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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Pattern recognition techniques for failure trend detection in SSME ground tests

The Space Shuttle Main Engine (SSME) is a complex power plant. To evaluate its performance 1200 hot-wire ground tests have been conducted, varying in duration from 0 to 500 secs. During the test some 500 sensors are sampled every 20 ms. The sensors are generally bounded by red lines so that an excursion beyond could lead to premature shutdown. In 27 tests it was not possible to effect an orderly premature shutdown, resulting in major incidents with serious damage to the SSME and test stand. The application of pattern recognition are investigated to detect SSME performance trends that may lead to major incidents. Based on the sensor data a set of (n) features is defined. At any time during the test, the state of the SSME is given by a point in the n-dimensional feature space. The history of a test can now be represented as a trajectory in the n-dimensional feature space. Portions of the normal trajectories and failed test trajectories would lie in different regions of the n-dimensional feature space. The latter can now be partitioned into regions of normal and failed tests. Thus, it is possible to examine the trajectory of a test in progress and predict if it is going into the normal or failure region.

Choudry, A.↗
NASA NTRS1992

Optimizing view/illumination geometry for terrestrial features using Space Shuttle and aerial polarimetry

This paper describes to relationship of polarimetric observations from orbital and aerial platforms and the determination optimum sun-target-sensor geometry. Polarimetric observations were evaluated for feature discrimination. The Space Shuttle experiment was performed using two boresighted Hasselblad 70 mm cameras with identical settings with linear polarizing filters aligned orthogonally about the optic axis. The aerial experiment was performed using a single 35 mm Nikon FE2 and rotating the linear polarizing filter 90 deg to acquire both minimum and maximum photographs. Characteristic curves were created by covertype and waveband for both aerial and Space Shuttle imagery. Though significant differences existed between the two datasets, the observed polarimetric signatures were unique and separable.

Israel, Steven A.↗
NASA NTRSDate not supplied

Simulating Micrometeoroid Bombardment on Mercury: Identifying New Space Weathering Features in the Laboratory

Airless bodies are continually exposed to the harsh interplanetary space environment, causing the alteration of their surfaces through a process known as space weathering (SW). SW is dominated by solar wind irradiation and micrometeoroid bombardment, which together alter the spectral, microstructural, and chemical characteristics of grains on the surface of airless bodies. The effects of SW are variable and depend on the heliocentric distance and the initial composition of the planetary surface, among other factors. On the Moon and S- type asteroids, it is well established that SW darkens and reddens the surface of these bodies and attenuates absorption bands across the Vis-NIR wavelengths. These spectral effects are largely the result of the production of metallic Fe nanoparticles (npFe). However, Mercury’s SW environment is unique compared to the Moon and S-type asteroids. Mercury is a geochemical endmember, with a surface composition low in Fe (<2 wt.%) and enriched in volatile components, including regions of the surface hypothesized to contain up to 4 wt.% graphite in the low reflectance material (LRM). In fact, the presence of graphite on Mercury has been hypothesized to act as a reducing agent for silicates during SW to produce Si-bearing Fe-rich metal. In addition, its location in the solar system exposes Mercury to an extreme SW environment, with the surface of the planet experiencing an intense solar wind flux and higher flux and velocity of micrometeoroid impactors compared to the Moon and S-type asteroids. The effects that such harsh SW has on materials with the unique compositional characteristics of Mercury are not well constrained. To better understand SW at Mercury, we must investigate these processes in the laboratory. Here, we present the results of our analyses of the spectral, microstructural and chemical characteristics of Mercury analog samples irradiated by pulsed laser to simulate the short duration, high temperature events associated with micrometeoroid impacts.

Nick Bott↗
NASA NTRSDate not supplied

Space weathering features of anhydrous minerals in fine grains from the C-type asteroid Ryugu

Materials exposed to the space environment are expected to show optically and chemically modified properties. This process is called as space weathering and is caused mainly by micrometeoroid bombardments and solar wind implantation [1]. Thus far, the space weathering of carbonaceous asteroids has not been well understood. Regolith samples were successfully recovered from C-type asteroid Ryugu by the Hayabusa mission [2]. Ryugu samples will provide insights into the ongoing space weathering of Ryugu [3]. In this study, we investigated the space weathering of anhydrous minerals including iron sulfides, magnetite, and carbonates, which are major reservoirs of volatiles including carbon, oxygen, and sulfur in Ryugu materials. We performed scanning electron microscopy (SEM) and transmission/scanning transmission electron microscopy (TEM/STEM) analysis for Ryugu samples.

Toru Matsumoto↗
NASA NTRS2023

Blade-Wake Interaction Noise for Small Hovering Rotors, Part I: Characterization Study

This work illustrates the use of artificial neural network modeling to study and characterize broadband blade-wake interaction noise from hovering small unmanned aerial systems rotors subject to varying airfoil geometries, rotor geometries, and operating conditions. Design of experiments was used to create input feature spaces, and a high-fidelity strategy was implemented at the discrete data points defined by the input feature spaces to design airfoils and rotor blades, predict the unsteady rotor aerodynamics and aeroacoustics, and isolate the blade-wake interaction noise from the acoustic broadband noise. A metric for the blade-wake interaction noise was developed, and the ANOPP2 Artificial Neural Network Tool was used to identify an optimal prediction model for the nonlinear relationship between the input features and the metric for blade-wake interaction noise. This optimal artificial neural network was then validated over training/test data and exhibited prediction accuracy over 91% for data previously unseen by the model. A sensitivity analysis was conducted, which showed that input features that directly modify the thrust coefficient had a dominant effect over blade-wake interaction noise. The optimal prediction model along with aerodynamic simulations were used to further study the effect of varying input features on blade-wake interaction noise, and three types of blade-wake interaction noise were identified

Christopher S. Thurman↗
NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗
NASA NTRS1985

Continental land cover classification using satellite data

Four different approaches to the classification of land cover for whole continents using multitemporal images of the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer of the NOAA series of satellites are discussed. The first approach uses only two dates from different seasons and classification dependent upon subdivision of the resultant two-dimensional feature space by an analyst using a track ball. The second approach involves a similar method of partitioning the feature space, but with the two dimensions being the first and second principal components derived from 13 four-week composite images. The third approach uses the maximum likelihood rule to derive the classified map. In the fourth approach, the amount of deviation from characteristic curves is used as a basis for classification.

Townshend, J. R. G.↗
NASA NTRS2023

Characterizing Space Weathering Features in Grains From Asteroid Ryugu

Airless bodies such as asteroids are characterized by a lack of a protective atmosphere to guard against the effects of micrometeorite bombardment and solar wind irradiation. These processes, cumulatively known as space weathering, alter the microstructural and chemical properties of grains on asteroidal surfaces, signatures of which are recognized as vesiculated textures, amorphous grain rims, and fe-bearing nanoparticles. The accumulation of these features also causes changes in the optical properties of the surface regolith, complicating the interpretation of remote sensing data and the characterization of returned samples.

L. E. Melendez↗
NASA NTRS1991

System requirements and design features of Space Station Remote Manipulator System mechanisms

The Space Station Remote Manipulator System (SSRMS) is a long robotic arm for handling large objects/payloads on the International Space Station Freedom. The mechanical components of the SSRMS include seven joints, two latching end effectors (LEEs), and two boom assemblies. The joints and LEEs are complex aerospace mechanisms. The system requirements and design features of these mechanisms are presented. All seven joints of the SSRMS have identical functional performance. The two LEES are identical. This feature allows either end of the SSRMS to be used as tip or base. As compared to the end effector of the Shuttle Remote Manipulator System, the LEE has a latch and umbilical mechanism in addition to the snare and rigidize mechanisms. The latches increase the interface preload and allow large payloads (up to 116,000 Kg) to be handled. The umbilical connectors provide power, data, and video signal transfer capability to/from the SSRMS.

Kumar, Rajnish↗
NASA NTRS1992

Small-scale fracture patterns on the volcanic plains of Venus

Linear features with a regular spacing of about 1 km in the gridded plains of Guinevere Planitia observed in Magellan radar images of Venus are examined. Many sets of parallel, regularly spaced lineations similar to the closely spaced features of the gridded plains were found. These sets of parallel lineations, which are interpreted to be fractures, typically cover areas with dimensions of hundreds of kilometers. Several examples of these regular lineations are characterized in terms of their average fracture spacing, which is between 1 and 2.5 km; the scatter in individual spacings is about +/- 1/3 the average. It is hypothesized that these features are extension fractures in the brittle upper layers of the volcanic plains material, which formed well after emplacement and solidification of the flows.

Banerdt, W. B.↗
NASA NTRSDate not supplied

Anomaly Detection in Flight Operational Data Using Deep Learning

In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].

anomaly detection↗
NASA NTRS1988

Processing Welding Images For Robot Control

Image data from two distinct windows used to locate weld features. Analyzer part of vision system described in companion article, "Image Control in Automatic Welding Vision System" (MFS-26035). Horizontal video lines define windows for viewing unwelded joint and weld pool. Data from picture elements outside windows not processed. Widely-separated local features carry no significance, but closely spaced features indicate welding feature. Image processor assigns confidence level to group of local features according to spacing and pattern.

Richardson, Richard W.↗
NASA NTRS2005

An Ensemble Approach to Building Mercer Kernels with Prior Information

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Srivastava, Ashok N.↗
NASA NTRS1984

Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation

The techniques used initially for the identification of cultivated crops from Landsat imagery depended greatly on the iterpretation of film products by a human analyst. This approach was not very effective and objective. Since 1978, new methods for crop identification are being developed. Badhwar et al. (1982) showed that multitemporal-multispectral data could be reduced to a simple feature space of alpha and beta and that these features would separate corn and soybean very well. However, there are disadvantages related to the use of alpha and beta parameters. The present investigation is concerned with a suitable method for extracting the required features. Attention is given to a profile model for crop discrimination, corn-soybean separation using profile parameters, and an automatic labeling (target recognition) method. The developed technique is extended to obtain a procedure which makes it possible to estimate the crop proportion of corn and soybean from Landsat data early in the growing season.

Badhwar, G. D.↗
NASA NTRS2004

Putting Priors in Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Srivastava, Ashok N.↗
NASA NTRS1973

The Space Shuttle Main Engine and its maintenance features

The Space Shuttle Main Engine (SSME) is a reusable, high-performance rocket engine being developed to satisfy the performance, life, reliability, and operational requirements of the Space Shuttle Orbiter. The design includes simple, low-cost maintenance features resulting from a viable maintainability program dedicated to minimizing engine cost per flight.

Wheelock, V. J.↗
NASA NTRS1976

Classification improvement by optimal dimensionality reduction when training sets are of small size

A computer simulation was performed to test the conjecture that, when the sizes of the training sets are small, classification in a subspace of the original data space may give rise to a smaller probability of error than the classification in the data space itself; this is because the gain in the accuracy of estimation of the likelihood functions used in classification in the lower dimensional space (subspace) offsets the loss of information associated with dimensionality reduction (feature extraction). A number of pseudo-random training and data vectors were generated from two four-dimensional Gaussian classes. A special algorithm was used to create an optimal one-dimensional feature space on which to project the data. When the sizes of the training sets are small, classification of the data in the optimal one-dimensional space is found to yield lower error rates than the one in the original four-dimensional space.

Starks, S. A.↗