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This presentation will cover grid integration and controls activities and capabilities at INL focusing on nuclear energy integration with details on microgrid capabilities.
Enhanced Geothermal Systems (EGS) have the capacity to broaden the accessible resource pool for geothermal power generation. Traditionally viewed as a “baseload” resource, their flexible operation might also enable dispatchable load‐following generation and long‐term energy storage, aligning them with the evolving landscape of decarbonized electricity systems. However, increasing permeability and extracting energy during EGS operations can induce microseismic events; for many prior EGS efforts, some associated seismicity has been observed. While energetically beneficial, the flexibility of EGS operations prompts our inquiry into whether new types of operations will yield previously unseen seismicity patterns. We demonstrate the use of distributed acoustic sensing (DAS) with real‐time edge computing to monitor seismicity during a pilot test of a cyclically operated EGS facility at the Blue Mountain geothermal field. Our focus lies in uncovering seismicity insights from the real‐time microseismic catalog, particularly during load‐following dispatchability tests simulating flexible EGS operation. Here, we find that variations in pore pressure consistently correlate with seismicity, and that controlling pressure cycles during flexible operations appears to constrain microseismic activity during subsequent cycles. The spatio‐temporal evolution of microseismic clouds recorded during cyclic injection cycles fits diffusive models over our available observation period. Additionally, seismicity elevation lags behind pore pressure increases, likely due to pressure diffusion to the fracture system boundary. Through real‐time monitoring, we offer novel insights into seismicity associated with flexibly operating EGS. Our findings suggest that leveraging DAS and edge computing can inform EGS operations and help mitigate induced seismicity.
We analyzed the performance of an electrochemical desorption unit to be implemented in an electrochemically mediated amine regeneration (EMAR) process, which separates CO 2 and regenerates the amine upon modulation of a targeted species that complexes with the amine molecules. Using a simplified one-dimensional boundary layer model, we combined the kinetic expressions of the faradaic reactions with mass transfer effects to analyze the polarization behavior of the flow cell. Mass transfer limitations become dominant at high current densities, leading to increases in energy consumption. An understanding of scaling laws with key parameters, such as the Sherwood number, is required to achieve high power operation with minimal mass transfer losses. Because the EMAR chemistry is sensitive to temperature, heat distribution in the unit is also examined. Our results indicate that the desorption performance increases with addition of a high-temperature heat source. Additionally, incorporation of membrane crossover suggests energy losses because of diffusion and migration of active species. Furthermore, these findings inform on scaling laws for at-scale desorber design and provide a model for scaling up the EMAR system for larger scale operations.
Spin-squeezed states provide a seminal example of how the structure of quantum mechanical correlations can be controlled to produce metrologically useful entanglement. These squeezed states have been demonstrated in a wide variety of quantum systems ranging from atoms in optical cavities to trapped ion crystals. By contrast, despite their numerous advantages as practical sensors, spin ensembles in solid-state materials have yet to be controlled with sufficient precision to generate targeted entanglement such as spin squeezing. Here we report the experimental demonstration of spin squeezing in a solid-state spin system. Our experiments are performed on a strongly interacting ensemble of nitrogen–vacancy colour centres in diamond at room temperature, and squeezing (−0.50 ± 0.13 dB) below the noise of uncorrelated spins is generated by the native magnetic dipole–dipole interaction between nitrogen–vacancy centres. To generate and detect squeezing in a solid-state spin system, we overcome several challenges. First, we develop an approach, using interaction-enabled noise spectroscopy, to characterize the quantum projection noise in our system without directly resolving the spin probability distribution. Second, noting that the random positioning of spin defects severely limits the generation of spin squeezing, we implement a pair of strategies aimed at isolating the dynamics of a relatively ordered sub-ensemble of nitrogen–vacancy centres. Furthermore, our results open the door to entanglement-enhanced metrology using macroscopic ensembles of optically active spins in solids.
Microcracking and residual stresses are key limitations of laser powder bed fusion (LPBF), an additive manufacturing (AM) technique that uses a high power laser to consolidate successive layers of metal powder. For tungsten, these microcracks find their origin in the combination of a ductile-to-brittle transition (DBT) between 200°C and 400°C and high residual stresses. This work utilized in situ high speed video to capture the cracking mechanisms, and combined the experimental results with thermomechanical modeling to unveil correlations between crack network morphology and process parameter-related variables, such as the local temperature and residual stress distributions. Consequently, preheating and alloying with rare earth oxides were adopted as possible crack mitigation strategies, the efficacy of which was tested against the initial fundamental baseline results. Preheating temperatures above 500°C eliminated all cracking, though this threshold is expected to be higher when tungsten powder (higher oxygen content) is introduced. Alloying can serve as an active oxygen getter in the system, but the rare earth oxides in this work were too large to have significant contributions to crack-mitigation.
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.
3D printing of fiber-reinforced composites has been receiving increasing attention as an efficient additive approach enabling lightweight, functional, and high-performance components required for industrial applications. The properties of fiber composites significantly depend on internal microstructures, including fiber orientation, distribution, and degree of alignment. Although multiple strategies have been introduced for controlling the fiber composites microstructures, these strategies are mostly active approaches relying on additional control of mechatronics parts, external forces, or intentionally introduced unstable flows inside narrow nozzles. These methods suffer from higher risk of nozzle clogging and requiring extra parts in 3D printing. Here, we introduce helicoid channels into extrusion system as a passive approach to control the fiber alignment without any additional moving parts in direct-write 3D printing. The helicoid channels automatically guide the composite inks and align fibers before flowing into narrow nozzle space, avoiding clogging and improving printability. The analyses indicate that both helicoid surface to volume ratio and helix angle affect the pre-alignment of fibers, leading to tunable mechanical properties of printed fiber-reinforced composites with increased stiffness and strength up to 77.6% and 47.8%, respectively.
Polymer-grafted nanoparticles (PGNPs) are versatile hybrid materials whose properties critically depend on brush dimensions, uniformity, and grafting density. Herein, we systematically investigated how initiator density, catalyst concentration, and nanoparticle curvature govern the growth of poly(methyl methacrylate) (PMMA) brushes grafted from spherical SiO 2 nanoparticles via surface-initiated activators regenerated by electron transfer atom transfer radical polymerization (SI-ARGET ATRP). By tuning the initiator density through a combination of “active” and “dummy” silane initiators anchored on the nanoparticles’ surface and controlling the catalyst concentration, we reveal that increased initiator crowding and smaller surface curvature amplify steric hindrance, leading to decreased initiation efficiency and broader molecular weight distributions. Correlation with the corresponding unattached chains by ARGET ATRP suggests the presence of permanently inaccessible (“buried”) initiation sites, which are a characteristic of surface-grafted systems. At sufficient Cu catalyst concentrations, uniform brush growth is attained across different initiator densities, whereas decreased catalyst concentrations accentuate nonconcurrent initiation and propagation. These findings provide mechanistic insights into the interplay of initiator density, catalyst concentration, and surface curvature, offering design principles for tailoring the PGNP architecture. These results can guide the structural engineering of densely grafted surfaces, including nanoparticles and flat substrates, for applications in nanocomposites, photonics, and functional coatings.
Oxidative coupling of methane (OCM) is an attractive direct route for upgrading methane to valuable chemicals. In this study, Temporal Analysis of Products (TAP) and steady state experiments are conducted to understand the role of individual oxide phases and their combinations in supported Mn-Na 2 WO 4 /SiO 2 catalysts for OCM. The results from TAP transient kinetic studies indicate that Mn plays an important role in promoting gas phase oxygen activation, while NaO x /SiO 2 and WO x /SiO 2 are relatively inert towards gas phase oxygen and methane activation. However, the supported catalyst combining Na and W in the form Na 2 WO 4 show enhanced gas phase oxygen activation exhibiting a much lower oxygen activation energy (148 kJ/mol) and enhanced activity toward methane activation as compared to the individual supported oxide catalysts. Addition of Mn to Na 2 WO 4 /SiO 2 further decreases the oxygen activation energy by 40 kJ/mol. Moreover, methane activation is also enhanced with CH 3 as the main intermediate but, with increasing Mn content, more CH 2 intermediates are observed. Different forms of oxygen (both dioxygen and atomic) are detected on the catalyst surface using isotopic pump/probe pulsing and their distribution is found to depend on the catalyst composition. An optimal Mn content in the Na 2 WO 4 /SiO 2 catalyst system is needed to enhance the amount of dioxide surface species (e.g., superoxide 16 O 2 - or peroxide 16 O 2 2- ) associated with the Na 2 WO 4 leading to high C 2 selectivity for OCM. When the Mn content is too high, the larger MnOx domains are shown to contribute to the formation of higher concertation of monoxide surface species that lead to nonselective OCM pathways. This insight from transient kinetic characterization using TAP combined with conventional steady state studies, provides a deeper understanding of the role of individual oxide phases and their combination on supported catalysts toward the formation of intermediate surface species and their impact on the OCM reaction mechanism. This knowledge is critical toward designing superior catalyst formulations for OCM.
Superstorm Sandy caused a major disruption to passenger-rail and other commuter systems throughout New York and New Jersey. To address this issue, New Jersey Transit (NJT) established the NJ TRANSITGRID project, an effort designed to power bus, ferry, and limited passenger-rail service during natural or man-made disasters. Given the importance of these transportation systems, NJT partnered with Sandia National Laboratories (Sandia) to assess the cyber-resilience of the information systems that monitor and control the electrical systems within the microgrid. The Sandia “tabletop” assessment is based on the most recent 20% design packages. From this assessment, the Sandia team identified several security areas that were undefined or did not implement industry best practices. Finally, the Sandia team presented possible follow-on assessment activities and recommended investigating multiple hardening technologies. Addressing these findings and adding state-of-the-art detection and mitigation technologies will help ensure the NJ TRANSITGRID is built with more comprehensive cyber-resilience features.
Tongue function is vital for chewing and swallowing and lingual dysfunction is often associated with dysphagia. Better treatment of dysphagia depends on a better understanding of hyolingual morphology, biomechanics, and neural control in humans and animal models. Recent research has revealed significant variation among animal models in morphology of the hyoid chain and suprahyoid muscles which may be associated with variation in swallowing mechanisms. The recent deployment of XROMM (X-ray Reconstruction of Moving Morphology) to quantify 3D hyolingual kinematics has revealed new details on flexion and roll of the tongue during chewing in animal models, movements similar to those used by humans. XROMM-based studies of swallowing in macaques have falsified traditional hypotheses of mechanisms of tongue base retraction during swallowing, and literature review suggests that other animal models may employ a diversity of mechanisms of tongue base retraction. There is variation among animal models in distribution of hyolingual proprioceptors but how that might be related to lingual mechanics is unknown. In macaque monkeys, tongue kinematics—shape and movement—are strongly encoded in neural activity in orofacial primary motor cortex, giving optimism for development of brain–machine interfaces for assisting recovery of lingual function after stroke. However, more research on hyolingual biomechanics and control is needed for technologies interfacing the nervous system with the hyolingual apparatus to become a reality.
Rechargeable Mg batteries are one of the most investigated polyvalent-metal storage batteries owing to the increased safety associated with the nondendritic nature of Mg electrodeposition, high volumetric capacity, and low cost. To realize the commercial applications of Mg batteries, there are still a number of challenges remaining unsolved, in particular, the lack of halogen free Mg electrolytes, as the use of the halogens remains a major limiting factor to achieving high voltage cathodes. The research presented here introduces an innovative approach to prepare a halogen-free Mg-based electrolyte in a simple, nonsynthetic method that can plate and strip Mg reversibly. Results suggest that by introducing a secondary amine cosolvent the magnesium bis(trifluoromethanesulfonyl)imide (Mg(TFSI) 2 ) salt can be easily dissolved into a wide array of polar but aprotic ether solvents. A systematic structural investigation of a representative Mg(TFSI) 2 electrolyte in the cosolvent systems with the secondary amine was performed using pair distribution function (PDF) analysis, single crystal diffraction analysis, and NMR. The experimental atomic scale understanding reveals an ion pair structure of Mg 2+ coordinated with six oxygen donors from the bis(trifluoromethanesulfonyl)imide (TFSI) anions and the THF solvent located in the first solvation shell. The as-formed neutral ion pair structure acts as the active component for reversible Mg deposition. We believe this new route of preparing Mg electrolytes can extend the current understanding of Mg electrolyte functionality for rechargeable Mg batteries and offers more guidance for the future electrolyte design.
Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.
In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous modes, which will produce new stresses on the grid-interfacing power electronics components, such as DC/AC inverters. In previous work, multiple DER devices were instrumented to evaluate additional component stress under multiple reactive power setpoints. We utilize quasi-static time-series simulations to determine voltage-reactive power mode (volt-var) mission profile of inverters in an active power system. Mission profiles and loss estimates are then combined to estimate the reduction of the useful life of inverters from different reactive power profiles. It was found that the average lifetime reduction was approximately 0.15% for an inverter between standard unity power factor operation and the IEEE 1547 default volt-var curve based on thermal damage due to switching in the power transistors. For an inverter with an expected 20-year lifetime, the 1547 volt-var curve would reduce the expected life of the device by 12 days. This framework for determining an inverter's useful life from experimental and modeling data can be applied to any failure mechanism and advanced inverter operation.
Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the operational gradient, radiological environment via activated components, and reliability of CEBAF’s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high levels of field emission based on control system data such as radiation levels, cryogenic readbacks, and vacuum loads. The gradients on the most offending cavities will be reduced and compensated for by increasing the gradients on least offensive cavities. Training data will be collected during this year’s operational program and initial implementation of AI models will be deployed. Preliminary results and future plans are presented.
Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the operational gradient, radiological environment via activated components, and reliability of CEBAF’s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high levels of field emission based on control system data such as radiation levels, cryogenic readbacks, and vacuum loads. The gradients on the most offending cavities will be reduced and compensated for by increasing the gradients on least offensive cavities. Training data will be collected during this year’s operational program and initial implementation of AI models will be deployed. Preliminary results and future plans are presented.
Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region.
Root detritusphere, that is, the soil in the vicinity of decomposing root residues, plays an important role in soil microbial activity and C sequestration. Pore structure (size distributions and connectivity of soil pores) in the detritusphere serves as a major driver for these processes and, in turn, is influenced by the physical characteristics of both soil and roots. This study compared pore structure characteristics in the root detritusphere of soils of contrasting texture and mineralogy subjected to > 6 years of contrasting vegetation: monoculture switchgrass and polyculture prairie systems. Soil samples were collected from five experimental sites in the US Midwest representing three soil types. Soil texture and mineralogy were measured using a hydrometer and x-ray powder diffraction, respectively. The intact cores were scanned with x-ray computed micro-tomography to identify visible soil pores, biopores, and particulate organic matter (POM). We specifically focused on pore structure within the detritusphere around the POM of root origin. Results showed that the detritusphere of coarser textured soils, characterized by high sand and quartz contents, had lower porosity in the vicinity of POM compared with finer textured soils. POM vicinities in finer textured soils had high proportions of large (> 300 μm diameter) pores, and their pores were better connected than in the coarser soils. Lower porosity in the outer (> 1 mm) parts of the detritusphere of switchgrass than of prairie suggested soil compaction by roots, with the effect especially pronounced in the coarser soils. Here, the results demonstrated that soil texture and mineralogy played a major, while vegetation played a more modest, role in defining the pore structure in the root detritusphere.