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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Measurements of Cyclotron Features and Pulse Periods in the High-Mass X-Ray Binaries 4U 1538-522 and 4U 1907+09 with the International Gamma-Ray Astrophysics Laboratory

We present a spectral and timing analysis of International Gamma-Ray Astrophysics Laboratory (INTEGRAL) observations of two high-mass X-ray binaries, 4U 1538−522 and 4U 1907+09. Our timing measurements for 4U 1538−522 find the pulse period to have exhibited a spin-up trend until approximately 2009, after which there is evidence for a torque reversal, with the source beginning to spin down to the most recently measured period of 525.407 plus or minus 0.001 seconds. The most recent INTEGRAL observations of 4U 1907+09 are not found to yield statistically significant pulse periods due to the significantly lower flux from the source compared with 4U 1538−522. A spectral model consisting of a power-law continuum with an exponential cutoff and modified by two cyclotron resonance scattering features is found to fit both sources well, with the cyclotron scattering features detected at approximately 22 and approximately 49 kiloelectronvolts for 4U 1538−522 and at approximately 18 and approximately 36 kiloelectronvolts for 4U 1907+09. The spectral parameters of 4U 1538−522 are generally not found to vary significantly with flux and there is little to no variation across the torque reversal. Examining our results in conjunction with previous work, we find no evidence for a correlation between cyclotron line energy and luminosity for 4U 1538−522. 4U 1907+09 shows evidence for a positive correlation between cyclotron line energy and luminosity, which would make it the fourth, and lowest luminosity, cyclotron line source to exhibit this relationship.

stars: magnetic field↗
NASA NTRS2016

Tapping the Brake for Entry, Descent, and Landing

A matrix of simulations of hypersonic flow over blunt entry vehicles with steady and pulsing retropropulsion jets is presented. Retropropulsion in the supersonic domain is primarily designed to reduce vehicle velocity directly with thrust. Retropropulsion in the hypersonic domain may enable significant pressure recovery through unsteady, oblique shocks while providing a buffer of reactant gases with relatively low total temperature. Improved pressure recovery, a function of Mach number squared and oblique shock angle, could potentially serve to increase aerodynamic drag in this domain. Pulsing jets are studied to include an additional degree of freedom to search for resonances in an already unsteady flow domain with an objective to maximize the time-averaged drag coefficient. In this paradigm, small jets with minimal footprints of the nozzle exit on the vehicle forebody may be capable of delivering the requisite perturbations to the flow. Simulations are executed assuming inviscid, symmetric flow of a perfect gas to enable a rapid assessment of the parameter space (nozzle geometry, plenum conditions, jet pulse frequency). The pulsed-jet configuration produces moderately larger drag than the constant jet configuration but smaller drag than the jet-off case in this preliminary examination of a single design point. The fundamentals of a new algorithm for this challenging application with time dependent, interacting discontinuities using the feature detection capabilities of Walsh functions are introduced.

Gnoffo, Peter A.↗
NASA NTRS2016

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.

Coltin, Brian↗
NASA NTRS2016

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee,a new free-flying robot designed to navigate autonomously on board the International Space Station (ISS). Astrobee will conduct experiments in microgravity, as well as assisst astronauts and ground controllers. Astrobee replaces the SPHERES robots which currently operate on the ISS, which were limited to operating in a small cube since their localization system relied on triangulation from ultrasonic transmitters. Astrobee localizes with only monocular vision and an IMU, enabling it to traverse the entire US segment of the station. Features detected on a previously-built map, optical flow information,and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise.Finally, we extensively evaluate the behavior of the filter on atwo-dimensional testing surface.

Coltin, Brian↗
NASA NTRS2015

Gear Tooth Wear Detection Algorithm

Vibration-based condition indicators continue to be developed for Health Usage Monitoring of rotorcraft gearboxes. Testing performed at NASA Glenn Research Center have shown correlations between specific condition indicators and specific types of gear wear. To speed up the detection and analysis of gear teeth, an image detection program based on the Viola-Jones algorithm was trained to automatically detect spiral bevel gear wear pitting. The detector was tested using a training set of gear wear pictures and a blind set of gear wear pictures. The detector accuracy for the training set was 75 percent while the accuracy for the blind set was 15 percent. Further improvements on the accuracy of the detector are required but preliminary results have shown its ability to automatically detect gear tooth wear. The trained detector would be used to quickly evaluate a set of gear or pinion pictures for pits, spalls, or abrasive wear. The results could then be used to correlate with vibration or oil debris data. In general, the program could be retrained to detect features of interest from pictures of a component taken over a period of time.

Delgado, Irebert R.↗
NASA NTRS2019

Confirming Interstellar C60(+) Using the Hubble Space Telescope

Recent advances in laboratory spectroscopy lead to the claim of ionized Buckminsterfullerene (C60(+)) as the carrier of two diffuse interstellar bands (DIBs) in the near-infrared. However, irrefutable identification of interstellar C60(+) requires a match between the wavelengths and the expected strengths of all absorption features detectable in the laboratory and in space. Here we present Hubble Space Telescope (HST) spectra of the region covering the C60(+) 9348, 9365, 9428, and 9577 Å absorption bands toward seven heavily reddened stars. We focus in particular on searching for the weaker laboratory C60(+) bands, the very presence of which has been a matter for recent debate. Using the novel STIS-scanning technique to obtain ultra-high signal-to-noise spectra without contamination from telluric absorption that afflicted previous ground-based observations, we obtained reliable detections of the (weak) 9365, 9428 Å and (strong) 9577 Å C60(+) bands. The band wavelengths and strength ratios are sufficiently similar to those determined in the latest laboratory experiments that we consider this the first robust identification of the 9428 Å band, and a conclusive confirmation of interstellar C60(+).

Cordiner, M. A.↗
NASA NTRS2021

On the origin & thermal stability of Arrokoth's and Pluto's ice

In this paper we discuss in a thermodynamic, geologically empirical way the long-term nature of the stable majority ices that could be present in Kuiper Belt object (KBO) 2014 MU69 (also called Arrokoth; hereafter “MU(69)”) after its 4.6 Gyr residence in the Edgeworth-Kuiper belt (EKB) as a cold classical object. We compare the upper bounds for the gas production rate (~10^(24) molecules/s) measured by the New Horizons (NH) spacecraft flyby on 01 Jan 2019 to estimates for the outgassing flux rates from a suite of common cometary and KBO ices at the average ~ 40 K sunlit surface temperature of MU69, but do not find the upper limit very constraining except for the most volatile of species (e.g. CO, N2, CH4). More constraining is the stability versus sublimation into vacuum requirement over Myr to Gyr, and from this we find only 3 common ices that are truly refractory: HCN, CH3OH, and H2O (in order of increasing stability), while NH3 and H2CO ices are marginally stable and may be removed by any positive temperature excursions in the EKB, as produced every 10^(8)–10^(9) years by nearby supernovae and passing O/B stars. To date the NH team has reported the presence of abundant CH3OH and H2O on MU69's surface (Stern et al., 2019; Grundy et al., 2020). NH3 has been searched for, but not found. We predict that future absorption feature detections, if any are ever derived from higher signal-to-noise ratio spectra, will be due to an HCN or poly-H2CO based species. Consideration of the conditions present in the EKB region during the formation era of MU69 lead us to state that it is highly likely that it “formed in the dark”, in an optically thick mid-plane, unable to see the nascent, variable, highly luminous Young Stellar Object (YSO)/TTauri Sun, and that KBOs contain HCN and CH3OH ice phases in addition to the H2O ice phases found in their short period (SP) comet descendants. Finally, when we apply our ice thermal stability analysis to bodies/populations related to MU69, we find that methanol ice is likely ubiquitous in the outer solar system; that if Pluto isn't a fully differentiated body, then it must have gained its hypervolatile ices from proto-planetary disk (PPD) sources in the first few Myr of the solar system's existence; and that hypervolatile rich, highly primordial comet C/2016 R2 was placed onto an Oort Cloud orbit on a similar few Myr timescale.

Pluto↗
NASA NTRS2022

Mitigation Strategies for the Impact of Low Energy Laser Pulses in CALIOP Calibration and Level 2 Retrievals

Since mid-2016, the frequency of low energy laser pulses emitted by the CALIPSO lidar has been slowly increasing due to pressure losses in the canister housing the laser. While originally confined primarily to the South Atlantic Anomaly (SAA) region, these low energy pulses now occur intermittently around the globe. Low energy pulses can cause calibration biases and degrade the science quality of level 2 retrievals. We describe a new low energy mitigation (LEM) algorithm that will be implemented incrementally in future versions of the CALIOP data processing to identify and reject affected profiles during calibration and feature detection. The LEM algorithm effectively eliminates low energy calibration biases, improves level 2 retrievals, and minimizes level 2 data loss.

Jason Tackett↗
NASA NTRS2018

Variability Timescale and Spectral Index of Sgr A* in the Near Infrared: Approximate Bayesian Computation Analysis of the Variability of the Closest Supermassive Black Hole

Sagittarius A* (Sgr A* ) is the variable radio, near-infrared (NIR), and X-ray source associated with accretion onto the Galactic center black hole. We present an analysis of the most comprehensive NIR variability data set of Sgr A* to date: eight 24 hr epochs of continuous monitoring of Sgr A* at 4.5 μm with the IRAC instrument on the Spitzer Space Telescope, 93 epochs of 2.18 μm data from Naos Conica at the Very Large Telescope, and 30 epochs of 2.12 μm data from the NIRC2 camera at the Keck Observatory, in total 94,929 measurements. A new approximate Bayesian computation method for fitting the first-order structure function extracts information beyond current fast Fourier transformation (FFT) methods of power spectral density (PSD) estimation. With a combined fit of the data of all three observatories, the characteristic coherence timescale of Sgr A* is b 243 57 82 t = - + minutes (90% credible interval). The PSD has no detectable features on timescales down to 8.5 minutes (95% credible level), which is the ISCO orbital frequency for a dimensionless spin parameter a = 0.92. One light curve measured simultaneously at 2.12 and 4.5 μm during a low flux-density phase gave a spectral index αs = 1.6 ± 0.1 (F µ n s n -a ). This value implies that the Sgr A* NIR color becomes bluer during higher flux-density phases. The probability densities of flux densities of the combined data sets are best fit by log-normal distributions. Based on these distributions, the Sgr A* spectral energy distribution is consistent with synchrotron radiation from a nonthermal electron population from below 20 GHz through the NIR.

accretion↗
NASA NTRSDate not supplied

Investigating Photogrammetric Accuracy of a Lunar-lander-induced Crater Measurement System

Laboratory measurements have been made to validate the performance of the Stereo CAmeras for Lunar Plume-Surface Studies (SCALPSS) stereo photogrammetry systems which will be flying to the moon on two of NASA’s upcoming Commercial Lunar Payload Services (CLPS) missions. Until recently, the system’s accuracy had only been studied using idealized geometric shapes as measurement targets. A realistic crater model of representative scale and an idealized ‘staircase’ target have been used to compare measurement accuracy of ideal versus lunar-like objects, with the commercial V-STARS® system being used to provide the known reference values for comparison. In a parametric study, altitude, lens focal length, and camera separation are varied to assess each parameter’s impact on photogrammetric accuracy in relation to the scaling law prediction developed previously. The SCALPSS 1.0 and 1.1 configurations have been validated on the crater model within acceptable accuracy for the missions, performing significantly better than the scaling law prediction in some cases. A semi-automated post-processing routine was developed in MATLAB® and proved successful for the cross-correlation of features between two stereo images. For some cases of extreme convergence angles between a camera pair, manual feature detection and matching was required. By using this manual process, the crater depth map was reconstructed but with worse accuracy than the idealized staircase measurements; refinements to the processing algorithm are expected to improve future results. Also examined in this work is the impact of illumination environments, both natural (e.g., Sun angles) and artificial (diffuse or structured illumination sources), on the camera system’s ability to measure the erosion of the lunar terrain.

Plume-surface interaction↗
NASA NTRSDate not supplied

Investigating Photogrammetric Accuracy of a Lunar-lander-induced Crater Measurement System

Laboratory measurements have been made to validate the performance of the Stereo CAmeras for Lunar Plume-Surface Studies (SCALPSS) stereo photogrammetry systems which will be flying to the moon on two of NASA’s upcoming Commercial Lunar Payload Services (CLPS) missions. Until recently, the system’s accuracy had only been studied using idealized geometric shapes as measurement targets. A realistic crater model of representative scale and an idealized ‘staircase’ target have been used to compare measurement accuracy of ideal versus lunar-like objects, with the commercial V-STARS® system being used to provide the known reference values for comparison. In a parametric study, altitude, lens focal length, and camera separation are varied to assess each parameter’s impact on photogrammetric accuracy in relation to the scaling law prediction developed previously. The SCALPSS 1.0 and 1.1 configurations have been validated on the crater model within acceptable accuracy for the missions, performing significantly better than the scaling law prediction in some cases. A semi-automated post-processing routine was developed in MATLAB® and proved successful for the cross-correlation of features between two stereo images. For some cases of extreme convergence angles between a camera pair, manual feature detection and matching was required. By using this manual process, the crater depth map was reconstructed but with worse accuracy than the idealized staircase measurements; refinements to the processing algorithm are expected to improve future results. Also examined in this work is the impact of illumination environments, both natural (e.g., Sun angles) and artificial (diffuse or structured illumination sources), on the camera system’s ability to measure the erosion of the lunar terrain.

Plume-surface interaction↗
NASA NTRSDate not supplied

Foundation AI Models for Science

Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be built on any type of sequence data. The use of self supervised learning removes the hurdle for developing a large labeled dataset for training. Most FM use transformer architecture utilizes the notion of self attention which allows the network to model the influence of distant data points to each other both in space and time. The FM models exhibit emergent properties that are induced from the data. FM can be an important tool for science. The scale of these models results in better performance for different downstream applications and these applications show better accuracy over models built from scratch. FM drastically reduces the cost of entry to build different downstream applications both in time and effort. FM for selected science datasets such as optical satellite data, can accelerate applications ranging from data quality monitoring, feature detection and prediction. FM can make it easier to infuse AI into scientific research by removing the training data bottleneck and increasing the use of science data.

Manil Maskey↗
NASA NTRSDate not supplied

A Procedure to Quantify Interlaminar Damage in Refractory Composites by In-Situ Micro-X-Ray Computed Tomography

Refractory composites are relevant for hot-structure applications on re-entry vehicles. To develop optimized high-performance hot structures, accurate material property data is required. Material properties that often limit design in two-dimensional (2-D) composites are interlaminar strengths. A better understanding of the failure mechanisms for interlaminar shear (ILS), interlaminar tension (ILT), and curved-beam interlaminar tension (CB-ILT) tests can provide insight on improving the design and fabrication of refractory composites. In-situ tests were performed to capture the progressive failure in American Society for Testing and Materials (ASTM) sized ILS (ASTM D3846), ILT (ASTM D7291), and CB-ILT (ASTM D6415) specimens. Before measuring internal damage of refractory composites specimens, the methodology of the test setup warrants a complete and thorough discussion. Detail on the test procedure, test stand design, micro-x-ray computed tomography (CT) configuration, data processing, and CT system uncertainty is discussed. Through keeping the many parameters of the CT system consistent, the uncertainty of the CT system was minimized, optimizing the quality of measurements. The overall quality of a CT scan can be quantified by three metrics: voxel resolution, minimal voxel resolution feature detection, and directional variability. A better understanding of the progressive failure mechanisms of refractory composites is provided by the results of both the present discussion and the one found in the accompanying paper titled “Analysis of Interlaminar Damage in Refractory Composites by In-Situ Micro-X-Ray Computed Tomography”.

In-situ micro-x-ray CT↗
DOE OSTI2021

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗
DOE OSTI2025

Advanced Interactive 3D Visualization Tool for Customizable Analyses of Tomography Datasets in Material Science

Current methods for visualizing and analyzing 3D tomography datasets in materials science often lack the interactivity and depth required for detailed structural insights. This limitation restricts a researchers' ability to accurately interpret complex data, which is critical for advancing material innovations and understanding structural properties. To address this issue, we have developed a novel, web-based interactive 3D visualization and analysis tool from the Trame framework that offers customizable features to enhance data interpretability. The tool allows users to adjust parameters such as visible range, slice planes, data rotation, and layering, providing a more detailed and dynamic view of complex structures. Its user-friendly web interface increases the accessibility and ease of use for both novice and experienced researchers, to visualize large volumetric datasets. The tool supports a diverse range of data formats, making it versatile for various research applications. Unique capabilities include real-time data manipulation, automated feature detection, context-sensitive feedback, and real-time volume calculations and distributions per sliced region or layer, alongside the ability to quickly generate high-quality screenshots and videos for presentations and reports. These advancements offer a comprehensive solution for enhanced 3D data exploration, significantly improving the analysis process and communication of results in materials science.

36 - MATERIALS SCIENCE↗
NASA NTRS1986

Space Shuttle cloud detection and earth feature classification experiment

The Feature Identification and Location Experiment (FILE) that is being designed for the detection and classification of four primary earth features (water, vegetation, bare land, and the clouds-snow-ice class) is described. Consideration is given to the FILE classification technology concept and the FILE instrument, which will use two solid-state CCD cameras operating at 0.65 and 0.85-micron center frequency wavelengths, with the camera outputs being functions of the earth surface material radiance. The classification is based on camera output radiance ratio values. The preliminary analysis of the data collected on the STS 41-G mission is discussed. The results demonstrated the suitability of using the two-channel-ratio detection technology and a simple (y = mx) algorithm to autonomously classify the four earth surface features. The technology is especially attractive as a cloud sensor, where, in advance of or during a mission, a threshold value for cloud cover percentage can be programmed and/or adaptively modified for use in the control of other remote sensors.

Sivertson, W. E., Jr.↗
NASA NTRS1990

Possible detection of an emission feature near 584 A in the direction of G191-B2B

A possible spectral emission feature is reported in the direction of the nearby hot white dwarf G191-B2B at 581.5 + or - 6 A with a significance of 3.8 sigma. This emission has been identified as He I 584.3 A. The emission cannot be due to local geocoronal emission or interplanetary backscatter of solar He I 584 A emission because the feature is not detected in a nearby sky exposure. Possible sources for this emission are examined, including the photosphere of G191-B2B, the comparison star G191-B2A, and a possible nebulosity near or around G191-B2B. The parameters required to explain the emission are derived for each case. All of these explanations require unexpected physical conditions; hence we believe this result must receive confirming verification despite the statistical likelihood of the detection.

Green, James↗
DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗