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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 199 records · Page 11

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NASA NTRS1984

Multiple spacecraft observations of interplanetary shocks Shock-normal oscillations and their effects

Observations of interplanetary shocks with multiple spacecraft and multiple instruments has permitted the determination of their average shock normals with unprecedented accuracy. Nevertheless, there are still local deviations from the best-fit normal. These deviations in general appear to be less than 5 deg but on occasion can be of the order of 20 deg or more. These fluctuating normals and the fluctuating upstream field can cause variations in the downstream field strength even when the upstream field strength is constant. This behavior has important consequences for the appearance of quasi-parallel shocks. Precursor waves which stand on the shock ramp and do not propagate or are not convected across the shock front do not affect the downstream field.

Russell, C. T.↗

NASA NTRS1995

Geomagnetic transmission of solar energetic protons during the geomagnetic disturbances of October 1989

Orbit-averaged geomagnetic transmission measurements during the large solar energetic particle events of October 1989 are presented using proton data from the NOAA-10 and GOES-7 satellies. The measurements are compared to geomagnetic transmission calculations determined by tracing particle trajectories through the combination of the International Geomagnetic Reference Field (IGRF) model and the 1989 Tsyganenko magnetospheric magnetic field model. The effective 'ring current' parameter in the 1989 Tsyganenko model based on the Dst data. Results are compared to calculations employing only the IGRF and to a parameterization of geomagnetically quiet-time cutoff rigidities derived from Cosmos/intercosmos observations. The 3-hour orbit-averaged results have approximately 15% accuracy during the October 1989 events.

Boberg, P. R.↗

NASA NTRS1999

Methane concentration and isotopic composition measurements with a mid-infrared quantum-cascade laser

A quantum-cascade laser operating at a wavelength of 8.1 micrometers was used for high-sensitivity absorption spectroscopy of methane (CH4). The laser frequency was continuously scanned with current over more than 3 cm-1, and absorption spectra of the CH4 nu 4 P branch were recorded. The measured laser linewidth was 50 MHz. A CH4 concentration of 15.6 parts in 10(6) ( ppm) in 50 Torr of air was measured in a 43-cm path length with +/- 0.5-ppm accuracy when the signal was averaged over 400 scans. The minimum detectable absorption in such direct absorption measurements is estimated to be 1.1 x 10(-4). The content of 13CH4 and CH3D species in a CH4 sample was determined.

Non-NASA Center↗

NASA NTRS2005

Space-based Swath Imaging Laser Altimeter for Cryospheric Topographic and Surface Property Mapping

Uncertainties in the response of the Greenland and Antarctic polar ice sheets to global climatic change inspired the development of ICESat/GLAS as part of NASA's Earth Observing System. ICESat's primary purpose is the measurement of ice sheet surface elevation profiles with sufficient accuracy, spatial density, and temporal coverage so that elevation changes can be derived with an accuracy of <1.5 cm/year for averages of measurements over the ice sheets with areas of 100 x 100 km. The primary means to achieve this elevation change detection is spatial averaging of elevation differences at cross-overs between ascending and descending profiles in areas of low ice surface slope. Additional information is included in the original extended abstract.

Abshire, James↗

NASA NTRS2006

Experimental and Computational Analysis of Unidirectional Flow Through Stirling Engine Heater Head

A high efficiency Stirling Radioisotope Generator (SRG) is being developed for possible use in long-duration space science missions. NASA s advanced technology goals for next generation Stirling convertors include increasing the Carnot efficiency and percent of Carnot efficiency. To help achieve these goals, a multi-dimensional Computational Fluid Dynamics (CFD) code is being developed to numerically model unsteady fluid flow and heat transfer phenomena of the oscillating working gas inside Stirling convertors. In the absence of transient pressure drop data for the zero mean oscillating multi-dimensional flows present in the Technology Demonstration Convertors on test at NASA Glenn Research Center, unidirectional flow pressure drop test data is used to compare against 2D and 3D computational solutions. This study focuses on tracking pressure drop and mass flow rate data for unidirectional flow though a Stirling heater head using a commercial CFD code (CFD-ACE). The commercial CFD code uses a porous-media model which is dependent on permeability and the inertial coefficient present in the linear and nonlinear terms of the Darcy-Forchheimer equation. Permeability and inertial coefficient were calculated from unidirectional flow test data. CFD simulations of the unidirectional flow test were validated using the porous-media model input parameters which increased simulation accuracy by 14 percent on average.

Wilson, Scott D.↗

NASA NTRS2002

DSN and GAVRT observations of Jupiter at 13 GHz and the calibration of the Cassini radar instrument for passive radiometry

One objective of the Cassini-Jupiter Microwave Observing Campaign observations was to measure Jupiter's average disk temperature with high accuracy at 13.78 GHz, which is the frequency of the radar receiver on the spacecraft. Preliminary results of the ground-based observations are reported. A second objective of the Cassini-JMOC project included an educational compment that allowed middle- and high school students to participate directly in the ground-based observations and data analysis.

radio astronomy Jupiter radio emission Cassini pas↗

NASA NTRS2012

Energy-dependent Orbital Modulation of X-rays and Constraints on Emission of the Jet in Cyg X-3

We study orbital modulation of X-rays from Cyg X-3, using data from Swift, INTEGRAL and RXTE. Using the wealth of the presently available data and an improved averaging method, we obtain energy-dependent folded and averaged light curves with unprecedented accuracy. We find that above ∼5 keV, the modulation depth decreases with the increasing energy, which is consistent with the modulation being caused by both bound-free absorption and Compton scattering in the stellar wind of the donor, with minima corresponding to the highest optical depth, which occurs around the superior conjunction. We find a decrease of the depth below ∼3 keV, which appears to be due to re-emission of the absorbed continuum by the wind in soft X-ray lines. Based on the shape of the folded light curves, any X-ray contribution from the jet in Cyg X-3, which emits γ-rays detected at energies > 0.1 GeV in soft spectral states, is found to be minor up to ∼100 keV. This implies the presence of a rather sharp low-energy break in the jet MeV-range spectrum.We also calculate phase-resolved RXTE X-ray spectra, and show the difference between the spectra corresponding to phases around the superior and inferior conjunctions can indeed be accounted for by a combined effect of bound-free absorption in an ionized medium and Compton scattering.

Energy-dependent Orbital↗

NASA NTRS2016

Drought Impacts on Agricultural Production and Land Fallowing in California's Central Valley in 2015

The ongoing drought in California substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to mitigation of drought impacts. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and increases in fallow acreage associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. We describe an approach for monthly and seasonal mapping of uncultivated agricultural acreage developed as part of a joint effort by USGS, USDA, NASA, and the California Department of Water Resources to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of uncultivated agricultural acreage from satellite data early in the season, we developed a decision tree algorithm and applied it to time-series data from Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus), OLI (Operational Land Imager), and MODIS (Moderate Resolution Imaging Spectroradiometer). Our effort has been focused on development of indicators of drought impacts in the March-August timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 650 fields from March-September in 2014 and 2015. We present the algorithm along with updated results from the accuracy assessment, and data and maps of land fallowing in the Central Valley in 2015.

Valleys↗

NASA NTRSDate not supplied

Aeroheating Predictions for a Hypersonic, Turbulent Near-Wake

The accuracy of heating predictions using various turbulence models is examined for an axisymmetric near-wake at Mach 6. The CFD predictions are compared with experimental data collected under AGARD Working Group 18 on the wake of a 70-degree sphere-cone. The impact of grid resolution and discretization error is estimated, which allows a comparison of stacked-block and conventional structured meshes. The accuracy of steady Reynolds-averaged Navier-Stokes (RANS) models is contrasted with that of a hybrid RANS/Large-Eddy Simulation model. The predictions are made with three different CFD codes (LAURA, FUN3D, and HyperSolve), to demonstrate the code-to-code variation in the results. Steady SST models capture the qualitative nature of the heating in the wake, including the increasing peak heating with increasing Reynolds number. Spalart-Allmaras models, including SA-Catris, under-predicted the peak heating in the wake. Hybrid RANS/LES models improve upon the SA results but have their own modeling difficulties near the shear layer impingement. These results are generally consistent across solvers and grid topologies.

RANS↗

NASA NTRSDate not supplied

Aeroheating Predictions for a Hypersonic, Turbulent Near-Wake

RANS↗

NASA NTRS1992

NASA Sea Ice Validation Program for the Defense Meteorological Satellite Program Special Sensor Microwave Imager

The history of the program is described along with the SSM/I sensor, including its calibration and geolocation correction procedures used by NASA, SSM/I data flow, and the NASA program to distribute polar gridded SSM/I radiances and sea ice concentrations (SIC) on CD-ROMs. Following a discussion of the NASA algorithm used to convert SSM/I radiances to SICs, results of 95 SSM/I-MSS Landsat IC comparisons for regions in both the Arctic and the Antarctic are presented. The Landsat comparisons show that the overall algorithm accuracy under winter conditions is 7 pct. on average with 4 pct. negative bias. Next, high resolution active and passive microwave image mosaics from coordinated NASA and Navy aircraft underflights over regions of the Beaufort and Chukchi seas in March 1988 were used to show that the algorithm multiyear IC accuracy is 11 pct. on average with a positive bias of 12 pct. Ice edge crossings of the Bering Sea by the NASA DC-8 aircraft were used to show that the SSM/I 15 pct. ice concentration contour corresponds best to the location of the initial bands at the ice edge. Finally, a summary of results and recommendations for improving the SIC retrievals from spaceborne radiometers are provided.

Cavalieri, Donald J.↗

NASA NTRS1994

The accuracy of temperature distributions used to derive the net transport for a zonally averaged model

Comparisons of satellite-derived temperatures with correlative temperatures indicate that the LIMS temperatures are accurate and contain more of the needed vertical resolution for calculating a residual mean circulation for transporting tracer-like species. Generally, the LIMS temperatures are accurate to at least 2 K. Other satellite data sets are comprised of temperatures with coarser vertical resolution, leading to biases that occur with an error pattern that is characteristic of their resolution. Their biases exceed 2 K at some altitudes. Retrievals of species using an infrared limb emission technique are sensitive to any temperature bias. Generally, the IMS comparisons with other data sets for ozone and water vapor are good to better than 20 percent; this represents an independent confirmation of the quality of LIMS and temperatures. Zonal mean comparisons between LIMS and SAMS temperatures also indicate agreement to better than 2 K from about 7 to 2hPa. Therefore, we are confident that SAMS N2O and CH4 are relatively free of temperature bias in that region. These factors support the generally good agreement in G90 between model N2O transported using a LIMS-derived RMC and the N2O contours from SAMS.

Remsberg, Ellis E.↗

DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗

NASA NTRS1979

The observation and interpretation of the profile of C IV lambda-1548 emitted from a quiet region of the sun

The average properties of the quiet chromospheric network as seen in the transition region line of C IV at 1548 A have been investigated. Line profile data for the study were taken with the OSO 8 High Resolution Ultraviolet Spectrometer, which has moderate angular resolution, high spectral resolution, and good relative photometric accuracy. The profiles, when classified and averaged according to their intensities, were found to be symmetric and Gaussian in shape at all intensity levels. A marginally significant increase in line width with line intensity was detected but the average relative redshift in the network that has been a feature of other OSO 8 studies was not found. The network-to-cell contrast ratio was of the order of 13:1 with the extreme extending to 50.1. The measured average width of 0.22 A (FWHM) is in good agreement with earlier work. Finally, a theoretical interpretation is presented, based on both the line width measurement and consideration of the profile symmetry properties. It is concluded that acoustic waves alone cannot supply enough energy to balance the radiative and conductive losses from the corona. The data are, however, consistent with heating by Alfven waves.

Bruner, E. C., Jr.↗

NASA NTRS1985

Error reduction in laser remote sensing - Combined effects of cross correlation and signal averaging

A systematic analysis is presented of the extent to which the accuracy of a differential-absorption lidar (DIAL) measurement may be improved by using the combined effects of signal averaging and temporal cross correlation. Previous studies which considered these effects separately are extended by incorporating both effects into a single analytical framework. In addition, experimental results involving lidar returns from a diffusely reflecting target using a dual-CO2 laser DIAL system with both heterodyne and direct detection are presented. These results are shown to be in good agreement with the theoretical analysis and help establish the limits of accuracy achievable under various experimental conditions.

Menyuk, N.↗

DOE OSTI2022

Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. Furthermore, this work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.

convolutional neural networks↗

DOE OSTI2026

Developing Science-based fueling protocols for 250-bar hydrogen tanks onboard hydrogen ferries: Experiments and modeling

Combined modeling and experimental studies are reported of the fueling of a large (28 kg capacity) 250-bar Type IV hydrogen tank of the type being deployed on early hydrogen ferries, such as the MV Sea Change. The primary goal was to determine how such tanks can be successfully fueled with hydrogen (state of charge greater than 97%) within 45 minutes without exceeding the 82 °C temperature limit for such tanks. The modeling studies show that a gas injector is needed to avoid thermal stratification during hydrogen fueling which can result in potential hot spots. Empirically, precooling of the hydrogen to 0 °C was found to be needed in some of the cases examined, as ambient conditions greatly affected the need for a precooling to achieve the 45-minute fill time desired by end users. The experimental results afforded a calibration of the engineering model SOFIL for these large 250-bar tanks, which now enables using SOFIL to predict volume-averaged hydrogen fueling temperatures to an accuracy of ±2.7°C for these tanks. The model can therefore be used to evaluate potential scenarios for development of a standardized fueling methodology for ferries utilizing large Type-IV tanks.

08 HYDROGEN↗

DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗