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Evolution of the electrothermal instability from thick rod z pinches subject to dynamically and statically applied axial magnetic field

LDRD Project 229427 aimed to determine how electrothermal instability (ETI) driven heating on a z-pinch rod pulsed with intense current evolves under mixed magnetic field (azimuthal + axial) conditions, which is pertinent to pulsed-power-driven magnetically-insulated transmission lines and physics targets. Experiments focused on diagnosing ETI-driven heating from deliberately-machined and well-characterized micron-scale surface defects (referred to as engineered defects or ED). Prior to the start of this project, understanding of how unmagnetized (B z =0) ED evolve had been obtained—simulations largely reproduce the experimentally observed high temperature spots which develop at the poles of bare/uncoated ED. Project 229427 extended the Mykonos Facility ED experimental platform to include axial field. In the first class of experiments, axial field was provided “dynamically” via a helical return can (HRC). In this case, B z and B θ rise at the same rate. Generally, the HRC generated magnetic field at a fixed polarization angle Φ B =arctan(B z /B θ )=15° on the rod's surface. In the second class of experiments, axial field was provided “statically” via a slow-rising (millisecond) external Helmholtz coil pair. In this case, B z was effectively constant/static throughout the 100 ns rise of the Mykonos current. For either case, a primary goal was to determine whether ETI provides a helical seed perturbation for the subsequent growth of the helical magneto Rayleigh-Taylor modes observed in MagLIF (static B z ) and dynamic screw pinch (DSP, dynamic B z ) experiments. When dynamic field was applied using an HRC, emissions from individual ED aligned toward Φ B , while emissions from ED within pairs elongated and preferentially merged along Φ B . These data strongly support that for a randomized defect distribution, heating from nearby current-density perturbations will favorably merge about Φ B to generate an extended seed perturbation that aligns toward the surface-field polarization, and this may impact the orientation of subsequent MRT growth on imploding liners. The results from the static field experiments were largely inconclusive, as any ETI heating rotation, if present, was obscured/overwhelmed by local/random heating from ED rim imperfections.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Evaluating opportunity for distributed wind energy in rural and agricultural areas

Wind energy is among the most mature renewable energy technologies, accounting for 11% of the current US electricity generation in 2024, with the lowest average levelized cost. While it is known that substantial opportunity exists for further development, a key question has been where wind energy is best suited compared to other technologies. This study leverages an immense dataset of parcel-resolved technoeconomic potential for the contiguous United States, focusing on distributed wind (DW) energy—a configuration where one or more turbines, typically 30–60 m in height are used to satisfy nearby energy needs. The analysis is conducted at multiple spatial scales and considers land use, crop land, census, and incentive program data to determine the most opportune areas for market development. The results show that rural, agricultural and residential areas are most suited to DW. Connection type (in front of, or behind the meter) and regulations determine the best application, while siting constraints, economics, demand and the wind resource determines the optimal size of turbine.

17 WIND ENERGY↗

Single-Photon Generation: Materials, Techniques, and the Rydberg Exciton Frontier

Due to their quantum nature, single-photon emitters (SPE) generate individual photons in bursts or streams. They are paramount in emerging quantum technologies such as quantum key distribution, quantum repeaters, and measurement-based quantum computing. Many such systems have been reported in the last three decades, from rubidium atoms coupled to cavities to semiconductor quantum dots and color centers implanted in waveguides. This review article highlights different solid-state and atomic systems with on-demand and controlled single-photon generation. We discuss and compare the performance metrics, such as purity and indistinguishability, for these sources and evaluate their potential for different applications. Finally, a new potential single-photon source, based on the Rydberg exciton in solid-state metal oxide thin films, is introduced, where we discuss its promising features and unique advantages in fabricating quantum chips for quantum photonic applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Stimulated emission tomography for efficient characterization of spatial entanglement

Stimulated-emission tomography (SET) is an excellent tool for characterizing the process of spontaneous parametric down conversion (SPDC), which is commonly used to create pairs of entangled photons for use in quantum information protocols. The use of stimulated emission increases the average number of detected photons by several orders of magnitude compared to the spontaneous process. In a SET measurement, the parametric down conversion is seeded by an intense signal field prepared with specified mode properties rather than by broadband multimodal vacuum fluctuations, as is the case for the spontaneous process. The SET process generates an intense idler field in a mode that is the complex conjugate to the signal mode. In this work we use SET to estimate the joint spatial mode distribution (JSMD) in the Laguerre-Gaussian (LG) basis of the two photons of an entangled photon pair. The pair is produced by parametric down conversion in a beta barium borate (BBO) crystal with type-II phase matching pumped at a wavelength of 405 nm along with a 780 nm seed signal beam prepared in a variety of LG modes to generate an 842 nm idler beam of which the spatial mode distribution is measured. We observe strong idler production and good agreement with the theoretical prediction of its spatial mode distribution. Our experimental procedure should enable the efficient determination of the photon-pair wavefunctions produced by low-brightness SPDC sources and the characterization of high-dimensional entangled-photon pairs. Published by the American Physical Society 2024

Xu, Yang (ORCID:0009000954547320)↗

Proactive demand-side participation: Centralized versus transactive demand-Supply coordination

The active participation of demand-side flexible resources in the wholesale market price formation and load dispatch process is crucial to encouraging demand-side participation. This calls for a joint supply-demand coordination mechanism to fully take advantage of the flexible resources in distribution systems, including distributed energy resources (DERs) and responsive loads (RLs). Here this paper aims at comparing and evaluating the centralized and transactive distribution-level market coordination mechanisms. We introduce the centralized and transactive demand-supply coordination mechanisms for the distribution-level market and elaborate on the structural difference between the two frameworks. Relevant metrics and test scenarios are proposed for a meaningful comparison. The key observations of the comparative study are generalized from the perspective of different entities in the market: fixed loads, flexible loads, DERs, and conventional generators. It is observed that while the centralized approach leads to socially optimum solutions, the transactive approach by allowing for competitive bidding at the distribution-level, results in clearing higher flexible demand, and thus higher electricity cost at the transmission-level. As a result, DERs and fixed loads receive a higher surplus in the centralized approach, while conventional generators and flexible loads are more profitable in the transactive approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Dynamics of the Wide-angle Tailed (WAT) Galaxy Cluster A562

We present the first in-depth dynamical analysis of the archetypal wide-angle tailed (WAT) cluster A562. We have combined Gemini observations with archival data from the literature to form a sample of 76 cluster members and derived a mean redshift of 0.1088 ± 0.0004 and a velocity dispersion of 919 ± 116 km s{sup −1}. This relatively large velocity dispersion suggests either a very massive cluster (M {sub dyn} > 6.9 × 10{sup 14} M {sub ☉}) and/or a merger system. The merger model is supported by a non-Gaussian galaxy velocity distribution, an elongated spatial distribution of likely cluster members, and an elongated X-ray emitting gas. This scenario would generate the bulk flow motion of the intracluster medium that can exert enough ram pressure to bend the radio jets. Thus, our observations support the model in which a recent off-axis merger event produced the cluster wide conditions needed to shape the WAT in A562.

79 ASTRONOMY AND ASTROPHYSICS↗

Enhancing flexibility for climate change using seasonal energy storage (aquifer thermal energy storage) in distributed energy systems

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. Furthermore, the design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

25 ENERGY STORAGE↗

DSGAN

This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low-and high- resolution simulations to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique. Results show that the SR technique produces results similar to those of the bilinear interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than the original high resolution simulations. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique do, their predictions are still not as close as the original high resolution simulations. The CNNs trained by CGAN generate more realistic and physically reasonable results, better capturing not only data variability in time and space but also extremes such as intense and long-lasting storms. The new proposed CNN-based downscaling approach can downscale precipitation from 50~km to 12~km in 14~min for 30~years

LIU, ZHENGCHUN↗

Vectorization techniques for probability distribution functions using VecCore

Probability distribution functions (PDFs) are very used in modeling random processes and physics simulations. Improving the performance of algorithms that generate many random numbers under complex PDFs is often a very challenging task when methods as direct functions are not available. In this work we present general strategies on how to vectorize some PDFs using VecCore library. We show the results for the Exponential, Gaussian, discrete Poisson and Gamma probability distributions.

Chaparro Amaro, Oscar R.↗

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Evidence for suprathermal ion distribution in burning plasmas

At the National Ignition Facility, inertial confinement fusion experiments aim to burn and ignite a hydrogen plasma to generate a net source of energy through the fusion of deuterium and tritium ions. The energy deposited by α-particles released from the deuterium–tritium fusion reaction plays the central role in heating the fuel to achieve a sustained thermonuclear burn. In the hydrodynamic picture, α-heating increases the temperature of the plasma, leading to increased reactivity because the mean ion kinetic energy increases. Therefore, the ion temperature is related to the mean ion kinetic energy. In this report we use the moments of the neutron spectrum to study the relationship between the ion temperature (measured by the variance in the neutron kinetic energy spectrum) and the ion mean kinetic energy (measured by the shift in the mean neutron energy). We observe a departure from the relationship expected for plasmas where the ion relative kinetic energy distribution is Maxwell–Boltzmann, when the plasma begins to burn. Understanding the cause of this departure from hydrodynamic behaviour could be important for achieving robust and reproducible ignition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

60 APPLIED LIFE SCIENCES↗

Summary: Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

The Clean Energy Investment Accelerator conducted a case study analysis of battery energy storage system (BESS) feasibility for an industrial park in Vietnam using the National Renewable Energy Laboratory's (NREL's) REopt platform (a distributed energy modeling and optimization tool) to evaluate how BESS may reduce electricity costs, increase utilization of onsite renewable energy generation, and improve resilience to utility grid outages. This presentation summarizes the analysis and key takeaways.

batteries↗

Solar Energy Integration in Buildings

Energy consumption in buildings has been steadily increasing and contributing up to 40% of the total energy use in developed countries [1]. In developing countries, the share of building energy consumption is smaller, but given population growth, urbanization, and rising demands for building services and comfort, the sharp rise of building energy use is probably inevitable. Thus, reducing building energy consumption plays a very important role in controlling global energy demand and mitigating climate change, so as to develop a sustainable environment. Solar energy, as the most important source of renewable energy, features the characteristics of clean, renewable, inexhaustible, and widely distributed energy, relative to other kinds of energy sources. Solar energy systems can now generate electricity at a cost equal to or lower than local grid-supplied electricity [2]. More importantly, solar energy can provide almost all forms of energy needed by buildings, through active or passive methods.

buildings↗

Synergies and trade-offs between storage, transmission, and sector coupling in high renewable energy systems

Energy storage, transmission, and sector coupling are some prominent flexibility solutions to support variable renewable energy (VRE) integration. However, investment cost uncertainties and public acceptance could hamper the deployment of these flexibility solutions. This raises questions about the development and cost-effectiveness of future energy systems, especially on how the dependence on local and cross-border solutions of flexibility would evolve if the uptake of these solutions is restricted. In this context, this paper identifies the synergies among flexibility options under restrictions on transmission expansion or increased costs of energy storage. It contributes to determining whether investments in energy storage and/or transmission expansion offer the least-cost transition and investigates the impact of sector coupling on these solutions. A long-term energy system planning and optimisation model towards 2050 is developed using the open-source energy system optimisation tool Balmorel, and a case study of the countries surrounding the Baltic Sea and the North Sea is established. Five cases with restrictions imposed on transmission expansion and higher energy storage technology costs are analysed at different levels of sector coupling. The results highlight the importance of transmission expansion at all levels of sector coupling. As the level of sector coupling increases, uncertainties around the cost of energy storage drive the least-cost pathways. Optimal investment solutions are found to have a mix of transmission and energy storage in capacity expansion at all levels of sector coupling.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Guest Editorial: Special Issue on recent advancements in electric power system planning with high-penetration of renewable energy resources and dynamic loads

The goal of this Special Issue is to present the state-of-the-art methodologies developed for expansion planning of all segments of the modern power systems, characterized by separated businesses, high penetration of renewable resources and new load types, as well as by application of technologically advanced solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

ReaxFF Reactive Force Field for Exploring Electronically Switchable Polarization in Zn 1– x Mg x O Ferroelectric Semiconductors

Cation misfit in traditional ferroelectric crystals offers a new material platform that can drive electronic components toward structural miniaturization and high-density integration, enabling deviation from von-Neumann architectures. Here, we explore ferroelectricity in Zn 1–x Mg x O, a nontraditional ferroelectric material with tunable properties. Using data from density-functional theory calculations, we have developed a ReaxFF reactive force field to explore the ferroelectric properties of Zn 1–x Mg x O and reveal the hysteresis behavior. We discover that ferroelectric switching can be observed at a critical thickness of 10 nm with a residual polarization of ~100 μC/cm 2 . Our analysis indicates that an increase in Mg-substitution correlates with a decrease in the coercive field. We also observe a strong temperature dependence of the coercive field in Zn 1–x Mg x O, with values decreasing as the temperature increases. Additionally, we find that the distribution of Mg atoms significantly impacts the coercive field, with a clustered distribution leading to a substantial increase. In particular, a decrease in coercive field values is observed when Mg atoms are randomly distributed, compared to uniform distribution. Furthermore, leveraging tunable hysteresis behavior offered by varied percentages and distribution of Mg-substitution provides valuable insights into the design of next-generation functional devices and will inspire further investigations.

36 MATERIALS SCIENCE↗

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. Additionally, to reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

36 MATERIALS SCIENCE↗