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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Influence of Aerosol Embedded in Shallow Cumulus Cloud Fields on the Surface Solar Irradiance

Ubiquitous shallow cumulus clouds are associated with complex variability in surface solar irradiance (SSI). Aerosol embedded in the cloud field typically has a much smaller overall radiative effect, but can significantly perturb the shape of the SSI probability density function (PDF). These perturbations have important implications for several applications that utilize SSI, but are poorly quantified and are the subject of this study. Multiple cases of shallow cumulus cloud fields with embedded aerosol are simulated at the Southern Great Plains Atmospheric Observatory using large eddy simulation (LES). The LES-derived cloud and aerosol fields are then ingested into Monte Carlo three-dimensional (3D) radiative transfer to simulate SSI. We find a variety of perturbations to the SSI PDF that depend on aerosol presence and optical properties. The processes leading to these perturbations include extinction of the direct beam that often increases from the clear-sky region toward cloud edge due to aerosol hygroscopic growth, and scattering of radiation by aerosol into cloud shadows. The ability to predict the SSI PDF in the presence of aerosol is assessed by adding three representative aerosol optical properties into an existing machine learning framework. We show that machine learning accurately predicts the SSI PDF across a wide range of conditions with negligible computational expense. Importance metrics reveal the relatively high influence of aerosol optical properties in making the predictions. These new findings highlight the important role that aerosol plays in SSI variability for highly 3D cloud-aerosol environments and provides a computationally efficient route forward for its simulation.

3D radiative transfer↗
DOE OSTI2023

The need for ion-exchange membranes with high charge densities

Ion-exchange membranes (IEMs) are essential for controlling ion transport in electrochemical membrane-based technologies for water purification, energy generation, and energy storage. In general, IEMs must exhibit high ionic conductivity and selectivity, both of which are significantly influenced by the charge and water content of the hydrated membrane. In this perspective, we compiled the charge and water contents of approximately 1,000 IEMs reported over the past several decades to assess their range and variability, as well as to reveal gaps and set clear targets for future membrane design. We compared the charge and water content of negatively and positively charged membranes, as well as membranes developed for desalination and energy applications. The majority of IEMs, regardless of structure or intended application, have volumetric charge densities below 3 mol/L[membrane]. This narrow range of charge densities limits the performance of IEMs and contributes to the observed tradeoff between ion throughput and selectivity. It is critical to develop IEMs with higher charge densities to overcome this performance limitation. Further, we highlighted several design strategies that yielded IEMs with fixed charge densities higher than 3 mol/L[membrane]. These strategies provide valuable insights and can guide the design of next-generation IEMs with high charge densities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Visual Analytics of Multivariate Networks With Representation Learning and Composite Variable Construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This article presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Long-term Variability of H{sub 2}CO Masers in Star-forming Regions

We present results of a multi-epoch monitoring program on variability of 6 cm formaldehyde (H{sub 2}CO) masers in the massive star-forming region NGC 7538 IRS 1 from 2008 to 2015, conducted with the Green Bank Telescope, the Westerbork Radio Telescope , and the Very Large Array. We found that the similar variability behaviors of the two formaldehyde maser velocity components in NGC 7538 IRS 1 (which was pointed out by Araya and collaborators in 2007) have continued. The possibility that the variability is caused by changes in the maser amplification path in regions with similar morphology and kinematics is discussed. We also observed 12.2 GHz methanol and 22.2 GHz water masers toward NGC 7538 IRS 1. The brightest maser components of CH{sub 3}OH and H{sub 2}O species show a decrease in flux density as a function of time. The brightest H{sub 2}CO maser component also shows a decrease in flux density and has a similar LSR velocity to the brightest H{sub 2}O and 12.2 GHz CH{sub 3}OH masers. The line parameters of radio recombination lines and the 20.17 and 20.97 GHz CH{sub 3}OH transitions in NGC 7538 IRS 1 are also reported. In addition, we observed five other 6 cm formaldehyde maser regions. We found no evidence of significant variability of the 6 cm masers in these regions with respect to previous observations, the only possible exception being the maser in G29.96−0.02. All six sources were also observed in the H{sub 2}{sup 13}CO isotopologue transition of the 6 cm H{sub 2}CO line; H{sub 2}{sup 13}CO absorption was detected in five of the sources. Estimated column density ratios [H{sub 2}{sup 12}CO]/[H{sub 2}{sup 13}CO] are reported.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Conformal High-Power-Density Half-Heusler Thermoelectric Modules: A Pathway toward Practical Power Generators

Thermoelectric generators (TEGs) exploiting the Seebeck effect provide a promising solution for waste heat recovery. Among the large number of thermoelectric (TE) materials, half-Heusler (hH) alloys are leading candidates for medium- to high-temperature power generation applications. However, the fundamental challenge in this field has been inhomogeneous material properties at large wafer diameters, insufficient power output from the modules, and rigid form factors of TE modules. This has restricted the transition of TEGs in practical applications for over three decades. Here, we successfully demonstrate large diameter wafers with uniform TE properties, high-power conformal hH TE modules for high-temperature application, and their direct integration on flue gas platforms, such as cylindrical tubes, to form large area flexible TEGs. This new conformal architecture design provides a breakthrough toward medium-/high-temperature TEGs over the conventional BiTe- and polymer-based flexible TEG design. In this work, a variable fill factor and greater flexibility due to the conformal design result in higher device performance as compared to conventional rigid TEG devices. Modules with 72-couple hH legs exhibit a device high-power-density of 3.13 W cm-2 and a total output power of 56.6 W under a temperature difference of 570 degrees C. These results provide a promising pathway toward widespread utilization of thermoelectric technology into the waste heat recovery application and will have a significant impact on the development of practical thermal to electrical converters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

High-Magnification Faraday Rotation Imaging and Analysis of X-pinch Implosion Dynamics

An X-pinch load driven by an intense current pulse (>100 kA in ~100 ns) can result in the formation of a small radius, runaway compressional micro-pinch. A micro-pinch is characterized by a hot (>1 keV), current-driven (>100 kA), high-density plasma column (near solid density) with a small neck diameter (1–10 μm), a short axial extent (<1 mm), and a short duration (.1 ns). With material pressures often well into the multi-Mbar regime, a micro-pinch plasma often radiates an intense, sub-ns burst of sub-keV to multi-keV x-rays. A low-density coronal plasma immediately surrounding the dense plasma neck could potentially shunt current away from the neck and thus reduce the magnetic drive pressure applied to the neck. To study the current distribution in the coronal plasma throughout the implosion process, a Faraday rotation imaging diagnostic (1064 nm) capable of producing simultaneous high-magnification polarimetric and interferometric images has been developed for the 1-MA, 100-ns MAIZE facility at the University of Michigan. Designed with a variable magnification (1–10&times;), this diagnostic achieves a spatial resolution of approximately 35 μm, which is useful for resolving the ~100-μm-scale coronal plasma region immediately surrounding the dense core. Combined with the corresponding interferogram, which provides the radial density distribution in the low-density coronal plasma, the measured rotation can be used to diagnose the delivery of the driver current, and thus the magnetic drive pressure, to the micro-pinch plasma as it approaches minimal radius. Using this technique on MAIZE, we have measured

Dowhan, G. V.↗
DOE OSTI2021

Adversarial sampling of unknown and high-dimensional conditional distributions

Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Herein, two approaches, stochastic estimation, and an external neural network are compared for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Over-Stoichiometric Metastabilization of Cation-Disordered Rock Salts

Cation-disordered rock salts (DRXs) are well known for their potential to realize the goal of achieving scalable Ni- and Co-free high-energy-density Li-ion batteries. Unlike in most cathode materials, the disordered cation distribution may lead to more factors that control the electrochemistry of DRXs. An important variable that is not emphasized by research community is regarding whether a DRX exists in a more thermodynamically stable form or a more metastable form. Moreover, within the scope of metastable DRXs, over-stoichiometric DRXs, which allow relaxation of the site balance constraint of a rock salt structure, are particularly underexplored. In this work, these findings are reported in locating a generally applicable approach to “metastabilize” thermodynamically stable Mn-based DRXs to metastable ones by introducing Li over-stoichiometry. Further, the over-stoichiometric metastabilization greatly stimulates more redox activities, enables better reversibility of Li deintercalation/intercalation, and changes the energy storage mechanism. The metastabilized DRXs can be transformed back to the thermodynamically stable form, which also reverts the electrochemical properties, further contrasting the two categories of DRXs. This work enriches the structural and compositional space of DRX families and adds new pathways for rationally tuning the properties of DRX cathodes.

25 ENERGY STORAGE↗
DOE OSTI2023

Combinatorial Growth of Vertically Aligned Nanocomposite Thin Films for Accelerated Exploration in Composition Variation

Combinatorial growth is capable of creating a compositional gradient for thin film materials and thus has been adopted to explore composition variation mostly for metallic alloy thin films and some dopant concentrations for ceramic thin films. This study uses a combinatorial pulsed laser deposition method to successfully fabricate two‐phase oxide–oxide vertically aligned nanocomposite (VAN) thin films of La 0.7 Sr 0.3 MnO 3 (LSMO)‐NiO with variable composition across the film area. The LSMO‐NiO compositional gradient across the film alters the two‐phase morphology of the VAN through varying nanopillar size and density. Additionally, the magnetic anisotropy and magnetoresistance properties of the nanocomposite thin films increase with increasing NiO composition. This demonstration of a combinatorial method for VAN growth can increase the efficiency of nanocomposite thin film research by allowing all possible compositions of thin film materials to be explored in a single deposition.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Insights into the Oxidation Mechanism of Vivianite to Metavivianite from First-Principles Calculations

Vivianite, a hydrous ferrous iron-phosphate mineral (Fe 3 (PO 4 ) 2 ·8H 2 O), readily oxidizes in contact with air yielding the less hydrous mixed-valent iron-phosphate mineral metavivianite. This topotactic transformation nominally occurs by oxidative dehydrogenation in which outgoing electrons from the iron sublattice are charge compensated by hydrolysis of structural water. However, the details of this internal charge balancing mechanism that allows the structure to remain electrostatically stable remain unknown. Here, in this study, we use density functional theory (DFT) calculations and ab initio thermodynamics (AIT) to evaluate the energetics of this process in terms of hydrogen release as a function of environmental variables such as partial pressure and temperature. The results show a thermodynamic driving force for vivianite phase transformation as oxidation progresses that is triggered by its rigid structure that has a limited accommodation for hydrogen vacancies. In contrast, metavivianite has a more flexible lattice and hydrogen bond network that stabilizes these hydrogen defects. Metavivianite is shown to be a stable intermediate for 66% residual Fe 2+ down to 33% Fe 2+ , below which other phases/structures such as santabarbarite should be more thermodynamically favorable. Our study provides a basis for experimental tests of our mechanistic findings and helps fill a basic knowledge gap about the solid-state process that defines how vivianite interacts with its surrounding environment.

Sassi, Michel [Pacific Northwest National Laborato↗
DOE OSTI2017

Native state volume fluctuations in proteins as a mechanism for dynamic allostery

Allostery enables tight regulation of protein function in the cellular environment. While existing models of allostery are firmly rooted in the current structure-function paradigm, the mechanistic basis for allostery in the absence of structural change remains unclear. In this study, we show that a typical globular protein is able to undergo significant changes in volume under native conditions while exhibiting no additional changes in protein structure. These native state volume fluctuations were found to correlate with changes in internal motions that were previously recognized as a source of allosteric entropy. This finding offers a novel mechanistic basis for allostery in the absence of canonical structural change. As a result, the unexpected observation that function can be derived from expanded, low density protein states has broad implications for our understanding of allostery and suggests that the general concept of the native state be expanded to allow for more variable physical dimensions with looser packing.

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

Long-term, large-scale experiment reveals the effects of seed limitation, climate, and anthropogenic disturbance on restoration of plant communities in a biodiversity hotspot

Ecological restoration is essential for maintaining biodiversity in the face of dynamic, global changes in climate, human land use, and disturbance regimes. Effective restoration requires understanding bottlenecks in plant community recovery that exist today, while recognizing that these bottlenecks may relate to complex histories of environmental change. Such understanding has been a challenge because few long-term, well-replicated experiments exist to decipher the demographic processes influencing recovery for numerous species against the backdrop of multiyear variation in climate and management. Here we address this challenge through a long-term and geographically expansive experiment in longleaf pine savannas, an imperiled ecosystem and biodiversity hotspot in the southeastern United States. Using 48 sites at three locations spanning 480 km, the 8-y experiment manipulated initial seed arrival for 24 herbaceous plant species and presence of competitors to evaluate the impacts of climate variability and management actions (e.g., prescribed burning) on plant establishment and persistence. Adding seeds increased plant establishment of many species. Cool and wet climatic conditions, low tree density, and reduced litter depth also promoted establishment. Once established, most species persisted for the duration of the 8-y experiment. Plant traits were most predictive when tightly coupled to the process of establishment. Our results illustrate how seed additions can restore plant diversity and how interannual climatic variation affects the dynamics of plant communities across a large region. The significant effects of temperature and precipitation inform how future climate may affect restoration and conservation via large-scale changes in the fundamental processes of establishment and persistence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Positivity enhancements to the P n transport equations

One of the common methods for solving the radiation transport equation is to use a polynomial expansion for the angle variable(s). Here, while each solution technique has its disadvantages, in some instances this method can result in oscillations large enough to give a negative radiation energy density, which is nonphysical. Some authors have recast the closure of the transport equations to guarantee positivity. Unfortunately, this results in messy nonlinear equations. Other authors post process each time step’s solution to improve positivity. In this work, the P n equations are modified to be more diffusive, less oscillatory, more positive, and more accurate. No new solution techniques are required, just the scaling of some terms with constant factors and the addition of constant filter opacities; therefore, the solutions are still linear. This new solution method is called stretched and filtered P n . Test problems are presented in one and two dimensions. Comparisons to the D n and TP n closures are made in one dimension. Modifications to D n and TP n are suggested.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2023

Variable Blue Straggler Stars in the Open Cluster NGC 6819 Observed in the Kepler “Superstamp” Field

Abstract NGC 6819 is an open cluster of age 2.4 Gyr that was in the NASA Kepler spacecraft’s field of view from 2009 to 2013. The central part of the cluster was observed in a 200 × 200 pixel “superstamp” during these four years in 30 minute cadence photometry, providing a unique, high-precision, long time-series data set. The cluster contains “blue straggler” stars, i.e., stars on the main sequence above the cluster turnoff that should have left the main sequence to become red giants. We present light curves and pulsation frequency analyses derived from custom photometric reductions for five confirmed cluster members—four blue stragglers and one star near the main-sequence turnoff. Two of these stars show a rich spectrum of δ Scuti pulsation modes, with 236 and 124 significant frequencies identified, respectively, while two stars show mainly low-frequency modes, characteristic of γ Doradus variable stars. The fifth star, a known active X-ray binary, shows only several harmonics of two main frequencies. For the two δ Scuti stars, we use a frequency separation–mean density relation to estimate their mean densities, and then use these values along with their effective temperature to derive their stellar masses and radii. For the two stars showing low frequencies, we searched for period-spacing sequences that may be representative of gravity-mode or Rossby-mode sequences, but found no clear sequences. The common age for the cluster members, considered along with the frequencies, will provide valuable constraints for asteroseismic analyses, and may shed light on the origin of the blue stragglers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Development of a Printable Prill Formulation Technique and Demonstration of Monomodal Prill Size on Compaction Density and Compressive Strength

Polymer-bonded explosive molding powder, or “prills,” are relied on for the fabrication of pressed high explosives since the 1950's. The wet granulation technique, also known as “slurry coating,” that is used to formulate prills, is a complex process that results in polydisperse and variable yields. This makes it difficult to study the mesoscale effect that prills have on the microstructure of a pressed article. The following study introduces a novel approach to energetic granulation that leverages techniques used in the additive manufacturing of paste-like energetic materials. This extrusion granulation, or prill printing technique, makes it possible to tailor the sizes and shapes of prills, allowing for their morphological influences to be studied in a controlled manner. The following work details the fabrication and characterization of four monomodal size lots of prills using an inert formulation (95 wt.% melamine, 5 wt.% polymer binder). Prills from each size lot were die-pressed using a fixed recipe to investigate how prill size impacts compaction density and therefore compressive strength. It was found that larger prills influence the pressing density by creating larger defects within the microstructure of a pressed article, resulting in a decrease in compressive strength.

direct ink write↗
DOE OSTI2022

Modeling of Cube Array Roughness: RANS, Large Eddy Simulation, and Direct Numerical Simulation

Abstract Flow over arrays of cubes is an extensively studied model problem for rough wall turbulent boundary layers. While considerable research has been performed in computationally investigating these topologies using direct numerical simulation (DNS) and large eddy simulation (LES), the ability of sublayer-resolved Reynolds-averaged Navier–Stokes (RANS) to predict the bulk flow phenomena of these systems is relatively unexplored, especially at low and high packing densities. Here, RANS simulations are conducted on six different packing densities of cubes in aligned and staggered configurations. The packing densities investigated span from what would classically be defined as isolated, up to those in the d-type roughness regime, filling in the gap in the present literature. Three different sublayer-resolved turbulence closure models were tested for each case: a low Reynolds number k–ϵ model, the Menter k–ω SST model, and a full Reynolds stress model. Comparisons of the velocity fields, secondary flow features, and drag coefficients are made between the RANS results and existing LES and DNS results. There is a significant degree of variability in the performance of the various RANS models across all comparison metrics. However, the Reynolds stress model demonstrated the best accuracy in terms of the mean velocity profile as well as drag partition across the range of packing densities.

Engineering↗
DOE OSTI2017

Local Variability of the Peierls Barrier of Screw Dislocations in Ta-10W.

It is well know that the addition of substitutional elements changes the mechanical behavior of metals, a effect referred to solid solution hardening. For body-centered-cubic (BCC) metals, screw dislocation play a key role in the mechanical properties. Here the detailed modification of the Peierls barrier for screw dislocation motion in Ta with W substitutional atoms is computing using density functional theory (DFT). A reduced order model (ROM) of the influence of W substitution on the Peierls barrier is developed. The mean field change in the Peierls barrier for a Ta10W alloy is determined and shown to be larger than anticipated based on simple elasticity considerations. The ROM could be used in future calculations to determine the local variability of the Peierls barrier and the resultant influence on the motion of screw dislocation in this alloy.

42 ENGINEERING↗