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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2026

Lyman-$α$ forest holography: 3D predictions from 1D measurements

Cosmological analyses of Lyman-$α$ forest clustering rely on either one-dimensional correlations along individual sightlines or three-dimensional correlations between different sightlines. Because these observables probe the matter distribution on very different scales, they have traditionally been analyzed independently. In this work, we bridge this gap using ForestFlow, an emulator trained on a suite of cosmological hydrodynamical simulations that provides a unified description of Lyman-$α$ forest clustering from linear to nonlinear scales. This framework enables us to determine the range of three-dimensional clustering models compatible with the DESI one-dimensional flux power spectrum ($P_{\rm 1D}$). The resulting predictions successfully reproduce the large-scale clustering measured by the DESI BAO analysis and provide physically motivated priors on nonlinear clustering that are used in a companion paper presenting the full-shape analysis of the DESI DR2 Lyman-$α$ forest. We validate our methodology using the large-volume, high-resolution hydrodynamical simulation ACCEL-2, demonstrating excellent agreement across the full range of scales considered. Finally, we combine constraints from the $P_{\rm 1D}$ and BAO analyses on the parameter combinations $b_δσ_8$ and $b_ηf σ_8$, finding that the two probes provide comparable constraining power while exhibiting complementary parameter degeneracies. Our results establish a direct connection between one- and three-dimensional Lyman-$α$ forest measurements through ForestFlow, an approach we term Lyman-$α$ holography by analogy with the reconstruction of higher-dimensional structure from lower-dimensional information.

Chaves-Montero, J. [Barcelona, IFAE] (ORCID:000000↗
DOE OSTI2024

Infrared Optical Anisotropy in Quasi‐1D Hexagonal Chalcogenide BaTiSe 3

Polarimetric infrared (IR) detection bolsters IR thermography by leveraging the polarization of light. Optical anisotropy, i.e., birefringence and dichroism, can be leveraged to achieve polarimetric detection. Recently, giant optical anisotropy is discovered in quasi-1D narrow-bandgap hexagonal perovskite sulfides, A 1+x TiS 3 , specifically BaTiS 3 and Sr 9/8 TiS 3 . In these materials, the critical role of atomic-scale structure modulations in the unconventional electrical, optical, and thermal properties raises the broader question of the nature of other materials that belong to this family. To address this issue, for the first time, high-quality single crystals of a largely unexplored member of the A 1+x TiX 3 (X = S, Se) family, BaTiSe 3 are synthesized. Single-crystal X-ray diffraction determined the room-temperature structure with the P31c space group, which is a superstructure of the earlier reported P6 3 /mmc structure. The crystal structure of BaTiSe 3 features antiparallel c-axis displacements similar to but of lower symmetry than BaTiS 3 , verified by the polarization dependent Raman spectroscopy. Fourier transform infrared (FTIR) spectroscopy is used to characterize the optical anisotropy of BaTiSe 3 , whose refractive index along the ordinary (E ⊥ c) and extraordinary (E ‖ c) optical axes is quantitatively determined by combining ellipsometry studies with FTIR. With a giant birefringence Δn ∼ 0.9, BaTiSe 3 emerges as a new candidate for miniaturized birefringent optics for mid-wave infrared to long-wave infrared imaging.

optical anisotropy↗
DOE OSTI2021

Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU

Synchrophasor measurement devices (SMDs) have been widely deployed to support real-time monitoring and control of power systems. In the meantime, data spoofing has emerged in recent years. Therefore, it is of great importance to study data authentication algorithms for detecting and defending the data spoofing effectively. Here, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract temporal signatures hidden in frequency, voltage angle and amplitude data; then the gated recurrent unit (GRU) employs these temporal signatures for data source authentication. In case studies, the performances of different algorithms are tested in large-scale power systems with numerous SMDs for the first time, and comparisons among different algorithms show that the proposed algorithm can achieve a higher accuracy of data source authentication with a shorter time window.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Design and simulation of n -type solar cells based on an iodine-doped CdTe absorber using SCAPS-1D

The performance of conventional p-type CdTe solar cells has plateaued in recent years, motivating exploration of n-type absorbers. Here, we evaluate a homojunction solar cell employing iodine-doped CdTe (CdTe:I) as the absorber in a Ti 3 C 2 T x MXene/p-CdTe:As/n-CdTe:I/indium structure through SCAPS-1D simulations and prototype devices. Optimized simulations predict efficiencies above 25% for thin CdTe:I absorbers (~0.7 μm). In contrast, the first prototype achieved only ~1.36% efficiency with V OC = 0.48 V, J SC = 6.45 mA/cm 2 and FF = 43.8%. When the simulation is adjusted to match the actual device structure, and the effective illumination is reduced to account for front-side light loss, the predicted V OC (0.48 V) and JSC (6.74 mA/cm 2 ) closely reproduce the experimental values. This suggests that the device performance is primarily limited by reduced front-side photon transmission and other material non-idealities. These results highlight the promise of iodine-doped n-type CdTe and identify clear pathways for further efficiency improvement.

14 SOLAR ENERGY↗
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↗
DOE OSTI2020

1D to 2D Transition in Tellurium Observed by 4D Electron Microscopy

A new microwave-enhanced synthesis method for the production of tellurium nanostructures is reported here—with control over products from the 1D regime (sub-5 nm diameter nanowires), to nanoribbons, to the 2D tellurene regime—along with a new methodology for local statistical quantification of the crystallographic parameters of these materials at the nanometer scale. Using a direct electron detector and image-corrected microscope, large and robust 4D scanning transmission electron microscopy datasets for accurate structural analysis are obtained. These datasets allow the adaptation of quantitative techniques originally developed for X-ray diffraction (XRD) refinement analyses to transmission electron microscopy, enabling the first demonstration of sub-picometer accuracy lattice parameter extraction while also obtaining both the size of the coherent crystallite domains and the nanostrain, which is observed to decrease as nanowires transition to tellurene. This new local analysis is commensurate with global powder XRD results, indicating the robustness of both the new synthesis approach and new structural analysis methodology for future scalable production of 2D tellurene and characterization of nanomaterials.

2D materials↗
DOE OSTI2023

Ti‐Modified Imogolite Nanotubes as Promising Photocatalyst 1D Nanostructures for H 2 Production

Imogolite nanotubes (INTs) are predicted as a unique 1D material with spatial separation of conduction and valence band edges but their large band gaps have inhibited their use as photocatalysts. The first step toward using these NTs in photocatalysis and exploiting the polarization-promoted charge separation across their walls is to reduce their band gap. Here, the modification of double-walled aluminogermanate INTs by incorporation of titanium into the NT walls is explored. The precursor ratio x = [Ti]/([Ge]+[Ti]) is modulated between 0 and 1. Structural and optical properties are determined at different scales and the photocatalytic performance is evaluated for H 2 production. Although the incorporation of Ti atoms into the structure remains limited, the optimal condition is found around x = 0.4 for which the resulting NTs reveal a remarkable hydrogen production of ≈1500 µmol g −1 after 5 h for a noble metal-free photocatalyst, a 65-fold increase relative to a commercial TiO 2 -P25. This is correlated to a lowering of the recombination rate of photogenerated charge carriers for the most active structures. These results confirm the theoretical predictions regarding the potential of modified INTs as photoactive nanoreactors and pave the way for investigating and exploiting their polarization properties for energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021

Abstract Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.

54 ENVIRONMENTAL SCIENCES↗