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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Predicting Geologic Behavior in Carbon Storage Projects Using Graph Neural Network

This study was invited to presented at NVIDIA's GTC conference to highlight the potential of Graph Neural Network as a novel and promising methodology for predicting pressure and saturation evolution in carbon storage projects. Carbon capture and storage (CCS) technology plays a pivotal role in mitigating greenhouse gas emissions, facilitating the transition to a low-carbon future. Effective management of subsurface reservoirs is essential to ensure the safe and efficient storage of captured carbon dioxide (CO₂). Accurate predictions of pressure and saturation over time are critical for evaluating the long-term performance and integrity of CCS projects. In recent years, Graph Neural Network (GNN) has emerged as a powerful framework for analyzing complex data in graph-structured domains. This abstract explores the application of GNN to forecast pressure and saturation evolution in carbon storage projects. Traditional numerical simulations of subsurface reservoirs have proven successful in providing pressure and saturation forecasts. However, these simulations involve massive amounts of computational effort and require extensive domain expertise for proper model calibration and validation. Graph Neural Operator offers an alternative approach that harnesses the inherent graph structure of reservoirs, where nodes represent reservoir grid cells and edges represent the geological connectivity between them.

Shih, Chung Yan↗
DOE OSTI2022

Examination of Haines Jump in Microfluidic Experiments via Evolution Graphs and Interface Tracking

This work examines a type of rapid pore-filling event in multiphase flow through permeable media that is better known as Haines Jump. While existing microfluidic experiments on Haines Jump mostly seek to maintain quasi-steady states through very low bulk flow rates over long periods of time, this work explores the combined use of a highly structured microscale transport network, high-speed fluorescent microscopy, displacement front segmentation algorithms, and a tracking algorithm to build evolution graphs that track displacement fronts as they evolve through high-speed video recording. The resulting evolution graph allows the segmentation of a high-speed recording in both space and time, potentially facilitating topology-cognitive computation on the transport network. Occurrences of Haines Jump are identified in the microfluidic displacement experiments and their significance in bulk flow rates is qualitatively analyzed. The bulk flow rate has little effect on the significance of Haines Jump during merging and splitting, but large bulk flow rates may obscure small bursts at the narrowest part of the throat.

permeable media↗
DOE OSTI2021

R Coronae Borealis Star Evolution: Simulating 3D Merger Events to 1D Stellar Evolution Including Large-scale Nucleosynthesis

R Coronae Borealis (RCB) stars are rare hydrogen-deficient carbon-rich variable supergiants thought to be the result of dynamically unstable white dwarf mergers. In this article, we attempt to model RCB stars through all the relevant timescales by simulating a merger event in Octo-tiger, a 3D adaptive mesh refinement (AMR) hydrodynamics code, and mapping the post-merger object into MESA, a 1D stellar evolution code. We then post-process the nucleosynthesis on a much larger nuclear reaction network to study the enhancement of s-process elements. We present models that match observations or previous studies in most surface abundances, isotopic ratios, early evolution, and lifetimes. We also observe similar mixing behavior to previous modeling attempts that result in the partial He-burning products visible on the surface in observations. However, we do note that our subsolar models lack any enhancement in s-process elements, which we attribute to a lack of hydrogen in the envelope. We also find that the 16 O/ 18 O isotopic ratio is very sensitive to initial hydrogen abundance and increases outside of the acceptable range with a hydrogen mass fraction greater than 10 -4 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Highly Structured Water Networks in Microhydrated Dodecaborate Clusters

Here, we report a combined photoelectron spectroscopy and theoretical investigation of a series of size-selected hydrated closo-dodecaborate clusters B 12 X 12 2– ·nH 2 O (X = H, F, or I; n = 1–6). Distinct structural arrangements of water clusters from monomer to hexamer can be achieved by using different B 12 X 12 2– bases, illustrating the evident solute specificity. Because B–H···H–O dihydrogen bonds are stronger than O···H–O hydrogen bonds in water, the added water molecules are arranged in a unified binding mode by forming highly structured water networks manipulated by B 12 H 12 2– . As a comparison, the hydrated B 12 F 12 2– clusters display similar water evolution for n values of 1 and 2 but different binding modes for larger clusters, while water networks in B 12 I 12 2– share similarities with the free water clusters. This finding provides a consistent picture of the structural diversity of hydrogen bonding networks in microhydrated dodecaborates and a molecular-level understanding of microsolvation dynamics in aqueous borate chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Materials processing often occurs under extreme dynamic conditions leading to a multitude of unique structural environments. These structural environments generally occur at high temperatures and/or high pressures, often under non-equilibrium conditions, which results in drastic changes in the material's structure over time. Computational techniques, such as molecular dynamics simulations, can probe the atomic regime under these extreme conditions. However, characterizing the resulting diverse atomistic structures as a material undergoes extreme changes in its structure has proved challenging due to the inherently non-linear relationship between structures as large-scale changes occur. Here, we introduce SODAS++, a universal graph neural network framework, that can accurately and intuitively quantify the atomistic structural evolution corresponding to the transition between any two arbitrary phases. We showcase SODAS++ for both solid–solid and solid–liquid transitions for systems of increasing geometric and chemical complexity, such as colloidal systems, elemental Al, rutile and amorphous TiO 2 , and the non-stoichiometric ternary alloy Ag 26 Au 5 Cu 19 . Finally, we show that SODAS++ can accurately quantify all transitions in a physically interpretable manner, showcasing the power of unsupervised graph neural network encodings for capturing the complex and non-linear pathway, a material's structure takes as it evolves.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Extreme Energy Dissipation via Material Evolution in Carbon Nanotube Mats

Abstract Thin layered mats comprised of an interconnected meandering network of multiwall carbon nanotubes (MWCNT) are subjected to a hypersonic micro‐projectile impact test. The mat morphology is highly compliant and while this leads to rather modest quasi‐static mechanical properties, at the extreme strain rates and large strains resulting from ballistic impact, the MWCNT structure has the ability to reconfigure resulting in extraordinary kinetic energy (KE) absorption. The KE of the projectile is dissipated via frictional interactions, adiabatic heating, tube stretching, and ultimately fracture of taut tubes and the newly formed fibrils. The energy absorbed per unit mass of the film can range from 7–12 MJ kg −1 , much greater than any other material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗
DOE OSTI2025

Water Clustering Modulates Activity and Enables Hydrogenated Product Formation during Carbon Monoxide Electroreduction in Aprotic Media

Water solvation plays a critical role in a wide range of electrochemical transformations, but its role is often convoluted since water is typically used as both a solvent and a proton source. Here, we experimentally control water speciation and activity using aprotic solvent media during the carbon monoxide reduction reaction (CORR). Remarkably, we show that aprotic solvents that support microheterogeneous water–water clusters lead to significant amounts of CORR products (methane and ethylene) with a maximum ethylene Faradaic efficiency of 22% in acetonitrile (χ H 2 O = 0.2). In contrast, microhomogeneous systems–where water integrates into the solvents’ intermolecular binding network and has lower activity–primarily support the undesired hydrogen evolution reaction (HER). As a result, insights gained expand our understanding of water activity and nonaqueous electrolyte design for other important transformation reactions beyond CO reduction, such as CO2RR and HER.

Electrolytes↗
DOE OSTI2022

Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments

Abstract. Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments. It aims to optimize the radar observation strategy for deep convection through the use of high-spatiotemporal cloud-resolving model simulations, which resolve the evolution of individual convective cells every 1 min, coupled with a radar simulator and a cell tracking algorithm. The radar simulation settings are based on the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaigns held in the Houston, TX, area but are generalizable to other field campaigns focusing on isolated deep convection. Our analysis produces the following four outcomes. First, a 5–7 m s−1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single-RHI (range height indicator) updraft retrieval technique for cells close to the radars, for which multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s−1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Inference of neutrino flavor evolution through data assimilation and neural differential equations

The evolution of neutrino flavor in dense environments such as core-collapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavy-element nucleosynthesis in these environments. In this paper, we build upon recent work to explore inference-based techniques for the estimation of model parameters and neutrino flavor evolution histories. Here, we combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for nonlinear dynamical systems. Using this architecture, and a simple two-neutrino-beam, two-flavor model, we compare the performances of nine different optimization algorithms and expand upon previous assessments of the efficacy of inference for tackling problems in flavor evolution. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the small-scale model and allows us to quickly explore both model parameters and initial flavor content. In future work we plan to extend these inference techniques to large numbers of neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Community Fabric: Visualizing communities and structure in dynamic networks

We present Community Fabric, a novel visualization technique for simultaneously visualizing communities and structure within dynamic networks. In dynamic networks, the structure of the network is continuously evolving throughout time and these underlying topological shifts tend to lead to communal changes. Community Fabric helps the viewer more easily interpret and understand the interplay of structural change and community evolution in dynamic graphs. To achieve this, we take a new approach, hybridizing two popular network and community visualizations. Community Fabric combines the likes of the Biofabric static network visualization method with traditional community alluvial flow diagrams to visualize communities in a dynamic network while also displaying the underlying network structure. Our approach improves upon existing state-of-the-art techniques in several key areas. We describe the methodologies of Community Fabric, implement the visualization using modern web-based tools, and apply our approach to three example data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Evolved Gas Analysis–Mass Spectrometry Exposes Polymer Network Structures

Polymer network structures in epoxy thermosets play an important role in the final thermoset material properties. However, analytical characterization of these network structures is difficult due to their amorphous nature. In this work, the application of evolved gas analysis–mass spectrometry (EGA-MS) to characterize the polymer network structures of bisphenol A (BPA)-based thermosets is demonstrated. Analytical characterization of the polymer network structures is accomplished by monitoring the Product-Specific Kinetics (PSK) of BPA monomer formation during thermal degradation investigations. We relate observed differences in the activation energy (E a ) of BPA monomer formation to the local packing environment around the BPA monomer units within the polymer network. Variations in the local environment related to the polymer networks manifest qualitatively as broadening in the thermal profile of the BPA monomer evolution and quantitatively as changes in the activation energy (E a ). Three BPA thermoset formulations were investigated; two amine-cured thermoset with 4,4′-diaminodiphenylmethane (DDM) or poly(propylene glycol) bis(2-amino-propyl ether) (PPG400) and a homopolymerized thermoset via curing with Epikure 3253 catalyst (3253). Results revealed that the 3253 thermoset contained two distinct packing densities in the polymer network, while DDM and PPG400 thermosets had uniform distributions of packing densities. Results from the DDM thermoset revealed a gradually decreasing E a , while the apparent E a of PPG400 was consistent over the entire degradation. Furthermore, these differences in E a were concluded to stem from the flexibility of the corresponding polymer networks and the ability of the network components to rearrange and occupy formed voids. Due to the minimal sample required for analysis (100–200 μg), this EGA-MS technique has great potential for postproduction evaluation of composite parts to identify changes in the polymer networks from use and aging, which could signal compromised performance.

Degradation↗
DOE OSTI2021

pnnl/grid_prediction

Two datadriven predictive approaches, namely, {\em Koopman Operator Theoretic (KOT)-based} model and {\em Graph Neural Network (GNN)-based model}, to enable effective power system state predictions. The KOT-based approaches (Robust DMD, deepDMD) capture the power system evolution as a linear dynamical system on an abstract space. The GNNs model the spatio-temporal correlations using graph convolutional network and are called Spatio-Temporal Graph Convolutional Network (STGCN). These predictive models are trained, tested and compared rigorously based on their predictions of frequencies in the IEEE 68 bus system when subjected to a disturbance. GridSTAGE framework developed at Pacific Northwest National Laboratory is leveraged to generate multiple datasets (in the form of PMU measurements) for training and testing by strategically creating load changes across the spatial locations of the network

Nandanoor, Sai Pushpak↗
DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗
DOE OSTI2024

Optimal control of the electron temperature profile in DIII-D using machine learning surrogate models

The viability of the tokamak as a potential fusion reactor depends on the ability to keep the plasma in a stable regime while achieving temperatures, densities, and confinement times that are as high as possible. Tokamak scenario development attempts to find plasma regimes that achieve all of these conditions and are accessible with a given set of hardware constraints. This requires the ability to control plasma properties such as the normalized beta, the internal inductance, safety factor, rotation, etc. One property that has received less attention than some of the others, but is no less critical to achieving high performance, is the electron temperature (T e ) profile. In this work, Linear Quadratic Integral (LQI) control is used to develop a controller for the electron temperature profile in DIII-D. The controller is based on a linearized model derived from the transport equation that describes the evolution of the electron temperature, and includes contributions from the neural network surrogate models NubeamNet and MMMnet. Furthermore, the controller is tested in simulation using COTSIM, and is proven capable of tracking a target T e profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Electronic Structures of Iron in Oxide Glasses via 1s3p Resonant Inelastic X-ray Scattering

Electronic structures of iron in glasses are essential for unraveling the effect of transition metals on amorphous networks and controlling the electro-optical and transport properties of advanced glasses and amorphous energy-storing materials. The electronic configurations around iron in glasses, however, remain not well understood due to the structural disorders arising from multiple iron species with distinct valence, coordination, and spin states. Here, the first 1s3p resonant inelastic X-ray scattering (RIXS) for oxide glasses identifies hidden electronic configurations for Fe 2+ and Fe 3+ in amorphous networks. The results allow us to quantify the composition-induced evolution of oxygen ligand–field interactions of high-spin Fe 3d states with varying valence and coordination environments in complex glasses. The distinct electronic structures account for the electronic origins of iron-induced changes in the glass properties. The results offer prospects for a simultaneous probing of valence, coordination, and spin states of transition metals in diverse multicomponent oxide glasses and functional amorphous solids via 1s3p RIXS.

Kim, Yong-Hyun [Seoul National Univ. (Korea, Repub↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗