Publications

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.

Abstract not provided.

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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.