would reduce the expected life of the device by 12 days. This framework for determining an inverter's useful life from experimental and modeling data can be applied to any failure mechanism and advanced inverter operation.

Calibrating a finite element model to test data is often required to accurately characterize a joint, predict its dynamic behavior, and determine fastener fatigue life. In this work, modal testing, model calibration, and fatigue analysis are performed for a bolted structure, and various joint modeling techniques are compared. The structure is designed to test a single bolt to fatigue failure by utilizing an electrodynamic modal shaker to axially force the bolted joint at resonance. Modal testing is done to obtain the dynamic properties, evaluate finite element joint modeling techniques, and assess the effectiveness of a vibration approach to fatigue testing of bolts. Results show that common joint models can be inaccurate in predicting bolt loads, and even when updated using modal test data, linear structural models alone may be insufficient in evaluating fastener fatigue.

In accident scenarios involving release of tritium during handling and storage, the level of risk to human health is dominated by the extent to which radioactive tritium is oxidized to the water form (T2O or THO). At some facilities, tritium inventories consist of very small quantities stored at sub-atmospheric pressure, which means that tritium release accident scenarios will likely produce concentrations in air that are well below the lower flammability limit. It is known that isotope effects on reaction rates should result in slower oxidation rates for heavier isotopes of hydrogen, but this effect has not previously been quantified for oxidation at concentrations well below the lower flammability limit for hydrogen. This work describes hydrogen isotope oxidation measurements in an atmospheric tube furnace reactor. These measurements consist of five concentration levels between 0.01% and 1% protium or deuterium and two residence times. Oxidation is observed to occur between about 550°C and 800°C, with higher levels of conversion achieved at lower temperatures for protium with respect to deuterium at the same volumetric inlet concentration and residence time. Computational fluid dynamics simulations of the experiments were used to customize reaction orders and Arrhenius parameters in a 1-step oxidation mechanism. The trends in the rates for protium and deuterium are extrapolated based on guidance from literature to produce kinetic rate parameters appropriate for tritium oxidation at low concentrations.

For the resiliency of both small and large distribution systems, the concept of microgrids is arising. The ability for sections of the distribution system to be 'self-sufficient' and operate under their own energy generation is a desirable concept. This would allow for only small sections of the system to be without power after being affected by abnormal events such as a fault or a natural disaster, and allow for a greater number of consumers to go through their lives as normal. Research is needed to determine how different forms of generation will perform in a microgrid, as well as how to properly protect an islanded system. While synchronous generators are well understood and generally accepted amongst utility operators, inverter-based resources (IBRs) are less common. An IBR's fault characteristic varies between manufacturers and is heavily based on the internal control scheme. Additionally, with the internal protections of these devices to not damage the switching components, IBRs are usually limited to only 1.1-2.5p.u. of the rated current, depending on the technology. This results in traditional protection methods such as overcurrent devices being unable to 'trip' in a microgrid with high IBR penetration. Moreover, grid-following inverters (commonly used for photovoltaic systems) require a voltage source to synchronize with before operating. Also, these inverters do not provide any inertia to a system. On the other hand, grid-forming inverters can operate as a primary voltage source, and provide an 'emulated inertia' to the system. This study will look at a small islanded system with a grid-forming inverter, and a grid-following inverter subjected to a line-to-ground fault.

Applications such as counterfeit identification, quality control, and non-destructive material identification benefit from improved spatial and compositional analysis. X-ray Computed Tomography is used in these applications but is limited by the X-ray focal spot size and the lack of energy-resolved data. Recently developed hyperspectral X-ray detectors estimate photon energy, which enables composition analysis but lacks spatial resolution. Moving beyond bulk homogeneous transmission anodes toward multi-metal patterned anodes enables improvements in spatial resolution and signal-to-noise ratios in these hyperspectral X-ray imaging systems. We aim to design and fabricate transmission anodes that facilitate confirmation of previous simulation results. These anodes are fabricated on diamond substrates with conventional photolithography and metal deposition processes. The final transmission anode design consists of a cluster of three disjoint metal bumps selected from molybdenum, silver, samarium, tungsten, and gold. These metals are chosen for their k-lines, which are positioned within distinct energy intervals of interest and are readily available in standard clean rooms. The diamond substrate is chosen for its high thermal conductivity and high transmittance of X-rays. The feature size of the metal bumps is chosen such that the cluster is smaller than the 100 m diameter of the impinging electron beam in the X-ray tube. This effectively shrinks the X-ray focal spot in the selected energy bands. Once fabricated, our transmission anode is packaged in a stainless-steel holder that can be retrofitted into our existing X-ray tube. Innovations in anode design enable an inexpensive and simple method to improve existing X-ray imaging systems.

This paper discusses the development and current status of a recommended practice by the members of IEEE Working Group P2688 on Energy Storage Management Systems (ESMS) in grid applications. The intent of this recommended practice is to provide a reference for ESMS designers and ESS integrators regarding the challenges in ESMS development and deployment, and to provide recommendations and best practices to address these challenges. This recommended practice will assist in the selection between design options by supplying the pros and cons for a range of technical solutions.

Expansion techniques are powerful tools that can take a limited measurement set and provide information on responses at unmeasured locations. Expansion techniques are used in dynamic environments specifications, full field stress measurements, model calibration, and other calculations that require response at locations not measured. However, the process of modal expansion techniques such as SEREP (System Equivalent Reduction Expansion Process) has error with the projection of the measurement set of degrees of freedom to the expanded degrees of freedom. Empirical evidence has been used in the past to qualitatively determine the error. In recent years, the modal projection error was developed to quantify the error through a projection between different domains. The modal projection error is used in this paper to demonstrate the use of the metric in quantifying the error of the expansion process and to quantify which modes of the expansion process are the most important.

Quantum computers can now run interesting programs, but each processor’s capability—the set of programs that it can run successfully—is limited by hardware errors. These errors can be complicated, making it difficult to accurately predict a processor’s capability. Benchmarks can be used to measure capability directly, but current benchmarks have limited flexibility and scale poorly to many-qubit processors. We show how to construct scalable, efficiently verifiable benchmarks based on any program by using a technique that we call circuit mirroring. With it, we construct two flexible, scalable volumetric benchmarks based on randomized and periodically ordered programs. We use these benchmarks to map out the capabilities of twelve publicly available processors, and to measure the impact of program structure on each one. We find that standard error metrics are poor predictors of whether a program will run successfully on today’s hardware, and that current processors vary widely in their sensitivity to program structure.

Abstract not provided.

Abstract not provided.

This paper describes how the performance of motion primitive-based planning algorithms can be improved using reinforcement learning. Specifically, we describe and evaluate a framework that autonomously improves the performance of a primitive-based motion planner. The improvement process consists of three phases: exploration, extraction, and reward updates. This process can be iterated continuously to provide successive improvement. The exploration step generates new trajectories, and the extraction step identifies new primitives from these trajectories. These primitives are then used to update rewards for continued exploration. This framework required novel shaping rewards, development of a primitive extraction algorithm, and modification of the Hybrid A* algorithm. The framework is tested on a navigation task using a nonlinear F-16 model. The framework autonomously added 91 motion primitives to the primitive library and reduced average path cost by 21.6 s, or 35.75% of the original cost. The learned primitives are applied to an obstacle field navigation task, which was not used in training, and reduced path cost by 16.3 s, or 24.1%. Additionally, two heuristics for the modified Hybrid A* algorithm are designed to improve effective branching factor.

Abstract not provided.

Abstract not provided.

We develop methods that could be used to qualify a training dataset and a data-driven turbulence closure trained on it. By qualify, we mean identify the kind of turbulent physics that could be simulated by the data-driven closure. We limit ourselves to closures for the Reynolds-Averaged Navier Stokes (RANS) equations. We build on our previous work on assembling feature-spaces, clustering and characterizing Direct Numerical Simulation datasets that are typically pooled to constitute training datasets. In this paper, we develop an alternative way to assemble feature-spaces and thus check the correctness and completeness of our previous method. We then use the characterization of our training dataset to identify if a data-driven turbulence closure learned on it would generalize to an unseen flow configuration – an impinging jet in our case. Finally, we train a RANS closure architected as a neural network, and develop an explanation i.e., an interpretable approximation, using generalized linear mixed-effects models and check whether the explanation resembles a contemporary closure from turbulence modeling.

A high-throughput experimental setup was used to characterize initiation threshold and growth to detonation in the explosives hexanitrostilbene (HNS) and pentaerythritol tetranitrate (PETN). The experiment sequentially launched an array of laser-driven flyers to shock samples arranged in a 96-well microplate geometry, with photonic Doppler velocimetry diagnostics to characterize flyer velocity and particle velocity at the explosive-substrate interface. Vapor-deposited films of HNS and PETN were used to provide numerous samples with various thicknesses, enabling characterization of the evolution of growth to detonation. One-dimensional hydrocode simulations were performed with reactions disabled to illustrate where the experimental data deviate from the predicted inert response. Prompt initiation was observed in 144 μm thick HNS films at flyer velocities near 3000 m/s and in 125 μm thick PETN films at flyer velocities near 2400 m/s. This experimental setup enables rapid quantification of the growth of reactions in explosive materials that can reach detonation at sub-millimeter length scales. These data can subsequently be used for parameterizing reactive burn models in hydrocode simulations, as discussed in Paper II [D. E. Kittell, R. Knepper, and A. S. Tappan, J. Appl. Phys. 131, 154902 (2022)].

Software is ubiquitous in society, but understanding it, especially without access to source code, is both non-trivial and critical to security. A specialized group of cyber defenders conducts reverse engineering (RE) to analyze software. The expertise-driven process of software RE is not well understood, especially from the perspective of workflows and automated tools. We conducted a task analysis to explore the cognitive processes that analysts follow when using static techniques on binary code. Experienced analysts were asked to statically find a vulnerability in a small binary that could allow for unverified access to root privileges. Results show a highly iterative process with commonly used cognitive states across participants of varying expertise, but little standardization in process order and structure. A goal-centered analysis offers a different perspective about dominant RE states. We discuss implications about the nature of RE expertise and opportunities for new automation to assist analysts using static techniques.

Quantum diamond microscope (QDM) magnetic field imaging is an emerging interrogation and diagnostic technique for integrated circuits (ICs). To date, the ICs measured with a QDM have been either too complex for us to predict the expected magnetic fields and benchmark the QDM performance or too simple to be relevant to the IC community. In this paper, we establish a 555 timer IC as a "model system"to optimize QDM measurement implementation, benchmark performance, and assess IC device functionality. To validate the magnetic field images taken with a QDM, we use a spice electronic circuit simulator and finite-element analysis (FEA) to model the magnetic fields from the 555 die for two functional states. We compare the advantages and the results of three IC-diamond measurement methods, confirm that the measured and simulated magnetic images are consistent, identify the magnetic signatures of current paths within the device, and discuss using this model system to advance QDM magnetic imaging as an IC diagnostic tool.

Time-resolved, absolute number densities of metastable N2(A3ς u +, v = 0, 1) molecules, ground state N2 and H atoms, and rotational-translational temperature have been measured by tunable diode laser absorption spectroscopy and two-photon absorption laser-induced fluorescence in diffuse N2 and N2-H2 plasmas during and after a nanosecond pulse discharge burst. Comparison of the measurement results with the kinetic modeling predictions, specifically the significant reduction of the N2(A3ς u +) populations and the rate of N atom generation during the burst, suggests that these two trends are related. The slow N atom decay in the afterglow, on a time scale longer than the discharge burst, demonstrates that the latter trend is not affected by N atom recombination, diffusion to the walls, or convection with the flow. This leads to the conclusion that the energy pooling in collisions of N2(A3ς u +) molecules is a major channel of N2 dissociation in electric discharges where a significant fraction of the input energy goes to electronic excitation of N2. Additional measurements in a 1% H2-N2 mixture demonstrate a further significant reduction of N2(A3ς u +, v = 0, 1) populations, due to the rapid quenching by H atoms accumulating in the plasma. Comparison with the modeling predictions suggests that the N2(A3ς u +) molecules may be initially formed in the highly vibrationally excited states. The reduction of the N2(A3ς u +) number density also diminishes the contribution of the energy pooling process into N2 dissociation, thus reducing the N atom number density. The rate of N atom generation during the burst also decreases, due to its strong coupling to N2(A3ς u +, v) populations. On the other hand, the rate of H atom generation, produced predominantly by the dissociative quenching of the excited electronic states of N2 by H2, remains about the same during the burst, resulting in a nearly linear rise in the H atom number density. Comparison of the kinetic model predictions with the experimental results suggests that the yield of H atoms during the quenching of the excited electronic state of N2 by molecular H2 is significantly less than 100%. The present results quantify the yield of N and H atoms in high-pressure H2-N2 plasmas, which have significant potential for ammonia generation using plasma-assisted catalysis.

We demonstrate an optical waveguide device capable of supporting the optical power necessary for trapping a single atom or a cold-atom ensemble with evanescent fields. Our photonic integrated platform successfully manages optical powers of ~30mW.

The emerging use of the physics-based athermal recombination-corrected displacement per atom (arc-dpa) model for the displacement damage efficiency has motivated a re-evaluation of the historical empirically-derived GaAs damage response function with the purpose of highlighting needs for future analytical and experimental work. The 1-MeV neutron damage equivalence methodology used in the ASTM E-722 standard for GaAs has been re-evaluated using updated nuclear data. This yielded a higher fidelity representation of the GaAs displacement kerma and, through the use of the refined PKA recoil energy-dependent damage efficiency model, an updated 1-MeV(GaAs) displacement damage function. This re-evaluation included use of the Norgett-Robinson-Torrens (NRT) model for an updated threshold treatment, rather than the sharp-threshold Kinchin-Pease model used in the current ASTM standard. The underlying nuclear data evaluations have been updated to use the ENDF/VIII.0 ⁷⁵As and TENDL-2019 ⁷¹Ga/⁶⁹Ga evaluations. The displacement kerma and 1-MeV-equivalent damage responses were calculated using a modified NJOY-2016 code which allowed for refinements in some of the damage models. This paper shows that an updated displacement damage function, based upon the latest nuclear data, is consistent with the experimental data used to develop the current ASTM E-722 GaAs standard. Using a double ratio approach to compare the available experimental data with the calculated response, the average legacy double ratio was found to be 0.97±0.05 and the average updated double ratio was found to be 0.94 ±0.05.

For the model-based control of low-voltage microgrids, state and parameter information are required. Different optimal estimation techniques can be employed for this purpose. However, these estimation techniques require knowledge of noise covariances (process and measurement noise). Incorrect values of noise covariances can deteriorate the estimator performance, which in turn can reduce the overall controller performance. This paper presents a method to identify noise covariances for voltage dynamics estimation in a microgrid. The method is based on the autocovariance least squares technique. A simulation study of a simplified 100 kVA, 208 V microgrid system in MATLAB/Simulink validates the method. Results show that estimation accuracy is close to the actual value for Gaussian noise, and non-Gaussian noise has a slightly larger error.

This paper demonstrates that a faster Automatic Generation Control (AGC) response provided by Inverter-Based Resources (IBRs) can improve a performance-based regulation (PBR) metric. The improvement in performance has a direct effect on operational income. The PBR metric used in this work was obtained from a California ISO (CAISO) example and is fully described herein. A single generator in a modified three area IEEE 39 bus system was replaced with a group of co-located IBRs to present possible responses using different plant controls and variable resource conditions. We show how a group of IBRs that rely on variable resources may negatively affect the described PBR metric of all connected areas if adequate plant control is not employed. However, increasing the dispatch rate of internal plant controls may positively affect the PBR metric of all connected areas despite variable resource conditions.

The Multi-Fidelity Toolkit (MFTK) is a simulation tool being developed at Sandia National Laboratories for aerodynamic predictions of compressible flows over a range of physics fidelities and computational speeds. These models include the Reynolds-Averaged Navier–Stokes (RANS) equations, the Euler equations, and modified Newtonian aerodynamics (MNA) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal code-and solution-verification activities. Code verification is performed on the MNA model by comparing with an analytical solution for flat-plate and inclined-plate geometries. Solution-verification activities include grid-refinement studies of HIFiRE-1 wind tunnel measurements, which are used for validation, for all model fidelities.

Kokkos is a C++ library and ecosystem for writing parallel programs on heterogeneous systems. One of the primary goals of Kokkos is portability: programs in Kokkos are expressed through general parallel constructs which can enable the same code to compile and execute on different parallel architectures. However, there is no known formal model of Kokkos's semantics, which must be generic enough to support current and future CPU and accelerator architectures. As a first step of formalizing Kokkos, We introduce MiniKokkos: a small language capturing the main features of Kokkos, and then prove that MiniKokkos ensures portability across all possible parallel executions. We also provide a case study of how MiniKokkos can help reason about Kokkos programs and help find a bug in the Kokkos implementation.

Abstract not provided.

Advances in wind-plant control have often focused on more effectively balancing power between neighboring turbines. Wake steering is one such method that provides control-based improvements in a quasi-static way, but this does little to fundamentally change the wake recovery process, and thus, it has limited potential. This study investigates use of another control paradigm known as dynamic wake control (DWC) to excite the mutual inductance instability between adjacent tip-vortex structures, thereby accelerating the breakdown of the structures. The current work carries this approach beyond the hypothetical by applying the excitation via turbine control vectors that already exist on all modern wind turbines: blade pitch and rotor speed control. The investigation leverages a free-vortex wake method (FVWM) that allows a thorough exploration of relevant frequencies and amplitudes of harmonic forcing for each control vector (as well as the phase difference between the vectors for a tandem configuration) while still capturing the essential tip-vortex dynamics. The FVWM output feeds into a Fourier stability analysis working to pinpoint candidate DWC strategies suggesting fastest wake recovery. Near-wake length reductions of >80% are demonstrated, although without considering inflow turbulence. Analysis is provided to interpret these predictions considering the presence of turbulence in a real atmospheric inflow.

Measurements of gas-phase pressure and temperature in hypersonic flows are important to understanding fluid–structure interactions on vehicle surfaces, and to develop compressible flow turbulence models. To achieve this measurement capability, femtosecond coherent anti-Stokes Raman scattering (fs CARS) is applied at Sandia National Laboratories’ hypersonic wind tunnel. After excitation of rotational Raman transitions by a broadband femtosecond laser pulse, two probe pulses are used: one at an early time where the collisional environment has largely not affected the Raman coherence, and another at a later time after the collisional environment has led to significant J-dependent dephasing of the Raman coherence. CARS spectra from the early probe are fit for temperature, while the later CARS spectra are fit for pressure. Challenges related to implementing fs CARS in cold-flow hypersonic facilities are discussed. Excessive fs pump energy can lead to flow perturbations. The output of a second-harmonic bandwidth compressor (SHBC) is spectrally filtered using a volume Bragg grating to provide the narrowband ps probe pulses and enable single-shot CARS measurements at 1 kHz. Measurements are demonstrated at temperatures and pressures relevant to cold-flow hypersonic wind tunnels in a low-pressure cryostat with an initial demonstration in the hypersonic wind tunnel.

Development of job scheduling algorithms, which directly influence High-Performance Computing (HPC) clusters performance, is hindered because popular scheduling quality metrics, such as Bounded Slowdown, poorly correlate with global scheduling objectives that include job packing efficiency and fairness. This report proposes Area Weighted Response Time, a metric that offers an unbiased representation of job packing efficiency, and presents a class of new metrics, Priority Weighted Specific Response Time, that assess both packing efficiency and fairness of schedules. The provided examples of simulation of scheduling of real workload traces and analysis of the resulting schedules with the help of these metrics and conventional metrics, demonstrate that although Bounded Slowdown can be readily improved by modifying the standard First Come First Served backfilling algorithm and by using existing techniques of estimating job runtime, these improvements are accompanied by significant degradation of job packing efficiency and fairness. In contrast, improving job packing efficiency and fairness over the standard backfilling algorithm, which is designed to target those objectives, is difficult. It requires further algorithm development and more accurate runtime estimation techniques that reduce frequency of underpredictions.

Applications such as counterfeit identification, quality control, and non-destructive material identification benefit from improved spatial and compositional analysis. X-ray Computed Tomography is used in these applications but is limited by the X-ray focal spot size and the lack of energy-resolved data. Recently developed hyperspectral X-ray detectors estimate photon energy, which enables composition analysis but lacks spatial resolution. Moving beyond bulk homogeneous transmission anodes toward multi-metal patterned anodes enables improvements in spatial resolution and signal-to-noise ratios in these hyperspectral X-ray imaging systems. We aim to design and fabricate transmission anodes that facilitate confirmation of previous simulation results. These anodes are fabricated on diamond substrates with conventional photolithography and metal deposition processes. The final transmission anode design consists of a cluster of three disjoint metal bumps selected from molybdenum, silver, samarium, tungsten, and gold. These metals are chosen for their k-lines, which are positioned within distinct energy intervals of interest and are readily available in standard clean rooms. The diamond substrate is chosen for its high thermal conductivity and high transmittance of X-rays. The feature size of the metal bumps is chosen such that the cluster is smaller than the 100 m diameter of the impinging electron beam in the X-ray tube. This effectively shrinks the X-ray focal spot in the selected energy bands. Once fabricated, our transmission anode is packaged in a stainless-steel holder that can be retrofitted into our existing X-ray tube. Innovations in anode design enable an inexpensive and simple method to improve existing X-ray imaging systems.

We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and boundary conditions, but for which only scattered observations of the source term are available. Scattered observations of the solution may also be used in the regression. The framework combines co-kriging with the linear transformation of a Gaussian process together with the use of kernels given by spectral expansions in eigenfunctions of the boundary value problem. Thus, it benefits from a reduced-rank property of covariance matrices. We demonstrate that the resulting framework yields more accurate and stable solution inference as compared to physics-informed Gaussian process regression without boundary condition constraints.

Traditional deep learning (DL) models are powerful classifiers, but many approaches do not provide uncertainties for their estimates. Uncertainty quantification (UQ) methods for DL models have received increased attention in the literature due to their usefulness in decision making, particularly for high-consequence decisions. However, there has been little research done on how to evaluate the quality of such methods. We use statistical methods of frequentist interval coverage and interval width to evaluate the quality of credible intervals, and expected calibration error to evaluate classification predicted confidence. These metrics are evaluated on Bayesian neural networks (BNN) fit using Markov Chain Monte Carlo (MCMC) and variational inference (VI), bootstrapped neural networks (NN), Deep Ensembles (DE), and Monte Carlo (MC) dropout. We apply these different UQ for DL methods to a hyperspectral image target detection problem and show the inconsistency of the different methods' results and the necessity of a UQ quality metric. To reconcile these differences and choose a UQ method that appropriately quantifies the uncertainty, we create a simulated data set with fully parameterized probability distribution for a two-class classification problem. The gold standard MCMC performs the best overall, and the bootstrapped NN is a close second, requiring the same computational expense as DE. Through this comparison, we demonstrate that, for a given data set, different models can produce uncertainty estimates of markedly different quality. This in turn points to a great need for principled assessment methods of UQ quality in DL applications.

Software sustainability is critical for Computational Science and Engineering (CSE) software. Measuring sustainability is challenging because sustainability consists of many attributes. One factor that impacts software sustainability is the complexity of the source code. This paper introduces an approach for utilizing complexity data, with a focus on hotspots of and changes in complexity, to assist developers in performing code reviews and inform project teams about longer-term changes in sustainability and maintainability from the perspective of cyclomatic complexity. We present an analysis of data associated with four real-world pull requests to demonstrate how the metrics may help guide and inform the code review process and how the data can be used to measure changes in complexity over time.

Multi-model Monte Carlo methods have been illustrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation amongst the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC.

A crucial component of field testing is the utilization of numerical models to better understand the system and the experimental data being collected. Meshing and modeling field tests is a complex and computationally demanding problem. Hexahedral elements cannot always reproduce experimental dimensions leading to grid orientation or geometric errors. Voronoi meshes can match complex geometries without sacrificing orthogonality. As a result, here we present a high-resolution 3D numerical study for the BATS heater test at the WIPP that compares both a standard non-deformed cartesian mesh along with a Voronoi mesh to match field data collected during a salt heater experiment.

This chapter deals with experimental dynamic substructures which are reduced order models that can be coupled with each other or with finite element derived substructures to estimate the system response of the coupled substructures. A unifying theoretical framework in the physical, modal or frequency domain is reviewed with examples. The major issues that have hindered experimental based substructures are addressed. An example is demonstrated with the transmission simulator method that overcomes the major historical difficulties. Guidelines for the transmission simulator design are presented.

Energy storage is an extremely flexible grid asset than can provide a wide range of services. Unfortunately, energy storage is often relatively expensive compared to other options. With the emphasis on decarbonization, energy storage is required to buffer the intermittency associated with variable renewable generation. This paper calculates the maximum potential revenue from an energy storage system engaged in day-ahead market arbitrage in the California Independent System Operator (CAISO) region and uses these results to estimate the distribution of break-even capital costs. Break-even cost data is extremely useful as it provides insight into expected market penetration given a target capital cost. This information is also valuable for setting policy related to energy storage incentives as well as for setting price targets for research and development initiatives. The potential annual revenue of a generic battery energy storage system (BESS) participating in the CAISO day-ahead energy market was analyzed for 2,145 nodes over a seven year period (2014-2020). This data was used to estimate the break-even capital cost for each node as well as the cost requirements for several internal rate of return scenarios. Based on the analysis, the capital costs of lithium-ion systems must be reduced by approximately 80% from current levels to enable arbitrage applications to have a reasonable rate of return.

Abstract not provided.

Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work leverages the underlying physics of emerging devices to develop probabilistic neural circuits for RNGs from a given distribution. However, codesign for novel circuits and systems that leverage inherent device stochasticity is a hard problem. This is mostly due to the large design space and complexity of doing so. It requires concurrent input from multiple areas in the design stack from algorithms, architectures, circuits, to devices. In this paper, we present examples of optimal circuits developed leveraging AI-enhanced codesign techniques using constraints from emerging devices and algorithms. Our AI-enhanced codesign approach accelerated design and enabled interactions between experts from different areas of the micro-electronics design stack including theory, algorithms, circuits, and devices. We demonstrate optimal probabilistic neural circuits using magnetic tunnel junction and tunnel diode devices that generate an RNG from a given distribution.

We present a new strategy for automatically exploring the design space of key CUDA + MPI programs and providing design rules that discriminate slow from fast implementations. In such programs, the order of operations (e.g., G PU kernels, MPI communication) and assignment of operations to resources (e.g., G PU streams) makes the space of possible designs enormous. Systems experts have the task of redesigning and reoptimizing these programs to effectively utilize each new platform. This work provides a prototype tool to reduce that burden. In our approach, a directed acyclic graph of CUDA and MPI operations defines the design space for the program. Monte-Carlo tree search discovers regions of the design space that have large impact on the program's performance. A sequence-to-vector transformation defines features for each explored im-plementation, and each implementation is assigned a class label according to its relative performance. A decision tree is trained on the features and labels to produce design rules for each class; these rules can be used by systems experts to guide their implementations. We demonstrate our strategy using a key kernel from scientific computing - sparse-matrix vector multiplication - on a platform with multiple MPI ranks and GPU streams.

Abstract not provided.

Iterative methods for solving linear systems serve as a basic building block for computational science. The computational cost of these methods can be significantly influenced by the round-off errors that accumulate as a result of their implementation in finite precision. In the extreme case, round-off errors that occur in practice can completely prevent an implementation from satisfying the accuracy and convergence behavior prescribed by its underlying algorithm. In the exascale era where cost is paramount, a thorough and rigorous analysis of the delay of convergence due to round-off should not be ignored. In this paper, we use a small model problem and the Jacobi iterative method to demonstrate how the Coq proof assistant can be used to formally specify the floating-point behavior of iterative methods, and to rigorously prove the accuracy of these methods.

Calibrating a finite element model to test data is often required to accurately characterize a joint, predict its dynamic behavior, and determine fastener fatigue life. In this work, modal testing, model calibration, and fatigue analysis are performed for a bolted structure, and various joint modeling techniques are compared. The structure is designed to test a single bolt to fatigue failure by utilizing an electrodynamic modal shaker to axially force the bolted joint at resonance. Modal testing is done to obtain the dynamic properties, evaluate finite element joint modeling techniques, and assess the effectiveness of a vibration approach to fatigue testing of bolts. Results show that common joint models can be inaccurate in predicting bolt loads, and even when updated using modal test data, linear structural models alone may be insufficient in evaluating fastener fatigue.

Detailed analysis of both the line-intensity ratios and line shapes of the K-lines of elements of different abundances (Fe, Cr, Ni, and Mn) emitted from the stagnation of a steel wire-array implosion on Z, were used to determine the line opacities. While the opacities at the early time of stagnation appear to be consistent with a nearly uniform hot-plasma cylinder on-axis surrounded by a colder annulus, the opacities during the peak K-emission strongly suggest that the main K-emission is due to small hot regions (spots) spread over the stagnating column. The spots are shown to be at least 4× denser than expected based on a uniform-cylinder emission (namely, ni > 3 ×1020 cm-3 ), are of diameters of about 200 μ or less (where the smaller the spots the higher are the densities), and are thousands in number. The total mass of the spots was determined to be 3-10 % of the load mass, and their total volume 3-15 % of the O 1.2-mm stagnation-column volume, both are less than the respective values for the earlier period of lower K power.

This work evaluates the benefits of using a 'smart' network interface card (SmartNIC) as a compute accelerator for the example of the MiniMD molecular dynamics proxy application. The accelerator is NVIDIA's BlueField-2 card, which includes an 8-core Arm processor along with a small amount of DRAM and storage. We test the networking and data movement performance of these cards compared to a standard Intel server host using microbenchmarks and MiniMD. In MiniMD, we identify two distinct classes of computation, namely core computation and maintenance computation, which are executed in sequence. We restructure the algorithm and code to weaken this dependence and increase task parallelism, thereby making it possible to increase utilization of the BlueField-2 concurrently with the host. We evaluate our implementation on a cluster consisting of 16 dual-socket Intel Broadwell host nodes with one BlueField-2 per host-node. Our results show that while the overall compute performance of BlueField-2 is limited, using them with a modified MiniMD algorithm allows for up to 20% speedup over the host CPU baseline with no loss in simulation accuracy.

High-performing teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams implicitly understand the different roles of heterogeneous team members and adapt their communication protocols accordingly. Multi-Agent Reinforcement Learning (MARL) seeks to develop computational methods for synthesizing such coordination strategies, but formulating models for heterogeneous teams with different state, action, and observation spaces has remained an open problem. Without properly modeling agent heterogeneity, as in prior MARL work that leverages homogeneous graph networks, communication becomes less helpful and can even deteriorate the cooperativity and team performance. We propose Heterogeneous Policy Networks (HetNet) to learn efficient and diverse communication models for coordinating cooperative heterogeneous teams. Building on heterogeneous graph-attention networks, we show that HetNet not only facilitates learning heterogeneous collaborative policies per existing agent-class but also enables end-to-end training for learning highly efficient binarized messaging. Our empirical evaluation shows that HetNet sets a new state of the art in learning coordination and communication strategies for heterogeneous multi-agent teams by achieving an 8.1% to 434.7% performance improvement over the next-best baseline across multiple domains while simultaneously achieving a 200× reduction in the required communication bandwidth.

The development of the High-Resolution Wavelet Transform (HRWT) is driven by the need of increasing the high-frequency resolution of widely used discrete Wavelet Transforms (WTs). Based on the Stationary Wavelet Transform (SWT), which is a modification of the Discrete Wavelet Transform (DWT), a novel WT that increases the number of decomposition levels (therefore increasing the previously mentioned frequency resolution) is proposed. In order to show the validity of the HRWT, this paper encompasses a theoretical comparison with other discrete WT methods. First, a summary of the DWT and the SWT, along with a brief explanation of the WT theory, is provided. Then, the concept of the HRWT is presented, followed by a discussion of the adherence of this new method to the WT's common properties. Finally, an example of the application is performed on a transient waveform analysis from a power system fault event, outlining the benefits that can be obtained from its usage compared to the SWT.

With the increase in penetration of inverter-based resources (IBRs) in the electrical power system, the ability of these devices to provide grid support to the system has become a necessity. With standards previously developed for the interconnection requirements of grid-following inverters (GFLI) (most commonly photovoltaic inverters), it has been well documented how these inverters 'should' respond to changes in voltage and frequency. However, with other IBRs such as grid-forming inverters (GFMIs) (used for energy storage systems, standalone systems, and as uninterruptable power supplies) these requirements are either: not yet documented, or require a more in deep analysis. With the increased interest in microgrids, GFMIs that can be paralleled onto a distribution system have become desired. With the proper control schemes, a GFMI can help maintain grid stability through fast response compared to rotating machines. This paper will present an experimental comparison of commercially available GFMIand GFLI ' responses to voltage and frequency deviation, as well as the GFMIoperating as a standalone system and subjected to various changes in loads.

Metal additive manufacturing allows for the fabrication of parts at the point of use as well as the manufacture of parts with complex geometries that would be difficult to manufacture via conventional methods (milling, casting, etc.). Additively manufactured parts are likely to contain internal defects due to the melt pool, powder material, and laser velocity conditions when printing. Two different types of defects were present in the CT scans of printed AlSi10Mg dogbones: spherical porosity and irregular porosity. Identification of these pores via a machine learning approach (i.e., support vector machines, convolutional neural networks, k-nearest neighbors’ classifiers) could be helpful with part qualification and inspections. The machine learning approach will aim to label the regions of porosity and label the type of porosity present. The results showed that a combination approach of Canny edge detection and a classification-based machine learning model (k-nearest neighbors or support vector machine) outperformed the convolutional neural network in segmenting and labeling different types of porosity.

In the summer of 2020, the National Aeronautics and Space Administration (NASA) launched a spacecraft as part of the Mars 2020 mission. The rover on the spacecraft uses a Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) to provide continuous electrical and thermal power for the mission. The MMRTG uses radioactive plutonium dioxide. NASA prepared a Supplemental Environmental Impact Statement (SEIS) for the mission in accordance with the National Environmental Policy Act. The SEIS provides information related to updates to the potential environmental impacts associated with the Mars 2020 mission as outlined in the Final Environmental Impact Statement (FEIS) for the Mars 2020 Mission issued in 2014 and associated Record of Decision (ROD) issued in January 2015. The Nuclear Risk Assessment (NRA) 2019 Update includes new and updated Mars 2020 mission information since the publication of the 2014 FEIS and the updates to the Launch Approval Process with the issuance of Presidential Memorandum on Launch of Spacecraft Containing Space Nuclear Systems, National Security Presidential Memorandum 20 (NSPM-20). The NRA 2019 Update addresses the responses of the MMRTG to potential accident and abort conditions during the launch opportunity for the Mars 2020 mission and the associated consequences. This information provides the technical basis for the radiological risks discussed in the SEIS. This paper provides a summary of the methods and results used in the NRA 2019 Update.

A survey of cadmium plated field return hardware showed ubiquitous cadmium whisker growth. The most worn and debris-covered hardware showed the densest whisker growth. Whiskers were often found growing in agglomerates of nodules and whiskers. The hardware was rinsed with alcohol to transfer whiskers and debris from the hardware to a flat stub. Fifty whiskers were studied individually by scanning electron microscopy (SEM), including energy dispersive spectroscopy (EDS) and electron backscatter diffraction (EBSD). Most of the whiskers were single crystal, though three were found to contain grain boundaries at kinks. The whiskers ranged from 5 to 600 μm in length and 80 pct showed a <1 ¯ 2 1 ¯ 0> type growth direction. This growth direction facilitates the development of low energy side faces of the whisker, (0001) and {1010}.

A high-speed, two-color pyrometer was developed and employed to characterize the temperature of the ejecta from pyrotechnic igniters. The pyrometer used a single objective lens, beamsplitter, and two high-speed cameras to maximize the spatial and temporal resolutions. The pyrometer used the integrated intensity of under-resolved particles to maintain a large region of interest to capture more particles. The spectral response of the pyrometer was determined based on the response of each optical component and the total system was calibrated using a black body source to ensure accurate intensity ratios over the range of interest.

Periodic arrays of nanoholes perforated in metallic thin films interact strongly with light and produce large electromagnetic near-field enhancements in their vicinity. As a result, the optical response of these systems is very sensitive to changes in their dielectric environment, thus making them an exceptional platform for the development of compact optical sensors. Given that these systems already operate at the shot-noise limit when used as optical sensors, their sensing capabilities can be enhanced beyond this limit by probing them with quantum light, such as squeezed or entangled states. Motivated by this goal, here, we present a comparative theoretical analysis of the quantum enhanced sensing capabilities of metallic nanohole arrays with one and two holes per unit cell. Through a detailed investigation of their optical response, we find that the two-hole array supports resonances that are narrower and stronger than its one-hole counterpart, and therefore have a higher fundamental sensitivity limit as defined by the quantum Cramér-Rao bound. We validate the optical response of the analyzed arrays with experimental measurements of the reflectance of representative samples. The results of this work advance our understanding of the optical response of these systems and pave the way for developing sensing platforms capable of taking full advantage of the resources offered by quantum states of light.

Despite their susceptibility to hydrogen-assisted fracture, ferritic steels make up a large portion of the hydrogen infrastructure. It is impractical and too costly to build large scale components such as pipelines and pressure vessels out of more hydrogen-resistant materials such as austenitic stainless steels. Therefore, it is necessary to understand the fracture behavior of ferritic steels in high-pressure hydrogen environments to manage design margins and reduce costs. Quenched and tempered (Q&T) martensite is the predominant microstructure of high-pressure hydrogen pressure vessels, and higher strength grades of this steel type are more susceptible to hydrogen degradation than lower strength grades. In this study, a single heat of 4340 alloy was heat treated to develop alternative microstructures for evaluation of fracture resistance in hydrogen gas. Fracture tests of several microstructures, such as lower bainite and upper bainite with similar strength to the baseline Q&T martensite, were tested at 21 and 105 MPa H2. Despite a higher MnS inclusion content in the tested 4340 alloy which reduced the fracture toughness in air, the fracture behavior in hydrogen gas fit a similar trend to other previously tested Q&T martensitic steels. The lower bainite microstructure performed similar to the Q&T martensite, whereas the upper bainite microstructure performed slightly worse. In this paper, we extend the range of high-strength microstructures evaluated for hydrogen-assisted fracture beyond conventional Q&T martensitic steels.

Abstract not provided.

A new small-scale pressure vessel with a 5×5 fuel assembly and axially truncated PWR hardware was created to simulate commercial vacuum drying processes. This test assembly, known as the Dashpot Drying Apparatus, was built to focus on the drying of a single PWR dashpot and surrounding fuel. Drying operations were simulated for three tests with the DDA based on the pressure and temperature histories observed in the HBDP. All three tests were conducted with an empty guide tube. One test was performed with deionized water as the fill fluid. The other two tests used 0.2 M boric acid as the fill fluid to accurately simulate spent fuel pool conditions. These tests proved the capability of the DDA to mimic commercial drying processes on a limited scale and detect the presence of bulk and residual water. Furthermore, for all tests, pressure remained below the 0.4 kPa (3 Torr) rebound threshold for the final evacuation step in the drying procedure. Results indicate that after bulk fluid is removed from the pressure vessel, residual water is verifiably measured through confirmatory measurements of pressure and water content using a mass spectrometer. The final pressure rebound behaviors for the three tests conducted were well below the established regulatory limit of less than 0.4 kPa (3 Torr) within 30 minutes of isolation. The water content measurements across all tests showed that despite observing high water content within the DDA vessel at the beginning of the vacuum isolations, the water content drastically drops to below 1,200 ppmv after the isolations were conducted. The data and operational experience from these tests will guide the next evolution of experiments on a prototypic-length scale with multiple surrogate rods in a full 17×17 PWR assembly. The insight gained through these investigations is expected to support the technical basis for the continued safe storage of spent nuclear fuel into long term operations.

Firmware emulation is useful for finding vulnerabil-ities, performing debugging, and testing functionalities. However, the process of enabling firmware to execute in an emulator (i.e., re-hosting) is difficult. Each piece of the firmware may depend on hardware peripherals outside the microcontroller that are inaccessible during emulation. Current practices involve painstakingly disentangling these dependencies or replacing them with developed models that emulate functions interacting with hardware. Unfortunately, both are highly manual and error-prone. In this paper, we introduce a systematic graph-based approach to analyze firmware binaries and determine which functions need to be replaced. Our approach is customizable to balance the fidelity of the emulation and the amount of effort it would take to achieve the emulation by modeling functions. We run our algorithm across a number of firmware binaries and show its ability to capture and remove a large majority of hardware dependencies.

Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work leverages the underlying physics of emerging devices to develop probabilistic neural circuits for RNGs from a given distribution. However, codesign for novel circuits and systems that leverage inherent device stochasticity is a hard problem. This is mostly due to the large design space and complexity of doing so. It requires concurrent input from multiple areas in the design stack from algorithms, architectures, circuits, to devices. In this paper, we present examples of optimal circuits developed leveraging AI-enhanced codesign techniques using constraints from emerging devices and algorithms. Our AI-enhanced codesign approach accelerated design and enabled interactions between experts from different areas of the micro-electronics design stack including theory, algorithms, circuits, and devices. We demonstrate optimal probabilistic neural circuits using magnetic tunnel junction and tunnel diode devices that generate an RNG from a given distribution.

This paper describes an efficient reverse-mode differentiation algorithm for contraction operations for arbitrary and unconventional tensor network topologies. The approach leverages the tensor contraction tree of Evenbly and Pfeifer (2014), which provides an instruction set for the contraction sequence of a network. We show that this tree can be efficiently leveraged for differentiation of a full tensor network contraction using a recursive scheme that exploits (1) the bilinear property of contraction and (2) the property that trees have a single path from root to leaves. While differentiation of tensor-tensor contraction is already possible in most automatic differentiation packages, we show that exploiting these two additional properties in the specific context of contraction sequences can improve eficiency. Following a description of the algorithm and computational complexity analysis, we investigate its utility for gradient-based supervised learning for low-rank function recovery and for fitting real-world unstructured datasets. We demonstrate improved performance over alternating least-squares optimization approaches and the capability to handle heterogeneous and arbitrary tensor network formats. When compared to alternating minimization algorithms, we find that the gradient-based approach requires a smaller oversampling ratio (number of samples compared to number model parameters) for recovery. This increased efficiency extends to fitting unstructured data of varying dimensionality and when employing a variety of tensor network formats. Here, we show improved learning using the hierarchical Tucker method over the tensor-train in high-dimensional settings on a number of benchmark problems.

Abstract not provided.

The Multi-Fidelity Toolkit (MFTK) is a simulation tool being developed at Sandia National Laboratories for aerodynamic predictions of compressible flows over a range of physics fidelities and computational speeds. These models include the Reynolds-Averaged Navier–Stokes (RANS) equations, the Euler equations, and modified Newtonian aerodynamics (MNA) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal model validation with uncertainty considerations that leverages experimental data from the HIFiRE-1 wind tunnel tests. The geometry is a multi-conic shape that produces complex flow phenomena under hypersonic conditions. A thorough treatment of the validation comparison with prediction error and validation uncertainty is also presented.

A first-of-its-kind model calibration was performed using Sandia National Laboratories' high-throughput initiation (HTI) experiment for two types of vapor-deposited explosive films consisting of hexanitrostilbene (HNS) or pentaerythritol tetranitrate (PETN). These films exhibit prompt initiation, and they reach steady detonation at sub-millimeter length scales. Following prior work on HNS, we test the hypothesis of approximating these explosive films as fine-grained homogeneous solids with simple Arrhenius kinetics burn models. The model calibration process is described herein using a single-step as well as a two-step Arrhenius rate law, and it consists of systematic parameter sampling leading to a reduction in the model degrees of freedom. Multiple local minima are observed; results are given for seven different optimized parameter sets. Each model set is further evaluated in a two-dimensional simulation of the critical failure thickness for a sustained detonation. Overall, the two-step Arrhenius kinetics model captures the observed behavior for HNS; however, neither model produces a good fit to the PETN data. We hypothesize that the HTI results for PETN correspond to a heterogeneous response, owing to the smaller reaction zone of PETN compared to HNS (i.e., it does not homogenize the fine-grained hot spots as well). Future work should consider using the ignition and growth model for PETN, as well as other reactive burn models such as xHVRB, AWSD, PiSURF, and CREST.

Carbon sequestration is a growing field that requires subsurface monitoring for potential leakage of the sequestered fluids through the casing annulus. Sandia National Laboratories (SNL) is developing a smart collar system for downhole fluid monitoring during carbon sequestration. This technology is part of a collaboration between SNL, University of Texas at Austin (UT Austin) (project lead), California Institute of Technology (Caltech), and Research Triangle Institute (RTI) to obtain real-time monitoring of the movement of fluids in the subsurface through direct formation measurements. Caltech and RTI are developing millimeter-scale radio frequency identification (RFID) sensors that can sense carbon dioxide, pH, and methane. These sensors will be impervious to cement, and as such, can be mixed with cement and poured into the casing annulus. The sensors are powered and communicate via standard RFID protocol at 902-928 MHz. SNL is developing a smart collar system that wirelessly gathers RFID sensor data from the sensors embedded in the cement annulus and relays that data to the surface via a wired pipe that utilizes inductive coupling at the collar to transfer data through each segment of pipe. This system cannot transfer a direct current signal to power the smart collar, and therefore, both power and communications will be implemented using alternating current and electromagnetic signals at different frequencies. The complete system will be evaluated at UT Austin's Devine Test Site, which is a highly characterized and hydraulically fractured site. This is the second year of the three-year effort, and a review of SNL's progress on the design and implementation of the smart collar system is provided.

This work presents a high-speed laser-absorption-spectroscopy diagnostic capable of measuring temperature, pressure, and nitric oxide (NO) mole fraction in shock-heated air at a measurement rate of 500 kHz. This diagnostic was demonstrated in the High-Temperature Shock Tube (HST) facility at Sandia National Laboratories. The diagnostic utilizes a quantum-cascade laser to measure the absorbance spectra of two rovibrational transitions near 5.06 µm in the fundamental vibration bands (v" = 0 and 1) of NO in its ground electronic state (X2 Π1/2 ). Gas properties were determined using scanned-wavelength direct absorption and a recently established fitting method that utilizes a modified form of the time-domain molecular free-induction-decay signal (m-FID). This diagnostic was applied to acquire measurements in shock-heated air in the HST at temperatures ranging from approximately 2500 to 5500 K and pressures of 3 to 12 atm behind both incident and reflected shocks. The measurements agree well with the temperature predicted by NASA CEA and the pressure measured simultaneously using PCB pressure sensors. The measurements presented demonstrate that this diagnostic is capable of resolving the formation of NO in shock-heated air and the associated temperature change at the conditions studied.