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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Geometric quenching of orbital pair breaking in a single crystalline superconducting nanomesh network

In a superconductor Cooper pairs condense into a single state and in so doing support dissipation free charge flow and perfect diamagnetism. In a magnetic field the minimum kinetic energy of the Cooper pairs increases, producing an orbital pair breaking effect. We show that it is possible to significantly quench the orbital pair breaking effect for both parallel and perpendicular magnetic fields in a thin film superconductor with lateral nanostructure on a length scale smaller than the magnetic length. By growing an ultra-thin (2 nm thick) single crystalline Pb nanowire network, we establish nm scale lateral structure without introducing weak links. Our network suppresses orbital pair breaking for both perpendicular and in-plane fields with a negligible reduction in zero-field resistive critical temperatures. Our study opens a frontier in nanoscale superconductivity by providing a strategy for maintaining pairing in strong field environments in all directions with important technological implications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Modulation of crystal growth and structure within cerium-based metal–organic frameworks

The intriguing catalytic properties of cerium-based materials have motivated the development of Ce-based metal–organic frameworks (MOFs). However, the controlled crystallization of Ce MOFs remains nascent due to complications with the high reduction potential of Ce 4+ species. Modulators offer a route in other well-studied coordination networks to slow down crystallization processes to allow for corrective, uniform crystal growth. Herein, we report an investigation of modulator identity and concentration on the synthesis of a Ce-UiO-type MOF with 2,6-naphthalenedicarboxylic acid (NDC) as a linker. At low concentrations using both benzoic acid and trifluoroacetic acid, we observed a mononuclear Ce 3+ MOF (NU-350) through single-crystal X-ray diffraction studies. Higher modulator concentrations yielded pure-phase Ce-UiO-NDC, with uniform particle sizes observed with utilizing benzoic acid as a modulator. Moreover, we demonstrated the transferability of this synthesis through the pure-phase synthesis of Ce-UiO-66 with benzoic acid as a modulator. High-resolution transmission electron microscopy showed a single crystalline domain within Ce-UiO-NDC.

36 MATERIALS SCIENCE↗
DOE OSTI2015

The Multicenter Aerobic Iron Respiratory Chain of Acidithiobacillus ferrooxidans Functions as an Ensemble with a Single Macroscopic Rate Constant

Electron transfer reactions among three prominent colored proteins in intact cells of Acidithiobacillus ferrooxidans were monitored using an integrating cavity absorption meter that permitted the acquisition of accurate absorbance data in suspensions of cells that scattered light. The concentrations of proteins in the periplasmic space were estimated to be 350 and 25 mg/ml for rusticyanin and cytochrome c, respectively; cytochrome a was present as one molecule for every 91 nm2 in the cytoplasmic membrane. All three proteins were rapidly reduced to the same relative extent when suspensions of live bacteria were mixed with different concentrations of ferrous ions at pH 1.5. The subsequent molecular oxygen-dependent oxidation of the multicenter respiratory chain occurred with a single macroscopic rate constant, regardless of the proteins' in vitro redox potentials or their putative positions in the aerobic iron respiratory chain. The crowded electron transport proteins in the periplasm of the organism constituted an electron conductive medium where the network of protein interactions functioned in a concerted fashion as a single ensemble with a standard reduction potential of 650 mV. The appearance of product ferric ions was correlated with the reduction levels of the periplasmic electron transfer proteins; the limiting first-order catalytic rate constant for aerobic respiration on iron was 7,400 s -1 . The ability to conduct direct spectrophotometric studies under noninvasive physiological conditions represents a new and powerful approach to examine the extent and rates of biological events in situ without disrupting the complexity of the live cellular environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Dimensionality Reduction Library v 0.2

Dimensionality Reduction Library is a C++ library for dimensionality reduction. In the context of this library, dimensionality reduction is considered to consist of 1)estimation of the intrinsic dimensionality using sampled data, 2) Finding maps that reduce the dimensionality of data (forward map) or increase the dimensionality of data (reverse map) and 3) mapping arbitray coordinates to high and low dimensionalities. The library is intended to provide a consistent interface to multiple dimensionality reduction algorithms with an efficient C++ interface that runs efficiently on multicore architectures. A few routines have been optimized with an option for GPU acceleration or distributed computation. Currently the library offers intrinsic dimensionality estimation using point-PCA reconstruction error and/ residual variance. The following dimensionality reduction methods have been implemented: Principal Component Analysis Multidimensional Scaling Locally Linear Embedding IsoMap Autoencoder Neutral Networks An executable is also supplied that can be built to allow for command-line access to the library routines. A description for an application of the library for molecular structure analysis has been published.

BROWN, WILLIAM↗
DOE OSTI2025

Identification and Classification of Fungal GPCR Gene Families

G protein-coupled receptors (GPCRs) are transmembrane proteins crucial for signal transduction in eukaryotes, responding to diverse extracellular signals. Researchers have found and systematically summarized 14 distinct types of GPCRs in fungi but their distribution among numerous fungal species remained largely unexamined. Additionally, three families of mammalian homologs (Rhodopsin, Glutamate, and Frizzled) have been found in previous studies, but they are not included in the systematic classification of fungal GPCRs. Our study establishes a unified classification of 17 GPCR classes in fungi, combining 14 fungal and 3 mammalian previously recognized groups, and classifies 28,294 GPCRs across 1357 fungal species, significantly expanding the scale of GPCRs in fungi and demonstrating their broader distribution. We found that mammalian homologs are notably more prevalent in Early Diverging Fungi (EDF), whereas the previous 14 classes are predominantly found in Ascomycota and Basidiomycota. The most abundant class detected in fungi was Pth11-like GPCRs, exclusively found in Pezizomycotina and involved in fungal pathogenicity. Our analysis suggested that Pezizomycotina ancestor possessed an extensive array of Pth11-like GPCRs, but over time, some species underwent considerable reductions in these GPCRs in conjunction with genome contractions. Utilizing a custom-built convolutional neural network (CNN) for the identification of fungal GPCRs, we identified several putative novel fungal GPCRs. Predicted interactions between these prospective new GPCRs and G-alpha proteins, as simulated by AlphaFold Multimer, provided additional support for their functional relevance. In conclusion, our work defines the first large-scale, unified classification of fungal GPCRs, reveals lineage-specific expansions and contractions, and uncovers previously unrecognized GPCR candidates with potential functional roles in fungal signaling.

G protein-coupled receptors↗
DOE OSTI2022

Measuring the β-decay Properties of Neutron-rich Exotic Pm, Sm, Eu, and Gd Isotopes to Constrain the Nucleosynthesis Yields in the Rare-earth Region

Abstract The β -delayed neutron-emission probabilities of 28 exotic neutron-rich isotopes of Pm, Sm, Eu, and Gd were measured for the first time at RIKEN Nishina Center using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The existing β -decay half-life ( T 1/2 ) database was significantly increased toward more neutron-rich isotopes, and uncertainties for previously measured values were decreased. The new data not only constrain the theoretical predictions of half-lives and β -delayed neutron-emission probabilities, but also allow for probing the mechanisms of formation of the high-mass wing of the rare-earth peak located at A ≈ 160 in the r -process abundance distribution through astrophysical reaction network calculations. An uncertainty quantification of the calculated abundance patterns with the new data shows a reduction of the uncertainty in the rare-earth peak region. The newly introduced variance-based sensitivity analysis method offers valuable insight into the influence of important nuclear physics inputs on the calculated abundance patterns. The analysis has identified the half-lives of 168 Sm and of several gadolinium isotopes as some of the key variables among the current experimental data to understand the remaining abundance uncertainty at A = 167–172.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

STARBASE: Database software for the automated plate scanner

The Automated Plate Scanner (APS) of the University of Minnesota, a unique high speed 'flying spot' laser scanner, is currently being used to scan and digitize the 936 O and E plate pairs of the first epoch Palomar Sky Survey. The resultant database will be used to produce a catalog of approximately a billion stars and several million galaxies. The authors describe the ongoing development of a dedicated APS database management system which will be made available to the astronomical community via INTERNET. A specialized DBMS called STARBASE has been written to provide fast access to the hundreds of millions of images collected by the APS. This system provides an initial reduction mode for parameterizing APS images and classifying image types using a novel set of neural network image classifiers. A second analysis mode, which will be that commonly used by the general user, provides for searches of the database which may be constrained by any combination of physical and positional parameters. Through the use of pointer hash trees, the system has been optimized for extremely fast positional searches using either right ascension and declination on the sky or linear X and Y positions on the POSS field. In addition to fast data retrieval, the system provides a graphical interface for displaying scatter plots or histograms of the collected data. In addition, a specialized image display system is being developed to allow the user to view densitometric data for all objects classified as extended by the neural network system. Finally, STARBASE has a flexible programmable interface which allows other programs to access information in the database. This allows users to write applications suited to their particular needs to process APS data.

Odewahn, S. C.↗
NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗
NASA NTRS2010

Advanced Navigation Strategies For Asteroid Sample Return Missions

Flyby and rendezvous missions to asteroids have been accomplished using navigation techniques derived from experience gained in planetary exploration. This paper presents analysis of advanced navigation techniques required to meet unique challenges for precision navigation to acquire a sample from an asteroid and return it to Earth. These techniques rely on tracking data types such as spacecraft-based laser ranging and optical landmark tracking in addition to the traditional Earth-based Deep Space Network radio metric tracking. A systematic study of navigation strategy, including the navigation event timeline and reduction in spacecraft-asteroid relative errors, has been performed using simulation and covariance analysis on a representative mission.

Getzandanner, K.↗
NASA NTRS2018

Action Pathways for Transforming Cities

Transformation is required for cities to fulfil their leadership potential on climate change. Five action pathways can guide them: integrate mitigation and adaptation; coordinate risk reduction and climate adaptation; cogenerate risk information; focus on disadvantaged populations; and improve governance and knowledge networks.

Civil engineering↗
NASA NTRS2000

COTS-Based Fault Tolerance in Deep Space: Qualitative and Quantitative Analyses of a Bus Network Architecture

Using COTS products, standards and intellectual properties (IPs) for all the system and component interfaces is a crucial step toward significant reduction of both system cost and development cost as the COTS interfaces enable other COTS products and IPs to be readily accommodated by the target system architecture. With respect to the long-term survivable systems for deep-space missions, the major challenge for us is, under stringent power and mass constraints, to achieve ultra-high reliability of the system comprising COTS products and standards that are not developed for mission-critical applications. The spirit of our solution is to exploit the pertinent standard features of a COTS product to circumvent its shortcomings, though these standard features may not be originally designed for highly reliable systems. In this paper, we discuss our experiences and findings on the design of an IEEE 1394 compliant fault-tolerant COTS-based bus architecture. We first derive and qualitatively analyze a -'stacktree topology" that not only complies with IEEE 1394 but also enables the implementation of a fault-tolerant bus architecture without node redundancy. We then present a quantitative evaluation that demonstrates significant reliability improvement from the COTS-based fault tolerance.

Tai, Ann T.↗
NASA NTRS2024

Usage of ChatGPT for Engineering Design and Analysis Tool Development

ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.

Design↗
NASA NTRS1996

Image analysis by geostatistical and neural-network methods applications in glaciology

The applicability of neural network techniques, in the classification of ice surfaces and crevasse patterns, was analyzed. The observations of the Bering Glacier (Alaska) obtained from a surface survey and from the global positioning system (GPS) were used. A geographical information system was applied to test the usefulness of standard approaches. The information in the image needed to be reduced prior to the classification. The reduction was performed with a fast variogram algorithm sampling in three oblique directions. The resultant vectors provided the input for the neural network.

Herzfeld, Ute Christina↗
DOE OSTI2019

Sticking-Free Reduction of Titanomagnetite Ironsand in a Fluidized Bed Reactor

Fluidized bed reduction of iron ore fines is typically inhibited by the onset of “sticking” at temperatures above 973 K, which leads to particle agglomeration and defluidization of the bed. Here, we report the sticking-free fluidized bed reduction of titanomagnetite (TTM) ironsand at 1223 K in Ar-H{sub 2} gas mixtures. We show that sticking is prevented by the formation of a protective titanium-rich oxide shell around each particle during the initial reduction stage. This protective shell prevents iron-iron contact between particles throughout the reduction process, enabling metallization degrees of 93 pct to be attained without sticking occurring. Phase evolution during the reaction has also been analyzed using q-X-ray diffraction and scanning electron microscope/energy dispersed spectroscopy. We find that the reduction proceeds through four separate stages. During the initial stage, approximately half of the initial TTM phase is converted to wüstite, forming a network of sub-micron wüstite channels which interlace the TTM matrix. During this stage, Ti and Al are enriched within the TTM matrix, due to the low solubility of both species in wüstite. This enrichment stabilizes the remaining TTM, meaning that wüstite is then preferentially reduced to metallic iron in stage 2 of the reduction. In stage 3, the remaining Ti-enriched TTM is reduced directly to metallic iron and ilmenite. The final stage of reduction involves the conversion of ilmenite into rutile and pseudobrookite. Our findings clarify the important role played by titanium species during the reduction of TTM and suggest that New Zealand ironsand can offer significant advantages over conventional hematite ores when used as a feedstock for fluidized bed direct-reduced iron processes.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Mechanism of oxygen reduction reaction on Pt(111) in alkaline solution: Importance of chemisorbed water on surface

Here, we report a detailed mechanistic study of the oxygen reduction reaction (ORR) on Pt(111) in alkaline solution, combining density functional theory and kinetic Monte Carlo simulations. A complex reaction network including four possible pathways via either 2e – or 4e – transfer is established and is able to reproduce the experimental measured polarization curve at both low- and high-potential regions. Our results show that it is essential to account for solvation by water and the dynamic coverage of *OH to describe the reaction kinetics well. In addition, a chemisorbed water (*H 2 O)-mediated mechanism including 4e – transfers is identified, where the reduction steps via *H 2 O on the surface are potential-independent and only the final removal of *OH from the surface in the form of OH – (aq) contributes to the current. For the ORR in alkaline solutions, such a mechanism is more competitive than the associative and dissociative mechanisms typically used to describe the ORR in acid solution. Finally, *OH and **O 2 intermediates are found to be critically important for tuning the ORR activity of Pt in alkaline solution. To enhance the activity, the binding of Pt should be tuned in such a way that *OH binding is weak enough to release more surface sites under working conditions, while **O 2 binding is strong enough to enable the ORR via the 4e – transfer mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Parameter Reduction of Composite Load Model Using Active Subspace Method

Over the past decades, the increasing penetration of distributed energy resources (DERs) has dramatically changed the power load composition in the distribution networks. The traditional static and dynamic load models can hardly capture the dynamic behavior of modern loads especially for fault-induced delayed voltage recovery (FIDVR) events. Thus, a more comprehensive composite load model with combination of static load, different types of induction motors, single-phase A/C motor, electronic load and DERs has been proposed by Western Electricity Coordinating Council (WECC). However, due to the large number of parameters and model complexity, the WECC composite load model (WECC CMLD) raises new challenges to power system studies. To overcome these challenges, in this paper, a cutting-edge parameter reduction (PR) approach for WECC CMLD based on active subspace method (ASM) is proposed. Firstly, the WECC CMLD is parameterized in a discrete-time manner for the application of the proposed method. Then, parameter sensitivities are calculated by discovering the active subspace, which is a lower-dimensional linear subspace of the parameter space of WECC CMLD in which the dynamic response is most sensitive. The interdependency among parameters can be taken into consideration by our approach. Finally, the numerical experiments validate the effectiveness and advantages of the proposed approach for WECC CMLD model.

active subspace↗