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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dynamic Mode Decomposition of Random Pressure Fields over Bluff Bodies

Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally varying random field. In this paper, a dynamic mode decomposition (DMD) was applied to extract dominant features embedded in these random pressure fields. Utilizing an unsupervised machine learning algorithm, spatial modes and their temporal variations were grouped into different clusters at scales, e.g., macro, meso, and micro. A proper orthogonal decomposition (POD) of the experimental data was carried out to observe commonalities and distinctive perspectives each decomposition offers. Here, a comprehensive examination of the DMD/POD for their convergence criteria, data sufficiency, and modal components analysis was conducted. The physical interpretation of the spatiotemporal pressure field based on these decomposition schemes was discussed. At different scales, the DMD modes can capture the evolution of aerodynamic features, e.g., convection of vortices (or vortex tubes) and other structures. The distribution of energy among these three broad scales also reflects an energy cascade in pressure fluctuations akin to turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

New interaction potentials for alkali and alkaline-earth aluminosilicate glasses

Here, we apply a recently developed optimization scheme to obtain effective potentials for alkali and alkaline-earth aluminosilicate glasses that contain lithium, sodium, potassium, or calcium as modifiers. As input data for the optimization, we used the radial distribution functions of the liquid at high temperature generated by means of ab initio molecular dynamics simulations and density and elastic modulus of glass at room temperature from experiments. The new interaction potentials are able to reproduce reliably the structure and various mechanical and vibrational properties over a wide range of compositions for binary silicates. Previously, we tested these potentials for various ternary systems and find that they are transferable and can be mixed, thus allowing us to reproduce and predict the structure and properties of multicomponent glasses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Measurement of Hard Exclusive Electroproduction of π 0 Meson Cross Section in Hall A of JLab with CEBAF at 12 GeV

A complete description of nucleon structure requires the simultaneous knowledge of both the spatial and momentum information of the ultimate constituents of the nucleon, the quarks and gluons. Generalized Parton Distributions (GPDs) provide such tools to describe nucleon structure. GPDs are measurable through hard exclusive processes like Deeply Virtual Compton Scattering (DVCS) and Deeply Virtual Meson Production (DVMP). GPDs can describe hard exclusive processes only if Bjorken factorization is achieved during the hard scattering process. While DVCS data have given hints of the factorization regime being attained, such hints have not been observed for DVMP data. Testing for factorization in DVMP processes is the topic of this thesis. Exclusive π 0 electroproduction has been measured by experiment E12-06-114 in Hall A of JLab. Cross sections have been measured at three fixed Bjorken- x ( x B ): 0.35, 0.48 and 0.6 in the Q 2 range 3 to 9 GeV 2 . In this document we present an analysis of a subset of the data: x B = 0.35 in the Q 2 range 3.1 to 4.5 GeV 2 . The different structure functions: unseparated cross section (σ T + ϵσ L ), longitudinal-transverse interference (σ LT ), transverse-transverse interference (σ TT ), and the polarized response (σ LT' ) terms were extracted. The data was compared to a transversity GPD model. The model fails to reproduce the data even though the order of magnitude is in agreement for both model and data. We observe a strong disagreement between the data and the model for σ LT in terms of both magnitude and the cross section sign. Our results are in a larger and wider Q 2 domain but they are in agreement with existing measurements.

Dlamini, Mongi↗
DOE OSTI2023

3P Program: Phenotyping X Prediction = Productivity (Final Scientific/Technical Report)

The goal of the 3P Program was to establish integrated, real-time phenotyping and to analyze above- and below-ground plant architecture and total carbon partitioning and allocation to predict heterosis and develop superior crop hybrids by fully leveraging the Sorghum gene pool. There were two overarching themes: 1) the development of a new crop improvement approach utilizing advances in high-throughput phenotyping (HTP), computing, and genomics for public dissemination and 2) leveraging this platform for sorghum crop improvement and commercialization. The Clemson team worked on creating genomic resources and using both statistical learning and high-throughput phenotyping in genomics-assisted breeding. Research was broadly interested in the genetics of carbon partitioning, with the aim of improving crop performance and achieving sustainability. The technology and resources created can be readily found in the public domain and serve to advance scientific understanding of crop genomics and breeding. Genomic prediction was able to identify top crosses to be made, and a hybrid prediction pipeline is in place to drive year-over-year genetic gain. Roots have long been ignored by plant breeders and agronomists, not because they are unimportant but because they are hard to measure. This is an untapped white space of potential insight and innovation. To address this, Hi Fidelity Genetics developed the RootTracker to measure roots in the field on a continuous basis. A database system called RootTracker Tracker was developed to handle data coming from the RootTrackers. In using this device, valuable data was observed for plant breeding, hydrochemical development, and other agricultural biology applications. Carnegie Mellon’s goal was developing new techniques to generate high-resolution 3D models of plants from data collected in the field. The idea was that more useful and more informative phenotypes could be extracted by resolving small features, such as seeds and flowers, and that by modeling in 3D, the spatial structure of plants could be examined. To achieve this, multiple images collected by a new small format structured light stereo imager were fused together. A sorghum panicle modeling pipeline was developed to allow the collection and processing of data. Carolina Seed Systems is an agricultural technology company focused on decarbonizing the agricultural system. Their technology pipeline serves to drive fundamental progress towards creation and distribution of carbon negative crops. The genomic and the engineering technology developed through the 3P Program was leveraged to deliver both value and sustainability from the grower to the consumer. Promising sorghum hybrids were scaled up and commercialized. The overall goal of our research was to integrate, create, and deploy genetic and engineering concepts and technologies to enhance crop productivity in a sustainable fashion. The combination of public and private partners allowed the basic research and hypothesis testing to be quickly accelerated for commercial application by the companies yet maintained that the core framework and academic insights remain in the public domain for continued market disruption, competition, and innovation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A spatially regularized detector for emergent/re-emergent disease outbreaks

Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multimodal Atomic Force Microscopy for the Characterization of Metallic Particulates

This study investigates the utility of multimodal, or functional, atomic force microscopy (AFM) for the characterization of individual particles and particle ensembles. In single-particle analyses, AFM imaging modes provided orthogonal insights into mechanical and magnetic properties that are not accessible through conventional electron microscopy. Although the experiments were inherently delicate and time-intensive, with an observed sample loss rate of approximately 30%, these techniques enabled the qualitative differentiation of grain structures and magnetic domains, underscoring the challenges of experimental robustness. For particle ensembles, AFM enabled the extraction of reliable 2D and 3D particle size distributions. However, efforts to chemically differentiate particles based on mechanical property contrasts were limited by scaling effects and the qualitative nature of the data. Overall, multimodal AFM offers valuable complementary information, but its application requires careful consideration of methodological limitations, particularly for sample integrity, calibration, and scalability of mechanical and magnetic measurements. This report is organized to first address the application of AFM to single-particle analysis using various imaging modalities, followed by its potential role in ensemble-level particle screening and differentiation.

36 MATERIALS SCIENCE↗
NASA NTRS1972

The EOLE experiment: Early results and current objectives

The EOLE experiment with 480 constant level balloons released in the Southern Hemisphere is described. Each balloon floating freely at approximately the 200 mb level, is a precise tracer of the horizontal motion of air masses, the accuracy of which is limited only by the laminated structure of the stratospheric flow, within an RMS uncertainty of 1.5 m/sec. The balloons were found after 2 months to distribute at random over the whole hemisphere outside the tropics, irrespective of their original launching site. Early results of Eulerian and Lagrangian averages of the EOLE wind data are given for describing the mean 200 mb zonal and meridional circulations. The effect of the small scale eddies of two-dimensional turbulence has been studied with respect to the relative eddy diffusion of pairs of balloons and the relative dispersion of triangular clusters. New estimates of the RMS divergence of the 200 mb flow are given, together with their scale dependence which was found to be a logarithmic law.

Morel, P.↗
NASA NTRS1973

The Eole experiment - Early results and current objectives.

The Eole experiment with 480 constant level balloons released in the Southern Hemisphere is described. Each balloon, floating freely at approximately the 200-mb level, is a precise tracer of the horizontal motion of air masses, the accuracy of which is limited only by the laminated structure of the stratospheric flow, within an rms uncertainty of 1.5 m/sec. The balloons were found after 2 months to distribute at random over the whole hemisphere outside the tropics, irrespective of their original launching site. Early results of Eulerian and Lagrangian averages of the Eole wind data are given for describing the mean 200-mb zonal and meridional circulations.

Morel, P.↗
NASA NTRS1974

Proper motions, cluster membership and reddening in NGC 6611

Approaches used in reductions for proper motion are based on methods developed by Vasilevskis et al. (1965). Probabilities of cluster membership were determined by fitting the distribution of stars in the proper motion vector diagram with two bivariate Gaussian distributions for the field and the cluster stars. The spectroscopic data indicate that the stellar content of the cluster is not anomalous. The color-magnitude diagram is considered along with questions regarding member stars lying above the main sequence, the cluster age, and aspects of spatial structure.

Kamp, L. W.↗
NASA NTRS1978

Effects of cloud size and cloud particles on satellite-observed reflected brightness

Satellite observations allowed obtaining data on the visible brightness of cumulus clouds over South Park, Colorado, while aircraft observations were made in cloud to obtain the drop size distributions and liquid water content of the cloud. Attention is focused on evaluating the relationship between cloud brightness, horizontal dimension, and internal microphysical structure. A Monte Carlo cloud model for finite clouds was run using different distributions of drop sizes and numbers, while varying the cloud depth and width to determine how theory would predict what the satellite would view from its given location in space. Comparison of these results to the satellite observed reflectances is presented. Theoretical results are found to be in good agreement with observations. For clouds of optical thickness between 20 and 60, monitoring cloud brightness changes in clouds of uniform depth and variable width gives adequate information about a cloud's liquid water content. A cloud having a 10:1 width to depth ratio is almost reaching its maximum brightness for a specified optical thickness.

Reynolds, D. W.↗
NASA NTRS1989

The Center for Astrophysics Redshift Survey - Recent results

Six strips of the CfA redshift survey extension are now complete. The data continue to support a picture in which galaxies are on thin sheets which nearly surround vast low-density voids. The largest structures are comparable with the extent of the survey. Voids like the one in Bootes are a common feature of the large-scale distribution of galaxies. The issue of fair samples of the galaxy distribution is discussed, examining statistical measures of the galaxy distribution including the two-point correlation functions.

Geller, Margaret J.↗
NASA NTRS1990

The diffuse galactic gamma ray emission

The EGRET (Energetic Gamma-Ray Experiment Telescope) detector will provide a much more detailed view of the diffuse galactic gamma ray intensity in terms of higher resolution, greater statistical significance, and broader energy range than earlier missions. These observations will furnish insight into a number of very important questions related to the dynamics and structure of the Galaxy. A diffuse emission model is being developed that incorporates the latest information on matter distribution and source functions. In addition, it is tailored to the EGRET instrument response functions. The analysis code of the model maintains flexibility to accommodate the quality of the data that is anticipated. The discussion here focuses on the issues of the distributions of matter, cosmic rays, and radiation fields, and on the important source functions that enter into the model calculation of diffuse emission.

Bertsch, David L.↗
NASA NTRS1995

A decentralized software bus based on IP multicas ting

We describe decentralized reconfigurable implementation of a conference management system based on the low-level Internet Protocol (IP) multicasting protocol. IP multicasting allows low-cost, world-wide, two-way transmission of data between large numbers of conferencing participants through the Multicasting Backbone (MBone). Each conference is structured as a software bus -- a messaging system that provides a run-time interconnection model that acts as a separate agent (i.e., the bus) for routing, queuing, and delivering messages between distributed programs. Unlike the client-server interconnection model, the software bus model provides a level of indirection that enhances the flexibility and reconfigurability of a distributed system. Current software bus implementations like POLYLITH, however, rely on a centralized bus process and point-to-point protocols (i.e., TCP/IP) to route, queue, and deliver messages. We implement a software bus called the MULTIBUS that relies on a separate process only for routing and uses a reliable IP multicasting protocol for delivery of messages. The use of multicasting means that interconnections are independent of IP machine addresses. This approach allows reconfiguration of bus participants during system execution without notifying other participants of new IP addresses. The use of IP multicasting also permits an economy of scale in the number of participants. We describe the MULITIBUS protocol elements and show how our implementation performs better than centralized bus implementations.

Callahan, John R.↗
NASA NTRS2001

Shielded Heavy-Ion Environment Linear Detector (SHIELD): an experiment for the Radiation and Technology Demonstration (RTD) Mission

Radiological assessment of the many cosmic ion species of widely distributed energies requires the use of theoretical transport models to accurately describe diverse physical processes related to nuclear reactions in spacecraft structures, planetary atmospheres and surfaces, and tissues. Heavy-ion transport models that were designed to characterize shielded radiation fields have been validated through comparison with data from thick-target irradiation experiments at particle accelerators. With the RTD Mission comes a unique opportunity to validate existing radiation transport models and guide the development of tools for shield design. For the first time, transport properties will be measured in free-space to characterize the shielding effectiveness of materials that are likely to be aboard interplanetary space missions. Target materials composed of aluminum, advanced composite spacecraft structure and other shielding materials, helium (a propellant) and tissue equivalent matrices will be evaluated. Large solid state detectors will provide kinetic energy and charge identification for incident heavy-ions and for secondary ions created in the target material. Transport calculations using the HZETRN model suggest that 8 g cm -2 thick targets would be adequate to evaluate the shielding effectiveness during solar minimum activity conditions for a period of 30 days or more.

NASA Center LaRC↗
NASA NTRS2009

Hybrid NN/SVM Computational System for Optimizing Designs

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Rai, Man Mohan↗
DOE OSTI2020

Tritium Transport Phenomena in Molten-Salt Reactors

In this work, we review phenomena relevant to tritium transport in molten-salt reactors, which produce tritium from lithium and beryllium salts at significantly higher levels than other reactor types. Modeling of such phenomena began following MSRE operations, and these early models attempted to predict measured tritium distributions in the MSRE, accounting for turbulent mass-transport processes (using established heat transfer correlations), permeation through a variety of metal structures such as heat exchanger tubes, and transport to and from bubbles introduced into the salt by gas sparging. The models reasonably reproduced the MSRE data, but did so best when the permeability of structures was reduced by about a factor of 1,000. This issue does not appear to have been conclusively resolved, and all of the more recent attempts to model tritium transport in molten salts appear to make use of the same methodology. MSRE remains, however, essentially our only source of integral tritium transport data relevant to MSRs. Here, we generalize the MSRE approach to permeation in order to include potential rate-limiting effects at interfaces, as well the effects of added hydrogen. Appropriately non-dimensionalized, this system of equations identifies two dimensionless numbers whose relative values clearly delineate the conditions under which mass transport, surface effects, permeation, and hydrogen swamping are expected to become rate-limiting. We also describe the preliminary conceptual design of a forced convection FLiBe loop, into which tritium would be introduced for the purpose of providing validation data for such a model. The primary purpose of this is to investigate the coupled transport phenomena described above and identify those that are rate-limiting in MSRs. Additional test section configurations are described that would address other transport phenomena relevant to MSRs, including bubbly flows and graphite interactions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2001

Remote Sensing of Atlanta's Urban Sprawl and the Distribution of Land Cover and Surface Temperature

Between 1973 and 1992, an average of 20 ha of forest was lost each day to urban expansion of Atlanta, Georgia. Urban surfaces have very different thermal properties than natural surfaces-storing solar energy throughout the day and continuing to release it as sensible heat well after sunset. The resulting heat island effect serves as catalysts for chemical reactions from vehicular exhaust and industrialization leading to a deterioration in air quality. In this study, high spatial resolution multispectral remote sensing data has been used to characterize the type, thermal properties, and distribution of land surface materials throughout the Atlanta metropolitan area. Ten-meter data were acquired with the Advanced Thermal and Land Applications Sensor (ATLAS) on May 11 and 12, 1997. ATLAS is a 15-channel multispectral scanner that incorporates the Landsat TM bands with additional bands in the middle reflective infrared and thermal infrared range. The high spatial resolution permitted discrimination of discrete surface types (e.g., concrete, asphalt), individual structures (e.g., buildings, houses) and their associated thermal characteristics. There is a strong temperature contrast between vegetation and anthropomorphic features. Vegetation has a modal temperature at about 20 C, whereas asphalt shingles, pavement, and buildings have a modal temperature of about 39 C. Broad-leaf vegetation classes are indistinguishable on a thermal basis alone. There is slightly more variability (+/-5 C) among the urban surfaces. Grasses, mixed vegetation and mixed urban surfaces are intermediate in temperature and are characterized by broader temperature distributions with modes of about 29 C. Thermal maps serve as a basis for understanding the distribution of "hotspots", i.e., how landscape features and urban fabric contribute the most heat to the lower atmosphere.

Laymon, Charles A.↗
DOE OSTI2019

XPCA - R Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2019-10591 M

Kincher-Winoto, Kina↗