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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

EMI testing adequacy and flight anomaly history

The effects of electromagnetic interference (EMI) and the space environment on the performance of long-duration spacecraft hardware are surveyed, and the implications for prelaunch test procedures are indicated. Topics addressed include the need for onboard environmental monitoring devices to aid in the identification of environmentally induced anomalies, environmental modeling methods, the existing spacecraft-anomaly data bases, electrostatic discharges, radiation effects, magnetic-field effects, and terrestrial sources of EMI. Statistical data on EMI-induced anomalies are presented in graphs and discussed in detail. It is recommended that testing beyond the current MIL-STD levels be performed on equipment for long-term missions such as the Space Station.

Kuberry, Richard W.↗
NASA NTRS1973

Electron-beam studies of Schottky-barrier detector surfaces

Review of the surface anomalies occurring in Schottky-barrier particle detectors identifiable by means of an electron beam technique employed by Czaja (1965) for analyzing defects in diode structures. The technique is shown to make possible the detection and identification of the following anomalies: (1) chemical contamination of the detector surface; (2) mechanical damage of the wafer substrates; (3) damage introduced in semiconductor surface preparation; (4) radiation damage; and (5) defective surface metallization.

Peckerar, M. C.↗
NASA NTRS1993

Regional mantle upwelling on Venus: The Beta-Atla-Themis anomaly and correlation with global tectonic patterns

The morphology and global distribution of volcanic centers and their association with other geological characteristics offers significant insight into the global patterns of geology, tectonic style, thermal state, and interior dynamics of Venus. Magellan data permit the detailed geological interpretation necessary to address questions about interior dynamics of Venus particularly as they reflect relatively physical, chemical, and thermal conditions of the interior. This paper focuses on the distribution of anomalous concentrations of volcanic centers on Venus and regional patterns of tectonic deformation as it may relate to the identification of global internal anomalies, including mantle dynamic, petrological, or thermal patterns.

Crumpler, L. S.↗
NASA NTRS2023

A Multi-Satellite Framework to Rapidly Evaluate Extreme Biosphere Cascades: The Western US 2021 Drought and Heatwave

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low-latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1–2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO 2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated 28 independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

Causal model↗
NASA NTRS2004

Identification of Interplanetary Coronal Mass Ejections at 1 AU Using Multiple Solar Wind Plasma Composition Anomalies

We investigate the use of multiple simultaneous solar wind plasma compositional anomalies, relative to the composition of the ambient solar wind, for identifying interplanetary coronal mass ejection (ICME) plasma. We first summarize the characteristics of several solar wind plasma composition signatures (O(+7)/O(+6), Mg/O, Ne/O, Fe charge states, He/p) observed by the ACE and WIND spacecraft within the ICMEs during 1996 - 2002 identsed by Cane and Richardson. We then develop a set of simple criteria that may be used to identify such compositional anomalies, and hence potential ICMEs. To distinguish these anomalies from the normal variations seen in ambient solar wind composition, which depend on the wind speed, we compare observed compositional signatures with those 'expected' in ambient solar wind with the same solar wind speed. This method identifies anomalies more effectively than the use of fixed thresholds. The occurrence rates of individual composition anomalies within ICMEs range from approx. 70% for enhanced iron and oxygen charge states to approx. 30% for enhanced He/p (> 0.06) and Ne/O, and are generally higher in magnetic clouds than other ICMEs. Intervals of multiple anomalies are usually associated with ICMEs, and provide a basis for the identification of the majority of ICMEs. We estimate that Cane and Richardson, who did not refer to composition data, probably identitied approx. 90% of the ICMEs present. However, around 10% of their ICMEs have weak compositional anomalies, suggesting that the presence of such signatures does not provide a necessary requirement for an ICME. We note a remarkably similar correlation between the Mg/O and O(7)/O(6) ratios in hourly-averaged data both within ICMEs and the ambient solar wind. This 'universal' relationship suggests that a similar process (such as minor ion heating by waves inside coronal magnetic field loops) produces the first-ionization potential bias and ion freezing-in temperatures in the source regions of both ICMEs and the ambient solar wind.

Richardson, I. G.↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗
NASA NTRS1976

Apollo-Soyuz Doppler-tracking experiment MA-089

The Doppler tracking experiment was designed to test the feasibility of improved mapping of the earth's gravity field by the low-low satellite-to-satellite tracking method and to observe variations in the electron density of the ionosphere between the two spacecraft. Data were taken between 1:01 and 14:37 GMT on July 24, 1975. Baseline data taken earlier, while the docking module was still attached to the command and service module, indicated that the equipment operated satisfactorily. The ionospheric data contained in the difference between the Doppler signals at the two frequencies are of excellent quality, resulting in valuable satellite-to-satellite observations, never made before, of wave phenomena in the ionosphere. The gravity data were corrupted by an unexpectedly high noise level of as-yet-undetermined origin, with periods greater than 150 seconds, that prevented unambiguous identification of gravity-anomaly signatures.

Weiffenbach, G. C.↗
NASA NTRS1985

An analysis of the Space Shuttle hypersonic entry trim anomaly

This paper reviews a parameter identification methodology developed to investigate the hypersonic longitudinal trim misprediction apparent in the NASA Space Shuttle Orbiter entry flights. The method combines an analysis using a measured versus predicted technique in conjunction with a multilinear regression analysis to identify prediction deficiencies using quasi-static longitudinal data in the hypersonic flight regime (Mach 6 through 26). In general, the results of this extraction confirm results previously obtained by other Shuttle investigators with the exception of elevon effectiveness. Further analysis and/or flight data will be required to resolve the conflicting elevon results. A combination of this analytical tool and other flight data will enable flight data interpretation with the potential for identifying the sources of the Shuttle's hypersonic trim misprediction to an accuracy consistent with updating preflight prediction methodology for future spacecraft.

Young, J. C.↗
NASA NTRS2021

Time series comparisons in Deep Space Network

The Deep Space Network (DSN) is NASA’s international array of antennas that support interplanetary spacecraft missions. DSN provides radar and radio astronomy observations that enhance our understanding of the solar system and the larger universe. A track is a block of continuous multi-dimensional time series from the beginning to end of DSN communication with the target spacecraft, containing 129 monitor data items lasting several hours at a frequency of 0.2-1Hz. Monitor data on each track reports on the performance of specific spacecraft operations and the DSN itself. DSN is receiving signals from 32 spacecraft across the solar system. DSN has pressure to reduce costs while maintaining the quality of support for DSN mission users. DSN operators need to simultaneously monitor multiple tracks and identify anomalies in real time. DSN has seen that as the number of missions increases, the data that needs to be processed increases over time. In this project, we look at the last 8 years of data for analysis. Any anomaly in the track indicates a problem with either the spacecraft, DSN equipment, or weather conditions. DSN operators typically write “discrepancy reports” for further analysis. It is recognized that it would be quite helpful to identify 10 similar historical tracks out of the huge database to quickly find/match anomalies. This tool has three functions: (1) identification of the top 10 similar historical tracks, (2) detection of anomalies compared to the reference normal track, and (3) comparison of statistical differences between two given tracks. The requirements for these features were confirmed by survey responses from 21 DSN operators and engineers. The preliminary machine learning model has shown promising performance (AUC=0.92). We plan to increase the number of data sets and perform additional testing to improve performance further before its planned integration into the Track Visualizer to assist DSN field operators and engineers.

Rebbapragada, Umaa↗
NASA NTRS1994

Separation of lithospheric, external, and core components of the south polar geomagnetic field at satellite altitudes

We present a new approach to producing scalar Magsat magnetic anomaly maps based on correlation coefficient filtering and the use of almost all of the available orbits. Our method differs from earlier techniques with respect to the following: (1) Passes are selected based on their variance properties rather than planetary indices such as Kp. (2) The core field model is least squares fit to individual passes and subsequently removed instead of substracting the model directly. This technique replaces band pass filtering or polynomial trend removal methods. (3) Each selected pass is sorted geographically and by local time, placed into one of four different altitude bands, and correlation coefficient filtered with the two adjacent passes. The filtering is the second step toward isolating the static lithospheric signal from the more dynamic external field signals. (4) Least squares collocation is used to grid the correlated passes; subsequently, the dawn and dusk maps are also correlation filtered providing another step toward removal of external fields. (5) The four resultant total field maps are continued to a common altitude and again correlation filtered for the static lithospheric anomalies. (6) The filtered results are then averaged together to provide a new total field map of the lithosphere south of 40 deg S latitude. Our total field map differs from previous efforts over the crustal blocks of West Antarctica. We obtained a positive anomaly over Edward VII Peninsula, extending into the Byrd subglacial basin and obtained a negative anomaly over the Ellsworth Mountains and parts of the Byrd subglacial basin. Also, a positive anomaly extending from the Ross Sea to offshore Wilkes Land is present in our map; however, this feature is absent in other maps. Positive anomalies marking the Weddell Sea in previous efforts are not present in our map. Prominent external field anomalies in the quadrant offshore of Wilkes Land are present in all previous efforts; however, these signatures are reduced in our total field map. This map significantly advances efforts for the identification and interpretation of magnetic anomalies of the south polar lithosphere.

Alsdorf, Douglas E.↗
NASA NTRS1984

Systematic data interpretation of remote sensing in the reception of hydrocarbons, volume 1

The utilization of MSS-LANDSAT and RADAR imagery in the definition of morphostructural anomalies, which are indicative of hydrocarbon entrapment sites in the limit of the Middle and Lower Amazons basins was systemized. The identification and classification of the morphostructural anomalies were accomplished by means of the drainage network interpretation, based on the criteria previously proposed. Thirty anomalies were recognized, being subdivided into twenty domes, two fault controlled domes, six structural depressions, one fault controlled structural depression and one structure developed on a tilted fault block. Many anomalies are not randomly located. Rather, they seem to be aligned according to directions ENE and NNW, suggesting the presence of morphstructural trends in this part of the Amazons Basin. Significant orientations of lineaments were determined through statistical analysis, which defined many regional trends. The directions coincide with morphostructural trends orientations and with the directions of important structures in the Precambrian basement.

Demiranda, F. P.↗
NASA NTRS2010

JMISR INteractive eXplorer

MISR (Multi-angle Imaging SpectroRadiometer) INteractive eXplorer (MINX) is an interactive visualization program that allows a user to digitize smoke, dust, or volcanic plumes in MISR multiangle images, and automatically retrieve height and wind profiles associated with those plumes. This innovation can perform 9-camera animations of MISR level-1 radiance images to study the 3D relationships of clouds and plumes. MINX also enables archiving MISR aerosol properties and Moderate Resolution Imaging Spectroradiometer (MODIS) fire radiative power along with the heights and winds. It can correct geometric misregistration between cameras by correlating off-nadir camera scenes with corresponding nadir scenes and then warping the images to minimize the misregistration offsets. Plots of BRF (bidirectional reflectance factor) vs. camera angle for points clicked in an image can be displayed. Users get rapid access to map views of MISR path and orbit locations and overflight dates, and past or future orbits can be identified that pass over a specified location at a specified time. Single-camera, level-1 radiance data at 1,100- or 275- meter resolution can be quickly displayed in color using a browse option. This software determines the heights and motion vectors of features above the terrain with greater precision and coverage than previous methods, based on an algorithm that takes wind direction into consideration. Human interpreters can precisely identify plumes and their extent, and wind direction. Overposting of MODIS thermal anomaly data aids in the identification of smoke plumes. The software has been used to preserve graphical and textural versions of the digitized data in a Web-based database.

Nelson, David L.↗
NASA NTRS2015

International Space Station (ISS) Anomalies Trending Study: Appendices - Volume II

The NASA Engineering and Safety Center (NESC) set out to utilize data mining and trending techniques to review the anomaly history of the International Space Station (ISS) and provide tools for discipline experts not involved with the ISS Program to search anomaly data to aid in identification of areas that may warrant further investigation. Additionally, the assessment team aimed to develop an approach and skillset for integrating data sets, with the intent of providing an enriched data set for discipline experts to investigate that is easier to navigate, particularly in light of ISS aging and the plan to extend its life into the late 2020s. This document contains the Appendices to the Volume I report.

Beil, Robert J.↗
NASA NTRS2015

International Space Station (ISS) Anomalies Trending Study

The NASA Engineering and Safety Center (NESC) set out to utilize data mining and trending techniques to review the anomaly history of the International Space Station (ISS) and provide tools for discipline experts not involved with the ISS Program to search anomaly data to aid in identification of areas that may warrant further investigation. Additionally, the assessment team aimed to develop an approach and skillset for integrating data sets, with the intent of providing an enriched data set for discipline experts to investigate that is easier to navigate, particularly in light of ISS aging and the plan to extend its life into the late 2020s. This report contains the outcome of the NESC Assessment.

Beil, Robert J.↗
NASA NTRS2016

Observational Constraints on the Identification of Shallow Lunar Magmatism: Insights from Floor-Fractured Craters

Floor-fractured craters are a class of lunar crater hypothesized to form in response to the emplacement of a shallow magmatic intrusion beneath the crater floor. The emplacement of a shallow magmatic body should result in a positive Bouguer anomaly relative to unaltered complex craters, a signal which is observed for the average Bouguer anomaly interior to the crater walls. We observe the Bouguer anomaly of floor-fractured craters on an individual basis using the unfiltered Bouguer gravity solution from GRAIL and also a degree 100-600 band-filtered Bouguer gravity solution. The low-magnitude of anomalies arising from shallow magmatic intrusions makes identification using unfiltered Bouguer gravity solutions inconclusive. The observed anomalies in the degree 100-600 Bouguer gravity solution are spatially heterogeneous, although there is spatial correlation between volcanic surface morphologies and positive Bouguer anomalies. We interpret these observations to mean that the spatial heterogeneity observed in the Bouguer signal is the result of variable degrees of magmatic degassing within the intrusions.

Jozwiak, L. M.↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗