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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Characteristics of microcracks in samples from the drill hole Noerdlingen 1973 in the Ries crater, Germany

Samples from the Noerdlingen 1973 drill core contain abundant shock-induced microfractures which exhibit varying amounts of healing and sealing. Many of the microcracks resemble morphologically the open microcracks present in returned lunar samples. Data derived from petrography, scanning electron microscopy, and differential strain analysis indicate that fewer microcracks were formed at greater depths and crack sealing processes are more effective for cracks associated with planar elements. Although the microcracks in the Ries core are now sealed, they are valid analogues of the open shock-induced cracks of lunar rocks and demonstrate that open cracks do form in rocks at depth during a naturally-occurring shock event. The healing of the shock-induced cracks in the Ries core also precludes their use for laboratory measurements of physical properties intended to be used as analogue measurements of lunar samples in situ.

Padovani, E. R.↗
NASA NTRS2014

Understanding the Effects of Spaceflight on Head-trunk Coordination during Walking and Obstacle Avoidance

Prolonged exposure to spaceflight conditions results in a battery of physiological changes, some of which contribute to sensorimotor and neurovestibular deficits. Upon return to Earth, functional performance changes are tested using the Functional Task Test (FTT), which includes an obstacle course to observe post‐flight balance and postural stability, specifically during turning. The goal of this study was to quantify changes in movement strategies during turning events by observing the latency between head‐and‐trunk coordinated movements. It was hypothesized that subjects experiencing neurovestibular adaptations would exhibit head‐to‐trunk locking ('en bloc' movement) during turning, exhibited by a decrease in latency between head and trunk movement. FTT data samples were collected from ISS missions. Samples were analyzed three times pre‐exposure, immediately postexposure (1 day post) and 2‐to‐3 times during recovery from the microgravity environment. Two 3D inertial measurements units (XSens MTx) were attached to subjects, one on the head and one on the upper back. This study focused primarily on the yaw movements about the subject's center of rotation. Time differences (latency) between head and trunk movement were calculated at two points on the obstacle course: the first turn to enter the obstacle course (approximately 90⁰ turn) and averaged across a slalom obstacle portion, consisting of three turns (approximately three 90⁰ turns). Preliminary analysis of the data shows a trend toward decreasing head‐to‐trunk movement latency during postflight ambulation in slalom turning after reintroduction to Earth gravity in ISS astronauts. It is clear that changes in movement strategies are adopted during exposure to the microgravity environment and upon reintroduction to a gravity environment. Most ISS subjects exhibit symptoms of neurovestibular changes ('en bloc head and trunk movement) which may impact their ability to perform post‐flight functional tasks.

Madansingh, S.↗
NASA NTRS2013

Revised Thickness of the Lunar Crust from GRAIL Data: Implications for Lunar Bulk Composition

High-resolution gravity data from GRAIL have yielded new estimates of the bulk density and thickness of the lunar crust. The bulk density of the highlands crust is 2550 kg m-3. From a comparison with crustal composition measured remotely, this density implies a mean porosity of 12%. With this bulk density and constraints from the Apollo seismic experiment, the average global crustal thickness is found to lie between 34 and 43 km, a value 10 to 20 km less than several previous estimates. Crustal thickness is a central parameter in estimating bulk lunar composition. Estimates of the concentrations of refractory elements in the Moon from heat flow, remote sensing and sample data, and geophysical data fall into two categories: those with refractory element abundances enriched by 50% or more relative to Earth, and those with abundances the same as Earth. Settling this issue has implications for processes operating during lunar formation. The crustal thickness resulting from analysis of GRAIL data is less than several previous estimates. We show here that a refractory-enriched Moon is not required

Taylor, G. Jeffrey↗
NASA NTRS1996

Cosmogenic Production of Be-7 and Be-10 in Water Targets

We have measured Be-10(t(sub 1/2) = 1.5 x 10(exp 6) years) and Be-7 (t(sub 1/2) = 53.28 days) concentrations in water targets exposed for 1 to 2 years at Echo Lake, Colorado (elevation = 3246 m) and at La Jolla, California (140 m). Neutron monitor data were used to normalize the measured concentrations in order to calculate production rates equivalent to the cosmic ray flux averaged over four solar cycles (43 years). The Be-7 production rates thus obtained correspond to 6.03 +/- 0.07 x 10(exp -6) atom/g.O/s at Echo Lake and 5.06 +/- 0.20 x 10(exp -7) atom/g.O/ s at La Jolla. The Be-10 production rates correspond to 3.14 +/- 0.18 x 10(exp -6) atom/g.O/s at Echo Lake and 2.68 +/- 0.47 x 10(exp -7) atom/g.O/s at La Jolla. When compared with Be-10 production rates determined in Be-10-saturated rocks from the Antarctic and with theoretical calculations based on meteorite and lunar sample data, we find that the million-year average production rate is about 14 - 17% greater than the present production rate averaged over the last four solar cycles. Comparison with production rates determined by measuring glacially polished rocks from the Sierra Nevada in California indicates that average production (based on a revised 13,000-year deglaciation age and a geographic latitude correction) is a about 11% greater than the average over the last four solar cycles. The measured Be-10/Be-7 production ratio in oxygen is 0.52 +/- 0.03 at Echo Lake and 0.55 +/- 0.07 at La Jolla.

Nishiizumi, K.↗
NASA NTRS1983

Observations of spectroscopic binaries at ultraviolet and red wavelengths

The use of IUE-satellite and ground-based-Reticon observations for the study of spectroscopic binaries is examined, and sample data from the Reticon are presented. IUE radial-velocity determinations with uncertainties as low as 2-5 km/sec should be possible for binaries with early-type secondary components, using UV spectral lines where contamination by the later-type primary is reduced. The Reticon solid-state photodiode-array detector has been used at the coudefocus of the 2.7-m telescope at McDonald Observatory for observation of complex multiple systems in the 400-950-nm range with a quantum efficiency of 40-80 percent and resolution of 20-30 pm at 440 pm/mm. The fundamental parameters of 15 systems observed with this apparatus are listed in a table, and two sample spectra are shown.

Fekel, F. C., Jr.↗
NASA NTRS2019

Principal Component Analysis of Up-The-Ramp Sampled Infrared Array Data

We describe the results of principal component analysis (PCA) of up-the-ramp sampled infrared (IR) array data from the Hubble Space Telescope wide field camera 3 (WFC3 IR), James Webb Space Telescope NIRSpec, and prototype Wide Field Infrared Survey Telescope’s wide field instrument detectors. These systems use, respectively, Teledyne H1R, H2RG, and H4RG-10 near-IR detector arrays with a variety of IR array controllers. The PCA shows that the Legendre polynomials approximate the principal components of these systems (i.e., they roughly diagonalize the covariance matrix). In contrast to the monomial basis that is widely used for polynomial fitting and linearization today, the Legendre polynomials are an orthonormal basis. They provide a quantifiable, compact, and (nearly) linearly uncorrelated representation of the information content of the data. By fitting a few Legendre polynomials, nearly all of the meaningful information in representative WFC3 astronomical datacubes can be condensed from 15 up-the-ramp samples down to 6 compressible Legendre coefficients per pixel. The higher order coefficients contain time domain information that is lost when one projects up-the-ramp sampled datacubes onto two-dimensional images by fitting a straight line, even if the data are linearized before fitting the line. Going forward, we believe that this time domain information is potentially important for disentangling the various nonlinearities that can affect IR array observations, i.e., inherent pixel nonlinearity, persistence, burn in, brighter-fatter effect, (potentially) nonlinear interpixel capacitance, and perhaps others.

Rauscher, Bernard J.↗
NASA NTRS1987

Solar object tracking for the Hubble Space Telescope

The Hubble Space Telescope (HST) is designed to carry five major scientific instruments to collect imagery, spectrographic, and photometric astronomical data. The Pointing Control System is to achieve pointing accuracies and line of sight jitter levels an order of magnitude less than can be achieved with ground mounted telescopes. In addition, the HST must be able to acquire and track solar system targets with apparent motion up to 0.21 arcsec/s. Such targets include planetary satellites, planetary surface features and comets. It is to perform this tracking with an accuracy under 0.03 arcsec at the maximum rate. Tracking of solar objects by the Space Telescope accounts for the effects of velocity aberration and parallax, as well as solar targeting a celestial object in a science instrument aperture. The design of the Pointing Control System solar object tracking features is discussed, with emphasis on the special timing and granulation problems inherent with a sampled-data, multirate digital control system.

Rodden, J. J.↗
NASA NTRS1987

An algorithmic approach to crustal deformation analysis

In recent years the analysis of crustal deformation measurements has become important as a result of current improvements in geodetic methods and an increasing amount of theoretical and observational data provided by several earth sciences. A first-generation data analysis algorithm which combines a priori information with current geodetic measurements was proposed. Relevant methods which can be used in the algorithm were discussed. Prior information is the unifying feature of this algorithm. Some of the problems which may arise through the use of a priori information in the analysis were indicated and preventive measures were demonstrated. The first step in the algorithm is the optimal design of deformation networks. The second step in the algorithm identifies the descriptive model of the deformation field. The final step in the algorithm is the improved estimation of deformation parameters. Although deformation parameters are estimated in the process of model discrimination, they can further be improved by the use of a priori information about them. According to the proposed algorithm this information must first be tested against the estimates calculated using the sample data only. Null-hypothesis testing procedures were developed for this purpose. Six different estimators which employ a priori information were examined. Emphasis was put on the case when the prior information is wrong and analytical expressions for possible improvements under incompatible prior information were derived.

Iz, Huseyin Baki↗
NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Zeng, Shan↗
NASA NTRSDate not supplied

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗
NASA NTRS2013

An Efficient, Highly Flexible Multi-Channel Digital Downconverter Architecture

In this innovation, a digital downconverter has been created that produces a large (16 or greater) number of output channels of smaller bandwidths. Additionally, this design has the flexibility to tune each channel independently to anywhere in the input bandwidth to cover a wide range of output bandwidths (from 32 MHz down to 1 kHz). Both the flexibility in channel frequency selection and the more than four orders of magnitude range in output bandwidths (decimation rates from 32 to 640,000) presented significant challenges to be solved. The solution involved breaking the digital downconversion process into a two-stage process. The first stage is a 2 oversampled filter bank that divides the whole input bandwidth as a real input signal into seven overlapping, contiguous channels represented with complex samples. Using the symmetry of the sine and cosine functions in a similar way to that of an FFT (fast Fourier transform), this downconversion is very efficient and gives seven channels fixed in frequency. An arbitrary number of smaller bandwidth channels can be formed from second-stage downconverters placed after the first stage of downconversion. Because of the overlapping of the first stage, there is no gap in coverage of the entire input bandwidth. The input to any of the second-stage downconverting channels has a multiplexer that chooses one of the seven wideband channels from the first stage. These second-stage downconverters take up fewer resources because they operate at lower bandwidths than doing the entire downconversion process from the input bandwidth for each independent channel. These second-stage downconverters are each independent with fine frequency control tuning, providing extreme flexibility in positioning the center frequency of a downconverted channel. Finally, these second-stage downconverters have flexible decimation factors over four orders of magnitude The algorithm was developed to run in an FPGA (field programmable gate array) at input data sampling rates of up to 1,280 MHz. The current implementation takes a 1,280-MHz real input, and first breaks it up into seven 160-MHz complex channels, each spaced 80 MHz apart. The eighth channel at baseband was not required for this implementation, and led to more optimization. Afterwards, 16 second stage narrow band channels with independently tunable center frequencies and bandwidth settings are implemented A future implementation in a larger Xilinx FPGA will hold up to 32 independent second-stage channels.

Goodhart, Charles E.↗
NASA NTRS1992

On the isotopic signature of recent solar-wind nitrogen

One of the most intriguing discoveries yielded by the Apollo samples was evidence pointing towards a significant long-term change in the composition of the sun. Such a change, of the size inferred from the lunar sample data, is inconsistent with present theories of solar evolution. Consequently, there is much interest in exploring this phenomenon as closely as possible, to determine exactly what compositional changes have taken place and whether those changes really did take place in the sun, or whether the cause lies elsewhere. The reason why we can use the moon to analyze the elements in the sun is that the sun emits a stream of ions, known as the solar wind, whose composition, on average, is believed to be the same as that in the surface regions of the sun. When the solar-wind ions hit the surface of the moon, many of them penetrate a short distance into the dust grains lying on the lunar surface. Thus, after a grain has sat on the lunar surface for a while, it has a rim of material that is partly lunar and partly solar in composition. For most chemical elements, the difference between lunar and solar composition is so sufficiently small that the solar elements cannot be detected, but for a handful of elements that are missing from the moon, their solar 'signature' can be observed in samples of lunar soil brought back by the astronauts. Among those elements is nitrogen, the most common element in the air we breathe, but very rare indeed on the moon. Our analytical techniques are not sophisticated enough yet to enable us to analyze individual lunar soil grains for nitrogen, much less to zero in on just the nitrogen in the surface of such a grain. Consequently we are forced to analyze samples consisting of many different grains, each of which could have experienced its own individual history. This makes it difficult to identify the nitrogen implanted in grain surfaces, and also to define the age of a sample.

Kim, Y.↗
NASA NTRS2018

Portable, Low-Cost, Column Carbon Dioxide and Methane Measurements for Validating Satellite Observations in Remote Locations

We present a low-cost (~$10K per instrument), portable solution to ground-based validation of satellite observations for difficult to reach locations with precisions of 1 ppm XCO2 and 10 ppb XCH4 for hourly data products. While Total Carbon Column Observing Network (TCCON) is the gold-standard for ground validation, there are locations where a ground column validation data point would be useful but conditions are not conducive to a permanent station. Examples include wetlands, thawing permafrost, the tropics, the Amazon, sub-Saharan Africa, as well as locations without a power grid or with geopolitical conflict. In addition, the low-cost and portability mean a geographical region can be studied in depth with multiple instruments. This passive, sun-pointing instrument is a miniaturized, laser heterodyne radiometer (mini-LHR) that has been under development by our team since 2009. It can be operated either in tandem with AERONET (a global network of 500 instruments that measure aerosol optical depth), or as a stand-alone instrument with a low-cost (~$3K), light-weight sun tracker. One of the main benefits of the mini-LHR is that it can quickly reach remote locations and provide a validation measurement even if there is limited or no infrastructure at the site. The instrument weighs ~10 lbs, fits into a backpack, and is powered by two folding solar panels and a battery pack. In clear conditions, the instrument can be set-up in under an hour. Portability means that mini-LHRs can be easily moved for side-by-side comparisons with other mini-LHRs and with TCCON which simplifies assessing instrument bias as well as accuracy. Like TCCON, the mini-LHR points directly at the sun with a narrow field-of-view and is its insensitivity to cloud and aerosol scattering that is common to nadir-pointing passive satellite approaches. Here we present a collection of sample data sets to demonstrate performance from locations that vary in climate, altitude, solar zenith angle, hours of sun per day, etc., as well as data from side-by-side TCCON comparisons. Retrievals of CO2 and CH4 were completed using the NASA/Goddard's Planetary Spectrum Generator (PSG) that incorporates meteorological inputs from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data set.

Wilson, Emily W.↗
NASA NTRS1968

Design techniques - Stochastic controllers

Analytic techniques aid in the design of nearly optimal linear time-varying sampled-data stochastic controllers. The techniques also aid in the simplification and automation of program designing for control computers.

Widnall, W. S.↗