Publications

Abstract not 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.

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.

Density fluctuations in compressible turbulent boundary layers cause aero-optical distortions that affect the performance of optical systems such as sensors and lasers. The development of models for predicting the aero-optical distortions relies on theory and reference data that can be obtained from experiments and time-resolved simulations. This paper reports on wall-modeled large-eddy simulations of turbulent boundary layers over a flat plate at Mach 3.5, 7.87, and 13.64. The conditions for the Mach 3.5 case match those for the DNS presented by Miller et al.1 The Mach 7.87 simulation match those inside the Hypersonic Wind Tunnel at Sandia National Laboratories. For the Mach 13.64, the conditions inside the Arnold Engineering Development Complex Hypervelocity Tunnel 9 are matched. Overall, adequate agreement of the velocity and temperature as well as Reynolds stress profiles with reference data from direct numerical simulations is obtained for the different Mach numbers. For all three cases, the normalized root-mean-square optical path difference was computed and compared with data obtained from the reference direct numerical simulations and experiments, as well as predictions obtained with a semi-analytical relationship by Notre Dame University. Above Mach five, the normalized path difference obtained from the simulations is above the model prediction. This provides motivation for future work aimed at evaluating the assumptions behind the Notre Dame model for hypersonic boundary layer flows.

Abstract not provided.

Neural networks (NN) have become almost ubiquitous with image classification, but in their standard form produce point estimates, with no measure of confidence. Bayesian neural networks (BNN) provide uncertainty quantification (UQ) for NN predictions and estimates through the posterior distribution. As NN are applied in more high-consequence applications, UQ is becoming a requirement. Automating systems can save time and money, but only if the operator can trust what the system outputs. BNN provide a solution to this problem by not only giving accurate predictions and estimates, but also an interval that includes reasonable values within a desired probability. Despite their positive attributes, BNN are notoriously difficult and time consuming to train. Traditional Bayesian methods use Markov Chain Monte Carlo (MCMC), but this is often brushed aside as being too slow. The most common method is variational inference (VI) due to its fast computation, but there are multiple concerns with its efficacy. MCMC is the gold standard and given enough time, will produce the correct result. VI, alternatively, is an approximation that converges asymptotically. Unfortunately (or fortunately), high consequence problems often do not live in the land of asymtopia so solutions like MCMC are preferable to approximations. We apply and compare MCMC-and VI-trained BNN in the context of target detection in hyperspectral imagery (HSI), where materials of interest can be identified by their unique spectral signature. This is a challenging field, due to the numerous permuting effects practical collection of HSI has on measured spectra. Both models are trained using out-of-the-box tools on a high fidelity HSI target detection scene. Both MCMC-and VI-trained BNN perform well overall at target detection on a simulated HSI scene. Splitting the test set predictions into two classes, high confidence and low confidence predictions, presents a path to automation. For the MCMC-trained BNN, the high confidence predictions have a 0.95 probability of detection with a false alarm rate of 0.05 when considering pixels with target abundance of 0.2. VI-trained BNN have a 0.25 probability of detection for the same, but its performance on high confidence sets matched MCMC for abundances >0.4. However, the VI-trained BNN on this scene required significant expert tuning to get these results while MCMC worked immediately. On neither scene was MCMC prohibitively time consuming, as is often assumed, but the networks we used were relatively small. This paper provides an example of how to utilize the benefits of UQ, but also to increase awareness that different training methods can give different results for the same model. If sufficient computational resources are available, the best approach rather than the fastest or most efficient should be used, especially for high consequence problems.

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 installation of digital sensors, such as advanced meter infrastructure (AMI) meters, has provided the means to implement a wide variety of techniques to increase visibility into the distribution system, including the ability to calibrate the utility models using data-driven algorithms. One challenge in maintaining accurate and up-to-date distribution system models is identifying changes and event occurrences that happen during the year, such as customers who have changed phases due to maintenance or other events. This work proposes a method for the detection of phase change events that utilizes techniques from an existing phase identification algorithm. This work utilizes an ensemble step to obtain predicted phases for windows of data, therefore allowing the predicted phase of customers to be observed over time. The proposed algorithm was tested on four utility datasets as well as a synthetic dataset. The synthetic tests showed the algorithm was capable of accurately detecting true phase change events while limiting the number of false-positive events flagged. In addition, the algorithm was able to identify possible phase change events on two real datasets.

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.

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.

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.

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.

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.

Abstract not provided.

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.

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.

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.

Remote assessment of physiological parameters has enabled patient diagnostics without the need for a medical professional to become exposed to potential communicable diseases. In particular, early detection of oxygen saturation, abnormal body temperature, heart rate, and/or blood pressure could affect treatment protocols. The modeling effort in this work uses an adding-doubling radiative transfer model of a seven-layer human skin structure to describe absorption and reflection of incident light within each layer. The model was validated using both abiotic and biotic systems to understand light interactions associated with surfaces consisting of complex topography as well as multiple illumination sources. Using literature-based property values for human skin thickness, absorption, and scattering, an average deviation of 7.7% between model prediction and experimental reflectivity was observed in the wavelength range of 500-1000 nm.

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.

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.

Magnetic skyrmions are nanoscale whirls of magnetism that can be propagated with electrical currents. The repulsion between skyrmions inspires their use for reversible computing based on the elastic billiard ball collisions proposed for conservative logic in 1982. In this letter, we evaluate the logical and physical reversibility of this skyrmion logic paradigm, as well as the limitations that must be addressed before dissipation-free computation can be realized.

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.

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.

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.

Abstract not provided.

Abstract not provided.

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.

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.

As presented above, because similar existing DOE-managed SNF (DSNF) from previous reactors have been evaluated for disposal pathways, we use this knowledge/experience as a broad reference point for initial technical bases for preliminary dispositioning of potential AR SNF. The strategy for developing fully-formed gap analyses for AR SNF entails the primary step of first obtaining all the defining characteristics of the AR SNF waste stream from the AR developers. Utilizing specific and accurate information/data for developing the potential disposal inventory to be evaluated is a key principle start for success. Once the AR SNF waste streams are defined, the initial assessments would be based on comparison to appropriate existing SNF/waste forms previously analyzed (prior experience) to make a determination on feasibility of direct disposal, or the need to further evaluate due to differences specific to the AR SNF. Assessments of criticality potential and controls would also be performed to assess any R&D gaps to be addressed in that regard as well. Although some AR SNF may need additional treatment for waste form development, these aspects may also be constrained and evaluated within the context of disposal options, including detailed gap analysis to identify further R&D activities to close the gaps.

Abstract not provided.

Measurements that occur within the internal layers of a quantum circuit—midcircuit measurements—are a useful quantum-computing primitive, most notably for quantum error correction. Midcircuit measurements have both classical and quantum outputs, so they can be subject to error modes that do not exist for measurements that terminate quantum circuits. Here we show how to characterize midcircuit measurements, modeled by quantum instruments, using a technique that we call quantum instrument linear gate set tomography (QILGST). We then apply this technique to characterize a dispersive measurement on a superconducting transmon qubit within a multiqubit system. By varying the delay time between the measurement pulse and subsequent gates, we explore the impact of residual cavity photon population on measurement error. QILGST can resolve different error modes and quantify the total error from a measurement; in our experiment, for delay times above 1000ns we measure a total error rate (i.e., half diamond distance) of ϵ⋄=8.1±1.4%, a readout fidelity of 97.0±0.3%, and output quantum-state fidelities of 96.7±0.6% and 93.7±0.7% when measuring 0 and 1, respectively.

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.

Abstract not provided.

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.

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.

The postclosure criticality safety assessment for the direct disposal of dual-purpose canisters (DPCs) in a geologic repository includes considerations of transient criticality phenomena. The power pulse from a hypothetical transient criticality event in an unsaturated alluvial repository is evaluated for a DPC containing 37 spent pressurized water reactor (PWR) assemblies. The scenario assumes that the conditions for baseline criticality are achieved through flooding with groundwater and progressive failure of neutron absorbing media. A preliminary series of steady-state criticality calculations is conducted to characterize reactivity feedback due to absorber degradation, Doppler broadening, and thermal expansion. These feedback coefficients are used in an analysis with a reactor kinetics code to characterize the transient pulse given a positive reactivity insertion for a given length of time. The time-integrated behavior of the pulse can be used to model effects on the DPC and surrounding barriers in future studies and determine if transient criticality effects are consequential.

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}.

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.

The penetration of renewable energy resources (RER) and energy storage systems (ESS) into the power grid has been accelerated in recent times due to the aggressive emission and RER penetration targets. The Integrated resource planning (IRP) framework can help in ensuring long-term resource adequacy while satisfying RER integration and emission reduction targets in a cost-effective and reliable manner. In this paper, we present pIRP (probabilistic Integrated Resource Planning), an open-source Python-based software tool designed for optimal portfolio planning for an RER and ESS rich future grid and for addressing the capacity expansion problem. The tool, which is planned to be released publicly, with its ESS and RER modeling capabilities along with enhanced uncertainty handling make it one of the more advanced non-commercial IRP tools available currently. Additionally, the tool is equipped with an intuitive graphical user interface and expansive plotting capabilities. Impacts of uncertainties in the system are captured using Monte Carlo simulations and lets the users analyze hundreds of scenarios with detailed scenario reports. A linear programming based architecture is adopted which ensures sufficiently fast solution time while considering hundreds of scenarios and characterizing profile risks with varying levels of RER and ESS penetration levels. Results for a test case using data from parts of the Eastern Interconnection are provided in this paper to demonstrate the capabilities offered by the tool.

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.

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.

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.

To keep pace with the demand for innovation through scientific computing, modern scientific software development is increasingly reliant upon a rich and diverse ecosystem of software libraries and toolchains. Research software engineers (RSEs) responsible for that infrastructure perform highly integrative work, acting as a bridge between the hardware, the needs of researchers, and the software layers situated between them; relatively little, however, has been written about the role played by RSEs in that work and what support they need to thrive. To that end, we present a two-part report on the development of half-precision floating point support in the Kokkos Ecosystem. Half-precision computation is a promising strategy for increasing performance in numerical computing and is particularly attractive for emerging application areas (e.g., machine learning), but developing practicable, portable, and user-friendly abstractions is a nontrivial task. In the first half of the paper, we conduct an engineering study on the technical implementation of the Kokkos half-precision scalar feature and showcase experimental results; in the second half, we offer an experience report on the challenges and lessons learned during feature development by the first author. We hope our study provides a holistic view on scientific library development and surfaces opportunities for future studies into effective strategies for RSEs engaged in such work.

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.

Neural networks (NN)s have been increasingly proposed as surrogates for approximation of systems with computationally expensive physics for rapid online evaluation or exploration. As these surrogate models are integrated into larger optimization problems used for decision making, there is a need to verify their behavior to ensure adequate performance over the desired parameter space. We extend the ideas of optimization-based neural network verification to provide guarantees of surrogate performance over the feasible optimization space. In doing so, we present formulations to represent neural networks within decision-making problems, and we develop verification approaches that use model constraints to provide increasingly tight error estimates. We demonstrate the capabilities on a simple steady-state reactor design problem.

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.

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.

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.

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.

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.

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.

Abstract not provided.

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.

Reinforcement Learning (RL) provides an ability to train an artificial intelligent agent in dynamic and uncertain environments. RL has demonstrated an impressive performance capability to learn nearly optimal policies in various application domains including aerospace. Despite the demonstrated performance outcomes of RL, characterizing performance boundaries, explaining the logic behind RL decisions, and quantifying resulting uncertainties in RL outputs are major challenges that slow down the adoption of RL in real-time systems. This is particularly true for aerospace systems where the risk of failure is high and performance envelopes of systems of interest may be small. To facilitate adoption of learning agents in real-time systems, this paper presents a three-part Test and Evaluation (T&E) framework for RL built from Systems engineering for artificial intelligence (SE4AI) perspective. This T&E framework introduces robustness testing approaches to characterize performance bounds on RL, employs Explainable AI techniques, namely Shapley Additive Explanations (SHAP) to examine RL decision-making, and incorporates validation of RL outputs with known and accepted solutions. This framework is applied to a high-speed aerospace vehicle emergency descent problem where RL is trained to provide an angle of attack command and the framework is utilized to comprehensively examine the impact of uncertainties in the vehicle's altitude, velocity, and flight path angle. The robustness testing characterizes acceptable ranges of disturbances in flight parameters, while SHAP exposes the most significant features that impact RL selection of angle of attack-in this case the vehicle altitude. Finally, RL outputs are compared to trajectory generated by indirect optimal control methods for validation.

The characterization of mixtures of non-interacting, spectroscopically similar quantum components has important applications in chemistry, biology, and materials science. We introduce an approach based on quantum tracking control that allows for determining the relative concentrations of constituents in a quantum mixture, using a single pulse which enhances the distinguishability of components of the mixture and has a length that scales linearly with the number of mixture constituents. To illustrate the method, we consider two very distinct model systems: mixtures of diatomic molecules in the gas phase, as well as solid-state materials composed of a mixture of components. A set of numerical analyses are presented, showing strong performance in both settings.

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.

Abstract not provided.

Frequent changes in penetration levels of distributed energy resources (DERs) and grid control objectives have caused the maintenance of accurate and reliable grid models for behind-the-meter (BTM) photovoltaic (PV) system impact studies to become an increasingly challenging task. At the same time, high adoption rates of advanced metering infrastructure (AMI) devices have improved load modeling techniques and have enabled the application of machine learning algorithms to a wide variety of model calibration tasks. Therefore, we propose that these algorithms can be applied to improve the quality of the input data and grid models used for PV impact studies. In this paper, these potential improvements were assessed for their ability to improve the accuracy of locational BTM PV hosting capacity analysis (HCA). Specifically, the voltage- and thermal-constrained hosting capacities of every customer location on a distribution feeder (1,379 in total) were calculated every 15 minutes for an entire year before and after each calibration algorithm or load modeling technique was applied. Overall, the HCA results were found to be highly sensitive to the various modeling deficiencies under investigation, illustrating the opportunity for more data-centric/model-free approaches to PV impact studies.

A primary objective of repository modeling is identification and assessment of features and processes providing safety performance. Sensitivity analyses typically provide information on how input parameters affect performance, not features and processes. To quantify the effects of features and processes, tracers can be introduced virtually in model simulations and tracked in informative ways. This paper describes five ways virtual tracers can be used to directly measure the relative importance of several features, processes, and combinations of features and processes in repository performance assessment modeling.

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.

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.

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.

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.

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.

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.

Abstract not provided.

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).

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.

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.

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.

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.

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.

Large scale non-intrusive inspection (NII) of commercial vehicles is being adopted in the U.S. at a pace and scale that will result in a commensurate growth in adjudication burdens at land ports of entry. The use of computer vision and machine learning models to augment human operator capabilities is critical in this sector to ensure the flow of commerce and to maintain efficient and reliable security operations. The development of models for this scale and speed requires novel approaches to object detection and novel adjudication pipelines. Here we propose a notional combination of existing object detection tools using a novel ensembling framework to demonstrate the potential for hierarchical and recursive operations. Further, we explore the combination of object detection with image similarity as an adjacent capability to provide post-hoc oversight to the detection framework. The experiments described herein, while notional and intended for illustrative purposes, demonstrate that the judicious combination of diverse algorithms can result in a resilient workflow for the NII environment.

This paper reports on Catalyst usability and initial adoption by SPARC analysts. The use case approach highlights the analysts' perspective. Impediments to adoption can be due to deficiencies in software capabilities, or analysts may identify mundane inconveniences and barriers that prevent them from fully leveraging Catalyst. With that said, for many analyst tasks Catalyst provides enough relative advantage that they have begun applying it in their production work, and they recognize the potential for it to solve problems they currently struggle with. The findings in this report include specific issues and minor bugs in ParaView Python scripting, which are viewed as having straightforward solutions, as well as a broader adoption analysis.

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.

This paper applies sensitivity and uncertainty analysis to compare two model alternatives for fuel matrix degradation for performance assessment of a generic crystalline repository. The results show that this model choice has little effect on uncertainty in the peak 129I concentration. The small impact of this choice is likely due to the higher importance of uncertainty in the instantaneous release fraction and differences in epistemic uncertainty between the alternatives.

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.

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.

The Ghareb Formation in the Yasmin Plain of Israel is under investigation as a potential disposal rock for nuclear waste disposal. Triaxial deformation tests and hydrostatic water-permeability tests were conducted with samples of the Ghareb to assess relevant thermal, hydrological, and mechanical properties. Axial deformation tests were performed on dry and water-saturated samples at effective pressures ranging from 0.7 to 19.6 MPa and temperatures of 23 ℃ and 100 ℃, while permeability tests were conducted at ambient temperatures and effective pressures ranging from 0.7 to 20 MPa. Strength and elastic moduli increase with increasing effective pressure for the triaxial tests. Dry room temperature tests are generally the strongest, while the samples deformed at 100 ℃ exhibit large permanent compaction even at low effective pressures. Water permeability decreases by 1-2 orders of magnitude under hydrostatic conditions while experiencing permanent volume loss of 4-5%. Permeability loss is retained after unloading, resulting from permanent compaction. A 3-D compaction model was used to demonstrate that compaction in one direction is associated with de-compaction in the orthogonal directions. The model accurately reproduces the measured axial and transverse strain components. The experimentally constrained deformational properties of the Ghareb will be used for 3-D thermal-hydrological-mechanical modelling of borehole stability.

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.

Modern day processes depend heavily on data-driven techniques that use large datasets clustered into relevant groups help them achieve higher efficiency, better utilization of the operation, and improved decision making. However, building these datasets and clustering by similar products is challenging in research environments that produce many novel and highly complex low-volume technologies. In this work, the author develops an algorithm that calculates the similarity between multiple low-volume products from a research environment using a real-world data set. The algorithm is applied to pulse power operations data, which routinely performs novel experiments for inertial confinement fusion, radiation effects, and nuclear stockpile stewardship. The author shows that the algorithm is successful in calculating similarity between experiments of varying complexity such that comparable shots can be used for further analysis. Furthermore, it has been able to identify experiments not traditionally seen as identical.

Abstract not provided.

This paper presents a visualization technique for incorporating eigenvector estimates with geospatial data to create inter-area mode shape maps. For each point of measurement, the method specifies the radius, color, and angular orientation of a circular map marker. These characteristics are determined by the elements of the right eigenvector corresponding to the mode of interest. The markers are then overlaid on a map of the system to create a physically intuitive visualization of the mode shape. This technique serves as a valuable tool for differentiating oscillatory modes that have similar frequencies but different shapes. This work was conducted within the Western Interconnection Modes Review Group (WIMRG) in the Western Electric Coordinating Council (WECC). For testing, we employ the WECC 2021 Heavy Summer base case, which features a high-fidelity, industry standard dynamic model of the North American Western Interconnection. Mode estimates are produced via eigen-decomposition of a reduced-order state matrix identified from simulated ringdown data. The results provide improved physical intuition about the spatial characteristics of the inter-area modes. In addition to offline applications, this visualization technique could also enhance situational awareness for system operators when paired with online mode shape estimates.

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.

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.

Abstract not provided.

It is impossible in practice to comprehensively test even small software programs due to the vastness of the reachable state space; however, modern cyber-physical systems such as aircraft require a high degree of confidence in software safety and reliability. Here we explore methods of generating test sets to effectively and efficiently explore the state space for a module based on the Traffic Collision Avoidance System (TCAS) used on commercial aircraft. A formal model of TCAS in the model-checking language NuSMV provides an output oracle. We compare test sets generated using various methods, including covering arrays, random, and a low-complexity input paradigm applied to 28 versions of the TCAS C program containing seeded errors. Faults are triggered by tests for all 28 programs using a combination of covering arrays and random input generation. Complexity-based inputs perform more efficiently than covering arrays, and can be paired with random input generation to create efficient and effective test sets. A random forest classifier identifies variable values that can be targeted to generate tests even more efficiently in future work, by combining a machine-learned fuzzing algorithm with more complex model oracles developed in model-based systems engineering (MBSE) software.

The detonation of explosives produces luminous fireballs often containing particulates such as carbon soot or remnants of partially reacted explosives. The spatial distribution of these particulates is of great interest for the derivation and validation of models. In this work, three ultra-high-speed imaging techniques: diffuse back-illumination extinction, schlieren, and emission imaging, are utilized to investigate the particulate quantity, spatial distribution, and structure in a small-scale fireball. The measurements show the evolution of the particulate cloud in the fireball, identifying possible emission sources and regions of high optical thickness. Extinction measurements performed at two wavelengths shows that extinction follows the inverse wavelength behavior expected of absorptive particles in the Rayleigh scattering regime. The estimated mass from these extinction measurements shows an average soot yield consistent with previous soot collection experiments. The imaging diagnostics discussed in the current work can provide detailed information on the spatial distribution and concentration of soot, crucial for validation opportunities in the future.

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.

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.

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.

Abstract not provided.

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.

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

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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.

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.

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.