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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine Learning Approach to Modeling of Neutral Particles Transport in Plasma

A propagator‐based approach is investigated for Monte‐Carlo (MC) modeling of neutral particle transport in fusion boundary plasmas. The propagator is based on a Green's function for the neutral kinetic equation, which depends on the plasma profiles. A neural network (NN)‐based model for the propagator provides a fast and accurate solution for the neutral distribution function in plasma. Preliminary results from a small 1D test problem look encouraging. The proposed approach, a propagator‐based NN model for neutral transport in plasma, has potential for generalization to higher dimensions and efficient coupling with plasma models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Synergistic Coupling Effect of Electronic Conductivity and Interphase Compatibility on High-Voltage Na 3 V 2 (PO 4 ) 2 F 3 Cathodes

Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) has been considered an up-and-coming cathode material candidate for sodium (Na) ion batteries in light of its high specific capacity and working voltage. However, an erratic cathode/electrolyte interface layer is inevitably formed, accompanied by continuous electrolyte decomposition on the NVPF surface, when the voltage exceeds 4.2 V vs Na + /Na. Herein, the interphase features of NVPF are obviously enhanced owing to the ameliorated electronic conductivity obtained by combining it with carbon nanotubes (CNT). The NVPF with 3 wt % CNT (NVPF@3% CNT) reduces the Na + diffusion kinetic energy barrier and electron transport resistance. Furthermore, the conducting network formed by CNT with sturdy structure strength can promptly accommodate the volumetric changes during sequential Na + extraction/insertion and thus effectively improve the long-term cyclic performance of NVPF/hard carbon full cells. The initial discharge capacity approaches 105 mA h g –1 at 0.5C, and it retains 94% capacity retention after 200 cycles at the temperature of –10 °C. The cathode/electrolyte interphase characterization results further demonstrate that the interphase layer on the NVPF@3% CNT cathode is thinner and more compact compared with pristine samples. Here, this research provides a competitive strategy to facilitate the interfacial compatibility between the NVPF and electrolytes and accelerate the commercialization of high-performance Na-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Tandem synthesis of tetrahydroquinolines and identification of the reaction network by operando NMR

The study of the reaction mechanism and complex network for heterogeneously catalyzed tandem reactions is challenging but can guide reaction design and optimization. Here, we describe a case study using bifunctional metal–organic framework supported Pd nanoparticles (Pd/UiO-66(HCl)) for the one-pot tandem synthesis of substituted tetrahydroquinolines via the Claisen–Schmidt condensation and reductive intramolecular cyclization. The directly observed evolution of intermediates and products, including reactive species containing hydroxylamine group and unstable intermediate 2-phenyl-3,4-dihydroquinoline, was enabled by operando magic angle spinning nuclear magnetic resonance studies under 50 bar H 2 . The reaction network of the tandem reaction is deduced based on reaction kinetic information obtained from the operando study. The optimized procedure was applied to various acetophenone and nitrobenzaldehyde derivatives carrying different functional groups, and eight valuable substituted tetrahydroquinolines were obtained in moderate to good yields. This work provides a molecular-level understanding of the catalytic system and brings up new opportunities for efficient and sustainable synthesis of medicinally relevant building blocks.

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

Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids

Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Some examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A physics-informed deep learning description of Knudsen layer reactivity reduction

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov–Fokker–Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, the predictions from the Vlasov–Fokker–Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot, thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov–Fokker–Planck PINN and the results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile, demonstrating that the Vlasov–Fokker–Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Modeling Combustion Reaction ODEs with Neural Networks

The chemistry of combustion reactions is complex as it involves many species and reactions. In practice, such a system is often modeled computationally using an empirically derived chemical mechanism. Given the large range of reaction rates, the system is then time evolved using a stiff ODE solver. However, even when reduced chemical mechanisms are employed, the system can become computationally expensive for two-dimensional or three-dimensional systems. Such systems can also face problems with instability. As such, it is desirable to find a cheaper, stable alternative to solving the reaction system. Neural networks offer the potential to learn these reaction ODEs and time evolve a combustion reaction in a more cost-efficient manner than stiff ODE solvers. In this study, a variety of neural networks are trained on zero-dimensional Cantera simulations of methane combustion with varying initial conditions. Several predictive approaches as well as several neural network architectures (artificial neural network with dropout, ResNet, and Neural ODE) are compared in their ability to predict combustion trajectories. Promising models are then identified.

combustion kinetics↗
DOE OSTI2025

Molecular Dynamics Simulation and Theoretical Analysis of Structural Relaxation, Bond Exchange Dynamics, and Glass Transition in Vitrimers

Vitrimers are a class of polymer networks featuring dynamic covalent cross-links that can undergo associative bond exchange. There has been recent interest in these materials due to their promise as recyclable thermosets or self-healing polymers because of the ability of vitrimer networks to rearrange at the molecular level and undergo macroscopic flow. However, the practical use of these materials often occurs in the supercooled regime or glassy state, where the implications of dynamic bonds are complicated by the interplay between slow activated segmental dynamics, cross-link (i.e., bond-exchange) kinetics, and ultimately material properties. Here, in this paper, we combine coarse-grained molecular dynamics simulation and microscopic statistical mechanical theory to understand how cross-linking kinetics affect material dynamics and how this couples to segmental relaxation of the polymeric network strands across a spectrum of length and time scales, especially in the supercooled regime. We characterize the Kuhn segmental alpha relaxation time and bond exchange time for vitrimer systems across various cross-link densities, temperatures, and bond exchange rates. Simulation and theory both exhibit a bending-up behavior for bond exchange time upon cooling, suggesting a coupling between bond exchange dynamics and segmental relaxation that intensifies with faster bond exchange kinetics. We also found bond exchange dynamics have an impact on Kuhn segment alpha relaxation time, which is most significant at higher cross-link densities. Both these effects are most prominent when the bond exchange time is similar to the Kuhn segment alpha relaxation time, and the resulting coupling of these two relaxation processes is tied to both the probability of a free end to find a bonded pair and the time scale of the constraints imposed by the dynamic cross-links. This relationship is reflected by a cross-link dependence of a theoretical parameter which represents the quantitative degree of coupling between bond exchange and segmental dynamics. Overall, the combination of simulation and theory clarifies the intricate interaction between bond kinetics and segmental relaxation and demonstrates the ability to provide molecular-level insights into vitrimer dynamics over a wide temperature range.

dynamic relaxation↗
DOE OSTI2021

Crystallization of the P 3 Sn 4 Phase upon Cooling P 2 Sn 5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

We performed molecular dynamics simulations to study the crystallization of the P3Sn4 phase from P 2 Sn 5 liquid using a machine learning (ML) interatomic potential with desirable efficiency and accuracy. Our results capture the liquid properties of P 2 Sn 5 at 1300 K, which is well above the melting temperature. The phase separation and crystallization are observed when P 2 Sn 5 liquid is cooled down below 832 and 505 K, respectively. The simulation results are in good agreement with the experimentally observed phase transformation behaviors and provide useful insights into the complex nucleation and crystallization process at the details of atomistic scale. Our work also demonstrated that ML interatomic potentials based on neural network deep learning are robust and capable of accurately describing the energetics and kinetics of complex materials through molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2015

Overview of the Design, Fabrication and Performance Requirements of Micro-Spec, an Integrated Submillimeter Spectrometer

Micro-Spec is a compact submillimeter (350-700 GHz) spectrometer which uses low loss superconducting niobium microstrip transmission lines and a single-crystal silicon dielectric to integrate all of the components of a grating-analog spectrometer onto a single chip. Here we present details of the fabrication and design of a prototype Micro-Spec spectrometer with resolution, R64, where we use a high-yield single-flip wafer bonding process to realize instrument components on a 0.45 m single-crystal silicon dielectric. We discuss some of the electromagnetic design concerns (such as loss, stray-light, cross-talk, and fabrication tolerances) for each of the spectrometer components and their integration into the instrument as a whole. These components include a slot antenna with a silicon lens for optical coupling, a phase delay transmission line network, parallel plate waveguide interference region, and aluminum microstrip transmission line kinetic inductance detectors with extremely low cross-talk and immunity to stray light. We have demonstrated this prototype spectrometer with design resolution of R64. Given the optical performance of this prototype, we will also discuss the extension of this design to higher resolutions suitable for balloon-flight.

spectrometer↗
NASA NTRS2005

A Preliminary Investigation of the E-Beam Induced Polymerization of Maleimide and Norbornene End-capped Polyimides

A research area of high activity in connection with aerospace engineering has been the development of polymer thermosetting resins that can resist temperature as high as 300 C while maintaining adequate toughness, and providing ease of processing to enable low temperature and low cost composite fabrication methods. In order to meet such requirements, sequential interpenetrating polymer networks (IPNs) based on bismaleimide (BMI) and cyanate ester (CE) monomers were investigated. In these systems, a polycyanurate network is first formed in the presence of BMI and appropriate reactive diluent monomers and in a second step, a network based on the BMI is created in the presence of a fully formed polycyanurate network. The materials developed can be processed at relatively low temperature (less than 150 C) and with the aid of electron beam (EB) curing. Of major importance to the success of this work was the identification of a reactive diluent that improves ease of processing and has tailored reactivity to allow for the controlled synthesis of CE-BMI sequential IPNs. Based on solubility and reactivity of a number of reactive diluents, N-acryloylmorpholine (AMP) was selected as a comonomer for BMI copolymerization. A donor-acceptoreaction mechanism was suggested to explain the relative reactivity of a variety of reactive diluents towards maleimide functionality. The optimum processing parameters for the formation of the first network were determined through the study of metal catalyzed cure and hydrolysis of cyanate esters, whereas the reaction behavior for second network formation in terms of the influence of EB dose rate and temperature was elucidated through an in-situ kinetics study of maleimide and AMP copolymerization. Structure-property relationships were developed which allowed for the design of improved resin systems. In particular, appropriate network coupler possessing cyanate ester and maleimide functionality was synthesized to link the polycyanurate first network to the BMI/AMP second network and thus form linked sequential IPNs (LIPNs). Consequently, Tg as high as 370 C was achieved and a fracture toughness of 120 Joules per square meters was obtained for resin systems that possess adequately low viscosity for processing using liquid molding techniques at low temperature.

Palmese, Giuseppe R.↗
DOE OSTI2021

Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. However, despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Finding Hidden Patterns in High Resolution Wind Flow Model Simulations

Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with 2× resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A limit on the total lepton number in the Universe from BBN and the CMB

At temperatures below the QCD phase transition, any substantial lepton number in the Universe can only be present within the neutrino sector. In this work, we systematically explore the impact of a non-vanishing lepton number on Big Bang Nucleosynthesis (BBN) and the Cosmic Microwave Background (CMB). Relying on our recently developed framework based on momentum averaged quantum kinetic equations for the neutrino density matrix, we solve the full BBN reaction network to obtain the abundances of primordial elements. We find that the maximal primordial total lepton number L allowed by BBN and the CMB is -0.12 (-0.10) ≤ L ≤ 0.13 (0.12) for NH (IH), while specific flavor directions can be even more constrained. This bound is complementary to the limits obtained from avoiding baryon overproduction through sphaleron processes at the electroweak phase transition since, although numerically weaker, it applies at lower temperatures and is obtained completely independently. We publicly release the C++ code COFLASY-C on GitHub (https://github.com/mariofnavarro/COFLASY/tree/COFLASY-C) which solves for the evolution of the neutrino quantum kinetic equations numerically.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A deep learning approach to fast analysis of collective Thomson scattering spectra

Fast analysis of collective Thomson scattering ion acoustic wave features using a deep convolutional neural network model is presented. The network was trained from spectra to predict the plasma parameters, including ion velocities, population fractions, and ion and electron temperatures. A fully kinetic particle-in-cell simulation was used to model a laboratory astrophysics experiment and simulate a diagnostic image of the ion acoustic wave feature. Network predictions were compared with Bayesian inference of the plasma model parameters for both the simulated and experimentally measured images. Both approaches were fairly accurate predicting the simulated image and the network predictions matched a good portion of the Bayesian results for the experimentally measured image. The Bayesian approach is more robust to noise and motivates future work to train deep learning models with realistic noise. The advantage of the deep learning model is making thousands of predictions in a few hundred milliseconds, compared to a few seconds to minutes per prediction for the optimization and Bayesian approaches presented here. The results demonstrate promising capabilities of deep learning models to analyze Thomson data orders of magnitude faster than conventional methods when using the neural network for standalone analysis. If more rigorous analysis is needed, neural network predictions can be used to quickly initialize other optimization methods and increase chances of success. This is especially useful when the dataset becomes very large or highly dimensional and manually refining initial conditions for the entire dataset are no longer tractable.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Rapid and efficient hydrogen clathrate hydrate formation in confined nanospace

Clathrate hydrates are crystalline solids characterized by their ability to accommodate large quantities of guest molecules. Although CH 4 and CO 2 are the traditional guests found in natural systems, incorporating smaller molecules (e.g., H 2 ) is challenging due to the need to apply higher pressures to stabilize the hydrogen-bonded network. Another critical limitation of hydrates is the slow nucleation and growth kinetics. Here, we show that specially designed activated carbon materials can surpass these obstacles by acting as nanoreactors promoting the nucleation and growth of H 2 hydrates. The confinement effects in the inner cavities promote the massive growth of hydrogen hydrates at moderate temperatures, using pure water, with extremely fast kinetics and much lower pressures than the bulk system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

First-principles investigation of high capacity, rechargeable CF x cathode batteries based on graphdiyne and “holey” graphene carbon allotropes

Batteries composed of CF x cathodes have high theoretical specific capacities (>860 mA h g -1 ). Attempts at realizing such batteries coupled with Li anodes have failed to deliver on this promise, however, due to a discharge voltage plateau below the theoretical maximum lowering the realized energy density and difficulties with recharging the system. Here, in this study, we use first-principles calculations to investigate novel carbon allotropes for these battery systems: graphdiyne and “holey” graphene. We first identify stable flourination structures and calculate their band gaps. We demonstrate that the holes in these carbon allotropes can induce the formation of an amorphous LiF network within the carbon and that this formation may, in fact, be kinetically favored. For structures where amorphous LiF forms within the carbon, we predict it is easier to recharge and higher discharge voltages can be achieved. If the LiF forms outside the carbon product, however, it will be crystalline in form and lead to lower discharge voltages and more difficulty in recharging the systems. Finally, we simulate XPS spectra of representative cases, demonstrating an experimental pathway for determining the reaction pathway of these systems. Our work suggests CF x allotropes with holes in them as potential targets for high capacity, rechargeable cathodes for Li batteries, provided they lead to the formation of amorphous LiF within the C structure.

25 ENERGY STORAGE↗
DOE OSTI2021

Hydrodynamics of nonintegrable systems from a relaxation-time approximation

We develop a general kinetic theory framework to describe the hydrodynamics of strongly interacting, nonequilibrium quantum systems in which integrability is weakly broken, leaving a few residual conserved quantities. This framework is based on a generalized relaxation-time approximation; it gives a simple, but surprisingly accurate, prescription for computing nonequilibrium transport even in strongly interacting systems. We validate the predictions of this approximation against matrix product operator calculations on chaotic quantum spin chains, finding surprisingly good agreement. Lastly, we show that despite its simplicity, our framework can capture phenomena distinctive to strongly interacting systems, such as widely separated charge and energy diffusion constants.

1-dimensional spin chains↗
NASA NTRS2000

The Structure of Ice Nanoclusters and Thin-films of Water Ice: Implications for Icy Grains in Cold Molecular Clouds

The cubic to hexagonal phase transformation in water ice (I(sub c) yields I(sub h)) is used to measure the extent to which surface structure and impurities control bulk properties. In pure crystalline (I(sub c)) water ice nanoclusters and in thin-films of impure water ice, I(sub c) yields I(sub h) occurs at lower temperatures than in thin-films of pure water ice. The disordered surface of the 20 nm diameter nanoclusters promotes transformations or reactions which would otherwise be kinetically hindered. Likewise, impurities such as methanol introduce defects into the ice network, thereby allowing sluggish structural transitions to proceed. Such surface-related phenomena play an important role in promoting chemical reactions on interstellar ice grains within cold molecular clouds, where the first organic compounds are formed.

Delzeit, Lance↗