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At least 1,261 records · Page 70

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Impact of chemical ordering on thermodynamic properties of point defects and Xe substitutional in U-10Mo

The accurate knowledge of defect energetics is critical to understanding the aging and irradiation behavior of U-10Mo nuclear fuel, which is selected as the fuel type for conversion of the United States High-Performance Research Reactors (HPRRs). Furthermore, using hybrid molecular dynamics and Monte Carlo (MDMC) simulation, we studied the impact of chemical ordering on the formation energies of vacancies, interstitials, and the solution energy of the Xe substitutionals. Instead of forming a random solid solution (RSS), substantial short-range-order (SRO) develops in U-10Mo, particularly at low temperatures. Mo atoms are found to repel each other and prefer U-rich local atomic environments within the 1st nearest neighbor (1NN) cutoff. Compared to the case of a RSS, the state with equilibrated Mo ordering shifts the distributions of vacancy and interstitial formation energies due to the dependence of defect energies on the local atomic environment, without a clear effect on Xe solution energy. In the operation temperature range (100–250 °C) of U-Mo fuels, neglecting SRO can lead to an inaccurate estimate of thermal equilibrium point defect concentrations by over an order of magnitude and incorrectly predict the preference among different types of dumbbells, highlighting the critical importance of accounting for the impact of chemical ordering for accurate atomistic calculations of defect properties.

Molecular dynamics and Monte Carlo (MDMC)↗

Comparative study with two reduced test facilities for air-cooled RCCS scaling law

Korea Atomic Energy Research Institute (KAERI) and Argonne National Laboratory (ANL) conducted steady-state tests at constant heat flux conditions to investigate the scale effect on heat removal behavior of the air-cooled Reactor Cavity Cooling System (RCCS). Two differently scaled-down test facilities were used in this study, KAERI’s ¼ scale with 4.0-m cavity height, and ANL’s ½ scale with 6.8-m cavity height. Two scaling laws were proposed to simulate the radiation across the cavity and the buoyancy-driven duct flow in the riser, respectively. The test matrix for KAERI and ANL test facilities focused on the scaling effect of the heated riser length with constant heat flux at the PMR200 RCCS design. The test results showed that mixed convection in the riser duct is an important factor for accurately extrapolating the thermo-fluid behavior in the prototype from the test results in the scale-down facilities. The system analysis code, GAMMA+, with improved heat transfer models predicted fairly well the air-cooled RCCS test data from two facilities. GAMMA+ analysis results showed that the predicted radiation fractions on the heated plate in the scale-down test conditions were larger than those on the reactor vessel in the prototype. The scaling law for air-cooled RCCS was improved by considering the mixed convection in the riser duct and the same radiation fraction on the heated plate. The mixed convection effect was calculated by the height ratio. The same radiation fraction provided a conservative extrapolation from the scale-down test results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Effects of B 2 O 3 on the Growth, Structural, and Magneto-Optical Properties of Yttrium Iron Garnet Single-Crystal Fibers

This study explores the fabrication of yttrium iron garnet (YIG) single crystal fibers using the laser heated pedestal growth (LHPG) method with the experimental addition of B 2 O 3 . The incorporation of B 2 O 3 facilitates the fiber fabrication process by lowering the required growth temperatures and likely modifying melt viscosity behavior, consistent with the established fluxing behavior of B2O3 and the comparative viscosity trend observed in the TMA−VFT analysis, thereby improving process efficiency while maintaining fiber quality. Structural characterization using EBSD and SC-XRD reveals a transition from polycrystalline to single-crystal behavior, with improved alignment along the [111] direction without altering the garnet structure. Magnetic measurements show increases in saturation magnetization in B 2 O 3 -assisted fibers. Three-dimensional anisotropy energy modeling, based on EBSD-derived Euler angles, indicates that the enhanced crystallinity and orientation contribute to reorientation of MCA energy distribution due to improved crystallographic alignment. Faraday rotation measurements show that the B 2 O 3 -assisted sample exhibits a rotation angle closer to reported values for high-quality YIG, suggesting improved phase purity and crystallographic quality. These findings demonstrate that B 2 O 3 -assisted LHPG growth is a scalable and nontoxic approach to producing high-performance YIG fibers for integrated photonic and magnetic field sensing applications.

Crystal structure↗

Molecular Driving Force for Facet Selectivity of Sequence-Defined Amphiphilic Peptoids at Au–Water Interfaces

Shape-controlled colloidal nanocrystal syntheses often require facet-selective solution-phase chemical additives to regulate surface free energy, atom addition/migration fluxes or particle attachment rates. Because of their highly tunable properties and robustness to a wide range of experimental conditions, peptoids represent a very promising class of next generation functional additives for control over nanocrystal growth. However, understanding the origin of facet selectivity at the molecular level is critical to generalizing their design. In this work, we employ molecular dynamics simulations and biased sampling methods and report stronger selectivity to Au(111) than to Au(100) for Nce3Ncp6, a peptoid that has been shown to assist the formation of five-fold twinned Au nanostars. We find that facet-selectivity is achieved through synergistic effects of both peptoid-surface and solvent-surface interactions. Moreover, the amphiphilic nature of Nce3Ncp6 together with the order of peptoid-peptoid and peptoid-surface binding energies, i.e., peptoid-Au(100) < peptoid-peptoid < peptoid-Au(111), further amplifies its distinct collective behavior on different Au surfaces. Our studies provide a fundamental understanding of the molecular origin of facet-selective adsorption and highlight the possibility of future designs and uses of sequence-defined peptoids for predictive syntheses of nanocrystals with designed shapes and properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Introducing dusty plasma particle growth of nanospherical titanium dioxide

In dusty plasma environments, spontaneous growth of nanoparticles from reactive gases has been extensively studied for over three decades, primarily focusing on hydrocarbons and silicate particles. Here, we introduce the growth of titanium dioxide, a wide bandgap semiconductor, as dusty plasma nanoparticles. The resultant particles exhibited a spherical morphology and reached a maximum monodisperse radius of 235 ± 20 nm after growing for 70 s. The particle grew linearly, and the growth displayed a cyclic behavior; that is, upon reaching their maximum radius, the largest particles fell out of the plasma, and the next growth cycle immediately followed. The particles were collected after being grown for different amounts of time and imaged using scanning electron microscopy. Further characterization was carried out using energy dispersive x-ray spectroscopy, x-ray diffraction, and Raman spectroscopy to elucidate the chemical composition and crystalline properties of the maximally sized particles. Initially, the as-grown particles exhibited an amorphous structure after 70 s. However, annealing treatments at temperatures of 400 and 800 °C induced crystallization, yielding anatase and rutile phases, respectively. Annealing at 600 °C resulted in a mixed phase of anatase and rutile. These findings open avenues for a rapid and controlled growth of titanium dioxide via dusty plasma.

Physics↗

Magnetoelastic coupling and spin contributions to entropy and thermal transport in biferroic yttrium orthochromite

Direct engineering of material properties through exploitation of spin, phonon, and charge-coupled degrees of freedom is an active area of development in materials science. However, the relative contribution of the competing orders to controlling the desired behavior is challenging to decipher. In particular, the independent role of phonons, magnons, and electrons, quasiparticle coupling, and relative contributions to the phase transition free energy largely remain unexplored, especially for magnetic phase transitions. Here, we study the lattice and magnetic dynamics of biferroic yttrium orthochromite using Raman, infrared, and inelastic neutron spectroscopy techniques, supporting our experimental results with first-principles lattice dynamics and spin-wave simulations across the antiferromagnetic transition at T N ~ 138 K. Spectroscopy data and simulations together with the heat capacity (C p ) measurements, allow us to quantify individual entropic contributions from phonons (0.01 ± 0.01k B atom -1 ), dilational (0.03 ± 0.01k B atom -1 ), and magnons (0.11 ± 0.01k B atom -1 ) across TN. High-resolution phonon measurements conducted in a magnetic field show that anomalous T-dependence of phonon energies across TN originates from magnetoelastic coupling. Additionally, phonon scattering is primarily governed by the phonon–phonon coupling, with little contribution from magnon–phonon coupling, short-range spin correlations, or magnetostriction effects; a conclusion further supported by our thermal conductivity measurements conducted up to 14 T, and phenomenological modeling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

High-Throughput Electrochemical Characterization of Aqueous Organic Redox Flow Battery Active Material

The development of redox-active organics for flow batteries providing long discharge duration energy storage requires an accurate understanding of molecular lifetimes. Herein we report the development of a high-throughput setup for the cycling of redox flow batteries. Using common negolyte redox-active aqueous organics, we benchmark capacity fade rates and compare variations in measured cycling behavior of nominally identical volumetrically unbalanced compositionally symmetric cells. We propose figures of merit for consideration when cycling sets of identical cells, and compare three common electrochemical cycling protocols typically used in battery cycling: constant current, constant current followed by constant voltage, and constant voltage. Redox-active organics exhibiting either high or low capacity fade rates are employed in the cell cycling protocol comparison, with results analyzed from over 50 flow cells.

Electrochemistry↗

FY22 Progress on Multicontinuum Methods in Containment

Estimation of two-phase fluid flow properties is important to understand and predict water and gas movement through the vadose zone for agricultural, hydrogeological, and engineering applications, such as containment transport and/or containment of gases in the subsurface. To estimate rock fluid flow properties and subsequently predict physically realistic processes such as patterns and timing of water, gas, and energy (e.g., heat) movement in the subsurface, laboratory spontaneous water imbibition with simultaneous temperature measurement and numerical modeling methods are presented in the FY22 progress report. A multiple-overlapping-continua conceptual model is used to explain and predict observed complex multi-phenomenological laboratory test behavior during spontaneous imbibition experiments. This report primarily addresses two complexities that arise during the experiments: 1) capturing the late-time behavior of spontaneous imbibition tests with dual porosity; and 2) understanding the thermal perturbation observed at or ahead of the imbibing wetting front, which are associated with adsorption of water in initially dry samples. We use numerical approaches to explore some of these issues, but also lay out a plan for further laboratory experimentation and modeling to best understand and leverage these unique observations.

58 GEOSCIENCES↗

Towards stacking fault energy engineering in FCC high entropy alloys

Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for many years as a key intrinsic mechanical property, its inference via experimental observations or prediction using, for example, computationally intensive first-principles methods is challenging. This difficulty precludes the explicit use of SFE as an alloy design parameter. In this work, we combine DFT calculations (with necessary configurational averaging), machine-learning (ML) and physics-based models to predict the SFE in the fcc CoCrFeMnNiV-Al high-entropy alloy space. The best-performing ML model is capable of accurately predicting the SFE of arbitrary compositions within this 7-element system. Finally, this efficient model along with a recently developed model to estimate intrinsic strength of fcc HEAs is used to explore the strength–SFE Pareto front, predicting new-candidate alloys with particularly interesting mechanical behavior.

36 MATERIALS SCIENCE↗

Pathways to High-Performance Salt Hydrate Thermochemical Energy Storage Materials and Systems

Thermochemical materials (TCMs) based on salt hydrates are promising for thermal energy storage as they combine high energy densities with low reaction temperatures. However, their adoption is hindered by poor structural integrity and degradation under hygrothermal cycling. Storage performance is governed not only by the chemical reaction, but also by the coupled thermo-chemo-mechanical behavior that evolves with cycling. Understanding and controlling this coupling across length scales (material-to-reactor) is necessary to improve TCM stability and lifetime. In this perspective, we discuss the shortcomings of current characterization approaches and emphasize the need for measuring transport properties and structural transformations using in situ techniques that capture the dynamic evolution of these materials. We also outline opportunities for multiscale modeling frameworks that link thermodynamics and mechanics, enabling predictive evaluation of composite architectures designed for cycling stability. We conclude by identifying research questions that must be addressed to transform TCMs into viable energy storage technologies.

Composites↗

Power Operations of the Mars Exploration Rovers

The rovers of the National Aeronautics and Space Administration’s (NASA) Mars Exploration Rovers (MER) project, Spirit (MER-A) and Opportunity (MER-B), safely landed on the surface of Mars three weeks apart during January 2004. Spirit and Opportunity were built and operated by the Jet Propulsion Laboratory (JPL), which is managed by the California Institute of Technology (Caltech) for NASA. Spirit landed at Gusev Crater, 14.8 degrees south of the equator, and operated continuously on the surface of Mars from January 4, 2004 until last contact from Spirit on March 22, 2010. Opportunity landed at Meridiani Planum, a location 2.5 degrees south of the Martian equator. Opportunity operated continuously on the Martian surface from January 25, 2004 until the last received transmission from Opportunity on June 10, 2018. The goal of the MER project was to determine if Mars ever had a habitable environment, in particular, if it ever had water. During their missions, both Spirit and Opportunity found evidence that liquid water once flowed on the surface of Mars. Both Spirit and Opportunity used a 1.33 m2 triple-junction solar array as their power sources. Based on observations of the original Mars rover, the Sojourner rover of the Mars Pathfinder mission that landed on Mars on July 4, 1997, the expectation was that the Martian dust would rapidly accumulate on the solar arrays of Spirit and Opportunity, and that the rovers would not have enough energy to continue operations after 90 Martian days (sols). Instead, due in part to lower dust accumulation rates than expected and numerous dust cleaning events, the Spirit and Opportunity rovers continued to operate on the Martian surface for over 2000 sols (MER-A) and 5000 sols (MER-B), respectively. During this time, the rovers experienced multiple Martian winters and several dust storms. Because the sources of solar array energy loss were known, the solar array energy output offered a method to scientifically estimate the loading and aeolian removal of dust from the solar arrays each sol. The MER Power operations team called this value that they calculated the solar array Dust Factor (DF). Dust Factor was defined as the fraction of sunlight that penetrates the accumulated dust on the surface of the solar array. A Dust Factor of 1.0 would indicate that the solar array was perfectly clean. A Dust Factor of 0.6 would indicate that only 60% of the available sunlight was able to penetrate the accumulated dust on the solar arrays. The MER Power subsystem operations team used the Multi-Mission Power Analysis Tool (MMPAT) to perform these Dust Factor calculations. The MMPAT software tool modeled the behavior of the solar arrays and the batteries as they interacted with the spacecraft power loads over the mission timeline. MMPAT also had knowledge (through telemetry and user inputs) of telemetered Power subsystem voltages and currents, atmospheric opacity (Tau), rover surface location, rover attitude, the planetary tilt and distance of Mars from the sun based on day of year, the instantaneous elevation of the sun based on time of day, temperatures (internal and external), terrain masking, and shadowing (due to the camera mast and antennas). Once all of the known sources of array energy loss are accounted, the remaining difference between the expected array energy and the actual array energy determines the solar array Dust Factor. The assumptions made while determining the Dust Factor are 1) that the single measured atmospheric opacity value (Tau) is constant over the course of the entire sol, 2) there is no measurable solar cell degradation, 3) there are no shorted strings and therefore, 4) all unexpected solar array energy losses are due to accumulated dust on the solar array. Although it cannot provide an absolute measure of dust loading, the determination of solar array Dust Factor provides a useful way of tracking dust accumulation and aeolian dust removal trends on Martian spacecraft. Any spacecraft on the Martian surface is vulnerable to dust, especially solar-powered spacecraft. The Spirit and Opportunity rovers were operational on the Martian surface for much longer than expected due in part to aeolian removal of dust from their solar arrays. The first few dust removal events were a pleasant surprise to the MER operations teams; however, over time a pattern began to arise. In over three Mars Years on the surface, the Power operations team tracked the solar array Dust Factor at Gusev Crater (location of MER-A) and observed that there were several significant dust removal and deposition events in Mars Year (MY) 27, even in the absence of a large dust storm. In MY 28 at Gusev Crater, the large atmospheric opacity (Tau) increase lagged significant dust removal events. Overall at Gusev Crater, there was a pattern of steady dust accumulation on the solar arrays, with a small number of significant dust cleaning events. At Meridiani Planum, where Opportunity rover operated for over seven Mars Years (late MY 26 to mid MY 34), a clear and consistent pattern of dust movement emerged. Meridiani Planum had a predictable, seasonally dependent pattern of gradual and continuous dust accumulation and removal. In summary, this paper explains the reasons for the development of the solar array Dust Factor and how it was used in mission operations. In particular, this paper describes the MMPAT software package, how it models array energy, including the important assumptions, model inputs and sources of error. And finally, this paper will show how the calculated solar array Dust Factor was used at Gusev Crater and Meridiani Planum to predict dust accumulation rates and weather patterns, and the importance of this generated data set for current and future solar- powered missions to Mars, such as the InSight lander and the planned Mars Sample Return rover.

Chin, Keith B.↗

VARI3D & PERSENT: Perturbation and Sensitivity Analysis

The nodal diffusion method is one of the most widely used approaches in modern reactor analysis. In the nodal diffusion method, a coarse multi-group set of “homogenized” parameters is constructed such that the complex geometry of a reactor core along with the energy dependence of neutron and gamma ray cross sections in a nuclear reactor are conserved in the simpler geometry. The homogenization is typically done on a fuel assembly level as is the case in the DIF3D code developed at Argonne National Laboratory. The nodal methodology is used primarily to predict fuel cycle behavior of nuclear systems of which there is a substantial amount of validation in the literature. Another use of the nodal method is to obtain reactivity coefficients and kinetics parameters for use in a safety analysis of a given nuclear reactor. While there are many ways to obtain reactivity worth and kinetics parameters, the work presented in this manuscript is unique as it provides the user with the ability to compute reactivity worths, kinetics parameters, and cross section sensitivities with a Cartesian and hexagonal geometry based transport code. This manuscript serves as a single manual for two separate codes: VARI3D and PERSENT. The VARI3D code (VARIational 3D) is based upon the classic finite difference diffusion theory solver available in DIF3D. The PERSENT code (PERturbation and SENitivity for Transport) is based upon the variational nodal method employed in DIF3D termed VARIANT. The VARIANT solver was added to DIF3D in 1995 and has seen continued development and use for the last 18 years. Because VARI3D primarily uses deprecated coding practices, rather than incorporating the perturbation and sensitivity treatments for transport within VARI3D, a new coding development was built using modern Fortran coding. The primary purpose of this manual is to describe the theory behind PERSENT (and by convenience, that of VARI3D) and discuss the input and output of PERSENT along with giving potential users an idea of how to use it. While this manuscript does describe the input and output of VARI3D, the PERSENT code is intended to be the replacement capability of VARI3D as PERSENT can generate nearly identical (if not superior) diffusion theory results. In this manuscript, the relevant aspects of generalized perturbation theory and exact perturbation theory that apply to both VARI3D and PERSENT are covered. The input and output of VARI3D is displayed by excerpting several of the example problems. Similarly, the input and output of PERSENT is displayed along with tips on how best to use the code. Note that the input and output of the inhomogeneous solver wrapped around DIF3D (DIF3D_IFS) is also discussed as it is needed to carry out some of the sensitivities in PERSENT such as reaction rate ratios. This manuscript describes several perturbation and sensitivity problems, and the results computed using PERSENT. From these sections, potential users should find that PERSENT provides not only the typical tables of numbers desired in perturbation and sensitivity analysis work, but also can visually plot the result for a more thorough understanding of the space and energy distribution (Section 5). Overall, PERSENT is observed to produce accurate reactivity worths and sensitivities for the displayed set of test problems and clearly demonstrates the need to have a transport-based sensitivity capability as evident from the thousands of percent errors observed in the 21-group hexagonal fast reactor problem (covered in Section 7). The uncertainty calculation capability is described in Section 3 and demonstrated in Section 7.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Drive-pressure optimization in ramp-wave compression experiments through differential evolution

Ramp-wave dynamic-compression experiments are used to examine quasi-isentropic loading paths in materials. The gradual and continuous increase in pressure created by ramp waves make these types of experiments ideal for studying nonequilibrium material behavior, such as solidification kinetics. In ramp-wave compression experiments, the input drive pressure to the experimental setup may be exerted through one of a number of different mechanisms (e.g., magnetic fields, gas-gun-driven impactors, or high-energy lasers) and is generally required for simulating such experiments. Yet, regardless of the specific mechanism, this drive pressure cannot be measured directly (measurements are generally taken at a location near the back of the experimental setup through a transparent window), leading to an inverse problem where one must determine the drive pressure at the front of the experimental setup (i.e., the input) that corresponds to the particle velocity (the output) measured near the back of the experimental setup. Furthermore, we solve this inverse problem using a heuristic optimization algorithm, known as differential evolution, coupled with a multiphysics, hydrodynamics code that simulates the compression of the experimental setup. By running many rounds of forward simulations of the experimental setup, our optimization process iteratively searches for a drive pressure that is optimized to closely reproduce the experimentally measured particle velocity near the back of the experimental setup. While our optimization methodology requires a significant number of hydrodynamics simulations to be conducted, many of these can be performed in parallel, which greatly reduces the time cost of our methodology. One novel aspect of our method for determining the drive pressure is that it does not require physical modeling of the drive mechanism and can thus be broadly applied to many types of ramp-compression experiments, regardless of the drive mechanism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

A survey on degradation modeling, prognosis, and prognostics-driven maintenance in wind energy systems

Wind energy generation proliferated over the past decades, introducing unique challenges and opportunities for failure prediction, operation and maintenance. Decision-makers are continuously looking into new methods to infer failure mechanisms and behaviors of wind turbine components to detect and intervene in the failures before they happen. Evidently, degradation modeling and prognosis become engaging topics for researchers and practitioners to prevent catastrophic failures. Prognostics-driven approaches predict the time of failure for the components (e.g., predicting remaining useful life), which provides significant insights for scheduling of operations and maintenance activities. Integrating these prognostics-driven insights into wind farm operations and maintenance presents a substantial challenge, demanding careful consideration of numerous factors such as accessibility, crew routing, and spare part logistics. This study provides state-of-the-art review for degradation modeling, prognosis, and prognostics-driven maintenance techniques for wind energy systems. The discussed techniques align with the United Nations' sustainable development goals, in particular Goal 7 (Affordable and Clean Energy), by enhancing effectiveness and sustainability of wind energy operations. This work also showcases open research questions related to degradation modeling, prognosis, and prognostics-driven maintenance.

Altinpulluk, Nur Banu↗

Resilient Inverter-Driven Black Start with Collective Parallel Grid-Forming Operation

As modern power systems are experiencing exceptional changes with increasing penetrations of inverter-based resources (IBRs), system restoration using IBRs has received attention. Using local grid-forming (GFM) assets near consumers, engineered to establish grid voltages in the absence of a stiff grid, i.e., bottom-up restoration, a distribution system could obtain high system resilience by not relying on the bulk power system restoration, which requires significant human intervention and procedure. This paper studies the technical feasibility of the novel approach with detailed electromagnetic transient (EMT) simulations. To thoroughly evaluate the potential of GFM inverters and the technical challenges in IBR-driven black start, a detailed three-phase inverter model is developed, including negative-sequence control for voltage balance and a phase-by- phase current limiter to sustain momentary overloading during the black start. To examine dynamic aspects of the black-start process, the EMT simulation also models transformer and motor dynamics to emulate their inrush and startup behaviors as well as network dynamics. In addition, active involvement of grid- following distributed energy resources is also studied to facilitate the black-start process. By allowing multiple GFM inverters to collectively black start without leader-follower coordination, we demonstrate that a system can achieve high resilience even with a fraction of assets lost. Two test cases of inverter-driven black start, using two and one GFM inverters, respectively, for a heavily unbalanced 2-MVA distribution feeder are demonstrated. Takeaways for further study and field deployment are provided.

grid-forming inverter↗

FE K EMISSION AND ABSORPTION FEATURES IN THE XMM-EPIC SPECTRUM OF THE SEYFERT GALAXY IC 4329A

We present a re-analysis of the XMM-Newton long-look of the X-ray bright Seyfert galaxy IC 4329a. The Fe K bandpass is dominated by two peaks, consistent with emission from neutral or near-neutral Fe Ka and KP. A relativistic diskline model whereby both peaks are the result of one doubly-peaked diskline profile is found to be a poor description of the data. Models using two relativistic disklines are found to describe the emission profile well. A low-inclination, moderately-relativistic dual-diskline model is possible if the contribution from narrow components, due to distant material, is small or absent. A high-inclination, moderately relativistic profile for each peak is possible if there are roughly equal contributions from both the broad and narrow components. Upper limits on Fe XXV and Fe XXVI emission and absorption at the systemic velocity of IC 4329a are obtained. We also present the results of RXTE monitoring of this source obtained so far; the combined XMM-Newton and RXTE data sets allow us to explore the time-resolved spectral behavior of this source on time scales ranging from hours to 2 years. We find no strong evidence for variability of the Fe Ka emission line on any time scale probed, likely due to the minimal level of continuum variability. We detect a narrow absorption line, at a energy of 7.68 keV in the rest frame of the source; its significance has been confirmed using Monte Carlo simulations. This feature is most likely due to absorption from Fe XXVI blueshifted to approximately 0.1c relative to the systemic velocity, making IC 4329a the lowest-redshift AGN known with a high-velocity, highly-ionized outflow component. As is often the case with similar outflows seen in high-luminosity quasars, the estimated mass outflow rate is larger than the inflow accretion rate, signaling that the outflow represents a substantial portion of the total energy budget of the AGN. The outflow could arise from a radiatively-driven disk wind, or it may be in the form of a discrete, transient blob of ejected material.

Markowitz, A.↗

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗