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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dynamic observation of dual-beam irradiated Fe and Fe-10Cr alloys at 435 °C

The attractive mechanical properties and superior resistance to void-swelling make ferritic/martensitic alloys a promising structural material for advanced nuclear reactors. However, one anomaly that has intrigued researchers for more than 50 years is the proportion of two types of dislocation loops in Fe and Fe-Cr alloys with Burger vectors b=½<111> and b=<100>. Although the possible mechanisms responsible for the presence of <100> loops continue to be the subject of intense modeling studies, there remains incomplete experimental understanding of fundamental irradiation processes in Fe(Cr) alloys. Here, the dose dependence of the irradiation-induced microstructural evolution was examined from 0 to 20 displacement per atom (dpa) in high purity Fe and Fe-10Cr during simultaneous dual-beam (1 MeV Kr + 10 appm He/dpa) irradiation at 435 °C. We experimentally revealed that the mechanism for the formation of <100> loops may not follow the conventional simple dislocation reaction between two 1½<111> loops. Real-time dynamic formation and evolution of defects including black dot loops, loop coarsening, loop decoration, network dislocations, and cavities were demonstrated. Several results indicated that the addition of Cr and He could impede dislocation loop motion. The evolution of the defect size/density and relative fraction of ½<111> vs <100> loops were quantitatively summarized. With increasing dose, ½<111> loops became the dominant type of loop in both materials. Notably, <100> loops were predominantly observed near grain boundaries only for pure Fe, while arrays of nanoscale black dot defects composing the <100> loop strings were observed in plenty in Fe-10Cr.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems

Here, we present a perspective on recent progress in machine-learning (ML) force-field approaches for large-scale Landau–Lifshitz–Gilbert (LLG) simulations of metallic spin systems. Building on a generalization of the Behler–Parrinello (BP) architecture originally developed for quantum molecular dynamics, we develop scalable and transferable ML models that faithfully capture the complex, environment-dependent electron-mediated exchange fields characteristic of itinerant magnets. A central ingredient of this framework is the implementation of symmetry-aware magnetic descriptors based on group-theoretical bispectrum formalisms. Leveraging these ML force fields, LLG simulations faithfully reproduce hallmark non-collinear magnetic orders—such as the 120° and tetrahedral states—on the triangular lattice, and successfully capture the complex spin textures emerging in the mixed-phase states of a square-lattice double-exchange model under thermal quench. We further discuss a generalized potential theory that extends the BP formalism to incorporate both conservative and nonconservative electronic torques, thereby enabling ML models to learn nonequilibrium exchange fields from computationally demanding microscopic approaches such as nonequilibrium Green’s-function techniques. This extension yields quantitatively accurate predictions of voltage-driven domain-wall motion and establishes a foundation for quantum-accurate, multiscale modeling of nonequilibrium spin dynamics and spintronic functionalities.

Descriptors↗
DOE OSTI2020

Sequentially coupled flow and geomechanical simulation with a discrete fracture model for analyzing fracturing fluid recovery and distribution in fractured ultra-low permeability gas reservoirs

More accurate characterization and prediction of the in-situ distribution of fracturing fluid in fractured reservoirs are needed for enhancing well productivity. In this study, an implicit-sequentially coupled flow/geomechanics simulator incorporating an efficient discrete fracture model is developed to model fluid distribution and recovery performance of ultra-low permeability gas reservoirs. The finite-volume and finite-element methods are used for space discretization of the flow and geomechanics equations, respectively, while the backward Euler method is employed for time discretization. The flow and geomechanics equations are solved sequentially based on fixed-stress splitting. An efficient discrete-fracture model is used to explicitly model the fractured system. Flexible unstructured gridding is employed to model arbitrarily-oriented fractures. The interrelations among pore volume, permeability and geomechanical conditions are considered dynamically using two-way coupled flow and geomechanics computations. The geometry of fracture (networks) due to hydraulic fracturing has significant impacts on the fracturing fluid recovery efficiency and ensuing fluid distribution. Under the same injection volume, the fracturing fluid recovery is higher when the fracture geometry is planar. Fluid recovery is relatively lower whenever natural fractures are activated during fracturing treatments; flowback time is also shortened when complex fracture network with enlarged fracture interface is present. Fracturing fluid in hydraulic fractures may leak off into the natural fractures and subsequently imbibes into the surrounding matrix due to capillarity effects. The fracturing fluid recovery and in-situ fluid distribution are sensitive to the shut-in duration and fracture closure behavior. This study analyzes the coupled flow-geomechanical responses of fractured gas reservoirs during the post-fracturing periods. Understanding the fate of the fracturing fluid can provide insights on, to some extent, the stimulated fracture volume, size of the water invasion zone, and efficiency of the fracturing design. The simulation predictions can also provide more accurate initial reservoir conditions (e.g. distributions of different phases and pressure) for long-term well performance estimation.

42 ENGINEERING↗
DOE OSTI2025

Orbital-Selective Instabilities and Spin Fluctuations at the Verge of Superconductivity in Interlayer-Expanded Iron Selenide

Understanding electron correlation-driven instabilities and their coupling to structural phases is essential for deciphering multiorbital pairing in unconventional superconductors. We investigate Li x (C 5 H 5 N) y Fe 2 Se 2 (x ∼ 0.6; y ∼ 0.7−0.9), a tetragonal β-FeSe intercalate with a superconducting transition temperature (T c = 39 K) closely tied to an expanded Fe-layer spacing (∼11.4 Å). High-resolution synchrotron Xray diffraction and core-level absorption spectroscopy reveal subtle lattice distortions on cooling without a symmetry-breaking transition. Instead, the material exhibits negative thermal expansion (NTE) in the two-dimensional Fe network below T S ∼ 70 K, and stiffening of local Se−Fe−Se bond dynamics near T c . The spatially incoherent rearrangement of FeSe 4 tetrahedra and the site-local fluctuations, signal reduced electron correlations compared to those of parent β-FeSe (T c = 8 K). Complementary X-ray emission spectroscopy, a fast local probe of Fe 3d valence states, detects persistent local Fe spin moments below T S , unlike quenching in related systems. These findings indicate that decoupling of Fe planes leads to an electronically driven lattice instability. The latter emerges as NTE induced from weak, orbital-selective localization of in-plane Fe 3d states rather than conventional transverse vibrations. Governed by Hund’s coupling, this selectivity permits coexistence of local spin fluctuations with itinerant d-electrons critical for enhancing T c . These results suggest that intercalation-driven d-orbital differentiation moderates electron correlations, providing a pathway to optimize the superconductivity in low-dimensional quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultra-fast Proton Conduction and Photocatalytic Water Splitting in a Pillared Metal–Organic Framework

Proton-exchange membrane fuel cells enable the portable utilization of hydrogen (H 2 ) as an energy resource. Current electrolytic materials have limitation, and there is an urgent need to develop new materials showing especially high proton conductivity. Here, we report the ultra-fast proton conduction in a novel metal–organic framework, MFM-808, which adopts an unprecedented topology and a unique structure consisting of two-dimensional layers of {Zr 6 }-clusters. By replacing the bridging formate with sulfate ligands within {Zr 6 }-layers, the modified MFM-808-SO 4 exhibits an exceptional proton conductivity of 0.21 S·cm –1 at 85 °C and 99% relative humidity. Modeling by molecular dynamics confirms that proton transfer is promoted by an efficient two-dimensional conducting network assembled by sulfate–{Zr 6 }-layers. MFM-808-SO 4 also possesses excellent photocatalytic activity for water splitting to produce H 2 , paving a new pathway to achieve a renewable hydrogen-energy cycle.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bedrock Vadose Zone Storage Dynamics Under Extreme Drought: Consequences for Plant Water Availability, Recharge, and Runoff

Bedrock vadose zone water storage (i.e., rock moisture) dynamics are rarely observed but potentially key to understanding drought responses. Exploiting a borehole network at a Mediterranean blue oak savanna site—Rancho Venada—we document how water storage capacity in deeply weathered bedrock profiles regulates woody plant water availability and groundwater recharge. The site is in the Northern California Coast Range within steeply dipping turbidites. In a wet year (water year 2019; 647 mm of precipitation), rock moisture was quickly replenished to a characteristic storage capacity, recharging groundwater that emerged at springs to generate streamflow. In the subsequent rainless summer growing season, rock moisture was depleted by about 93 mm. In two drought years that followed (212 and 121 mm of precipitation) the total amount of rock moisture gained each winter was about 54 and 20 mm, respectively, and declines were documented exceeding these amounts, resulting in progressively lower rock moisture content. Oaks, which are rooted into bedrock, demonstrated signs of water stress in drought, including reduced transpiration rates and extremely low water potentials. In the 2020–2021 drought, precipitation did not exceed storage capacity, resulting in variable belowground water storage, increased plant water stress, and no recharge or runoff. Rock moisture deficits (rather than soil moisture deficits) explain these responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Proximate spin liquid and fractionalization in the triangular antiferromagnet KYbSe 2

The Heisenberg triangular-lattice quantum spin liquid and its phase transitions to nearby magnetic orders have received much theoretical attention, but clear experimental manifestations of these states are rare. Here we demonstrate that a spin-half delafossite material, namely, KYbSe 2 , shows close proximity to the triangular-lattice Heisenberg quantum spin liquid. Using neutron scattering, we identify a diffuse continuum with a sharp lower bound within the measured spectra. Applying entanglement witnesses to the data indicates multipartite entanglement spread between its neighbours, and an analysis of its magnetic-exchange couplings reveals close proximity to the theoretical quantum spin-liquid phase. The key features of the data are reproduced by Schwinger boson theory and tensor network calculations with a substantial next-nearest-neighbour coupling. The strength of the dynamical structure factor at the Brillouin-zone K point shows a scaling collapse down to 0.3 K, indicating the existence of a second-order quantum phase transition. Finally, comparing this with previous theoretical work suggests that the proximate phase at a larger next-nearest-neighbour coupling is a gapped $\mathbb{Z}_{2}$ spin liquid, resolving a long-debated issue. Similar content being viewed by others

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

High-strength 3D printed poly(lactic acid) composites reinforced by shear-aligned polymer-grafted cellulose nanofibrils

This work demonstrates the application of pilot-scale surface functionalization of cellulose nanofibrils (CNFs) by aqueous grafting-through polymerization and subsequent spray drying in 3D printed poly(lactic acid) (PLA) composites. Grafted-CNF composites attain an ultimate tensile strength of 88 ± 3 MPa and a tensile modulus of elasticity of 7.8 ± 1.3 GPa in the printing direction at 20 wt% reinforcement loading. These increases, 42% and 139% over neat PLA, respectively, represent the strongest reported 3D printed CNF/PLA composite to date in the literature. The mechanisms behind these improvements are investigated by comparisons to neat PLA and unmodified spray-dried CNF/PLA controls using melt rheology, dynamic mechanical analysis, and assessment of the reinforcement dispersion. These experiments reveal that improved network formation and shear-induced alignment of the grafted CNFs facilitate the remarkable tensile properties of the printed composites.

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

Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning

We identify effective stochastic differential equations (SDEs) for coarse observables of fine-grained particle- or agent-based simulations; these SDEs then provide useful coarse surrogate models of the fine scale dynamics. We approximate the drift and diffusivity functions in these effective SDEs through neural networks, which can be thought of as effective stochastic ResNets. The loss function is inspired by, and embodies, the structure of established stochastic numerical integrators (here, Euler–Maruyama and Milstein); our approximations can thus benefit from backward error analysis of these underlying numerical schemes. They also lend themselves naturally to “physics-informed” gray-box identification when approximate coarse models, such as mean field equations, are available. Existing numerical integration schemes for Langevin-type equations and for stochastic partial differential equations can also be used for training; we demonstrate this on a stochastically forced oscillator and the stochastic wave equation. Our approach does not require long trajectories, works on scattered snapshot data, and is designed to naturally handle different time steps per snapshot. We consider both the case where the coarse collective observables are known in advance, as well as the case where they must be found in a data-driven manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computationally guided experimental validation of divacancy defect formation in 4H-SiC

Recent research into solid-state qubits for quantum information science has focused on optically addressable spin defects such as the negatively charged nitrogen-vacancy center in diamond and the neutrally charged divacancy (VV) in 4H-SiC as scalable quantum sensors and networking qubits. Within this context, direct investigations of the structural origin and defect formation dynamics of a sub-set of the VV center in 4H-SiC remain lacking. Here, we take a systematic experimental approach guided by predictions from first-principles simulations to gain a thorough mechanistic understanding of the VV defect formation and control in 4H-SiC. We study the effect of annealing time and temperature on VV formation in high-purity semi-insulating 4H-SiC samples following electron irradiation. Three different temperatures (1123, 1273, and 1473 K) and annealing duration (from 0.5 to 72 h) are chosen to explore VV formation in different regions. We find that samples annealed at 1273 K give the highest VV-related photoluminescence (PL) intensities, in agreement with the prediction from first-principles calculations. Furthermore, the logarithmic dependence of VV-related PL intensities on the annealing duration at 1273 K indicates that 1273 K provides sufficient thermal energy for silicon vacancy migration but not for VV migration. Together, these results suggest that efficient VV formation occurs above the V Si migration temperature and below the VV migration threshold.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Decoding diffraction and spectroscopy data with machine learning: A tutorial

This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Temporal Convolutional Network Using Empirical Mode Decomposition to Detect Faults in Grid Connected Systems

Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.

Sutton, Elizabeth [ORNL] (ORCID:0009000078885935)↗
DOE OSTI2020

Advances in High-Power Wireless Charging Systems: Overview and Design Considerations

Wireless charging systems are foreseen as an effective solution to improve the convenience and safety of conventional conductive chargers. As this technology has matured, recent broad applications of wireless chargers to electrified transportation have indicated a trend toward higher power, power density, modularity, and scalability of designs. In this article, commercial systems and laboratory prototypes are reviewed, focusing mostly on the advances in high-power wireless charging systems. The recent endeavors in magnetic pad designs, compensation networks, power electronics converters, control strategies, and communication protocols are illustrated. Both stationary and dynamic (in-motion) wireless charging systems are discussed, and critical differences in their designs and applications are emphasized. On that basis, the comparisons among different solutions and design considerations are summarized to present the essential elements and technology roadmap that will be necessary to support large-scale deployment of high-power wireless charging systems. The review is concluded with the discussion of several fundamental challenges and prospects of high-power wireless power transfer (WPT) systems. Foreseen challenges include utilization of advanced materials, electric and electromagnetic field measurement and mitigation, customization, communications, power metering, and cybersecurity.

25 ENERGY STORAGE↗
DOE OSTI2020

The plant endoplasmic reticulum: an organized chaos of tubules and sheets with multiple functions

The endoplasmic reticulum is a fascinating organelle at the core of the secretory pathway. It is responsible for the synthesis of one third of the cellular proteome and, in plant cells, it produces receptors and transporters of hormones as well as the proteins responsible for the biosynthesis of critical components of a cellulosic cell wall. The endoplasmic reticulum structure resembles a spider-web network of interconnected tubules and cisternae that pervades the cell. The study of the dynamics and interaction of this organelles with other cellular structures such as the plasma membrane, the Golgi apparatus and the cytoskeleton, have been permitted by the implementation of fluorescent protein and advanced confocal imaging. In this review,we report on the findings that contributed towards the understanding of the endoplasmic reticulum morphology and function with the aid of fluorescent proteins, focusing on the contributions provided by pioneering work from the lab of the late Professor Chris Hawes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Machine Learning Solutions for a Stable Grid Recovery

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗