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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Validating Arc-jet Condition Targets Using Inverse 1D Material Response Models

Experiments were conducted using both the Interaction Heating Facility (IHF) and Aerodynamic Heating Facility (AHF), two hypersonic arc-jet facilities at NASA Ames. These tests were run to understand the material response of ablative thermal protection system materials by exposing them to high-enthalpy flows. Using thermocouple data from the materials, test conditions were inversely determined and compared with expected results from a one-dimensional model using facility target conditions; this allowed an approximate environmental scale factor to be determined and applied to more accurately predict material response in additional tests.

material response↗
NASA NTRS2005

Carbon nanotubes and nanowires for biological sensing

This chapter reviews the recent development in biological sensing using nanotechnologies based on carbon nanotubes and various nanowires. These 1D materials have shown unique properties that are efficient in interacting with biomolecules of similar dimensions, i.e., on a nanometer scale. Various aspects including synthesis, materials properties, device fabrication, biofunctionalization, and biological sensing applications of such materials are reviewed. The potential of such integrated nanobiosensors in providing ultrahigh sensitivity, fast response, and high-degree multiplex detection, yet with minimum sample requirements is demonstrated. This chapter is intended to provide comprehensive updated information for people from a variety of backgrounds but with common interests in the fast-moving interdisciplinary field of nanobiotechnology.

Review↗
NASA NTRS2014

Evaluation of Dielectric-Barrier-Discharge Actuator Substrate Materials

A key, enabling element of a dielectric barrier discharge (DBD) actuator is the dielectric substrate material. While various investigators have studied the performance of different homogeneous materials, most often in the context of related DBD experiments, fundamental studies focused solely on the dielectric materials have received less attention. The purpose of this study was to conduct an experimental assessment of the body-force-generating performance of a wide range of dielectric materials in search of opportunities to improve DBD actuator performance. Materials studied included commonly available plastics and glasses as well as a custom-fabricated polyimide aerogel. Diagnostics included static induced thrust, electrical circuit parameters for 2D surface discharges and 1D volume discharges, and dielectric material properties. Lumped-parameter circuit simulations for the 1D case were conducted showing good correspondence to experimental data provided that stray capacitances are included. The effect of atmospheric humidity on DBD performance was studied showing a large influence on thrust. The main conclusion is that for homogeneous, dielectric materials at forcing voltages less than that required for streamer formation, the material chemical composition appears to have no effect on body force generation when actuator impedance is properly accounted for.

Wilkinson, Stephen P.↗
NASA NTRS2002

Fractional-order Viscoelasticity (FOV): Constitutive Development Using the Fractional Calculus: First Annual Report

This is the first annual report to the U.S. Army Medical Research and Material Command for the three year project "Advanced Soft Tissue Modeling for Telemedicine and Surgical Simulation" supported by grant No. DAMD17-01-1-0673 to The Cleveland Clinic Foundation, to which the NASA Glenn Research Center is a subcontractor through Space Act Agreement SAA 3-445. The objective of this report is to extend popular one-dimensional (1D) fractional-order viscoelastic (FOV) materials models into their three-dimensional (3D) equivalents for finitely deforming continua, and to provide numerical algorithms for their solution.

Freed, Alan↗
NASA NTRS1993

Acousto-Ultrasonic analysis of failure in ceramic matrix composite tensile specimens

Three types of acousto-ultrasonic (AU) measurements, stress-wave factor (SWF), lowest antisymmetric plate mode group velocity (VS), and lowest symmetric plate mode group velocity (VL), were performed on specimens before and after tensile failure. Three different Nicalon fiber architectures with ceramic matrices were tested. These composites were categorized as 1D (unidirectional fiber orientation) SiC/CAS glass ceramic, and 2D and 3D woven SiC/SiC ceramic matrix materials. SWF was found to be degraded after tensile failure in all three material categories. VS was found to be degraded only in the 1D SiC/CAS. VL was difficult to determine on the irregular specimen surfaces but appeared unchanged on all failed specimens. 3D woven specimens with heat-treatment at high temperature exhibited degradation only in SWF.

Kautz, Harold E.↗
NASA NTRS1994

The FC-1D: The profitable alternative Flying Circus Commercial Aviation Group

The FC-1D was designed as an advanced solution for a low cost commercial transport meeting or exceeding all of the 1993/1994 AIAA/Lockheed request for proposal requirements. The driving philosophy behind the design of the FC-1D was the reduction of airline direct operating costs. Every effort was made during the design process to have the customer in mind. The Flying Circus Commercial Aviation Group targeted reductions in drag, fuel consumption, manufacturing costs, and maintenance costs. Flying Circus emphasized cost reduction throughout the entire design program. Drag reduction was achieved by implementation of the aft nacelle wing configuration to reduce cruise drag and increase cruise speeds. To reduce induced drag, rather than increasing the wing span of the FC-1D, spiroids were included in the efficient wing design. Profile and friction drag are reduced by using riblets in place of paint around the fuselage and empennage of the FC-1D. Choosing a single aisle configuration enabled the Flying Circus to optimize the fuselage diameter. Thus, reducing fuselage drag while gaining high structural efficiency. To further reduce fuel consumption a weight reduction program was conducted through the use of composite materials. An additional quality of the FC-1D is its design for low cost manufacturing and assembly. As a result of this design attribute, the FC-1D will have fewer parts which reduces weight as well as maintenance and assembly costs. The FC-1D is affordable and effective, the apex of commercial transport design.

Meza, Victor J.↗
NASA NTRS2002

Finite-Strain Fractional-Order Viscoelastic (FOV) Material Models and Numerical Methods for Solving Them

Fraction-order viscoelastic (FOV) material models have been proposed and studied in 1D since the 1930's, and were extended into three dimensions in the 1970's under the assumption of infinitesimal straining. It was not until 1997 that Drozdov introduced the first finite-strain FOV constitutive equations. In our presentation, we shall continue in this tradition by extending the standard, FOV, fluid and solid, material models introduced in 1971 by Caputo and Mainardi into 3D constitutive formula applicable for finite-strain analyses. To achieve this, we generalize both the convected and co-rotational derivatives of tensor fields to fractional order. This is accomplished by defining them first as body tensor fields and then mapping them into space as objective Cartesian tensor fields. Constitutive equations are constructed using both variants for fractional rate, and their responses are contrasted in simple shear. After five years of research and development, we now possess a basic suite of numerical tools necessary to study finite-strain FOV constitutive equations and their iterative refinement into a mature collection of material models. Numerical methods still need to be developed for efficiently solving fraction al-order integrals, derivatives, and differential equations in a finite element setting where such constitutive formulae would need to be solved at each Gauss point in each element of a finite model, which can number into the millions in today's analysis.

Freed, Alan D.↗
NASA NTRS1988

Modeling and simulation of InP homojunction solar cells

Modeling and simulation of single-crystal InP homojunction solar cells has been performed using the PC-1D code. Cell design and performance have been optimized using the best available estimates of the various materials parameters. A comparison has been made of the predictions of the PC-1D model to those of other models. The optimum performance is predicted to give an efficiency approaching 21 percent at AM0. It is shown that in order to describe the performance of actual cells it is necessary to use larger values of the intrinsic carrier concentration and the surface recombination velocity (SRV) than have been reported in the literature. However, even with a near-maximum value of SRV (e.g., 10 to the 7th cm/s) it is necessary to reduce the minority carrier diffusion length in the emitter to only 0.01 micron in order to account for the relatively low quantum efficiency in the blue part of the spectrum. This indicates that improvement in the emitter bulk properties could be much more important than the SRV. Other loss mechanisms are also discussed; in particular, it is shown that recombination in the base, for good quality material, is relatively insignificant.

Yahia, A. H.↗
NASA NTRS2020

Composite Piezoelectric Resonator 1D Modeling with Loss

Abstract – Maximizing the benefit of applying piezoelectric materials in composite structures, which are a mix of piezoelectric and elastic elements, requires analytical modeling. In this paper we describe two main classes of resonators and how to model using network equivalent including material losses. These include extensional devices where the layer areas are parallel to the direction of wave travel as found in a variety of thickness or length mode transducers (BC. Stress T1 = T2 at boundary) and transverse mode resonators (BC. Strain S1 = S2 at boundary) where the major layer area is perpendicular to the wave direction as is found in radial mode or length thickness mode composite resonators. In order to illustrate these models we will use Mason’s equivalent circuits with elastic, dielectric and piezoelectric loss. It is noted that in order to maintain consistency with the linear equations of piezoelectricity and the wave equation care is required when applying complex loss coefficients to the models. Although these techniques are applicable to other models we use Mason’s network equivalent circuit because it is intuitive for composite modeling due to the frequency independent turns ratio and the independence of the equations describing the elastic layers from the adjoining layer properties which aids in understanding the boundary conditions to impose on the layer boundaries. We will present network models for a variety of applications with loss and show how to calculate their impedance spectra, interface stress, strain velocity and displacement spectra.

Sherrit, Stewart↗
NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRS2007

Extended-Range Ultrarefractive 1D Photonic Crystal Prisms

A proposal has been made to exploit the special wavelength-dispersive characteristics of devices of the type described in One-Dimensional Photonic Crystal Superprisms (NPO-30232) NASA Tech Briefs, Vol. 29, No. 4 (April 2005), page 10a. A photonic crystal is an optical component that has a periodic structure comprising two dielectric materials with high dielectric contrast (e.g., a semiconductor and air), with geometrical feature sizes comparable to or smaller than light wavelengths of interest. Experimental superprisms have been realized as photonic crystals having three-dimensional (3D) structures comprising regions of amorphous Si alternating with regions of SiO2, fabricated in a complex process that included sputtering. A photonic crystal of the type to be exploited according to the present proposal is said to be one-dimensional (1D) because its contrasting dielectric materials would be stacked in parallel planar layers; in other words, there would be spatial periodicity in one dimension only. The processes of designing and fabricating 1D photonic crystal superprisms would be simpler and, hence, would cost less than do those for 3D photonic crystal superprisms. As in 3D structures, 1D photonic crystals may be used in applications such as wavelength-division multiplexing. In the extended-range configuration, it is also suitable for spectrometry applications. As an engineered structure or artificially engineered material, a photonic crystal can exhibit optical properties not commonly found in natural substances. Prior research had revealed several classes of photonic crystal structures for which the propagation of electromagnetic radiation is forbidden in certain frequency ranges, denoted photonic bandgaps. It had also been found that in narrow frequency bands just outside the photonic bandgaps, the angular wavelength dispersion of electromagnetic waves propagating in photonic crystal superprisms is much stronger than is the angular wavelength dispersion obtained by use of conventional prisms and diffraction gratings and is highly nonlinear.

Ting, David Z.↗
NASA NTRS1992

Computational modeling of nonlinear electromagnetic phenomena

A new algorithm has been developed that permits, for the first time, the direct time integration of the full-vector nonlinear Maxwell's equations. This new capability permits the modeling of linear and nonlinear, instantaneous and dispersive effects in the electric polarization material media. Results are presented of first-time calculations in 1D of the propagation and collision of femtosecond electromagnetic solitons that retain the optical carrier.

Goorjian, Peter M.↗