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At least 343 records · Page 19

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DOE OSTI2024

GLIMPSE of Future Power Grid Models

Power grid is one of the critical national infrastructures with social, economics, and national security impacts. Particularly, power distribution systems represent part of the infrastructure between power distribution substations and customers such as residential, commercial, and industrial. To address various grid modernization challenges, state-of-the-art algorithms and methodologies have been developed to deploy, operate, and expand a secure and resilient power grid. At the same time, there is a need to develop capabilities to assist power grid stakeholders to quickly get insight into the design and structure of the grid. Data visualization is a key approach to comprehend and understand complex systems such as the power grid. We present GLIMPSE Grid Layout Interface for Model Preview and System Exploration), a graph-based semantic-aware application to visualize and update distribution power grid models. The GLIMPSE can be used with standard IEEE models to search and highlight power grid objects such as generators, loads, overhead lines, etc. Additionally, it supports updating attributes and model export to integrate with GridLAB-D simulations.

Cybersecurity, power grid, visualization↗

DOE OSTI2024

Interfacial Ice Density Fluctuations Inform Surface Ice-Philicity

The propensity of a surface to nucleate ice or bind to ice is governed by its ice-philicity─its relative preference for ice over liquid water. However, the relationship between the features of a surface and its ice-philicity is not well understood, and for surfaces with chemical or topographical heterogeneity, such as proteins, their ice-philicity is not even well-defined. In the analogous problem of surface hydrophobicity, it has been shown that hydrophobic surfaces display enhanced low water-density (vapor-like) fluctuations in their vicinity. To interrogate whether enhanced ice-like fluctuations are similarly observed near ice-philic surfaces, here we use molecular simulations and enhanced sampling techniques. Using a family of model surfaces for which the wetting coefficient, k , has previously been characterized, we show that the free energy of observing rare interfacial ice-density fluctuations decreases monotonically with increasing k . By utilizing this connection, we investigate a set of fcc systems and find that the (110) surface is more ice-philic than the (111) or (100) surfaces. By additionally analyzing the structure of interfacial ice, we find that all surfaces prefer to bind to the basal plane of ice, and the topographical complementarity of the (110) surface to the basal plane explains its higher ice-philicity. Using enhanced interfacial ice-like fluctuations as a measure of surface ice-philicity, we then characterize the ice-philicity of chemically heterogeneous and topologically complex systems. In particular, we study the spruce budworm antifreeze protein (sbwAFP), which binds to ice using a known ice-binding site (IBS) and resists engulfment using nonbinding sites of the protein (NBSs). We find that the IBS displays enhanced interfacial ice-density fluctuations and is therefore more ice-philic than the two NBSs studied. We also find the two NBSs are similarly ice-phobic. By establishing a connection between interfacial ice-like fluctuations and surface ice-philicity, our findings thus provide a way to characterize the ice-philicity of heterogeneous surfaces.

crystals↗

DOE OSTI2022

Uncertainty Aware Deep Learning for Particle Accelerators

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

Rajput, Kishansingh↗

DOE OSTI2020

Tailoring Spin-Wave Channels in a Reconfigurable Artificial Spin Ice

Artificial spin ices are ensembles of geometrically arranged interacting nanomagnets that have shown promising potential for the realization of reconfigurable magnonic crystals. Such systems allow for the manipulation of spin waves on the nanoscale and their potential use as information carriers. However, there are presently two general obstacles to the realization of artificial spin-ice-based magnonic crystals: the magnetic state of artificial spin ices is difficult to reconfigure and the magnetostatic interactions between the nanoislands are often weak, preventing mode coupling. We demonstrate, using micromagnetic modeling, that coupling a reconfigurable artificial spin-ice geometry made of weakly interacting nanomagnets to a soft magnetic underlayer creates a complex system exhibiting dynamically coupled modes. These give rise to spin-wave channels in the underlayer at well-defined frequencies, based on the artificial spin-ice magnetic state, which can be reconfigured. Finally, these findings open the door to the realization of reconfigurable magnonic crystals with potential applications for data transport and processing in magnonic-based logic architectures.

25 ENERGY STORAGE↗

DOE OSTI2024

Foundation Models for the Electric Power Grid

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.

AI-based power flow simulation↗

DOE OSTI2026

Hydrogen Analysis by Gas Chromatography–Mass Spectrometry

The detection of hydrogen in complex gas mixtures is essential for many applications. Conventional approaches such as gas chromatography (GC) with thermal conductivity detection (TCD) and residual gas analyzers (RGAs) face significant limitations: TCD exhibits poor response when helium is used as a carrier gas, whereas RGAs lack chromatographic separation, preventing reliable quantification of hydrogen because of interference from other species. Traditional GC methods rely on dual-column configurations with packed or molecular-sieve porous layer open tubular (PLOT) columns to separate hydrogen from O2, N2, CO, CO2, CH4, and other hydrocarbons, increasing system complexity and limiting compatibility with mass spectrometry (MS) detection. In this work, we developed and validated a robust GC–MS method capable of directly detecting and quantifying hydrogen using electron ionization (EI) without dopants, reagent gases, or ion–molecule reaction schemes. By integrating a modified EI source and a cryogenically cooled single capillary column configuration, we achieved baseline separation of hydrogen from all major permanent gases and hydrocarbons in a refinery gas mixture using helium as the carrier gas. The method demonstrated excellent linearity, high sensitivity, and exceptional reproducibility. Adjustable sample-loop volumes and split ratios enabled optimization of peak shape and signal-to-noise performance for trace-level and percent-level hydrogen concentrations. Beyond hydrogen quantitation, the method provides simultaneous compositional profiling of other gases in a mixture in a single run, making it valuable for a wide range of tasks.

Lobodin, Vlad [ORNL]↗

DOE OSTI2022

Comparative uptake, translocation, and plant mediated transport of Tc-99, Cs-133, Np-237, and U-238 in Savannah River Site soil columns for the grass species Andropogon virginicus

Here, this study examines the ability of the grass species Andropogon virginicus to alter the subsurface transport and redistribution of a suite of radionuclides ( 99 Tc, 133 Cs (stable analog for 135 Cs and 137 Cs), 237 Np, 238 U) with varying chemical behaviors in a Savannah River Site soil via the use of vegetated and unvegetated soil columns. After an acclimation period, a small volume of solution containing all radionuclides was introduced into the columns via Rhizon© pore water sampling tubes. Plants were grown for an additional 4 weeks before shoots were harvested, and columns were prepared for sampling. Plant presence led to decreased radionuclide release from the columns, mainly due to radionuclide specific combinations of system hydrology differences resulting from plant transpiration as well as plant uptake. For the most mobile radionuclides, 99 Tc followed by 237 Np, plant presence resulted in significantly different soil concentration profiles between vegetated and unvegetated columns, including notable upward migration for 237 Np in columns with plants. Additionally, plant uptake of 99 Tc was the greatest of all the radionuclides, with plant tissues containing an average of 44 % of the 99 Tc, while plant uptake only accounted for <2 % of 237 Np and <0.5 % of 133 Cs and 238 U in the system. Although overall plant uptake of 133 Cs and 238 U were similar, the majority of 133 Cs taken up by plants was associated with 133 Cs already available in the aqueous phase while 238 U uptake was mainly associated with the solid phase, meaning that plant activity resulted in a fraction of the native 238 U being mobilized and thus, made available for plant uptake. Overall, this study quantified the influence of several plant-mediated physical and biogeochemical factors that have significant influence on radionuclide mobility and transport in this complex system which can be further utilized in future system or site-specific environmental transport and risk assessment models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Geochemical Modeling of Celestite (SrSO 4 ) Precipitation and Reactive Transport in Shales

Celestite (SrSO 4 ) precipitation is a prevalent example of secondary sulfate mineral scaling issues in hydraulic fracturing systems, particularly in basins where large concentrations of naturally occurring strontium are present. Herein, we present a validated and flexible geochemical model capable of predicting celestite formation under such unconventional environments. Simulations were built using CrunchFlow and guided by experimental data derived from batch reactors. These data allowed the constraint of key kinetic and thermodynamic parameters for celestite precipitation under relevant synthetic hydraulic fracturing fluid conditions. Effects of ionic strength, saturation index, and the presence of additives were considered in the combined experimental and modeling construction. This geochemical model was then expanded into a more complex system where interactions between hydraulic fracturing fluids and shale rocks were allowed to occur subject to diffusive transport. We find that the carbonate content of a given shale and the presence of persulfate breaker in the system strongly impact the location and extent of celestite formation. The results of this study provide a novel multicomponent reactive transport model that may be used to guide future experimental design in the pursuit of celestite and other sulfate mineral scale mitigation under extreme conditions typical of hydraulic fracturing in shale formations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Minimal cyclic behavior in sheared amorphous solids

Although jammed packings of soft spheres exist in potential energy landscapes with a vast number of minima, when subjected to cyclic shear they may revisit the same configurations repeatedly. Simple hysteretic spin models, in which particle rearrangements are represented by interacting spin flips called hysterons, capture many features of this periodic behavior. Yet it has been unclear to what extent individual rearrangements can be described by such binary objects and how such objects interact with one another. Using a particularly sensitive algorithm, we identify rearrangements in simulated jammed packings and select pairs of rearrangements that undo one another to create periodic cyclic behavior. We find that the rearrangement pairs surprisingly persist down to the smallest increments in strain, even in the smallest systems we can study. We explore the statistics of these rearrangement pairs and find that there is a relation between the amount of hysteresis and the energy drop and mean-square displacement of the particles; these results are inconsistent with the scaling found in models that treat rearrangements as localized buckling events. Finally, our analysis shows that there is no clean distinction between the particle motions that represent the identity of a single, individual rearrangement and the particle motions that lead to interactions between separated rearrangements or hysterons. These results offer insight into how complex systems such as amorphous solids can reach a limit cycle.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Adaptive Power System Emergency Control using Deep Reinforcement Learning

Power system emergency control is generally regarded as the final safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenarios or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper proposes a novel adaptive emergency control scheme using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities DRL has for complex systems with high-dimensional variations. Furthermore, an open-source platform named DeepGrid has been designed for the first time to assist the DRL development and benchmarking processes in power system emergency control. Details of the platform, DRL, and emergency control schemes that use dynamic braking or under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.

Deep reinforcement learning, emergency control, lo↗

DOE OSTI2022

Mitigating cerium migration for perfluorosulfonic acid membranes using organic ligands

Improving the electrochemical stability of proton exchange membranes is a pressing priority for heavy-duty fuel cell vehicles. The lifetime of the most widely used perfluorosulfonic acid membranes is limited by reactive free radicals generated inside the system. Cerium has been found to reduce the chemical degradation of the membranes. However, cerium migration during fuel cell operation limits the chemical durability enhancement effect expected from the radical scavenging activity of cerium. Here we investigate a wide range of organic immobilizers for cerium, measuring their suitability concerning cerium retention, radical scavenging activity, and fuel cell performance. We report that partially fluorinated phosphonic acids enhance cerium retention up to 45 times and reduce fluoride emission rate by 38% compared to the commercial Nafion™ XL membrane pre-impregnated with cerium. The energetics of cerium-phosphonic acid complex systems by density functional theory calculations rationalizes effective cerium immobilization.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Mitigating cerium migration for perfluorosulfonic acid membranes using organic ligands

Improving the electrochemical stability of proton exchange membranes is a pressing priority for heavy-duty fuel cell vehicles. The lifetime of the most widely used perfluorosulfonic acid membranes is limited by reactive free radicals generated inside the system. Cerium has been found to reduce the chemical degradation of the membranes. However, cerium migration during fuel cell operation limits the chemical durability enhancement effect expected from the radical scavenging activity of cerium. Here we investigate a wide range of organic immobilizers for cerium, measuring their suitability concerning cerium retention, radical scavenging activity, and fuel cell performance. Further, we report that partially fluorinated phosphonic acids enhance cerium retention up to 45 times and reduce fluoride emission rate by 38% compared to the commercial Nafion™ XL membrane pre-impregnated with cerium. The energetics of cerium-phosphonic acid complex systems by density functional theory calculations rationalizes effective cerium immobilization.

25 ENERGY STORAGE↗

DOE OSTI2024

Basic Energy Sciences Roundtable: Foundational Science to Accelerate Nuclear Energy Innovation

Energy security, availability, and reliability are among the greatest challenges facing the nation and the planet. An abundant potential source of energy resides in the fundamental atomic building blocks of the universe in the form of nuclear fission and fusion reactions. In fact, energy from nuclear fission currently provides the majority of the world’s zero-carbon electricity, and future fusion energy systems offer great promise; carbon-free nuclear energy technologies can be key to the world’s decarbonized energy future. Although contemporary fission systems use well-established technologies to supply safe and efficient baseload power, they could be more fuel efficient and less costly. Moving beyond massive light-water fission reactors to a variety of advanced nuclear systems—which will vary in size and operate in extremes of temperature, corrosivity, and other parameters—will place stringent conditions on materials and chemical systems. New demands will be placed on the coolants and solvents, the materials, and the monitoring tools used in these reactors. Fusion-based nuclear energy will require superior materials to withstand extremely high temperatures, plasma exposure, radiation damage, and implanted gases. The advantages associated with these new fission and fusion technologies will be realized only through continued advancements in the fundamental science underpinning our knowledge of the physics and chemistry of nuclear systems gained via improved experimental and computational methods. In July 2022, the U.S. Department of Energy’s Office of Basic Energy Sciences—in coordination with the Offices of Nuclear Energy, Fusion Energy Sciences, and Advanced Scientific Computing Research—held a virtual roundtable titled “Foundational Science to Accelerate Nuclear Energy Innovation” to discuss the scientific and technical barriers for advanced nuclear energy systems. Five priority research opportunities were identified to address these scientific and technical challenges and to accelerate progress toward the realization of next-generation fusion and fission energy systems. The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous Basic Energy Sciences workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Understanding and controlling water-organic co-transport in amorphous microporous materials

The transport of molecules in microporous material is a significant and active area of research in separation applications. The movement of vapor/liquid molecules in a high-loading condition in microporous spaces is especially challenging to interrogate. This project aims to understand and control the transport of complex water and organic solvent mixtures in varying structures of microporous carbon molecular sieve (CMS) and activated carbon membranes. Modeling of transport behavior in such carbonaceous samples using computational modeling is difficult due to their amorphous structure. Therefore, this research intends to study the transport mechanism of water-organic mixtures by experimentally revealing fundamental transport properties, such as guest sorption amounts as well as diffusion and permeation rates. Considering the different size and guest-host affinity of water and organic solvent molecules, the objective of this research is to understand the structural conditions within the ultramicropores and micropores that generate different types of molecular transport and selection mechanisms within such complex systems. Carbonaceous materials are developed using tailored pyrolysis techniques to pyrolyze polymeric precursors, including polyvinylidene chloride (PVDC),polyvinylidene fluoride (PVDF), polymer of intrinsic microporosity (PIM) -1, and fully aromatic polyamide. The initial stage of the research will focus on the microscopic diffusion and sorption studies of pure component water, xylene isomers, and n,n- dimethylformamide within various carbons. Additional structural investigation on CMS, such as gas physisorption and neutron scattering studies, will be executed to gain deeper insight into these structure-transport relationships. Scanning electron microscopy and X-ray photoelectron microscopy are also used to further characterize these materials. The research will progress to probe mutual diffusion of complex water-organic mixtures within various CMS microstructures. Competitive sorption and permeation studies of the binary mixtures in different microstructural CMS will be followed. The obtained binary mixture transport parameters were used to predict binary water-organic mixtures transport.

36 MATERIALS SCIENCE↗

DOE OSTI2020

In situ nanoscale imaging of moiré superlattices in twisted van der Waals heterostructures

Abstract Direct visualization of nanometer-scale properties of moiré superlattices in van der Waals heterostructure devices is a critically needed diagnostic tool for study of the electronic and optical phenomena induced by the periodic variation of atomic structure in these complex systems. Conventional imaging methods are destructive and insensitive to the buried device geometries, preventing practical inspection. Here we report a versatile scanning probe microscopy employing infrared light for imaging moiré superlattices of twisted bilayers graphene encapsulated by hexagonal boron nitride. We map the pattern using the scattering dynamics of phonon polaritons launched in hexagonal boron nitride capping layers via its interaction with the buried moiré superlattices. We explore the origin of the double-line features imaged and show the mechanism of the underlying effective phase change of the phonon polariton reflectance at domain walls. The nano-imaging tool developed provides a non-destructive analytical approach to elucidate the complex physics of moiré engineered heterostructures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2026

Alchemy: A Model-Based Approach for 2D to 3D Autonomous Nuclear System Design

Engineering design of nuclear power plant (NPP) piping and equipment systems frequently bypasses crucial 2D system planning, instead moving straight to 3D modeling. This often leads to designs that exceed building envelope constraints, forcing expensive and time-consuming redesigns. When 2D modeling is employed, it typically involves labor-intensive manual workflows that convert 2D drawings into 3D models, resulting in inefficiencies and errors across design iterations. These workflows further suffer from poor software interoperability and dependence on proprietary software ecosystems, thereby contributing to schedule delays and cost overruns. This paper presents Alchemy, an autonomous framework that transforms 2D system definitions into Industry Foundation Classes (IFC)-compliant 3D building information models (BIMs) for expediting nuclear facility design at the conceptual preliminary phase. Using a model-based approach, the framework treats the 2D system diagram as the central reference model employed to automatically generate all subsequent outputs, ensuring consistency between the system definition and the resulting physical design. A web-based interface enables engineers to define hierarchical system topologies including associated equipment, geometric properties, and connectivity requirements. A two-phase equipment layout optimization algorithm automatically computes collision-free spatial configurations within predefined building envelopes. An artificial intelligence (AI)-assisted pipe routing module then generates orthogonal, collision-free routing paths, allowing the user to select either an A* search-based method or an Ant Colony Optimization (ACO)-based method. All outputs are authored natively in IFC format, relying on open-source technologies and standardized formats in order to ensure extensibility and eliminate proprietary software dependencies. The proposed framework is validated on two representative pressurized-water reactor (PWR)-based case studies, for which it autonomously generates IFC-compliant 3D models in minutes, drastically reducing workflows that typically require hours of manual effort. The generated model demonstrates topologically correct equipment placement, physically plausible spatial relationships, and collision-free pipe routing consistent with known PWR loop configurations. This work represents a foundational step toward digital engineering for nuclear facility preliminary design, with future ongoing development targeting design code compliance and expanded system complexity.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The quest to understand the fundamental structure of nuclear matter – outlook to QCD and the Electron-Ion Collider

Nuclear matter is made of quarks that are bound by gluons that also bind themselves. Unlike with the more familiar atomic and molecular matter, the interactions and structures in nuclear matter are inextricably mixed up, and observed properties of nucleons and nuclei, such as mass and spin, emerge out of this complex system. Ongoing and future QCD research offers the exciting prospect to obtain a multi-dimensional picture of the inner quark-gluon structure of protons and atomic nuclei and to inform us how the properties and structure of nuclear matter have emerged from the dynamics of QCD. This program is initiated at the 12-GeV Upgraded Jefferson Lab, concentrating on imaging the region in nucleon and nuclear structure where quarks prevail. A future Electron-Ion Collider (EIC) is planned at Brookhaven National Lab, in partnership with Jefferson Lab. The EIC will have a versatile range of beam energies, polarizations, and ion species, as well as high luminosity, to precisely image quarks, gluons, and their interactions in protons and complex atomic nuclei. The goal is an understanding of the internal structure of nuclear matter comparable to our knowledge of the electronic structure of atoms. The present status of the EIC will also be presented.

Ent, Rolf↗