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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
NASA NTRS1998

Oxidative Attack of Carbon/Carbon Substrates through Coating Pinholes

A critical issue with oxidation protected carbon/carbon composites used for spacecraft thermal protection is the formation of coating pinholes. In laboratory experiments, artificial pinholes were drilled through SiC-coatings on a carbon/carbon material and the material was oxidized at 600, 1000, and 1400 C at reduced pressures of air. The attack of the carbon/carbon was quantified by both weight loss and a novel cross-sectioning technique. A two-zone, one dimensional diffusion control model was adapted to analyze this problem. Agreement of the model with experiment was reasonable at 1000 and 1400 C; however results at lower temperatures show clear deviations from the theory suggesting that surface reaction control plays a role.

Jacobson, Nathan S.↗
NASA NTRS2011

Radiation Belt Environment Model: Application to Space Weather and Beyond

Understanding the dynamics and variability of the radiation belts are of great scientific and space weather significance. A physics-based Radiation Belt Environment (RBE) model has been developed to simulate and predict the radiation particle intensities. The RBE model considers the influences from the solar wind, ring current and plasmasphere. It takes into account the particle drift in realistic, time-varying magnetic and electric field, and includes diffusive effects of wave-particle interactions with various wave modes in the magnetosphere. The RBE model has been used to perform event studies and real-time prediction of energetic electron fluxes. In this talk, we will describe the RBE model equation, inputs and capabilities. Recent advancement in space weather application and artificial radiation belt study will be discussed as well.

Fok, Mei-Ching H.↗
NASA NTRS1999

Implicit Space-Time Conservation Element and Solution Element Schemes

Artificial numerical dissipation is in important issue in large Reynolds number computations. In such computations, the artificial dissipation inherent in traditional numerical schemes can overwhelm the physical dissipation and yield inaccurate results on meshes of practical size. In the present work, the space-time conservation element and solution element method is used to construct new and accurate implicit numerical schemes such that artificial numerical dissipation will not overwhelm physical dissipation. Specifically, these schemes have the property that numerical dissipation vanishes when the physical viscosity goes to zero. These new schemes therefore accurately model the physical dissipation even when it is extremely small. The new schemes presented are two highly accurate implicit solvers for a convection-diffusion equation. The two schemes become identical in the pure convection case, and in the pure diffusion case. The implicit schemes are applicable over the whole Reynolds number range, from purely diffusive equations to convection-dominated equations with very small viscosity. The stability and consistency of the schemes are analysed, and some numerical results are presented. It is shown that, in the inviscid case, the new schemes become explicit and their amplification factors are identical to those of the Leapfrog scheme. On the other hand, in the pure diffusion case, their principal amplification factor becomes the amplification factor of the Crank-Nicolson scheme.

Chang, Sin-Chung↗
DOE OSTI2021

Atomistic Mechanism Underlying the Si ( 111 ) - ( 7 × 7 ) Surface Reconstruction Revealed by Artificial Neural-Network Potential

The 7×7 reconstruction of the Si(111) surface represents arguably the most fascinating surface reconstruction so far observed in nature. Yet, the atomistic mechanism underpinning its formation remains unclear after it was discovered sixty years ago. Experimentally, it is observed post priori so that analysis of its formation mechanism can only be carried out in analogy with archaeology. Theoretically, density-functional theory (DFT) correctly predicts the Si(111)-(7×7) ground state but is impractical to simulate its formation process; while empirical potentials failed to produce it as the ground state. Developing an artificial neural-network potential of DFT quality, we carried out accurate large-scale simulations to unravel the formation of the Si(111)-(7×7) surface. We reveal a possible step-mediated atom-pop rate-limiting process that triggers massive nonconserved atomic rearrangements, most remarkably, a critical process of collective vacancy diffusion that mediates a sequence of selective dimer, corner-hole, stacking-fault, and dimer-line pattern formation, to fulfill the 7×7 reconstruction. Our findings may not only solve the long-standing mystery of this famous surface reconstruction but they also illustrate the power of machine learning in studying complex structures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Exploring NaCl-PuCl 3 molten salts with machine learning interatomic potentials and graph theory

Actinide molten salts are the basis of the liquid fuels used in molten salt reactors. Due to the inherent difficulties associated with high temperature and hazardous conditions, experimental investigations of fundamental properties of these materials are usually challenging. In this work, we describe the structure and transport of NaCl-PuCl 3 mixtures using computational techniques. Three compositions were considered (16, 25, and 36 mol% PuCl 3 ) over a temperature range (730 – 1257K) using ab initio molecular dynamics, which provided the necessary data sets for training machine learned interatomic potentials. Further, molecular dynamics simulations based on these potentials were then used to determine structure and transport properties. A substantial change was noted in the structure factor when increasing the PuCl 3 content from 25 to 36 mol%. This change is linked to the aggregation of larger Pu 3+ clusters. In addition, the similarity of the atomic environments of metal cations in molten salt systems to their solid states counterparts was investigated using an unsupervised learning technique. Finally, graph theory was employed to explore the structure and size of actinide networks. Consistent with the structure factor, a dense Pu 3+ intermolecular structure is observed within the 36 mol% PuCl 3 mixture. The structure of cation-cation inter-junctions is also discussed. In all cases, the diffusion of Pu 3+ is significantly lower than that of Na + and Cl - .

36 MATERIALS SCIENCE↗
DOE OSTI2023

Overcoming Barriers Associated with Oral Delivery of Differently Sized Fluorescent Core‐Shell Silica Nanoparticles

Oral delivery, while a highly desirable form of nanoparticle-drug administration, is limited by challenges associated with overcoming several biological barriers. Here, in this study, the authors study how fluorescent and poly(ethylene glycol)-coated (PEGylated) core-shell silica nanoparticles sized 5 to 50 nm interact with major barriers including intestinal mucus, intestinal epithelium, and stomach acid. From imaging fluorescence correlation spectroscopy studies using quasi-total internal reflection fluorescence microscopy, diffusion of nanoparticles through highly scattering mucus is progressively hindered above a critical hydrodynamic size around 20 nm. By studying Caco-2 cell monolayers mimicking the intestinal epithelia, it is observed that ultrasmall nanoparticles below 10 nm diameter (Cornell prime dots, [C’ dots]) show permeabilities correlated with high absorption in humans from primarily enhanced passive passage through tight junctions. Particles above 20 nm diameter exclusively show active transport through cells. After establishing C’ dot stability in artificial gastric juice, in vivo oral gavage experiments in mice demonstrate successful passage through the body followed by renal clearance without protein corona formation. Results suggest C’ dots as viable candidates for oral administration to patients with a proven pathway towards clinical translation and may generate renewed interest in examining silica as a food additive and its effects on nutrition and health.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integrative mapping reveals molecular features underlying the mechanism of nucleocytoplasmic transport

Nuclear pore complexes (NPCs) enable rapid, selective, and robust nucleocytoplasmic transport. To explain how transport emerges from the system components and their interactions, we used experimental data and theoretical information to construct an integrative Brownian dynamics model of transport through an NPC, coupled to a kinetic model of transport in the cell. The model recapitulates key aspects of transport for a wide range of molecular cargoes, including preribosomes and viral capsids. Our model quantifies how flexible phenylalanine-glycine (FG) repeat proteins create an entropic barrier to passive diffusion and how this barrier is selectively lowered in facilitated diffusion by the many transient interactions of nuclear transport receptors with the FG repeats. Selective transport is enhanced by “fuzzy” multivalent interactions, redundant FG repeat mass, coupling to the energy-dependent RanGTP concentration gradient, and exponential dependence of transport kinetics on the transport barrier. Our model will facilitate rational modulation of the NPC and its artificial mimics.

Raveh, Barak [School of Computer Science and Engin↗
DOE OSTI2023

Investigation of interfacial strength in nacre-mimicking tungsten heavy alloys for nuclear fusion applications

Abstract Tungsten heavy alloys have been proposed as plasma facing material components in nuclear fusion reactors and require experimental investigation in their confirmation. For this purpose, a 90W–7Ni–3Fe alloy has been selected and microstructurally manipulated to present a multiphase brick-and-mortar structure of W-phase ‘bricks’ surrounded by a ductile ‘mortar’. This work draws inspiration from nature to artificially imitate the extraordinary combination of strength and stiffness exhibited by mollusks and produce a nacre-mimicking metal matrix composite capable of withstanding the extremely hostile environment of the reactor interior and maintaining structural integrity. The underlying mechanisms behind this integrity have been probed through high-resolution structural and chemical characterization techniques and have revealed chemically diffuse phase boundaries exhibiting unexpected lattice coherency. These features have been attributed to an increase in the energy required for interfacial decohesion in these systems and the simultaneous expression of high strength and toughness in tungsten heavy alloys.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Neural networks for calibration tomography

Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. Computed tomography is compared with neural net calibration tomography for estimating density from its x-ray transform. X-ray transforms are measured, for example, in diffuse-illumination, holographic interferometry of fluids. Computed tomography and neural net calibration tomography are shown to have comparable performance for a 10 degree viewing cone and 29 interferograms within that cone. The system of tomography discussed is proposed as a relevant test of neural networks and other parallel processors intended for using flow visualization data.

Decker, Arthur↗
DOE OSTI2025

Ensemble‐Based, Large‐Eddy Reconstruction of Wind Turbine Inflow in a Near‐Stationary Atmospheric Boundary Layer Through Generative Artificial Intelligence

ABSTRACT To validate the second‐by‐second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time‐resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral‐based models of the atmosphere. Here, we develop a technique for time‐resolved inflow reconstruction that is rooted in a large‐eddy simulation model of the atmosphere. Our “large‐eddy reconstruction” technique blends observations and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate probabilistic ensembles of reconstructions for a single 10‐min observational period. Our generated inflows can be used directly by aeroelastic codes or as inflow boundary conditions in a large‐eddy simulation. We verify the second‐by‐second reconstruction capability of our technique in three synthetic field campaigns, finding positive Pearson correlation coefficient values () between ground‐truth and reconstructed streamwise velocity, as well as smaller positive correlation coefficient values for unobserved fields (spanwise velocity, vertical velocity, and temperature). We validate our technique in three real‐world case studies by driving large‐eddy simulations with reconstructed inflows and comparing to independent inflow measurements. The reconstructions are visually similar to measurements, follow desired power spectra properties, and track second‐by‐second behavior ().

17 WIND ENERGY↗
DOE OSTI2024

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1986

Shock-implanted noble gases - An experimental study with implications for the origin of Martian gases in shergottite meteorites

The shock-implantation of gases is studied by artificially shocking whole rock and power samples of terrestrial basalt to pressures of 2-40 GPa. Ar, Kr, Xe, and Ne were implanted into the silicate. It is observed that the amount of implanted gas is linearly proportional to its partial pressure over a pressure range of 0.0001 to 0.1 atmosphere. The fractionation effect in the implanted gas and the gas diffusion properties are examined. The amounts of gas that would have been implanted with 100 percent efficiency are calculated from the measured porosities of the power samples and are compared to observed abundances. It is determined that the implantation efficiencies are approximately 0.5 percent at 2 GPa, 7 percent at 5 GPa, and greater than 50 percent at both 20 and 35 GPa. The experimental data correlate with the shock implantation of Martian gases without mass fractionation into the shock-melted phase of meteorite EETA 79001.

Bogard, Donald D.↗
NASA NTRS2004

Water cycle and its management for plant habitats at reduced pressures

Experimental and mathematical models were developed for describing and testing temperature and humidity parameters for plant production in bioregenerative life support systems. A factor was included for analyzing systems operating at low (10-101.3 kPa) pressure to reduce gas leakage and structural mass (e.g., inflatable greenhouses for space application). The expected close relationship between temperature and relative humidity was observed, along with the importance of heat exchanger coil temperature and air circulation rate. The presence of plants in closed habitats results in increased water flux through the system. Changes in pressure affect gas diffusion rates and surface boundary layers, and change convective transfer capabilities and water evaporation rates. A consistent observation from studies with plants at reduced pressures is increased evapotranspiration rates, even at constant vapor pressure deficits. This suggests that plant water status is a critical factor for managing low-pressure production systems. The approach suggested should help space mission planners design artificial environments in closed habitats.

NASA Center KSC↗
NASA NTRS2010

Simplified Vicarious Radiometric Calibration

A measurement-based radiance estimation approach for vicarious radiometric calibration of spaceborne multispectral remote sensing systems has been developed. This simplified process eliminates the use of radiative transfer codes and reduces the number of atmospheric assumptions required to perform sensor calibrations. Like prior approaches, the simplified method involves the collection of ground truth data coincident with the overpass of the remote sensing system being calibrated, but this approach differs from the prior techniques in both the nature of the data collected and the manner in which the data are processed. In traditional vicarious radiometric calibration, ground truth data are gathered using ground-viewing spectroradiometers and one or more sun photometer( s), among other instruments, located at a ground target area. The measured data from the ground-based instruments are used in radiative transfer models to estimate the top-of-atmosphere (TOA) target radiances at the time of satellite overpass. These TOA radiances are compared with the satellite sensor readings to radiometrically calibrate the sensor. Traditional vicarious radiometric calibration methods require that an atmospheric model be defined such that the ground-based observations of solar transmission and diffuse-to-global ratios are in close agreement with the radiative transfer code estimation of these parameters. This process is labor-intensive and complex, and can be prone to errors. The errors can be compounded because of approximations in the model and inaccurate assumptions about the radiative coupling between the atmosphere and the terrain. The errors can increase the uncertainty of the TOA radiance estimates used to perform the radiometric calibration. In comparison, the simplified approach does not use atmospheric radiative transfer models and involves fewer assumptions concerning the radiative transfer properties of the atmosphere. This new technique uses two neighboring uniform ground target areas having different reflectance values. The target areas can be natural or artificial and must be large enough to minimize adjacent-pixel contamination effects. The radiative coupling between the atmosphere and the terrain needs to be approximately the same for the two targets. This condition can be met for relatively uniform backgrounds when the distance between the targets is within a few hundred meters. For each target area, the radiance leaving the ground in the direction of the satellite is measured with a radiometrically calibrated spectroradiometer. Using the radiance measurements from the two targets, atmospheric adjacency and atmospheric scattering effects can be subtracted, thereby eliminating many assumptions about the atmosphere and the radiative interaction between the atmosphere and the terrain. In addition, the radiometrically calibrated spectroradiometer can be used with a known reflectance target to estimate atmospheric transmission and diffuse- to-global ratios without the need for ancillary sun photometers. Several comparisons between the simplified method and traditional techniques were found to agree within a few percent. Hence, the simplified method reduces the overall complexity of performing vicarious calibrations and can serve as a method for validating traditional radiative transfer models

Stanley, Thomas↗
NASA NTRS1982

ADI on staggered mesh - A method for the calculation of compressible convection

An alternating direction implicit (ADI) method has been applied to a staggered grid for the computation of convection in a highly stratified fluid. Since artificial viscosity is not needed, subtle effects like the onset of convection can be studied. These computations compare well with the 2-D results by Graham (1975) and also agree with standard Boussinesq results when taken to that limit. Good efficiency has been achieved with a time step hundreds of times larger than the stability limit imposed by the explicit treatment of diffusion and the Courant number is not restricted to be below 1. The Navier-Stokes equation contains cross spatial derivatives which are treated explicitly in most ADI schemes. The destabilizing effect of such a practice on a 2-D model system with second-order spatial derivative terms only was analyzed and found to be not excessive.

Chan, K. L.↗
DOE OSTI2021

AENET–LAMMPS and AENET–TINKER : Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

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

Over-Potential Tailored Thin and Dense Lithium Carbonate Growth in Solid Electrolyte Interphase for Advanced Lithium Ion Batteries

A stable solid electrolyte interphase (SEI) is highly desired to prevent parasitic reactions during normal operation of lithium-ion batteries (LIBs). Lithium carbonate (Li 2 CO 3 ) is one of the most significant components for smooth SEI passivation layers; while the formation mechanism and special distribution of the Li 2 CO 3 layer has not yet been illustrated. In this study, an over-potential tailored Li 2 CO 3 growth mechanism based on the typical hard carbon anode is demonstrated. With an increase in the over-potential, the size of Li 2 CO 3 decreases gradually as the amount increases. When the over-potential is large (potential at 0.01 V), a Li 2 CO 3 -rich thin and dense inorganic layer with the average thickness of 4.4 nm in the SEI is constructed. The special SEI the completely wraps the boundaries of the anode enables a larger Li-ion de-solvation energy barrier and a lower Li-ion diffusion energy barrier, which supports low self-discharge behavior and a fast kinetic rate at the anode. More generally, this Li 2 CO 3 growth mechanism is also applicable to commercialized graphite anodes and similar results are also obtained. Therefore, this work provides a new insight into the Li 2 CO 3 growth mechanism in SEIs, as well as a guideline for the design of stable artificial SEIs.

25 ENERGY STORAGE↗
DOE OSTI2023

Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements

Abstract Terrestrial ring current dynamics are a critical part of the near‐space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time‐varying distribution of ring current proton using geomagnetic indices and solar wind parameters with their history as input. We train an artificial neural network (ANN) model to reproduce proton fluxes measured by the Radiation Belt Storm Probes Ion Composition Experiment instrument onboard Van Allen Probes. By choosing optimal feature parameters and their history length, the model results show a high correlation and a small error between model specifications and satellite measurements. The modeled results well capture energy‐dependent proton dynamics in association with geomagnetic storms, including inward radial diffusion, acceleration and decay. Our ANN model produces proton fluxes with their corresponding 3D spatiotemporal variations, capturing the latitudinal distribution and local time asymmetry that are consistent with observations and that can further inform theory.

Li, Jinxing↗