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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Reactivity and Structural Dynamics of Supported Metal Nanoclusters Using Electron Microscopy, in-situ X-Ray Spectroscopy, Electronic Structure Theories, and Molecular Dynamics Simulations

This research will provide new methods to determine the structures and compositions of heterogeneous catalysts as they exist in reaction conditions. The new tools will make it possible to understand how extremely small material structures—ones in a size range spanning from single atoms to larger polyatomic clusters—make it possible to carry out highly selective chemical transformations required to synthesize useful materials as well as for efficient energy production and utilization. The tools will make it possible to see how the atomic structures of catalytic materials evolve over time under the conditions of their use, and in this way provide understandings to guide the design of new energy-efficient catalysts. The work will make it possible to answer long standing questions that include the following. How does catalyst structure help guide molecules along specific reaction pathways? What are the important intermediates involved in these transformations? How do the structures and reactivity of catalytic materials, as well as the chemistry of their interfaces, vary with reaction conditions? Is it possible to develop new catalytic materials using new knowledge of the active structures and reaction mechanisms? The work will provide heretofore unavailable experimental tools that can be applied in advanced programs of research addressing such questions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Preparation of asymmetric porous materials

As asymmetric porous film structure formed by depositing a porous material film on a flexible substrate, and applying an anisotropic stress to the porous media on the flexible substrate, where the anisotropic stress results from a stress such as an applied mechanical force, a thermal gradient, and an applied voltage, to form an asymmetric porous material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules

Advances in machine learning provide the ability to leverage data from expensive simulations of high-energy-density experiments to significantly cut down on computational time and costs associated with the search for optimal target designs. This study presents an application of cutting-edge Bayesian optimization methods to the one-dimensional (1D) design optimization of double shell graded layer targets for inertial confinement fusion experiments. This investigation attempts to reduce hydrodynamic instabilities while retaining high yields for future NIF experiments. Machine learning methods can use predictive physics simulations to identify graded layer designs from within the vast design space that demonstrate high predicted performance, including novel designs with high uncertainty in performance that may hold unexpected promise. By applying machine learning tools to the simulation design, we map the trade-off between 1D yield and instability, specifically isolating parameter ranges, which maintain high performance while showing significantly improved Rayleigh–Taylor stability over the point design. Furthermore, the groundwork laid in this study will be a useful design tool for future NIF experiments with graded layer targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Datasets for DOE 2023 Communities LEAP

This data is aligned to eligibility criteria outlined in the United States Department of Energy (DOE) 2023 Communities LEAP (Local Energy Action Program). Please visit the LEAP website (https://www.energy.gov/communitiesLEAP/communities-leap) to learn more about LEAP and gain additional contextual information for how these data may be used. The data provided approximates how the eligibility criteria apply at the census tract level across the United States. This EDX submission provides access to information pertaining to each of the four eligibility criteria outlined (average energy burden, percent low income, communities with a historic economic dependence on fossil fuel industrial facilities, and disadvantaged communities) for all census tracts within the 50 U.S. States, the District of Columbia (D.C.), and Puerto Rico. This information can be access in a detailed excel spreadsheet or through the linked interactive web application (https://arcgis.netl.doe.gov/portal/apps/experiencebuilder/experience/?id=2a77f443d72b4a4d82474b3ffe33b8cd). Please note that while these data are provided at the census tract level, census tracts do not necessarily have the same physical boundaries as a community but were used as they provide the closest proxy based on publicly available information collected using an empirically robust method. U.S. territories are not listed but are eligible to apply to Communities LEAP. As stated in the Opportunity Announcement, applying communities should describe how they meet the eligibility criteria in their application even if these data do not specifically show that they are eligible.

2023↗
DOE OSTI2026

An overview of 3D field optimization for control of transport and edge instabilities on KSTAR

An international team from several laboratories and universities has made key advances over the last few years in the control of plasma transport and edge instabilities with applied 3D fields in the KSTAR tokamak to optimize long pulse operation scenarios. This overview begins with the optimization of both core and edge resonant magnetic perturbations (RMP) to improve fast ion confinement to avoid excessive limiter heat loads due to fast ion losses and successful modeling of the experimental results. Integrated and advanced plasma control techniques with machine learning (ML) and adaptive control were then used to optimize the 3D field spectrum in real-time to control edge localized modes (ELMs) while avoiding core locked modes that could disrupt the plasma. Accelerating the offline model of 3D fields with a surrogate ML model can optimize ELM suppression in the edge while limiting the impact of the applied RMP fields deeper in the plasma core in real-time. In addition, the impact of the 3D fields on the divertor heat load has been modeled and compared with experimental measurements. An analysis of a multi-machine database including KSTAR has been performed to better understand the metrics for the observed RMP thresholds for ELM suppression and the resulting plasma performance. Predictive modeling of the operational space for ELM suppression and density pumpout due to RMP has shown the importance of magnetic islands in the plasma edge and their impact on plasma turbulence. This research has culminated in the development of successful long pulse operational scenarios on KSTAR while attempting to overcome challenges of the new tungsten divertor.

3D fields↗
DOE OSTI2025

Measurement of phonon angular momentum

In condensed matter, angular momentum is intimately related to the emergence of topological quantum states, including chiral superconductivity, quantum spin liquids and various chiral quasiparticles. Recently, it has been predicted that microscopic lattice excitations, known as phonons, can carry finite angular momentum, leading to specific physical properties of materials. However, phonon angular momentum has not yet been observed directly. Here, in this study, we demonstrate that angular momentum conservation results in a macroscopic mechanical torque when applying a time-reversal symmetry-breaking thermal gradient along the chiral axis of single-crystal tellurium. We probe this torque using a cantilever-based device and establish that it changes sign by flipping the chirality or thermal gradient. This behavior disappears in polycrystalline samples that lack a preferred chirality. Our experimental results align well with theoretical calculations. We provide compelling evidence for phonon angular momentum, which might enable quantum states with potential applications in microelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Anelasticity and Phase Transition During Ramp-Release in Tin

This study examines the qualitative features of an anelasticity model associated with the bowing of dislocations in the presence of phase transition. A simple physically plausible mechanism is introduced to describe the interaction of anelasticity and the transformation. Varying the anelastic parameters results in strong differences in the deviatoric stress response. The model is applied to study the behavior of tin (Sn) and compared to data from ramp driven compression-release experiments. Tin exhibits a complex phase diagram within a relatively accessible range of temperature and pressures and the characterization of its phases is considered an open problem with significant scientific merit. The coupling between anelasticity, plasticity, and phase transformation contributes to release wave features traditionally associated with the phase transition effect alone suggesting the importance of accounting for the effects jointly. Posterior distributions of the plastic and anelastic parameters are computed using Bayesian-inference-based methods, further highlighting the importance of anelasticity in this regime.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantum Lock Technologies: Innovation Crossroads Final Report

At Quantum Lock Technologies, our mission is to use future-proof hardware and software to bridge the gap between physical access control and cyber security. Physical security includes access to doors, lockboxes/containers, and machinery/robots. Connecting physical access control to the cloud allows for remote detection, fast ledger updates, and mobile or remote access. However, this also opens physical security up to the world of cyber-attacks. At Quantum Lock, we use quantum random number generation to generate completely random and unpredictable digital keys to be used by connected equipment in a facility. This quantum technology is then paired with end-to-end encryption and a one-time-key communication protocol to ensure the highest level of security. Through the Innovation Crossroads program at Oak Ridge National Laboratory, we have developed benchtop prototypes of our technology, connected with utility boards as our first target customers, and prepared for our first pilot with customers (target end of summer 2022). Below is a photograph of myself at an energy substation where we plan to eventually apply our technology.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics–Informed Neural Networks

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Furthermore, the improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Isometries and the double copy

In the standard derivation of the Kerr-Schild double copy, the geodicity of the Kerr-Schild vector and the stationarity of the spacetime are presented as assumptions that are necessary for the single copy to satisfy Maxwell’s equations. However, it is well known that the vacuum Einstein equations imply that the Kerr-Schild vector is geodesic and shear-free, and that the spacetime possesses a distinguished vector field that is simultaneously a Killing vector of the full spacetime and the flat background, but need not be timelike with respect to the background metric. We show that the gauge field obtained by contracting this distinguished Killing vector with the Kerr-Schild graviton solves the vacuum Maxwell equations, and that this definition of the Kerr-Schild double copy implies the Weyl double copy when the spacetime is Petrov type D. When the Killing vector is taken to be timelike with respect to the background metric, we recover the familiar Kerr-Schild double copy, but the prescription is well defined for any vacuum Kerr-Schild spacetime and we present new examples where the Killing vector is null or spacelike. While most examples of physical interest are type D, vacuum Kerr-Schild spacetimes are generically of Petrov type II. We present a straightforward example of such a spacetime and study its double copy structure. Our results apply to real Lorentzian spacetimes as well as complex spacetimes and real spacetimes with Kleinian signature, and provide a simple correspondence between real and self-dual vacuum Kerr-Schild spacetimes. This correspondence allows us to study the double copy structure of a self-dual analog of the Kerr spacetime. We provide evidence that this spacetime may be diffeomorphic to the self-dual Taub-NUT solution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Mapping Glacier Basal Sliding Applying Machine Learning

During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

58 GEOSCIENCES↗
DOE OSTI2023

MAS: A versatile Landau-fluid eigenvalue code for plasma stability analysis in general geometry

We have developed a new global eigenvalue code, Multiscale Analysis for plasma Stabilities (MAS), for studying plasma problems with wave toroidal mode number (n) and frequency (ω) in a broad range of interest in general tokamak geometry, based on a five-field Landau-fluid description of thermal plasmas. Beyond keeping the necessary plasma fluid response, we further retain the important kinetic effects including diamagnetic drift, ion finite Larmor radius, finite parallel electric field (E||), ion and electron Landau resonances in a self-consistent and non-perturbative manner without sacrificing the attractive efficiency in computation. The physical capabilities of the code are evaluated and examined in the aspects of both theory and simulation. In theory, the comprehensive Landau-fluid model implemented in MAS can be reduced to the well-known ideal MHD model, electrostatic ion-fluid model, and drift-kinetic model in various limits, which clearly delineates the physics validity regime. In simulation, MAS has been well benchmarked with theory and other gyrokinetic and kinetic-MHD hybrid codes in a manner of adopting the unified physical and numerical framework, which covers the kinetic Alfv\'en wave (KAW), ion sound wave (ISW), low-n kink, high-n ion temperature gradient mode (ITG) and kinetic ballooning mode (KBM). Moreover, MAS is successfully applied to model the Alfv\'en eigenmode (AE) activities in DIII-D discharge #159243, which faithfully captures the frequency sweeping of reversed shear Alfv\'en eigenmode (RSAE), the tunneling damping of toroidal Alfv\'en eigenmode (TAE), as well as the polarization characteristics of kinetic beta-induced Alfv\'en eigenmode (KBAE) and beta-induced Alfv\'en-acoustic eigenmode (BAAE) being consistent with former gyrokinetic theory and simulation. With respect to the key progress contributed to the community, MAS has the advantage of combining rich physics ingredients, realistic global geometry and high computation efficiency together for plasma stability analysis in linear regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. Here, this work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.

42 ENGINEERING↗
DOE OSTI2025

Electroplated Polymer‐Modified Carbon Fiber for Performance‐Enhancing Composite Interfaces

Carbon fiber composite performance relies on the fiber-matrix interface for effective load transfer. To enhance interfacial properties between the fiber and matrix, often carbon fiber surfaces are oxidatively and covalently modified to incorporate chemical functional groups. By contrast, here, noncovalent electrodeposition of functional polyelectrolyte is applied onto conducting carbon fibers from aqueous solutions. A natural polymer, chitosan (CS), is electro-deposited onto the fiber surface, which undergoes multi-scale physical interactions. The bound CS layer with abundant amine functionalities reacts with epoxy moieties within the matrix to improve the interfacial properties. The scalable and energy-efficient electrodeposition eliminates traditional functionalization and sizing requirements of carbon fiber while delivering significantly higher mechanical performance with enhanced consistency. For continuous fiber reinforced composites, compared to conventional fibers, apparent interlaminar shear strength increases by 27%, reaching ≈86 MPa. The short fiber composites with only 2–11 wt.% fibers exhibit ≈20% increase in tensile strength with a peak performance of 120 MPa. Unlike traditionally treated and sized carbon fiber, this approach delivers coated fibers with long shelf-life and allows recovery of both CS in electrolyte form and carbon fiber by continuous electrochemical processing of the modified fibers with inverse polarity; thus, it promotes overall fiber recyclability.

36 MATERIALS SCIENCE↗
DOE OSTI2023

$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Efficient high-fidelity TRISO statistical failure analysis using Bison: Applications to AGR-2 irradiation testing

The ability of tri-structural isotropic (TRISO) fuel to contain fission products is largely dictated by the quality of the manufacturing process, since most of the fission product release is expected to occur due to coating layer failure in a small number of particles containing defects. The Bison fuel performance code has capabilities to predict failure in individual particles, accounting for the presence of defects, and to apply statistical analysis methods to compute the probability of failure in a set of fuel particles. Bison has recently undergone significant development both to improve its physical representations of fuel particle behavior and to improve the efficiency of its statistical failure calculations. Physical model improvements include new capabilities to account for the pressure generated by fission gases on inner pyrolytic carbon (IPyC) crack surfaces and to use local material coordinate orientation to accurately incorporate the anisotropy in the material properties in aspherical particles. To improve statistical modeling efficiency, a direct integration approach which involves directly integrating the failure probability function associated with statistically varying parameters has been developed. The direct integration approach is much more efficient than the Monte Carlo (MC) schemes commonly employed, and allows Bison to directly run high-dimensional fuel performance models, which improves the accuracy of failure probability calculations. Finally, a set of benchmark problems is considered here to compare the MC and direct integration approaches, and a statistical failure analysis of compacts in the Advanced Gas Reactor (AGR)-2 experiments is performed using the direct integration approach.

36 MATERIALS SCIENCE↗