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With increasing penetration of variable renewable generation, battery energy storage systems (BESS) are becoming important for power system stability due to their operational flexibility. In this paper, we propose a method for determining the minimum BESS rated power that guarantees security constraints in a grid subject to disturbances induced by variable renewable generation. The proposed framework leverages sensitivity-based inverse uncertainty propagation where the dynamical responses of the states are parameterized with respect to random variables. Using this approach, the original nonlinear optimization problem for finding the security-constrained uncertainty interval may be formulated as a quadratically-constrained linear program. The resulting estimated uncertainty interval is utilized to find the BESS rated power required to satisfy grid stability constraints.

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The Sliding Scale of Cybersecurity is a framework for understanding the actions that contribute to cybersecurity. The model consists of five categories that provide varying value towards cybersecurity and incur varying implementation costs. These categories range from offensive cybersecurity measures providing the least value and incurring the greatest cost, to architecture providing the greatest value and incurring the least cost. This paper presents an application of the Sliding Scale of Cybersecurity to the Tiered Cybersecurity Analysis (TCA) of digital instrumentation and control systems for advanced reactors. The TCA consists of three tiers. Tier 1 is design and impact analysis. In Tier 1 it is assumed that the adversary has control over all digital systems, components, and networks in the plant, and that the adversary is only constrained by the physical limitations of the plant design. The plant’s safety design features are examined to determine whether the consequences of an attack by this cyber-enabled adversary are eliminated or mitigated. Accident sequences that are not eliminated or mitigated by security by design features are examined in Tier 2 analysis. In Tier 2, adversary access pathways are identified for the unmitigated accident sequences, and passive measures are implemented to deny system and network access to those pathways wherever feasible. Any systems with remaining susceptible access pathways are then examined in Tier 3. In Tier 3, active defensive cybersecurity architecture features and cybersecurity plan controls are applied to deny the adversary the ability to conduct the tasks needed to cause a severe consequence. Earlier application of the TCA in the design process provides greater opportunity for an efficient graded approach and defense-in-depth.

This study investigated the durability of four high temperature coatings for use as a Gardon gauge foil coating. Failure modes and effects analysis have identified Gardon gauge foil coating as a critical component for the development of a robust flux gauge for high intensity flux measurements. Degradation of coating optical properties and physical condition alters flux gauge sensitivity, resulting in flux measurement errors. In this paper, four coatings were exposed to solar and thermal cycles to simulate real-world aging. Solar simulator and box furnace facilities at the National Solar Thermal Test Facility (NSTTF) were utilized in separate test campaigns. Coating absorptance and emissivity properties were measured and combined into a figure of merit (FOM) to characterize the optical property stability of each coating, and physical coating degradation was assessed qualitatively using microscope images. Results suggest rapid high temperature cycling did not significantly impact coating optical properties and physical state. In contrast, prolonged exposure of coatings to high temperatures degraded coating optical properties and physical state. Coatings degraded after 1 hour of exposure at temperatures above 400 °C and stabilized after 6-24 hours of exposure. It is concluded that the combination of high temperatures and prolonged exposure provide the energy necessary to sustain coating surface reactions and alter optical and physical coating properties. Results also suggest flux gauge foil coatings could benefit from long duration high temperature curing (>400 °C) prior to sensor calibration to stabilize coating properties and increase measurement reliability in high flux and high temperature applications.

While research in multiple-input/multiple-output (MIMO) random vibration testing techniques, control methods, and test design has been increasing in recent years, research into specifications for these types of tests has not kept pace. This is perhaps due to the very particular requirement for most MIMO random vibration control specifications – they must be narrowband, fully populated cross-power spectral density matrices. This requirement puts constraints on the specification derivation process and restricts the application of many of the traditional techniques used to define single-axis random vibration specifications, such as averaging or straight-lining. This requirement also restricts the applicability of MIMO testing by requiring a very specific and rich field test data set to serve as the basis for the MIMO test specification. Here, frequency-warping and channel averaging techniques are proposed to soften the requirements for MIMO specifications with the goal of expanding the applicability of MIMO random vibration testing and enabling tests to be run in the absence of the necessary field test data.

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Carbonaceous particulate produced by a diesel engine and turbojet engine combustor are analyzed by transmission electron microscopy (TEM) for differences in nanostructure before and after pulsed laser annealing. Soot is examined between low/high diesel engine torque and low/high turbojet engine thrust. Small differences in nascent nanostructure are magnified by the action of high-temperature annealing induced by pulsed laser heating. Lamellae length distributions show occurrence of graphitization while tortuosity analyses reveal lamellae straightening. Differences in internal particle structure (hollow shells versus internal graphitic ribbons) are interpreted as due to higher internal sp3 and O-atom content under the higher power conditions with hypothesized greater turbulence and resulting partial premixing. TEM in concert with fringe analyses reveal that a similar degree of annealing occurs in the primary particles in soot from both diesel engine and turbojet engine combustors—despite the aggregate and primary size differences between these sources. Implications of these results for source identification of the combustion particulate and for laser-induced incandescence (LII) measurements of concentration are discussed with inter-instrument comparison of soot mass from both diesel and turbojet soot sources.

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Extreme meteorological events, such as hurricanes and floods, cause significant infrastructure damage and, as a result, prolonged grid outages. To mitigate the negative effect of these outages and enhance the resilience of communities, microgrids consisting of solar photovoltaics (PV), energy storage (ES) technologies, and backup diesel generation are being considered. Furthermore, it is necessary to take into account how the extreme event affects the systems' performance during the outage, often referred to as black-sky conditions. In this paper, an optimization model is introduced to properly size ES and PV technologies to meet various duration of grid outages for selected critical infrastructure while considering black-sky conditions. A case study of the municipality of Villalba, Puerto Rico is presented to identify the several potential microgrid configurations that increase the community's resilience. Sensitivity analyses are performed around the grid outage durations and black-sky conditions to better decide what factors should be considered when scoping potential microgrids for community resilience.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

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Although enhancing permeability is vital for successful development of an Enhanced Geothermal System (EGS) reservoir, high-permeability pathways between injection and production wells can lead to short-circuiting of the flow, resulting in inefficient heat exchange with the reservoir rock. For this reason, the permeability of such excessively permeable paths needs to be reduced. Controlling the reservoir permeability away from wells, however, is challenging, because the injected materials need to form solid plugs only after they reach the target locations. To control the timing of the flow-diverter formation, we are developing a technology to deliver one or more components of the diverter-forming chemicals in microparticles (capsules) with a thin polymer shell. The material properties of the shell are designed so that it can withstand moderately high temperatures (up to ~200°C) of the injected fluid for a short period of time (up to ~30 minutes), but thermally degrades and releases the reactants at higher reservoir temperatures. A microfluidic system has been developed that can continuously produce reactant-encapsulating particles. The diameter of the produced particles is in the range of ~250-650 μm, which can be controlled by using capillary tubes with different diameters and by adjusting the flow rates of the encapsulated fluid and the UV-curable epoxy resin for the shell. Preliminary experiments have demonstrated that (1) microcapsules containing chemical activators for flow-diverter (silicate gel or metal silicate) formation can be produced, (2) the durability of the shell can be made to satisfy the required conditions, and (3) thermal degradation of the shell allows for release of the reaction activators and control of reaction kinetics in silica-based diverters.

In recent years, high-altitude infrasound sensing has become more prolific, demonstrating an enormous value especially when utilized over regions inaccessible to traditional ground-based sensing. Similar to ground-based infrasound detectors, airborne sensors take advantage of the fact that impulsive atmospheric events such as explosions can generate low frequency acoustic waves, also known as infrasound. Due to negligible attenuation, infrasonic waves can travel over long distances, and provide important clues about their source. Here, we report infrasound detections of the Apollo detonation that was carried on 29 October 2020 as part of the Large Surface Explosion Coupling Experiment in Nevada, USA. Infrasound sensors attached to solar hot air balloons floating in the stratosphere detected the signals generated by the explosion at distances 170–210 km. Three distinct arrival phases seen in the signals are indicative of multipathing caused by the small-scale perturbations in the atmosphere. We also found that the local acoustic environment at these altitudes is more complex than previously thought.

A method is presented to detect clear-sky periods for plane-of-array, time-averaged irradiance data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data intervals, and by detecting clear periods in plane-of-array data, which is novel. We illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

Polymers are widely used as damping materials in vibration and impact applications. Liquid crystal elastomers (LCEs) are a unique class of polymers that may offer the potential for enhanced energy absorption capacity under impact conditions over conventional polymers due to their ability to align the nematic phase during loading. Being a relatively new material, the high rate compressive properties of LCEs have been minimally studied. Here, we investigated the high strain rate compression behavior of different solid LCEs, including cast polydomain and 3D-printed, preferentially oriented monodomain samples. Direct ink write (DIW) 3D printed samples allow unique sample designs, namely, a specific orientation of mesogens with respect to the loading direction. Loading the sample in different orientations can induce mesogen rotation during mechanical loading and subsequently different stress-strain responses under impact. We also used a reference polymer, bisphenol-A (BPA) cross-linked resin, to contrast LCE behavior with conventional elastomer behavior.

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan—University at Buffalo Research Center’s Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. In this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier–Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

Modern Industrial Control Systems (ICS) attacks evade existing tools by using knowledge of ICS processes to blend their activities with benign Supervisory Control and Data Acquisition (SCADA) operation, causing physical world damages. We present Scaphy to detect ICS attacks in SCADA by leveraging the unique execution phases of SCADA to identify the limited set of legitimate behaviors to control the physical world in different phases, which differentiates from attacker's activities. For example, it is typical for SCADA to setup ICS device objects during initialization, but anomalous during process-control. To extract unique behaviors of SCADA execution phases, Scaphy first leverages open ICS conventions to generate a novel physical process dependency and impact graph (PDIG) to identify disruptive physical states. Scaphy then uses PDIG to inform a physical process-aware dynamic analysis, whereby code paths of SCADA process-control execution is induced to reveal API call behaviors unique to legitimate process-control phases. Using this established behavior, Scaphy selectively monitors attacker's physical world-targeted activities that violates legitimate process-control behaviors. We evaluated Scaphy at a U.S. national lab ICS testbed environment. Using diverse ICS deployment scenarios and attacks across 4 ICS industries, Scaphy achieved 95% accuracy & 3.5% false positives (FP), compared to 47.5% accuracy and 25% FP of existing work. We analyze Scaphy's resilience to futuristic attacks where attacker knows our approach.

This paper elaborates the results of the hardware implementation of a traveling wave (TW) protection device (PD) for DC microgrids. The proposed TWPD is implemented on a commercial digital signal processor (DSP) board. In the developed TWPD, first, the DSP board's Analog to Digital Converter (ADC) is used to sample the input at a 1 MHz sampling rate. The Analog Input card of DSP board measures the pole current at the TWPD location in DC microgrid. Then, a TW detection algorithm is applied on the output of the ADC to detect the fault occurrence instance. Once this instance is detected, multi-resolution analysis (MRA) is performed on a 128-sample data butter that is created around the fault instance. The MRA utilizes discrete wavelet transform (DWT) to extract the high-frequency signatures of measured pole current. To quantity the extracted TW features, the Parseval theorem is used to calculate the Parseval energy of reconstructed wavelet coefficients created by MRA. These Parseval energy values are later used as inputs to a polynomial linear regression tool to estimate the fault location. The performance of the created TWPD is verified using an experimental testbed.

A quantum-cascade-laser-absorption-spectroscopy (QCLAS) diagnostic was used to characterize post-detonation fireballs of RP-80 detonators via measurements of temperature, pressure, and CO column pressure at a repetition rate of 1 MHz. Scanned-wavelength direct-absorption spectroscopy was used to measure CO absorbance spectra near 2008.5 cm−1 which are dominated by the P(0,31), P(2,20), and P(3,14) transitions. Line-of-sight (LOS) measurements were acquired 51 and 91 mm above the detonator surface. Three strategies were employed to facilitate interpretation of the LAS measurements in this highly nonuniform environment and to evaluate the accuracy of four post-detonation fireball models: (1) High-energy transitions were used to deliberately bias the measurements to the high-temperature outer shell, (2) a novel dual-zone absorption model was used to extract temperature, pressure, and CO measurements in two distinct regions of the fireball at times where pressure variations along the LOS were pronounced, and (3) the LAS measurements were compared with synthetic LAS measurements produced using the simulated distributions of temperature, pressure, and gas composition predicted by reactive CFD modeling. The results indicate that the QCLAS diagnostic provides high-fidelity data for evaluating post-detonation fireball models, and that assumptions regarding thermochemical equilibrium and carbon freeze-out during expansion of detonation gases have a large impact on the predicted chemical composition of the fireball.

A microgrid is characterized by a high R/X ratio, making the voltage more sensitive to active power changes unlike in bulk power systems where voltage is mostly regulated by reactive power. Because of its sensitivity to active power, control approach should incorporate active power as well. Thus, the voltage control approach for microgrids is very different from conventional power systems. The energy costs associated with these power are different. Furthermore, because of diverse generation sources and different components such as distributed energy resources, energy storage systems, etc, model-based control approaches might not perform very well. This paper proposes a reinforcement learning-based voltage support framework for a microgrid where an agent learns control policy by interacting with the microgrid without requiring a mathematical model of the system. A MATLAB/Simulink simulation study on a test system from Cordova, Alaska shows that there is a large reduction in voltage deviation (about 2.5-4.5 times). This reduction in voltage deviation can improve the power quality of the microgrid: ensuring a reliable supply, longer equipment lifespan, and stable user operations.

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System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that can be used for provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. Also, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. We demonstrate the details of the dataset events and dependency analysis.