The design of thermal protection systems (TPS), including heat shields for reentry vehicles, rely more and more on computational simulation tools for design optimization and uncertainty quantification. Since high-fidelity simulations are computationally expensive for full vehicle geometries, analysts primarily use reduced-physics models instead. Recent work has shown that projection-based reduced-order models (ROMs) can provide accurate approximations of high-fidelity models at a lower computational cost. ROMs are preferable to alternative approximation approaches for high-consequence applications due to the presence of rigorous error bounds. The following paper extends our previous work on projection-based ROMs for ablative TPS by considering hyperreduction methods which yield further reductions in computational cost and demonstrating the approach for simulations of a three-dimensional flight vehicle. We compare the accuracy and potential performance of several different hyperreduction methods and mesh sampling strategies. This paper shows that with the correct implementation, hyperreduction can make ROMs up to 1-3 orders of magnitude faster than the full order model by evaluating the residual at only a small fraction of the mesh nodes.
The Big Hill SPR site has a rich data set consisting of multi-arm caliper (MAC) logs collected from the cavern wells. This data set provides insight into the on-going casing deformation at the Big Hill site. This report summarizes the MAC surveys for each well and presents well longevity estimates where possible. Included in the report is an examination of the well twins for each cavern and a discussion on what may or may not be responsible for the different levels of deformation between some of the well twins. The report also takes a systematic view of the MAC data presenting spatial patterns of casing deformation and deformation orientation in an effort to better understand the underlying causes. The conclusions present a hypothesis suggesting the small-scale variations in casing deformation are attributable to similar scale variations in the character of the salt-caprock interface. These variations do not appear directly related to shear zones or faults.
Filamentous fungi can synthesize a variety of nanoparticles (NPs), a process referred to as mycosynthesis that requires little energy input, do not require the use of harsh chemicals, occurs at near neutral pH, and do not produce toxic byproducts. While NP synthesis involves reactions between metal ions and exudates produced by the fungi, the chemical and biochemical parameters underlying this process remain poorly understood. Here, the role of fungal species and precursor salt on the mycosynthesis of zinc oxide (ZnO) NPs is investigated. This data demonstrates that all five fungal species tested are able to produce ZnO structures that can be morphologically classified into i) well-defined NPs, ii) coalesced/dissolving NPs, and iii) micron-sized square plates. Further, species-dependent preferences for these morphologies are observed, suggesting potential differences in the profile or concentration of the biochemical constituents in their individual exudates. This data also demonstrates that mycosynthesis of ZnO NPs is independent of the anion species, with nitrate, sulfate, and chloride showing no effect on NP production. Finally, these results enhance the understanding of factors controlling the mycosynthesis of ceramic NPs, supporting future studies that can enable control over the physical and chemical properties of NPs formed through this “green” synthesis method.
The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) - probabilistic graphical models consisting of a set of variables and their conditional dependencies - for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof.
As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
Mann, James B.; Mohanty, Debapriya P.; Kustas, Andrew K.; Stiven Puentes Rodriguez, B.; Issahaq, Mohammed N.; Udupa, Anirudh; Sugihara, Tatsuya; Trumble, Kevin P.; M'Saoubi, Rachid; Chandrasekar, Srinivasan
Machining-based deformation processing is used to produce metal foil and flat wire (strip) with suitable properties and quality for electrical power and renewable energy applications. In contrast to conventional multistage rolling, the strip is produced in a single-step and with much less process energy. Examples are presented from metal systems of varied workability, and strip product scale in terms of size and production rate. By utilizing the large-strain deformation intrinsic to cutting, bulk strip with ultrafine-grained microstructure, and crystallographic shear-texture favourable for formability, are achieved. Implications for production of commercial strip for electric motor applications and battery electrodes are discussed.
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.
Multiple Input Multiple Output (MIMO) vibration testing provides the capability to expose a system to a field environment in a laboratory setting, saving both time and money by mitigating the need to perform multiple and costly large-scale field tests. However, MIMO vibration test design is not straightforward oftentimes relying on engineering judgment and multiple test iterations to determine the proper selection of response Degree of Freedom (DOF) and input locations that yield a successful test. This work investigates two DOF selection techniques for MIMO vibration testing to assist with test design, an iterative algorithm introduced in previous work and an Optimal Experiment Design (OED) approach. The iterative-based approach downselects the control set by removing DOF that have the smallest impact on overall error given a target Cross Power Spectral Density matrix and laboratory Frequency Response Function (FRF) matrix. The Optimal Experiment Design (OED) approach is formulated with the laboratory FRF matrix as a convex optimization problem and solved with a gradient-based optimization algorithm that seeks a set of weighted measurement DOF that minimize a measure of model prediction uncertainty. The DOF selection approaches are used to design MIMO vibration tests using candidate finite element models and simulated target environments. The results are generalized and compared to exemplify the quality of the MIMO test using the selected DOF.
Conference Record of the IEEE Photovoltaic Specialists Conference
Hobbs, William B.; Black, Chloe L.; Holmgren, William F.; Anderson, Kevin
Subhourly changes in solar irradiance can lead to energy models being biased high if realistic distributions of irradiance values are not reflected in the resource data and model. This is particularly true in solar facility designs with high inverter loading ratios (ILRs). When resource data with sufficient temporal and spatial resolution is not available for a site, synthetic variability can be added to the data that is available in an attempt to address this issue. In this work, we demonstrate the use of anonymized commercial resource datasets with synthetic variability and compare results with previous estimates of model bias due to inverter clipping and increasing ILR.
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.
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.
Laser-induced photoemission of electrons offers opportunities to trigger and control plasmas and discharges [1]. However, the underlying mechanisms are not sufficiently characterized to be fully utilized [2]. We present an investigation to characterize the effects of photoemission on plasma breakdown for different reduced electric fields, laser intensities, and photon energies. We perform Townsend breakdown experiments assisted by high-speed imaging and employ a quantum model of photoemission along with a 0D discharge model [3], [4] to interpret the experimental measurements.
High-altitude electromagnetic pulse events are a growing concern for electric power grid vulnerability assessments and mitigation planning, and accurate modeling of surge arrester mitigations installed on the grid is necessary to predict pulse effects on existing equipment and to plan future mitigation. While some models of surge arresters at high frequency have been proposed, experimental backing for any given model has not been shown. This work examines a ZnO lightning surge arrester modeling approach previously developed for accurate prediction of nanosecond-scale pulse response. Four ZnO metal-oxide varistor pucks with different sizes and voltage ratings were tested for voltage and current response on a conducted electromagnetic pulse testbed. The measured clamping response was compared to SPICE circuit models to compare the electromagnetic pulse response and validate model accuracy. Results showed good agreement between simulation results and the experimental measurements, after accounting for stray testbed inductance between 100 and 250 nH.
Unlike traditional base excitation vibration qualification testing, multi-axis vibration testing methods can be significantly faster and more accurate. Here, a 12-shaker multiple-input/multiple-output (MIMO) test method called intrinsic connection excitation (ICE) is developed and assessed for use on an example aerospace component. In this study, the ICE technique utilizes 12 shakers, 1 for each boundary condition attachment degree of freedom to the component, specially designed fixtures, and MIMO control to provide an accurate set of loads and boundary conditions during the test. Acceleration, force, and voltage control provide insight into the viability of this testing method. System field test and ICE test results are compared to traditional single degree of freedom specification development and testing. Results indicate the multi-shaker ICE test provided a much more accurate replication of system field test response compared with single degree of freedom testing.
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.
Phosphor thermometry has become an established remote sensing technique for acquiring the temperature of surfaces and gas-phase flows. Often, phosphors are excited by a light source (typically emitting in the UV region), and their temperature-sensitive emission is captured. Temperature can be inferred from shifts in the emission spectra or the radiative decay lifetime during relaxation. While recent work has shown that the emission of several phosphors remains thermographic during x-ray excitation, the radiative decay lifetime was not investigated. The focus of the present study is to characterize the lifetime decay of the phosphor Gd2O2S:Tb for temperature sensitivity after excitation from a pulsed x-ray source. These results are compared to the lifetime decays found for this phosphor when excited using a pulsed UV laser. Results show that the lifetime of this phosphor exhibits comparable sensitivity to temperature between both excitation sources for a temperature range between 21 °C to 140 °C in increments of 20 °C. This work introduces a novel method of thermometry for researchers to implement when employing x-rays for diagnostics.
The structure-property linkage is one of the two most important relationships in materials science besides the process-structure linkage, especially for metals and polycrystalline alloys. The stochastic nature of microstructures begs for a robust approach to reliably address the linkage. As such, uncertainty quantification (UQ) plays an important role in this regard and cannot be ignored. To probe the structure-property linkage, many multi-scale integrated computational materials engineering (ICME) tools have been proposed and developed over the last decade to accelerate the material design process in the spirit of Material Genome Initiative (MGI), notably crystal plasticity finite element model (CPFEM) and phase-field simulations. Machine learning (ML) methods, including deep learning and physics-informed/-constrained approaches, can also be conveniently applied to approximate the computationally expensive ICME models, allowing one to efficiently navigate in both structure and property spaces effortlessly. Since UQ also plays a crucial role in verification and validation for both ICME and ML models, it is important to include UQ in the picture. In this paper, we summarize a few of our recent research efforts addressing UQ aspects of homogenized properties using CPFEM in a big picture context.