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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Fast Proton Insertion in Layered H 2 W 2 O 7 via Selective Etching of an Aurivillius Phase

H 2 W 2 O 7 , a metastable material synthesized via selective etching of the Aurivillius-related Bi 2 W 2 O 9 , is shown as an electrode for high power proton-based energy storage. Comprehensive structural characterization is performed to obtain a high-fidelity crystal structure of H 2 W 2 O 7 using an iterative approach that combines X-ray diffraction, neutron pair distribution function, scanning transmission electron microscopy, Raman spectroscopy, and density functional theory modeling. Electrochemical characterization shows a capacity retention of ≈80% at 1000 mV s –1 (1.5-s charge/discharge time) as compared to 1 mV s –1 (≈16-min charge/discharge time) with cyclability for over 100 000 cycles. Energetics from density functional theory calculations indicate that proton storage occurs at the terminal oxygen sites within the hydrated interlayer. Last, optical micrographs collected during in situ Raman spectroscopy show reversible, multicolor electrochromism, with color changes from pale yellow to blue, purple, and last, orange as a function of proton content. These results highlight the use of selective etching of layered perovskites for the synthesis of metastable transition metal oxide materials and the use of H 2 W 2 O 7 as an anode material for proton-based energy storage or electrochromic applications.

25 ENERGY STORAGE↗
DOE OSTI2020

Possibility of 14 C cluster as a building block of medium-mass nuclei

The possibility of the 14 C cluster being a basic building block of medium-mass nuclei is discussed. Although α cluster structures have been widely discussed in the light N ≈ Z mass region, the neutron-to-proton ratio deviates from unity in the nuclei near β-stability line and in neutron-rich nuclei. Thus, more neutron-rich objects with N > Z could become the building blocks of cluster structures in such nuclei. The 14 C nucleus is strongly bound and can be regarded as such a candidate. In addition, the path to the lowest shell-model configuration at short relative distances is closed for the 14 C+ 14 C structure, contrary to the case of the 12 C+ 12 C structure; this allows an appreciable separation distance between the 14 C clusters. The recent development of the antisymmetrized quasicluster model (AQCM) allows us to utilize a j j-coupling shell-model wave function for each cluster in a simplified way. The AQCM results for the 14 C+ 14 C structure in 28 Mg are compared with the ones of cranked relativistic mean field (CRMF) calculations. Although theoretical frameworks of these two models are quite different, they give similar results for the nucleonic densities and rotational properties of the structure under investigation. The existence of a linear chain three- 14 C cluster structure in 42 Ar has also been predicted in AQCM. Furthermore, these results confirm the role of the 14 C cluster as a possible building block of cluster structures in medium-mass nuclei.

20 ≤ A ≤ 38↗
DOE OSTI2015

Quantum mechanics without potential function

In the standard formulation of quantum mechanics, one starts by proposing a potential function that models the physical system. The potential is then inserted into the Schrödinger equation, which is solved for the wavefunction, bound states energy spectrum, and/or scattering phase shift. In this work, however, we propose an alternative formulation in which the potential function does not appear. The aim is to obtain a set of analytically realizable systems, which is larger than in the standard formulation and may or may not be associated with any given or previously known potential functions. We start with the wavefunction, which is written as a bounded infinite sum of elements of a complete basis with polynomial coefficients that are orthogonal on an appropriate domain in the energy space. Using the asymptotic properties of these polynomials, we obtain the scattering phase shift, bound states, and resonances. This formulation enables one to handle not only the well-known quantum systems but also previously untreated ones. Illustrative examples are given for two- and three-parameter systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Real-time capable modeling of neutral beam injection on NSTX-U using neural networks

A new model of heating, current drive, torque and other effects of neutral beam injection on NSTX-U that uses neural networks has been developed. The model has been trained and tested on the results of the Monte Carlo code NUBEAM for the database of experimental discharges taken during the first operational campaign of NSTX-U. By projecting flux surface quantities onto empirically derived basis functions, the model is able to efficiently and accurately reproduce the behavior of both scalars, like the total neutron rate and shine through, and profiles, like beam current drive and heating. With this being said, the model has been tested on the NSTX-U real-time computer, demonstrating a rapid execution time orders of magnitude faster than the Monte Carlo code that is well suited for the iterative calculations needed to interpret experimental results, optimization during scenario development activities, and real-time plasma control applications. Simulation results of a proposed design for a nonlinear observer that embeds the neural network calculations to estimate the poloidal flux profile evolution, as well as Z eff and fast ion diffusivity, are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Real-time capable modeling of neutral beam injection on NSTX-U using neural networks

A new model of heating, current drive, torque and other effects of neutral beam injection on NSTX-U that uses neural networks has been developed. The model has been trained and tested on the results of the Monte Carlo code NUBEAM for the database of experimental discharges taken during the first operational campaign of NSTX-U. By projecting flux surface quantities onto empirically derived basis functions, the model is able to efficiently and accurately reproduce the behavior of both scalars, like the total neutron rate and shine through, and profiles, like beam current drive and heating. The model has been tested on the NSTX-U real-time computer, demonstrating a rapid execution time orders of magnitude faster than the Monte Carlo code that is well suited for the iterative calculations needed to interpret experimental results, optimization during scenario development activities, and real-time plasma control applications. Simulation results of a proposed design for a nonlinear observer that embeds the neural network calculations to estimate the poloidal flux profile evolution, as well as effective charge and fast ion diffusivity, are presented.

Neural networks↗
DOE OSTI2026

Constraints on White Dwarf Hydrogen Layer Masses Using Gravitational Redshifts

The hydrogen envelope is the outermost layer of a DA white dwarf; it makes up the entirety of the stellar photosphere, and yet its typical extent is difficult to model theoretically and remains poorly observationally constrained. As a result, hydrogen envelope mass is a substantial source of systematic uncertainty in the physical properties of white dwarfs, including overall masses and cooling ages. In this work, we fit a Gaussian mixture model to gravitational redshifts from high-resolution spectroscopy, paired with radius measurements from Gaia BP/RP spectra, to measure the mass–radius relation for a sample of 468 white dwarfs. Our results are in excellent agreement with the predicted mass–radius relations of state-of-the-art evolutionary models, including those from the MESA Isochrones and Stellar Tracks (MIST) library. We find that mass–radius relations such as those from MIST that assume a thick and mass-dependent hydrogen envelope are preferred by the observed probability density function over models that assume a hydrogen envelope of constant mass. Proper treatment of the evolution of white dwarf progenitors is thus important for accurately modeling the mass–radius relation. Our results indicate that gravitational redshift measurements of large samples of white dwarfs in wide binaries are promising probes of the hydrogen envelope masses of DA white dwarfs.

Astronomy and AstroPhysics↗
NASA NTRS2016

Active Full-Shell Grazing-Incidence Optics

MSFC has a long history of developing full-shell grazing-incidence x-ray optics for both narrow (pointed) and wide field (surveying) applications. The concept presented in this paper shows the potential to use active optics to switch between narrow and wide-field geometries, while maintaining large effective area and high angular resolution. In addition, active optics has the potential to reduce errors due to mounting and manufacturing lightweight optics. The design presented corrects low spatial frequency error and has significantly fewer actuators than other concepts presented thus far in the field of active x-ray optics. Using a finite element model, influence functions are calculated using active components on a full-shell grazing-incidence optic. Next, the ability of the active optic to effect a change of optical prescription and to correct for errors due to manufacturing and mounting is modeled.

Full-shell↗
DOE OSTI2023

CRITICAL FUNCTION ASSURANCE: Understanding Critical Function and Critical Function Delivery is Foundational for Meaningful ICS Security Improvement and Policy Efforts

Modern life is enabled by a complex and interdependent web of critical functions, including energy, communications, transportation, food, and water. Automation has significantly reduced or replaced human interactions in the delivery of these functions, resulting in a web of goods and services that are made available 24/7 only through unique and intentional deployments of microprocessors, software, and firmware technologies. The prospect of cyber-enabled sabotage of these processes disrupts traditional risk determination models. Critical Function Assurance (CFA) is a foundational approach to identifying, prioritizing, and mitigating the risk that is inherent in the delivery of critical functions that depend on digital technology. It provides rapid focus to what matters most and illuminates elements and areas of risk that otherwise are often overlooked. This focus enables effective application of available security resources and optimizes security strategy and policy efforts. This paper introduces CFA to decision makers and risk executives (including CEOs, COOs, CFOs, and CISOs) whose organizations support and deliver the critical functions that underpin national defense, societal health and safety, and a vibrant economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Digital Twin for Hydropower System Object Modeling: Alder Dam (FY2023)

Hydropower is the world's largest source of renewable electricity, and hydropower plants are distributed all over the world. Typical major components of a hydropower plant are the governor, excitation, generator, thrust bearing, hydraulic turbine, transformer, the main lead, metering and control, tailwater depression, and dissolved oxygen. For each component, various measures are taken. The measurements are acquired by various heterogeneous systems, including standalone sensors, programmable logic controllers (PLC), Supervisory control and data acquisition (SCADA), Internet of Things (IoT), and data acquisition and integration platforms such as OSI/PI. The measured data are often archived within the plant by a data management platform, and many institutions have cloud-based archive systems, such as Hydropower Research Institution (HRI), U.S. Army Corps of Engineers (USACE), and Columbia River Data Access in Real Time (DART). Object Modeling is a general framework for designing information systems. It focuses on objects, the actions they perform, and the messages they send to one another to cause those actions to be taken. The major differences among object modeling, network modeling, data modeling, and process modeling are that in the first we focus on the actions in response to information, objects which form the system, the actions they perform, and how they pass information to one another, while in the second we concentrate on where, when and how much information is moved, while in the third we focus on what information is moved and where it is moved, while in the last we focus on how it is moved and when it is moved. Object modeling was developed basically as a method to develop object-oriented systems and to support object-oriented programming. It describes the static structure of the system. The object Modeling Technique is easy to draw and use. That is why we choose object modeling to connect physical hydropower plants to Digital Twin. It recognizes the objects and the relationship between them. It identifies the attributes and functions of each class. Dynamic Modeling: It explains how objects respond to events. Functional Modeling indicates the processes executed in an object and how data changes when it moves to objects. It has been used in many applications like telecommunication, transportation, etc.

13 HYDRO ENERGY↗
DOE OSTI2018

Software Requirements of SNAP for Editing MELCOR 2.2 Models

Applications of the severe accident analysis code MELCOR, developed for the U.S. Nuclear Regulatory Commission (NRC) by Sandia National Laboratories (SNL), have been supported by the graphical user-interface and post-processing suite Symbolic Nuclear Analysis Package (SNAP), developed for the NRC by Applied Programming Technology (APT). With the release of MELCOR 2.2, new user functionality and models have been introduced and an update to the SNAP MELCOR plugin user interface is necessary to access these new features. This document relates all new features introduced into MELCOR to the development team at APT as well as the NRC.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Combining phase-field crystal methods with a Cahn-Hilliard model for binary alloys

Diffusion-induced phase transitions typically change the lattice symmetry of the host material. In battery electrodes, for example, Li ions (diffusing species) are inserted between layers in a crystalline electrode material (host). This diffusion induces lattice distortions and defect formations in the electrode. The structural changes to the lattice symmetry affect the host material’s properties. We propose a 2D theoretical framework that couples a Cahn-Hilliard (CH) model, which describes the composition field of a diffusing species, with a phase-field crystal (PFC) model, which describes the host-material lattice symmetry. We couple the two continuum models via coordinate transformation coefficients. We introduce the transformation coefficients in the PFC method to describe affine lattice deformations. These transformation coefficients are modeled as functions of the composition field. Using this coupled approach, we explore the effects of coarse-grained lattice symmetry and distortions on a diffusion-induced phase transition process. In this paper, we demonstrate the working of the CH-PFC model through three representative examples: First, we describe base cases with hexagonal and square symmetries for two composition fields. Next, we illustrate how the CH-PFC method interpolates lattice symmetry across a diffuse phase boundary. Finally, we compute a Cahn-Hilliard type of diffusion and model the accompanying changes to lattice symmetry during a phase transition process.

25 ENERGY STORAGE↗
DOE OSTI2015

Simulations of hybrid system varying solar radiation and microturbine response time

Hybrid power systems, such as combinations of renewable power sources with intermittent power production and non-renewable power sources, theoretically increase the reliability and thus integration of renewable sources in the electrical system. However, a recent increase in the number of hybrid installations has sparked interest in the effects of their connection to the grid, especially in remote areas. This paper analyses a photovoltaic-gas microturbine hybrid system dimensioned to be installed in La Paz (Mexico).The research presented in this paper studies and quantifies the effects on the total electric power produced, varying both the solar radiation and the gas microturbine response time. The gas microturbine and the photovoltaic panels are modelled using Matlab/Simulink software, obtaining a platform where different tests to simulate real conditions have been executed. They consist of diverse ramps of irradiance that replicate solar radiation variations, and different microturbine response times reproduced by the time constants of a first order transfer function that models the microturbine dynamic response. The results obtained show that when radiation varies quickly it does not produce significant differences in the power guarantee or the microturbine gas consumption, to any microturbine response time. However, these two parameters are highly variable with smooth radiance variations. The maximum total power variation decreases greatly as the radiation variation gets lower. In addition, by decreasing the microturbine response time, it is possible to appreciably increase the power guarantee although the maximum power variation and gas consumption increase. Only in cases of low radiation variation is there no appreciable difference in the maximum power variation obtained by the different turbine response times.

14 SOLAR ENERGY↗
DOE OSTI2018

Deep learning: A guide for practitioners in the physical sciences

Machine learning is finding increasingly broad applications in the physical sciences. This most often involves building a model relationship between a dependent, measurable output, and an associated set of controllable, but complicated, independent inputs. We present a tutorial on current techniques in machine learning—a jumping-off point for interested researchers to advance their work. We focus on deep neural networks with an emphasis on demystifying deep learning. We begin with background ideas in machine learning and some example applications from current research in plasma physics. We discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, and then advancing to more sophisticated deep learning methods. We also address unsupervised learning and techniques for reducing the dimensionality of input spaces. Along the way, we describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We describe classes of tasks—predicting scalars, handling images, and fitting time-series—and prepare the reader to choose an appropriate technique. We finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Translational Genomics for the Improvement of Switchgrass

Our objectives were to apply bioinformatics and high throughput sequencing technologies to identify and classify the genes involved in cell wall formation in maize and switchgrass. Targets for genetic modification were to be identified and cell wall materials isolated and assayed for enhanced performance in bioprocessing. We annotated and assembled over 750 maize genes into gene families predicted to function in cell wall biogenesis. Comparative genomics of maize, rice, and Arabidopsis sequences revealed differences in gene family structure. In addition, differences in expression between gene family members of Arabidopsis, maize and rice underscored the need for a grass-specific genetic model for functional analyses.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Interference in astronomical speckle patterns

Astronomical speckle patterns are examined in an atmospheric-optics context in order to determine what kind of image quality is to be expected from several different imaging techniques. The model used to describe the instantaneous complex field distribution across the pupil of a large telescope regards the pupil as a deep phase grating with a periodicity given by the size of the cell of uniform phase or the refractive index structure function. This model is used along with an empirical formula derived purely from the physical appearance of the speckle patterns to discuss the orders of interference in astronomical speckle patterns.

Breckinridge, J. B.↗
NASA NTRS1993

Heat transfer in rotating serpentine passages with selected model orientation for smooth or skewed trip walls

Experiments were conducted to determine the effects of model orientation as well as buoyancy and Coriolis forces on heat transfer in turbine blade internal coolant passages. Turbine blades have internal coolant passage surfaces at the leading and trailing edges of the airfoil with surfaces at angles which are as large as +/- 50 to 60 degrees to the axis of rotation. Most of the previously-presented, multiple-passage, rotating heat transfer experiments have focused on radial passages aligned with the axis of rotation. Results from serpentine passages with orientations 0 and 45 degrees to the axis of rotation which simulate the coolant passages for the mid chord and trailing edge regions of the rotating airfoil are compared. The experiments were conducted with rotation in both directions to simulate serpentine coolant passages with the rearward flow of coolant or with the forward flow of coolant. The experiments were conducted for passages with smooth surfaces and with 45 degree trips adjacent to airfoil surfaces for the radial portion of the serpentine passages. At a typical flow condition, the heat transfer on the leading surfaces for flow outward in the first passage with smooth walls was twice as much for the model at 45 degrees compared to the model at 0 degrees. However, the differences for the other passages and with trips were less. In addition, the effects of buoyancy and Coriolis forces on heat transfer in the rotating passage were decreased with the model at 45 degrees, compared to the results at 0 degrees. The heat transfer in the turn regions and immediately downstream of the turns in the second passage with flow inward and in the third passage with flow outward was also a function of model orientation with differences as large as 40 to 50 percent occurring between the model orientations with forward flow and rearward flow of coolant.

Johnson, B. V.↗
NASA NTRS2006

NASA Taxonomies for Searching Problem Reports and FMEAs

Many types of hazard and risk analyses are used during the life cycle of complex systems, including Failure Modes and Effects Analysis (FMEA), Hazard Analysis, Fault Tree and Event Tree Analysis, Probabilistic Risk Assessment, Reliability Analysis and analysis of Problem Reporting and Corrective Action (PRACA) databases. The success of these methods depends on the availability of input data and the analysts knowledge. Standard nomenclature can increase the reusability of hazard, risk and problem data. When nomenclature in the source texts is not standard, taxonomies with mapping words (sets of rough synonyms) can be combined with semantic search to identify items and tag them with metadata based on a rich standard nomenclature. Semantic search uses word meanings in the context of parsed phrases to find matches. The NASA taxonomies provide the word meanings. Spacecraft taxonomies and ontologies (generalization hierarchies with attributes and relationships, based on terms meanings) are being developed for types of subsystems, functions, entities, hazards and failures. The ontologies are broad and general, covering hardware, software and human systems. Semantic search of Space Station texts was used to validate and extend the taxonomies. The taxonomies have also been used to extract system connectivity (interaction) models and functions from requirements text. Now the Reconciler semantic search tool and the taxonomies are being applied to improve search in the Space Shuttle PRACA database, to discover recurring patterns of failure. Usual methods of string search and keyword search fall short because the entries are terse and have numerous shortcuts (irregular abbreviations, nonstandard acronyms, cryptic codes) and modifier words cannot be used in sentence context to refine the search. The limited and fixed FMEA categories associated with the entries do not make the fine distinctions needed in the search. The approach assigns PRACA report titles to problem classes in the taxonomy. Each ontology class includes mapping words - near-synonyms naming different manifestations of that problem class. The mapping words for Problems, Entities and Functions are converted to a canonical form plus any of a small set of modifier words (e.g. non-uniformity NOT + UNIFORM.) The report titles are parsed as sentences if possible, or treated as a flat sequence of word tokens if parsing fails. When canonical forms in the title match mapping words, the PRACA entry is associated with the corresponding Problem, Entity or Function in the ontology. The user can search for types of failures associated with types of equipment, clustering by type of problem (e.g., all bearings found with problems of being uneven: rough, irregular, gritty ). The results could also be used for tagging PRACA report entries with rich metadata. This approach could also be applied to searching and tagging failure modes, failure effects and mitigations in FMEAs. In the pilot work, parsing 52K+ truncated titles (the test cases that were available), has resulted in identification of both a type of equipment and type of problem in about 75% of the cases. The results are displayed in a manner analogous to Google search results. The effort has also led to the enrichment of the taxonomy, adding some new categories and many new mapping words. Further work would make enhancements that have been identified for improving the clustering and further reducing the false alarm rate. (In searching for recurring problems, good clustering is more important than reducing false alarms). Searching complete PRACA reports should lead to immediate improvement.

Malin, Jane T.↗
NASA NTRS2016

Influence of Molecular Simulation Model Accuracy on the Interfacial Properties of an Ionic Liquid: Overview of Recommended Practices

Increasing the energy storage capability of ionic liquid supercapacitors will require better understanding of ion-electrode interactions. We have probed the influence of these interactions on the structure and differential capacitance of of an ionic liquid ([EMIM][BF4]) at an ideal graphite interface as a function of model accuracy. Of note, differential capacitance is determined through newly derived and validated fluctuation formulas. In terms of model accuracy, we test electrostatic techniques, electrode charging techniques, and electrolyte interatomic potentials. For electrostatic summations, we employ high cost, high fidelity techniques as well as less expensive, approximate techniques for summation in slab geometry. For electrode charging, uniform, constant-charge and environmentally responsive, constant-potential conditions are employed. For the ionic liquid, constant charge and atomically polarizable models are employed. We comment on the role of model accuracy on the structure and energetics of the electric double layer as well as on the magnitude and shape of differential capacitance.

simulations↗