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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

The production of O(1D) from dissociative recombination of O2(+)

The results of large scale ab initio calculations of the rates for production of O(1D) by dissociative combination of O2(+) are presented for electron temperatures in the range 100 to 3000 K. A 1-delta-u state is the dominant dissociative route from v = 0 and a 3-sigma-u(-) state is the most important route from v = 1 and v = 2. The calculated total rate for O(1D) production from v = 0 is 2.21(+0.21, -0.24) x 10(-7) x (T sub e/300) exp -.46 near room temperature. The v = 1 and v = 2 rates are about 17 percent and 47 percent smaller respectively, than the v = 0 rate at 300 K.

Guberman, Steven L.↗
NASA NTRS1989

Ground-based measurements of O 1D and the H2O production rate from comets

High spectral resolution 6300.3 A line profiles of Comet Halley are modeled to determine the relative contributions to the emission that can be expected from the distant O 1D daughters produced by photodissociation H2O, CO2, and CO. The results are compared with profiles measured with a single etalon Fabry-Perot interferometer interfaced with a 0.4-m telescopre. To accurately calculate Q O 1D using the 6300.3 A intensity, the line profile must be known. It is shown that the outflow velocity of the parent molecules from the nucleus can be determined using the interferometer profiles.

Kerr, R. B.↗
NASA NTRS1989

Aeronomical determinations of the quantum yields of O (1S) and O (1D) from dissociative recombination of O2(+)

Data from the visible-airglow experiment on the Atmosphere Explorer-E satellite have been used to determine the quantum yields of O (1S) and O (1D) from the dissociative recombination of O2(+) based on a constant total recombination rate from each vibrational level. A range of values between 0.05 and 0.18 has been obtained for the quantum yield of O (1S) and shows a positive correlation with the extent of the vibrational excitation of O2(+). The quantum yield of O (1D) has been measured to be 0.9 + or - 0.2, with no apparent dependence on the vibrational distribution of O2(+).

Yee, Jeng-Hwa↗
NASA NTRS1992

Comet Austin (1989c1) O(1D) and H2O production rates

The dual-etalon Fabry-Perot spectrometer of Kitt Peak's McMath solar telescope has been used to conduct Comet Austin observations with spectral scan resolutions of 0.21 A; this sufficed for resolution of cometary forbidden O I 6300 A emissions from nearby NH2, as well as telluric emissions of the same type. For these data, an O(1D) production rate is obtained which is noted to be nearly model-independent. The H2O production rate is determined by taking into account the photodissociation of H2O and OH as sources of O(1D).

Schultz, D.↗
NASA NTRS1992

Actinometric measurement of j(O3-O(1D)) using a luminol detector

The photolysis frequency of ozone to singlet D oxygen atoms has been measured by means of a chemical actinometer using a luminol based detector. The instrument measures j(O3-O(1D)) with a precision of 10 percent. The data collected in winter and spring of 1991 is in agreement with model predictions and previously measured values. Data from a global solar radiometer can be used to estimate the effects of local cloudiness on j(O3-O(1D)).

Bairai, Solomon T.↗
NASA NTRS1994

Calibration of Axisymmetric and Quasi-1D Solvers for High Enthalpy Nozzles

The proposed paper will present a numerical investigation of the flow characteristics and boundary layer development in the nozzles of high enthalpy shock tunnel facilities used for hypersonic propulsion testing. The computed flow will be validated against existing experimental data. Pitot pressure data obtained at the entrance of the test cabin will be used to validate the numerical simulations. It is necessary to accurately model the facility nozzles in order to characterize the test article flow conditions. Initially the axisymmetric nozzle flow will be computed using a Navier Stokes solver for a range of reservoir conditions. The calculated solutions will be compared and calibrated against available experimental data from the DLR HEG piston-driven shock tunnel and the 16-inch shock tunnel at NASA Ames Research Center. The Reynolds number is assumed to be high enough at the throat that the boundary layer flow is assumed turbulent at this point downstream. The real gas affects will be examined. In high Mach number facilities the boundary layer is thick. Attempts will be made to correlate the boundary layer displacement thickness. The displacement thickness correlation will be used to calibrate the quasi-1D codes NENZF and LSENS in order to provide fast and efficient tools of characterizing the facility nozzles. The calibrated quasi-1D codes will be implemented to study the effects of chemistry and the flow condition variations at the test section due to small variations in the driver gas conditions.

Papadopoulos, P. E.↗
NASA NTRS2001

Measurement of persistence in 1D diffusion

Using a novel NMR scheme we observed persistence in 1D gas diffusion. Analytical approximations and numerical simulations have indicated that for an initially random array of spins undergoing diffusion, the probability p(t) that the average spin magnetization in a given region has not changed sign (i.e., "persists") up to time t follows a power law t(-straight theta), where straight theta depends on the dimensionality of the system. Using laser-polarized 129Xe gas, we prepared an initial "quasirandom" 1D array of spin magnetization and then monitored the ensemble's evolution due to diffusion using real-time NMR imaging. Our measurements are consistent with analytical and numerical predictions of straight theta approximately 0.12.

NASA Discipline Life Sciences Technologies↗
NASA NTRS2012

Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data

The main point of this study was to use realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE satellite mission, was applied to CloudSat, CALIPSO, and MODIS satellite data thus producing 3D cloudy atmospheres measuring 60 km wide by 13,000 km long at 1 km grid-spacing. Broadband solar fluxes and radiances for each (1 km)2 column where then produced by a Monte Carlo photon transfer model run in both full 3D and independent column approximation mode (i.e., a 1D model).

Barker, Howard W.↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗
NASA NTRS2025

The Shape Effect: Influence of 1D and 2D Boron Nitride Nanostructures on the Radiation Shielding, Thermal, and Damping Properties of High-Temperature Epoxy Composites

In space exploration, lightweight multifunctional materials capable of shielding neutron radiation, dissipating heat, and providing damping are essential. Polymer composites reinforced with boron nitride (BN) nanomaterials—specifically one-dimensional boron nitride nanotubes (BNNTs) and two-dimensional boron nitride nanoplatelets (BNNPs)—offer promising solutions. This study investigates how BN nanomaterial morphology influences the performance of high-temperature (HT) epoxy composites. We developed ultralightweight, three-dimensional BN foams comprising 1D BNNTs, 2D BNNPs, and hybrid 1D BNNT/2D BNNP structures via freeze-drying, then infiltrated them with HT epoxy to form dense composites. The BNNT foam exhibited the highest neutron radiation shielding, with a mass absorption coefficient of 26.64 cm2 g −1 , outperforming the hybrid foam (18.18 cm 2 g −1 ) and the BNNP foam (11.12 cm 2 g −1 ). A similar trend was observed in the HT epoxy composites; incorporating these foams at least doubled the mass absorption coefficient compared to the neat polymer. In terms of thermal conductivity, the BNNT/BNNP foam-epoxy composite achieved the highest value of 0.34 W m −1 K −1 , a 2.13-fold increase over neat HT epoxy. The BNNT/BNNP foam-epoxy composites also improved by 1.88 and 1.75 times, respectively. Mechanical testing revealed that BNNP foams withstood the highest loads during nanoindentation (3.53 kN), followed by BNNT/BNNP foams (1.93 kN) and BNNT foams (1.56 kN). All BN foam-epoxy composites exhibited enhanced damping properties, with tan δ increasing by at least 30 % compared to neat HT epoxy. These findings elucidate the impact of BN nanomaterial morphology on the multifunctional performance of HT epoxy composites, offering insights for developing high-performance, tailorable materials for demanding environments.

Kazue Orikasa↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗
NASA NTRSDate not supplied

1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗
NASA NTRS1986

Excitation of O(1D) atoms in aurorae and emission of the forbidden OI 6300-A line

The electron aurora leads to six processes capable of exciting the O(1D2) metastable state of the atomic-oxygen ground-state configuration, the parent state of the 6300-A red line. Altitude profiles of the volume emission rate resulting from each process are computed for Maxwellian electron spectra with characteristic energies between 0.1 and 2.0 keV. Since each process peaks at a different altitude, the sum or total volume emission rate extends over a wide altitude range. Measurements of 6300-A emission obtained by rocket and satellite-borne instruments are summarized, and it is shown that the chemical reaction of N(2D) with O2 is the major source of O(1D) atoms in the electron aurora. New calculations of the 6300-A:4728-A column emission-rate ratio are presented for a range of characteristic energies in an assumed Maxwellian electron spectrum. An approximate equation for the red-line emission per unit energy input is given as a function of electron-spectrum characteristic energy.

Rees, M. H.↗