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At least 1,225 records · Page 68

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ∼2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the “standard” reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove non-linearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

Impedance Scan of Inverter-Based Resources and Diesel Generator for Stability Analysis: Preprint

Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline- based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive- sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.

grid following inverter↗

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Impedance Scan of Inverter-Based Resources and Diesel Generator for Stability Analysis

Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline-based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive-sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.

current measurement↗

Three-Dimensional Grid Visualization for Planning Activities: A Dubai Case Study

National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Multilayered microstructures with shape memory effects for vertical deployment

This paper presents a fabrication and characterization of multilayered microstructures with shape memory effects enabling large vertical deployment under electro-thermal actuation. Our previous research demonstrated vertical deployment of such microstructures by the effect of thermal mismatch. Development of equiatomic NiTi layers in the multilayered microstructure is investigated further for shape memory effects. Multilayered microstructures are built by sputtered NiTi layers and a lift-off process. Negative photoresist ma-N1420 enables clean lift-off of 500 nm thick NiTi layers by forming a significant undercut profile after development. The parametric study on co-sputter powers for Ti and Ni 50 Ti 50 targets suggests that 100 W RF on Ti target and 200 W DC on Ni 50 Ti 50 target can deposit Ni 49.62 Ti 50.38 layers. X-Ray Diffraction (XRD) and Atomic Force Microscopy (AFM) were used to study the crystal structures and surface topography of NiTi layers. XRD results of post-annealed Ni 49.62 Ti 50.38 layers show coexistence of austenite and martensitic phases at room temperature, suggesting that the transformation temperature of such NiTi layers should be approximate 20 °C. The surface topography of Ni 49.62 Ti 50.38 layers reveals substantial increase of surface roughness at ambient conditions after the annealing. Experimental verification of the multilayered microstructure for vertical deployment was carried out by Signatone Probe Station and Dual Scanning Electron Microscope/Focused Ion Beam (SEM/FIB) system. Finally, a vertical deployment of the two-dimensional (2D) multilayered microstructures for three-dimensional (3D) can be detected by applying a constant voltage of 0.04 V, and the expected 3D deployment displacement is enlarged from 2 μm to 10 μm by introducing the shape memory effect.

42 ENGINEERING↗

Of Actors, Cities and Energy Systems: Advancing the Transformative Potential of Urban Electrification

The electrification of transportation and the integration of electric vehicles (EVs) with buildings connected to clean grids has been touted as one of the key solutions to the global decarbonization challenge. Cities are on the frontlines of current and future electrification, as they depend on and drive electricity generation, distribution, and use. City actors also occupy a central role in the actions to enable electrification to support energy transitions in efficient, equitable, environmentally sound, and resilient ways. Currently, however, research and development on the interactions between actors, cities and energy systems is predominantly conducted in disciplinary siloes. This topical review analyzes the transformational potential of urban electrification. It focuses on efforts to electrify transportation and integrate EVs with buildings connected to a clean grid. We find that actions in these area are driving change; they are adopted by wealthier populations and on an experimental basis by specific communities. Their larger-scale growth is constrained by institutional, behavioral, and infrastructural factors. We also find that existing siloed disciplinary approaches are often incompatible with advancing holistic research. To achieve that, divergent communities of scholars need to come together to integrate their research and create broader perspectives. Through incorporation of the social sciences, these perspectives need to consider the societal limits and potentials brought to bear by human behavior and decision making. Only then can urban electrification be understood as the empirically rich and socially complex topic that it is. And only with this understanding will innovations and smart policy actions be able to tap into the transformational potential of urban electrification.

cities↗

How to Build a Connected Community: Policies to Promote Grid-interactive Efficient Buildings and Demand Flexibility

Efficient, connected, grid-interactive, smart, and flexible buildings are key to decarbonizing the U.S. energy economy, optimizing energy use, reducing electric consumers’ bills, integrating variable renewable energy resources, and improving the reliability and performance of the nation’s electricity grids. Such grid-interactive efficient buildings have high levels of energy efficiency layered with other distributed energy resources (DERs) and intelligent controls to provide demand flexibility. Policy support is unfolding at the federal, state, and local levels to transform homes and workplaces into state-of-the-art energy-efficient buildings and community-level grid services. This paper starts by describing the potential benefits. Next, it highlights existing policies — with a focus on state-level actions — that support grid-interactive efficient building deployment and demand flexibility. Finally, it identifies current trends and gaps, policies and programs that promote grid-interactive efficient buildings, and aggregations of grid-interactive efficient buildings referred to as Virtual Power Plants.

Schwartz, Lisa C↗

Oxidation‐Driven Enhancement of Intrinsic Properties in MXene Electrodes for High‐Performance Flexible Energy Storage

Abstract MXenes, a novel class of 2D materials, exhibit great potential for energy storage due to their unique layered structure and excellent electrical conductivity. However, improving the intrinsic electrochemical storage capacity of MXenes remains a significant challenge, often requiring the incorporation of other Faradaic materials. Oxidation, in particular, poses a key issue that impacts the capacity of MXene devices. In this study, controlled oxidation is employed to create nanoscale holes within MXene, transforming them into holey MXene (H‐MXene) nanosheets. These porous structures shorten ion transport distances and increase ion transport pathways, thereby significantly enhancing the electrochemical storage capacity of MXenes. The resulting H‐MXene micro‐supercapacitors (MSCs) demonstrate exceptional performance, achieving an aerial capacitance 2.5 times that of unmodified MXene electrodes, along with excellent cycling stability, retaining 91.7% of their capacitance after 10 000 cycles. Additionally, a flexible integrated system combining energy storage and sensing functionalities is developed, showcasing its scalability in self‐powered sensing applications. The incorporation of self‐healing polyurethane (PU) enables the device to retain 90% of its storage capacity after undergoing self‐healing. This study presents a novel approach for developing high‐performance MXene‐based energy storage devices and provides valuable insights into efficient ion transport and storage in 2D materials.

Cheng, Yongfa [Department of Materials Science and↗

Combining model-based and model-free methods for stochastic control of distributed energy resources

Modern distribution systems are experiencing a fast transformation with the growing penetration of distributed energy resources (DERs). Along with the economic and environmental benefits of DERs, challenges arise to address the uncertainties caused by their inherent volatility. If properly coordinated, however, DERs have the potential to provide the controllability that grid operators need. Here, we propose a hierarchical control framework that combines the model-based and model-free methods for stochastic DER control in distribution systems. The upper-level scheduler considers a chance-constrained optimal power flow problem (model-based) that schedules DER setpoints to minimize the operational cost and maintain the operating reserve. The lower-level distributed DER controllers absorb real-time disturbances and uncertainties using the extremum seeking control (model-free) to achieve grid objectives. The combination of model-based and model-free methods allows us to take the advantages of both methods to effectively manage the uncertainty in distribution systems. The proposed work is demonstrated on the IEEE 13-node feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A synthetic co-culture for bioproduction of ammonia from methane and air

Abstract Fixed nitrogen fertilizers feed 50% of the global population, but most fixed nitrogen production occurs using energy-intensive Haber–Bosch-based chemistry combining nitrogen (N2) from air with gaseous hydrogen (H2) from methane (CH4) at high temperatures and pressures in large-scale facilities sensitive to supply chain disruptions. This work demonstrates the biological transformation of atmospheric N2 into ammonia (NH3) using CH4 as the sole carbon and energy source in a single vessel at ambient pressure and temperature, representing a biological “room-pressure and room-temperature” route to NH3 that could ultimately be developed to support compact, remote, NH3 production facilities amenable to distributed biomanufacturing. The synthetic microbial co-culture of engineered methanotroph Methylomicrobium buryatense (now Methylotuvimicrobium buryatense) and diazotroph Azotobacter vinelandii converted three CH4 molecules to l-lactate (C3H6O3) and powered gaseous N2 conversion to NH3. The design used division of labor and mutualistic metabolism strategies to address the oxygen sensitivity of nitrogenase and maximize CH4 oxidation efficiency. Media pH and salinity were central variables supporting co-cultivation. Carbon concentration heavily influenced NH3 production. Smaller-scale NH3 production near dispersed, abundant, and renewable CH4 sources could reduce disruption risks and capitalize on untapped energy resources. One-Sentence Summary Co-culture of engineered microorganisms Methylomicrobium buryatense and Azotobacter vinelandii facilitated the use of methane gas as a sole carbon feedstock to produce ammonia in an ambient temperature, atmospheric pressure, single-vessel system.

Biotechnology & Applied Microbiology↗

Scalable Nano-Scaffold SOFC Anode Architecture Enabling Direct Hydrocarbon Utilization

This project is based on WVU’s pending patents, technology and aims to design and modify the internal surfaces of the Ni/YSZ anode from currently commercially viable Solid Oxide Fuel Cells (SOFCs) using the additive manufacturing process of Atomic Layer Deposition (ALD). The surface architecture/scaffold added onto the internal surface of the anode possesses an engineered nanostructure but it features only commonly-used oxide conductors and electro-catalyst materials. The surface layer possesses a minimum thickness of ~2-40 nm and is solely designed to control the surface reforming reactions and to increase catalytic activity. Three-dimensional (3D) nano scaffold architectures with the noble metal nano-catalyst, low-cost bimetallic catalytic alloys, and nano-scale ionic conducting oxide fully compatible with the state-of-the-art Ni/YSZ anode, were applied to the internal surface of the entire porous SOFC anode using ALD. In the present work, the surface scaffold architecture is essentially multi-functional at the nano-scale, facilitated by the multiple heterostructured interfaces. It will significantly enhance the power density and cell durability for direct hydrocarbon utilization by (1) increasing the number of electrochemical reaction sites to enhance the hydrogen/hydrocarbon oxidation reactions; (2) reducing carbon formation; (3) mitigating the coarsening of backbone Ni phase and the oxidation attack of Ni from oxidants (e.g., H2O, CO2); and (4) promoting the internal reforming capabilities, especially for natural gas applications. ALD is employed to generate stable anode surface architectures that are uniform, precisely controllable at the atomic scale, and accurately repeatable for processing. The engineered anode surface nano-scaffold architecture was cataloged and analyzed using High-resolution Transmission Electron Microscopy (TEM), and cell power/durability performance assessed via comprehensive electrochemical performance testing with commercial specimens and relevant environments using hydrocarbon fuels. To the best of our knowledge, this project is the First Report on ALD of Ni/YSZ. The actual achievement of this Project includes (1). Successful demonstration of 7 types of ALD layers on Ni/YSZ anode, including Co, Ni, Mn, Pt, Ru, ZrOx and multi-functional nano-composite. (1). Conformal coating and subsequently spontaneously pinning the discrete nano-catalyst, including the precious metal nano-catalyst and the Ni and Co catalysts, on the YSZ surface upon the electrochemical operation in the reduced atmosphere. Those nano-catalysts on the ionic-conducting YSZ provided excellent sites for promoting internal reforming; (2). Demonstrated ALD coating increased both catalytic activity and conductivity of Ni/YSZ. Conformal coating provided dopants and introduced additional electrical conducting pathways on the YSZ ionic conductor. The doped surface layer of YSZ with mixed conductivity thus further introduces the active triple phase boundaries adjacent to the ALD-coated nano-catalysts such as Pt, Co, and Ni that are pinned on the YSZ surface. The nano-composite ALD coating on Ni/YSZ anode has significantly increased cell durability; and (3). ALD coating of Ni/YSZ anode increased the power density of the entire cell by 300%. For a long time, the SOFC performance, such as the power density, was deemed hindered by the cathode. The sluggish oxygen reduction reaction (ORR) in the cathode was deemed as hindering the power density of the SOFCs. For the anode-supported commercial SOFCs, the cell performance is considered to be limited by the cathode's performance. For the first time in the field of SOFC, this project has demonstrated that (1). the performance of commercial SOFCs can be further increased by the ALD coating on Ni/YSZ anode backbone. (2). ALD coating on Ni/YSZ fuel electrodes results in the enhancement of power density, and increased reliability, robustness, and endurance of SOFCs, for their application using both hydrogen and hydrocarbon fuels over the entire operating temperature range of 650-800ºC for the inherently functional commercial cells. (3). ALD coating provides alternative approaches of exsolutions for introducing the stable catalyst onto the internal surface of the Ni/YSZ electrode. ALD coating could be much more versatile than exsolution in employing the catalysts with various chemistries onto the various backbones. (4). Due to the negligible amount of ALD materials coated onto the internal surface of the porous cathode of the as-fabricated cells, a peak power density increase up to 300 % induced by ALD coating was simultaneously achieved in terms of both power density and specific power. (5). The ALD coating developed through this project was applied to both the SOFC and Solid Oxide Electrolysis Cells (SOEC). SOEC’s face a similar but more demanding need to improve the fuel electrode's performance. It opens further research directions for electrocatalytic surface nanoionics with a wide range of chemistry. It will revolutionize our ability to render the formation of a nanostructured electrode that has been constantly pursued yet barely achieved for practical SOFC/SOEC applications. The research is also immediately transformative since both the preliminary data and the proposed work are on the direct implantation of nanoionics into the state-of-the-art inherently functional SOCs. It represents an immediate impact on the commercial sectors in SOC technology since the applied ALD processing is computer-controlled ALD coating using the commercial ALD systems, and it is scalable to both the single cells and SOC stacks.

36 MATERIALS SCIENCE↗

Microreactor Automated Control System - Digital Twin Models and Advanced Control Systems Updates

Automation of control systems is expected to be important in the economic and safe operation of microreactors. Therefore, there is a need to develop and demonstrate automated control for microreactors, along with the development of testbeds for this purpose. This report provides updates on the status of a nonnuclear microreactor automated control system (MACS)—a real-time, hardware-in-the-loop testbed for non-nuclear testing of microreactor control system automation. A real-time hardware-in-the-loop testbed incorporates the realistic dynamics of physical systems into control system development and testing. The collaborative effort between Oak Ridge National Laboratory (ORNL) and Idaho National Laboratory (INL) resulted in the development of a prototypic microreactor plant-level digital twin that includes the reactor and a balance of plant system. Advanced control strategies were incorporated to demonstrate testing of control automation solutions. The gRPC communication protocol, which was implemented in the hardware-in-the-loop testbed by INL, was coupled to a digital twin model developed using the TRANsient Simulation Framework of Reconfigurable Models (TRANSFORM) library in Modelica. This digital twin simulation was tested with the ViBRANT hardware for realistic feedback and visual representation of control action in real time. A modular Python client structure was developed to manage functional mock-up unit-based simulation and real-time gRPC communication. Hardware-in-the-loop testing indicated that the modeled reactor—a natural-convection, molten-salt coolant loop configuration—responds well to control of drum positioning for modulation of reactor core power, as well as system-level control and downstream demand changes. Ongoing research is focused on integrating additional control algorithms that utilize data from newly included sensors within the MACS hardware testbed, as well as demonstrating and assessing the performance of the different automated control algorithms on multiple additional operational scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Detecting Anomalies for Fire Prevention in Distribution Systems: Challenges and Analytical Techniques

Electric utilities in California have historically been linked to up to 10% of wildfires. To mitigate this risk, Southern California Edison has invested significantly in wildfire prevention strategies, including undergrounding cables and enhancing equipment inspections. This article explores a novel approach to fire prevention by detecting anomalies in the distribution system that may indicate potential fire hazards. The focus is on identifying arcing conditions through high-resolution point-on-wave (POW) measurements. Arcing, a precursor to fires, is challenging to detect due to its subtle transients and complex system topology. The article discusses the use of advanced signal processing and machine learning techniques, such as spectral correlation function and discrete wavelet transform, to extract features from POW data and accurately identify arcing events. The study demonstrates a high accuracy rate in detecting arcing, paving the way for improved fire prevention measures in electric distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Anomalous polarization reversal in strained thin films of CuIn⁢P 2 ⁢S 6

Strain-induced transitions of polarization reversal in thin films of a ferrielectric CuIn⁢P 2 ⁢S 6 (CIPS) with ideally conductive electrodes are explored using the Landau-Ginzburg-Devonshire approach with an eighth-order free energy expansion in polarization powers. Due to multiple potential wells, the height and position of which are temperature and strain dependent, the energy profiles of CIPS can flatten in the vicinity of the nonzero polarization states. Thereby, we reveal an unusually strong effect of a mismatch strain on the out-of-plane polarization reversal, hysteresis loop shape, dielectric susceptibility, and piezoelectric response of CIPS films. By varying the sign of the mismatch strain and its magnitude in a fairly narrow range, quasistatic hysteresisless paraelectric curves can transform into double, triple, and other types of pinched and single hysteresis loops. The strain effect on the polarization reversal is opposite, i.e., anomalous, in comparison with many other ferroelectric films, for which the out-of-plane remanent polarization and coercive field increase strongly for tensile strains and decrease or vanish for compressive strains. For definite values of temperature and mismatch strain, the low-frequency hysteresis loops of polarization may exhibit negative slope in a relatively narrow range of external field amplitude and frequency. The low-frequency susceptibility hysteresis loops, which correspond to the negative slope of polarization loops, contain only positive values, which can be giant in the entire range of field changes. In conclusion, the corresponding piezoresponse also reaches giant values, being maximal near coercive fields.

Dielectric properties↗

Validating simulated mountain wave impacts on hub-height wind speed using SoDAR observations

The ascent of stably stratified air over a mountain barrier can trigger the generation of mountain waves. Mountain waves occur frequently over the Columbia River Gorge in western North America and can impact wind power generation over the area. Therefore, predicting the details of mountain waves events (e.g., dominant wavelength, timing, and duration) can be very valuable for the wind energy community. In this study, the ability of the Weather Research and Forecasting (WRF) model to simulate mountain waves and their impact on hub-height wind speed is investigated. Our results suggest that the WRF model has moderate skill in simulating observed mountain wave. Further, given WRF predictions of wavelength range and wave period, the Fast Fourier Transform can calculate the simulated mountain wave impact on hub-height wind speed. The resulting wind speeds agree well with SoDAR observations in terms of both magnitude and pattern. Finally, for the simulated cases, WRF consistently predicts impacts of significant mountain wave events about an hour earlier than the actual observations. Overall, in this work, the sensitivities as well as uncertainties associated with our methodology are discussed in detail.

17 WIND ENERGY↗