REMOTELY: A HOME ENERGY SCORE APP
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29 ENERGY PLANNING, POLICY, AND ECONOMY↗
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A New Residual Strain Mapping Program Using Energy Dispersive X-Ray Diffraction at the Advanced Photon Source
Here, the ability to non-destructively map the residual strain field inside an engineering component is important for predicting its fatigue life or developing processing methods to prevent failure or enhance performance. In this paper, we describe a new residual strain mapping program at the Advanced Photon Source, Argonne National Laboratory. The new program is based on energy dispersive x-ray diffraction (EDXRD). It is capable of non-destructively penetrating a several- cm thick polycrystalline sample fabricated from engineering alloys using high-energy x-rays and measuring the residual strain field with mm or better spatial resolution and approximately ±1 x 10 -4 strain resolution. A multi-element detector array is employed to measure multiple strain components simultaneously. The residual strain mapping setup is augmented with a high-energy tomography capability, allowing precise alignment of the material volume of interest for residual strain mapping and providing a complementary view of the structure to understand the measured strain field. These measurement capabilities are demonstrated using several strain mapping examples ranging from polycrystalline structural alloys to biological materials. We also provide some guidance for the future users of the program for a successful residual strain mapping experiment. We are expanding the capabilities of the new setup with various in situ capabilities including thermo-mechanical loading.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Simulating Energy and Security Interactions in Semiconductor Manufacturing: Insights from the Intel Minifab Model
Semiconductor manufacturing is a highly complex. Fabrication plants must deal with re-entrant flows to support multiple types of wafers being produced simultaneously, each with their own deadlines and specifications. The manufacturing process itself depends upon the ability to control and programmatically adjust a variety of environmental conditions including temperature, humidity, and air quality. In addition, wafer fabrication consumes large amounts of electricity. Emerging technologies may help reduce the energy footprint of such facilities but can introduce cybersecurity risks. Therefore, this paper presents a modeling and simulation framework to quantify tradeoffs between operational measures of performance, energy consumption, and cybersecurity controls. We augment the Intel Minifab model, with the Purdue Enterprise Reference Architecture (PERA) for cybersecurity as well as tool-level energy consumption data from a real-world semiconductor manufacturing testbed. In this manner, we intend to provide stakeholders with a systematic, data-driven approach to evaluate emerging risks within the manufacturing process.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Enhanced dilepton emission from a phase transition in dense matter
It is demonstrated that the presence of a phase transition in heavy-ion collisions, at beam energies that probe dense quantum chromodynamics (QCD) matter, leads to a significant enhancement of the dilepton yield at low invariant mass and requires the embedding of low beam energies per produced pion due to the extended emission time. In addition, the temperature of low-mass dileptons shows a modest decrease due to the mixed phase. The emission of dileptons in the SIS18–SIS100 beam energy range is studied by augmenting the ultra-relativistic quantum molecular dynamics (UrQMD) transport model with a realistic density-dependent equation of state, as well as two different phase transitions. This is achieved by extending the molecular dynamics interaction part of the UrQMD model to a density-dependent interaction potential with a high-density minimum, leading to a phase transition and metastable coexisting high-density states. Together with a high-precision measurement, these simulations will be able to constrain the existence of a phase transition in QCD up to densities of several times nuclear saturation density.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Feasibility of an Active Interrogation System to Classify Waste with He-4 Neutron Spectroscopy
This work investigates a 4 He-detector active interrogation system that leverages neutron spectroscopy to classify nuclear waste streams. MCNP models tested the concept through the simulation of a D-D neutron generator, an array of 4 He detectors, and various waste compositions. The fast-neutron Differential Die-Away signature was augmented with a neutron-energy discrimination signature. This signature isolates induced fission neutrons, the energy of which is greater than that of the D-D monoenergetic spectrum. With the incorporation of this spectroscopic technique, the measurement time decreased by 3–9% (depending on the degree of neutron moderation and absorption presented by the sample), demonstrating how neutron spectroscopy can enhance active interrogation methods. The reduced measurement times would have significant financial and logistical benefits for facilities with large footprints of low-level waste production.
4He detectors↗
Dynamical Unification of Tidal Disruption Events
The ~100 tidal disruption events (TDEs) observed so far exhibit a wide range of emission properties both at peak and over their lifetimes. Some TDEs radiate predominantly at X-ray energies, while others radiate chiefly at UV and optical wavelengths. While the peak luminosities across TDEs show distinct properties, the evolutionary behavior can also vary between TDEs with similar peak emission properties. In particular, for optical TDEs, while their UV and optical emissions decline somewhat following the fallback pattern, some events can greatly rebrighten in X-rays at late time. In this Letter, we conduct three-dimensional general relativistic radiation magnetohydrodynamics simulations of TDE accretion disks at varying accretion rates in the regime of super-Eddington accretion. We make use of Monte Carlo radiative transfer simulations to calculate the reprocessed spectra at various inclinations and at different evolutionary stages. We confirm the unified model proposed by Dai et al., which predicts that the observed emission largely depends on the viewing angle of the observer with respect to the disk orientation. Furthermore, we find that disks with higher accretion rates have elevated wind and disk densities, which increases the reprocessing of the high-energy radiation and thus generally augments the optical-to-X-ray flux ratio along a particular viewing angle. This implies that at later times, as the accretion level declines, we expect that more X-rays will leak out along intermediate viewing angles. Such dynamical model for TDEs can provide a natural explanation for the diversity in the emission properties observed in TDEs at peak and along their temporal evolution.
79 ASTRONOMY AND ASTROPHYSICS↗
A Guide to Current Limiting and Stability with Grid-Forming Inverters
The electric power grid is changing. For many decades, the synchronous generator - an electromechanical device invented in 1887 - has been the workhorse and backbone of power grids across the globe. Our entire infrastructure is built around it. But times change, and so does the power grid. Since the early 21st century, we have seen a gradual shift in modern power grids away from synchronous generators to ones dominated by power electronic inverter-based resources (IBRs). Sources such as photovoltaics, wind turbines, battery storage, fuel cells, and other technologies like high-voltage DC transmission interconnections all rely on an inverter to connect and interface with the grid. They are increasingly being installed on the grid to augment, or even replace, traditional energy sources. This change is a fundamental shift that brings tremendous technical challenges and questions: Can a power grid remain stable with many more inverter-based resources? How do we avoid more blackouts on the grid? How do we keep the grid secure and resilient during disturbances? After all, power electronic inverters are nothing like the big, rotating, iron-and-copper machines that the grid heavily relies on. Many of these questions can be answered by using grid-forming (GFM) inverters, yet many research challenges remain. This document explores GFM inverters and how they can help stabilize the future grid, especially during disturbances and contingencies. It summarizes a two-year research and development fellowship program at NREL. We point interested readers to more detailed works developed during the project along the way. Let's dive in
24 POWER TRANSMISSION AND DISTRIBUTION↗
Electro-mechanical Modeling of Wind Turbine and Energy Storage Systems with Enhanced Inertial Response
In this paper, a coordinated control scheme for wind turbine generator (WTG) and supercapacitor energy storage system (ESS) is proposed for temporary frequency supports. Inertial control is designed by using generator torque limit considering the security of WTG system, while ESS releases its energy to compensate the sudden active power deficit during the recovery process of turbine rotor. WTG is modeled using the fatigue, aerodynamic, structure, turbulence (FAST) code, which identifies the mechanical loadings of the turbine and addresses electro-mechanical interactions in the wind energy system. A damping controller is augmented to the inertial control to suppress severe mechanical oscillations in the shaft and tower of the turbine during frequency supports. Furthermore, the result of small-signal stability analysis shows that the WTG-ESS tends to improve the stability of the whole multi-energy power grid. The major contributions of this paper will be validated by utilizing the proposed control method that combines the grid support capability and maintaining the integrity of structural design of the turbine for normal operations.
17 WIND ENERGY↗
Tight-binding study of beryllium
In this work, we have applied the Naval Research Laboratory Tight-binding (NRL-TB) Method to the hcp alkaline-earth metal Beryllium. This approach consists of fitting to energy band and total energy data generated by the Linearized Augmented Plane Wave (LAPW) for the fcc, bcc, sc, and hcp structures as a function of volume. First, we found that using the Generalized Gradient Approximation (GGA) for the input data secures a better agreement to experimental volumes than the LDA. Second, we found that including small volumes to the fit is needed to get TB parameters that are transferable enough to successfully predict the energies of structures that were not fitted, and to perform molecular dynamics (MD) simulations. The non-orthogonal Hamiltonian obtained, is successful in correctly predicting the hcp lattice as the ground state of Be and finding eight other structures, that were not fitted to LAPW, positioned at higher energies. In addition, the NRL-TB produces accurate energy bands, densities of states, elastic constants, the Bain path and several quantities derived from MD simulations.
36 MATERIALS SCIENCE↗
Immersive Analytics in Critical Spatial Domains: From Materials to Energy Systems
Immersive analytics (IA) leverages virtual reality, augmented reality, and mixed reality to transform how users interact with complex datasets across domains such as science, industry, and education. These immersive technologies offer spatial and multimodal environments that foster intuitive exploration, but they also introduce challenges related to cognitive load, interface design, and system performance. Here, this article presents a comprehensive review of visualization techniques, interaction models, and multimodal inputs utilized in IA. Drawing on case studies in scientific visualization, industrial training, and educational communication, we examine both the potential and limitations of current systems. Finally, we propose future research directions, focusing on real‐time collaboration, adaptive user interfaces, and scalable data exploration strategies to advance the field.
99 - GENERAL AND MISCELLANEOUS↗
Assessment of NuScale SMR Steam Heat Augmentation for Chemical Plant Decarbonization
Nearly 50% of the total energy consumed by the industrial sector in the United States is used to produce process steam with natural gas and coal-fired boilers1 . This project conducts a technoeconomic assessment of a NuScale Small Modular Reactor (SMR) coupled with a chemical plant as an Integrated Energy System (IES) where nuclear produces steam and electric power to meet the requirements of a large chemical plant. In a 2020 study, ORNL evaluated the feasibility of using advanced SMRs, including the NuScale design, to supply energy to the Eastman Chemical Plant. However, since that report was published, NuScale received NRC approval for its uprated 77 MWe design with 56% more power and has also introduced a high-temperature, high pressure, steam heat-augmentation system, a key focus of the new study. The new study also benefits from revised capital costs, a 10-day refueling outage time, reduced plant staffing, higher capacity factors, and a site boundary Emergency Planning Zone methodology. The study consists of a techno-economic assessment of two possible energy sources (nuclear and natural gas) in a number of steam and power generation configurations (NuScale Power Modules (NPMs), boilers and combinations of both) to satisfy the steam and power demand with the most reliable and cost competitive system. A total of 2,947.3 klb/hr of steam and 72.5 MWe of electricity are required for the demonstration case. A range of scenarios and solutions are explored, from a 12-NPM plant (3,000 MWth)—with excess capacity and redundancy, capable of supplying a significant amount of extra power to the grid—to a 4-NPM (1,000 MWth) plant—supplemented with existing boilers or grid power for redundancy. The study uses historical steam and power data from a chemical plant and examines the sensitivity to natural gas and grid power cost variations. Scenarios with up to two times gas and electricity costs were considered. Profitability in a 60-year time horizon was analyzed, consistent with NuScale’s design life specification. A steady-state site integration and reliability analysis was performed, and trade-offs were identified.
20 FOSSIL-FUELED POWER PLANTS↗
Glucose feeds the tricarboxylic acid cycle via excreted ethanol in fermenting yeast
Ethanol and lactate are typical waste products of glucose fermentation. In mammals, glucose is catabolized by glycolysis into circulating lactate, which is broadly used throughout the body as a carbohydrate fuel. Individual cells can both uptake and excrete lactate, uncoupling glycolysis from glucose oxidation. Here we show that similar uncoupling occurs in budding yeast batch cultures of Saccharomyces cerevisiae and Issatchenkia orientalis. Even in fermenting S. cerevisiae that is net releasing ethanol, media 13 C-ethanol rapidly enters and is oxidized to acetaldehyde and acetyl-CoA. This is evident in exogenous ethanol being a major source of both cytosolic and mitochondrial acetyl units. 2 H-tracing reveals that ethanol is also a major source of both NADH and NADPH high-energy electrons, and this role is augmented under oxidative stress conditions. Furthermore, uncoupling of glycolysis from the oxidation of glucose-derived carbon via rapidly reversible reactions is a conserved feature of eukaryotic metabolism.
36 MATERIALS SCIENCE↗
Influence of Photovoltaic Shading on Rooftop Heat Transfer, Building Energy Loads, and Photovoltaic Power Output
Photovoltaic (PV) modules on building rooftops provide shade from summer heating, leading to a reduction in cooling load during hot seasons. However, PV shading also reduces passive solar heating during winter months, leading to an increase in the building heating load during cold seasons. In this study, the heat transfer performance of an enclosure formed by adjacent PV modules is analyzed for three locations in the United States, comparing the daily heat flux through rooftops for the case of a PV-shaded rooftop as well as an unshaded roof. Several result metrics are developed as part of this work, including the saved energy load (SEL), or the energy conserved by adding PV-shading to the rooftop, and the additional energy load (AEL), or the supplemental building energy required to replace shaded solar heating. Lastly, this work calculates the utility factor, being the ratio of SEL and PV power output to AEL as a metric of PV effectiveness. SELs are 5.2, 6.2, and 11.7 kWh/m 2 · year for Dayton, Boise, and Phoenix, respectively, while the AELs for the same locations are 1.6, 1.5, and 2.1 kWh/m 2 · year. The utility factors for the same three locations are 61, 71, and 79. In general, locations with hot, non-cloudy summers and clear skies in winter see the largest utility factor. Further, it is shown that PV shading can conserve building energy during the winter months by preventing radiative losses to cold winter skies.
13 HYDRO ENERGY↗
Data for Glucose Feeds the Tricarboxylic Acid Cycle via Excreted Ethanol in Fermenting Yeast
Ethanol and lactate are typical waste products of glucose fermentation. In mammals, glucose is catabolized by glycolysis into circulating lactate, which is broadly used throughout the body as a carbohydrate fuel. Individual cells can both uptake and excrete lactate, uncoupling glycolysis from glucose oxidation. Here we show that similar uncoupling occurs in budding yeast batch cultures of Saccharomyces cerevisiae and Issatchenkia orientalis . Even in fermenting S. cerevisiae that is net releasing ethanol, media 13C-ethanol rapidly enters and is oxidized to acetaldehyde and acetyl-CoA. This is evident in exogenous ethanol being a major source of both cytosolic and mitochondrial acetyl units. 2H-tracing reveals that ethanol is also a major source of both NADH and NADPH high-energy electrons, and this role is augmented under oxidative stress conditions. Thus, uncoupling of glycolysis from the oxidation of glucose-derived carbon via rapidly reversible reactions is a conserved feature of eukaryotic metabolism.
Conversion↗
Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations
Addressing the "Red-AI" trend of rising energy consumption by large-scale neural networks, this study investigates the measured energy consumption of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset, containing energy consumption and performance data from 41,129 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (trainable parameters), 8 "shapes", and 14 depths on both CPUs and GPUs using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use. Our analysis uncovers a surprising, hardware-mediated non-linear relationship between energy efficiency and network design, challenging the assumption that reducing the number of parameters or FLOPs is the best way to achieve greater energy efficiency. We propose a straightforward and effective energy model that accounts for network size, computing, and memory hierarchy. Highlighting the need for cache-considerate algorithm development, we suggest a codesign approach to energy efficient network, algorithm, and hardware design. This work contributes to the fields of sustainable computing and Green AI, offering practical guidance for creating more energy-efficient neural networks and promoting sustainable AI.
97 MATHEMATICS AND COMPUTING↗
Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops
Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.
97 MATHEMATICS AND COMPUTING↗
Liquid-like solid-state diffusion of lithium ions in super-halide-rich argyrodite
The development of solid electrolytes with high ionic conductivity is essential for advancing safer, high-energy-density solid-state batteries, where lithium site distribution in the sublattice strongly affects ion transport. Here, we report a super-halide-rich argyrodite, Li 5.3 PS 4.3 Cl 1.7 , with remarkable room-temperature ionic conductivity (11.4 ± 0.7 mS cm -1 ) due to population of two additional interstitial lithium sites induced by vacancy redistribution. Prominent lithium density between lithium sites and elevated atomic displacement parameters indicate liquid-like diffusive behavior resembling sublattice melting. Combining electrochemical impedance spectroscopy, pulsed-field gradient NMR, and T 1 relaxation methods, we demonstrate that the augmented conductivity partly arises from a low energy barrier (0.08 eV) at the local scale, attributed to a three-site lithium distribution that drives correlated lithium dynamics. This work advances our understanding of the structure-dynamics interplay in super-halide-rich argyrodites, and highlighting their potential as solid-state battery electrolytes in cells with a coated single-crystal NMC82 cathode that achieve 170 mAh/g capacity at a 0.2 C rate .
25 ENERGY STORAGE↗
Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation
In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. Further, this physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. In conclusion, not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗