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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract

Materials Science↗
DOE OSTI2021

Convergent Hydraulic Redistribution and Groundwater Access Supported Facilitative Dependency Between Trees and Grasses in a Semi‐Arid Environment

Abstract Hydraulic redistribution is the transport of water from wet to dry soil layers, upward or downward, through plant roots. Often in savanna and woodland ecosystems, deep‐rooted trees, and shallow‐rooted grasses coexist. The degree to which these different species compete for or share soil‐water derived from precipitation or groundwater, as well as how these interactions are altered by hydraulic redistribution, is unknown. We use a multilayer canopy model and field observations to examine how the presence of deep, but tree‐root accessible, groundwater impacts seasonal patterns of hydraulic redistribution, and interaction between coexisting vegetation species in a semiarid riparian woodland (US‐CMW). Based on the simulation, trees absorb moisture at the water table (∼10 m depth) and release it in the shallow soil depth (0–3 m) during the dry pre‐monsoon season. We observed the occurrence of a new convergent hydraulic redistribution pattern during the monsoon season, where moisture is transported from both the near‐surface (0–0.5 m) and the water table to intermediate soil layers (1–5 m) through tree roots. We found that hydraulic redistribution demonstrates a growth facilitation effect at this site, supporting 49% of growing season tree transpiration and 14% of the grass transpiration. Compared to a similarly structured upland savanna without accessible groundwater, the riparian site shows an increased amount of hydraulically redistributed water and more facilitative water use between coexisting grasses and trees. These results shed light on the linkage between accessible groundwater and the role of hydraulic redistribution on the interaction between deep‐rooted and shallow‐rooted vegetation.

Lee, E.↗
DOE OSTI2021

Surface Salinity Under Transitioning Ice Cover in the Canada Basin: Climate Model Biases Linked to Vertical Distribution of Fresh Water

Abstract The Canada Basin has exhibited a significant trend toward a fresher surface layer and thus a more stratified upper‐ocean over the past three decades. State‐of‐the‐art ice‐ocean models, by contrast, tend to simulate a surface layer that is saltier and less stratified than observed. Here, we examine decadal changes to seasonal processes that may contribute to this wide‐reaching model bias using climate model simulations from the Community Earth System Model and below‐ice observations from the Arctic Ice Dynamics Joint Experiment in 1975 and Ice Tethered Profilers in 2006–2012. In contrast to the observations, the models simulate salinity profiles that show relatively little variation between 1975 and 2012. We demonstrate that this bias can be mainly attributed to unrealistically deep vertical mixing in the model, creating a surface layer that is saltier than observed. The results provide insight for climate model improvement with broad implications for Arctic sea ice and ecosystem dynamics.

58 GEOSCIENCES↗
DOE OSTI2021

Mixed‐Phase Clouds Over the Southern Ocean as Observed From Satellite and Surface Based Lidar and Radar

Abstract This study investigates the occurrence of mixed‐phase clouds (MPC, i.e., cloud layers containing both liquid and ice water at sub‐freezing temperatures) over the Southern Ocean (SO) using space‐ and surface‐based lidar and radar observations. The occurrence of supercooled clouds is dominated by geometrically thin (<1 km) layers that rarely contain ice. We diagnose layers that are geometrically thicker than 1 km to contain ice ~65% and ~4% of the time from below by surface remote sensors and from above by orbiting remote sensors, respectively. We examine the discrepancy in MPC occurrence statistics as diagnosed from below and above the cloud layer. From above, we find that MPC occurrence has a gradient associated with the Antarctic Polar Front near 55°S with a rare occurrence of satellite‐derived MPC south of that latitude. In contrast, surface sensors find ice in 33% of supercooled liquid water layers. We infer using observing system simulation experiments and data analysis that space‐based lidar cannot identify the occurrence of MPC except when secondary ice‐forming processes operate in convection that is, sufficiently strong to loft ice crystals to cloud tops. We conclude that the CALIPSO phase statistics of MPC have a severe low bias in MPC occurrence. Based on surface‐based statistics in the SO, we present a parameterization of the frequency of MPC as a function of cloud top temperature that differs substantially from that used in recent climate model simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Spectral broadening from turbulence in multiscale lower hybrid current drive simulations

Abstract The scattering of lower hybrid (LH) waves due to scrape-off layer filaments is investigated. It is revealed that scattering can account for the LH spectral gap without any ad hoc modification to the wave-spectrum. This is shown using a multiscale simulation approach which allows, for the first time, the inclusion of full-wave scattering physics in ray-tracing/Fokker–Planck calculations. In this approach, full-wave scattering probabilities are calculated for a wave interacting with a statistical ensemble of filaments. These probabilities are coupled to ray-tracing equations using radiative transfer theory. This allows the modeling of scattering along the entire ray-trajectory, which can be important in the multi-pass regime. Simulations are conducted for LH current drive (LHCD) in Alcator C-Mod, resulting in excellent agreement with experimental current and hard x-ray profiles. A region in filament parameter space is identified in which the impact of scattering on LHCD is saturated. Such a state coincides with experimental LHCD measurements, suggesting saturation indeed occurs in C-Mod, and therefore the exact statistical properties of the filaments are not important.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Three-Dimensional Permeability of Thick-Section Glass Fabric Reinforced Polymer Composite by Vacuum-Assisted Resin Infusion Molding

Abstract Determination of permeability of thick-section glass fabric preforms with fabric layers of different architectures is critical for manufacturing large, thick composite structures with complex geometry, such as wind turbine blades. The thick-section reinforcement permeability is inherently three-dimensional and needs to be obtained for accurate composite processing modeling and analysis. Numerical simulation of the liquid stage of vacuum-assisted resin infusion molding (VARIM) is important to advance the composite manufacturing process and reduce processing-induced defects. In this research, the 3D permeability of thick-section E-glass fabric reinforcement preforms is determined, and the results are validated by a comparison between flow front progressions from experiments and from numerical simulations using ansys fluent software. The orientation of the principal permeability axes were unknown prior to experiments. The approach used in this research differs from those in literature in that the through-thickness permeability is determined as a function of flow front positions along the principal axes and the in-plane permeabilities and is not dependent on the inlet radius. The approach was tested on reinforcements with fabric architectures which vary through-the-thickness direction, such as those in a spar cap of a wind turbine blade. The computational simulations of the flow-front progression through-the-thickness were consistent with experimental observations.

Engineering↗
DOE OSTI2021

The Scale and Activity of Symmetric Instability Estimated from a Global Submesoscale-Permitting Ocean Model

Abstract Symmetric instability (SI) extracts kinetic energy from fronts in the surface mixed layer (SML), potentially affecting the SML structure and dynamics. Here, a global submesoscale-permitting ocean model named MITgcm LLC4320 simulation is used to examine the Stone linear prediction of the maximum SI scale to estimate grid spacings needed to begin resolving SI. Furthermore, potential effects of SI on the usable wind work are estimated roughly: this estimate of SI “activity” is useful for assessing if these modes should be resolved or parameterized. The maximum SI scale varies by latitude with median values from 568 to 23 m. Strong seasonality is observed in the SI scale and activity. The median scale in winter is 188 m globally, 2.5 times of that of summer (75 m). SI is more active in winter: 15% of the time compared with 6% in summer. The strongest SI activity is found in the western Pacific, western Atlantic, and Southern Oceans. The required grid spacings for a global model to begin resolving SI eddies in the SML are 24 m (50% of regions resolved) and 7.9 m (90%) in winter, decreasing to 9.4 m (50%) and 3.6 m (90%) in summer. It is also estimated that SI may reduce usable wind work by an upper bound of 0.83 mW m −2 globally, or 5% of the global magnitude. The sensitivity of these estimates to empirical thresholds is provided in the text.

Dong, Jihai↗
DOE OSTI2023

Long‐Life Lithium‐Metal Batteries with an Ultra‐High‐Nickel Cathode and Electrolytes with Bi‐Anion Activity

Abstract Anion chemistry in electrolytes can greatly dictate the nature and quality of passivation layers on both cathode and anode surfaces. This will be more significant when it comes to highly reactive Li‐metal anode and aggressive high‐nickel cathodes. Herein, a competitive bi‐anion activity is found in electrolytes with the co‐existence of two anions, which leads to a controlled Li‐salt decomposition kinetics and entirely favorable interphasial chemistry on both Li‐metal anode and ultrahigh‐nickel cathode. The proposed bi‐anion localized high‐concentration electrolytes are demonstrated to exhibit superior electrochemical compatibility toward Li metal and long‐term cycling stabilities under both 4.4 and 4.6 V in Li‐metal batteries with ultrahigh‐nickel cathode. This study sheds fresh light on dendrite‐free Li‐metal anodes and provides guidance to achieve high‐energy‐density batteries.

Chemistry↗
DOE OSTI2025

Atomic‐Scale Mechanisms of Nucleation and Stabilization in CuCrO 2 and CuFeO 2 Delafossite Thin Films on Al 2 O 3

Abstract Delafossite thin films exhibit a range of intriguing physical properties derived from their layered structure, which consists of alternating noble metal ( A + ) and ( B O 2 − ) sublayers in an AB O 2 stoichiometry. The integration of these properties into functional devices requires the successful epitaxial growth of delafossites as thin films on appropriate substrates. Unfortunately, their unique lattice geometry complicates growth, as different delafossites display variable behavior on the same substrate, often unrelated to lattice mismatch. This suggests the presence of yet unidentified stabilization mechanisms that enable the selective growth of certain delafossites, allowing them to be grown either as films themselves or to be used as buffer layers for subsequent deposition. In this study, advanced scanning transmission electron microscopy (STEM) is employed to investigate the nucleation mechanisms governing the stable growth of Cu‐based delafossites on Al 2 O 3 , specifically CuCrO 2 and CuFeO 2 thin films synthesized via molecular‐beam epitaxy. These findings reveal that a combination of a misfit dislocation network at the interface and monolayer‐deep chemical intermixing effectively relieves lattice mismatch strain. This mechanism, which differs fundamentally from that observed in Pd‐based delafossites, provides key insights into the controlled epitaxy of delafossite materials.

Scheid, Anna [Max Planck Institute for Solid State↗
DOE OSTI2021

Mathematical modeling and parameter estimation of MFI membranes for para/ortho‐xylene separation

Abstract A mathematical model of a membrane with a thin, oriented, and selective MFI layer, which also includes contributions from defects, pore blockages, support layer, and external mass transfer, was formulated based on the Maxwell‐Stefan equations. It was validated using reported (Kim et al., Angew Chemie Int Ed, 2018, 57:480–485; Jeon et al., Nature, 2017, 543:690–694) para/ortho‐xylene separation data from five MFI membranes. The diffusivities of the xylenes were considered to be the same for all membranes, while the thickness and the density of defects and pore blockage were treated as unique properties of each membrane, and their contributions were estimated by fitting the model to the corresponding separation data. The effects of these properties and the role of permeate pressure on the separation performance were subsequently analyzed. The proposed modeling, parameter estimation, and analysis framework allow one to quantitatively interpret the variation of separation performance, to understand separation bottlenecks, and to provide guidance for designing membranes with desired performance.

Liu, Jingjun↗
DOE OSTI2024

Applying Machine‐Learning Methods to Laser Acceleration of Protons: Lessons Learned From Synthetic Data

ABSTRACT In this study, we consider three different machine‐learning methods—a three‐hidden‐layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine‐learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine‐learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine‐learning model we considered, support vector regression performed very well in our tests.

Desai, Ronak↗
DOE OSTI2024

Aerosol Vertical Turbulent Mass Flux Retrievals Through Novel Remote Sensing Algorithm

Abstract Integrated measurements of aerosol, radiation, cloud, and turbulent transport in the planetary boundary layer (PBL) are essential for understanding and modeling climate and air quality. Here, we developed a new technique for the identification of convective turbulent regions and deriving the vertical distribution of aerosol turbulent mass fluxes within PBL. The algorithm uses retrievals from coherent Doppler lidars and a high spectral resolution lidar. The technique was applied to study particle mass fluxes over 2 months (November–December 2020) during the campaign conducted at the DOE Atmospheric Radiation Measurement Southern Great Plains (SGP) site in Lamont, Oklahoma. The algorithm developed here is capable of continuously deriving vertically resolved (curtains) aerosol mass fluxes. Our data analysis shows that at the site, the 30‐min averaged fluxes at 135 m above the surface were mainly positive (upward) at ∼1 μg m −2 s −1 , suggesting that the surface is the primary source of the particle mass supplied to the boundary layer at the SGP site. Analyses of the individual case studies have revealed that not all the derived fluxes can be linked to surface emissions. Both positive and negative values in a range of ±5 μg m −2 s −1 can be caused by convective thermals interacting between the residual layer and the mixed layer and by rotation of the horizontal wind with the height. Large erroneous negative fluxes can also be caused by drizzling/precipitating clouds. We anticipate that the application of the current technique will lead to a more realistic representation of aerosol mass budgets and bidirectional mixing rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Probing the role of CNTs in Pt nanoparticle/CNT/graphene nanohybrids H 2 sensors

Abstract In the carbon nanotubes film/graphene heterostructure decorated with catalytic Pt nanoparticles using atomic layer deposition (Pt-NPs/CNTs/Gr) H 2 sensors, the CNT film determines the effective sensing area and the signal transport to Gr channel. The former requires a large CNT aspect ratio for a higher sensing area while the latter demands high electric conductivity for efficient charge transport. Considering the CNT’s aspect ratio decreases, while its conductivity increases ( i.e. , bandgap decreases), with the CNT diameter, it is important to understand how quantitatively these effects impact the performance of the Pt-NPs/CNTs/Gr nanohybrids sensors. Motivated by this, this work presents a systematic study of the Pt-NPs/CNTs/Gr H 2 sensor performance with the CNT films made from different constituent CNTs of diameters ranging from 1 nm for single-wall CNTs, to 2 nm for double-wall CNTs, and to 10–30 nm for multi-wall CNTs (MWCNTs). By measuring the morphology and electric conductivity of SWCNT, DWCNT and MWCNT films, this work aims to reveal the quantitative correlation between the sensor performance and relevant CNT properties. Interestingly, the best performance is obtained on Pt-NPs/MWCNTs/Gr H 2 sensors, which can be attributed to the compromise of the effective sensing area and electric conductivity on MWCNT films and illustrates the importance of optimizing sensor design.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Armor for Steel: Facile Synthesis of Hexagonal Boron Nitride Films on Various Substrates

Abstract While hexagonal boron nitride (hBN) has been widely used as a buffer or encapsulation layer for emerging electronic devices, interest in utilizing it for large‐area chemical barrier coating has somewhat faded. A chemical vapor deposition process is reported here for the conformal growth of hBN on large surfaces of various alloys and steels, regardless of their complex shapes. In contrast to the previously reported very limited protection by hBN against corrosion and oxidation, protection of steels against 10% HCl and oxidation resistance at 850 °C in air is demonstrated. Furthermore, an order of magnitude reduction in the friction coefficient of the hBN coated steels is shown. The growth mechanism is revealed in experiments on thin metal films, where the tunable growth of single‐crystal hBN with a selected number of layers is demonstrated. The key distinction of the process is the use of N 2 gas, which gets activated exclusively on the catalyst's surface and eliminates adverse gas‐phase reactions. This rate‐limiting step allowed independent control of activated nitrogen along with boron coming from a solid source (like elemental boron). Using abundant and benign precursors, this approach can be readily adopted for large‐scale hBN synthesis in applications where cost, production volume, and process safety are essential.

2D materials↗
DOE OSTI2024

Airborne Measurements of Scale‐Dependent Latent Heat Flux Impacted by Water Vapor and Vertical Velocity Over Heterogeneous Land Surfaces During the CHEESEHEAD19 Campaign

Abstract The water vapor transport associated with latent heat flux (LE) in the planetary boundary layer (PBL) is critical for the atmospheric hydrological cycle, radiation balance, and cloud formation. The spatiotemporal variability of LE and water vapor mixing ratio ( r v ) are poorly understood due to the scale‐dependent and nonlinear atmospheric transport responses to land surface heterogeneity. Here, airborne in situ measurements with the wavelet technique are utilized to investigate scale‐dependent relationships among LE, vertical velocity ( w ) variance (), and r v variance () over a heterogeneous surface during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign. Our findings reveal distinct scale distributions of LE, , and at 100 m height, with a majority scale range of 120 m–4 km in LE, 32 m–2 km in , and 200 m–8 km in . The scales are classified into three scale ranges, the turbulent scale (8–200 m), large‐eddy scale (200 m–2 km), and mesoscale (2–8 km) to evaluate scale‐resolved LE contributed by and . The large‐eddy scale in PBL contributes over 70% of the monthly mean total LE with equal parts (50%) of contributions from and . The monthly temporal variations mainly come from the first two major contributing classified scales in LE, , and . These results confirm the dominant role of the large‐eddy scale in the PBL in the vertical moisture transport from the surface to the PBL, while the mesoscale is shown to contribute an additional ∼20%. This analysis complements published scale‐dependent LE variations, which lack detailed scale‐dependent vertical velocity and moisture information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multiscale computational understanding and growth of 2D materials: a review

Abstract The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.

Momeni, Kasra↗
DOE OSTI2022

Divertor plasma detachment: roles of plasma momentum, energy, and particle balances

Abstract Contrary to statements from some recent papers, the bifurcation of the scrape-off layer and divertor plasma parameters, as described within a 1D model, does exist. The analysis of both numerical simulations and experimental data shows that the real physical reasons that can cause a strong reduction of the plasma particle flux to the target j d are impurity radiation loss and volumetric plasma recombination. Fitting the parameter describing the ‘plasma momentum removal’, based on the results of 2D numerical simulations, then using this fit in subsequent analytic expressions for j d , and finally interpreting the evolution of j d as the result of elastic plasma-neutral interactions break the causality principle and lead to a wrong physical picture of divertor plasma detachment.

Physics↗