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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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NASA NTRS1998

Relative size perception at a distance is best at eye level

Relative size judgments were collected for two objects at 30.5 m and 23.8 from the observer in order to assess how performance depends on the relationship between the size of the objects and the eye level of the observer. In three experiments in an indoor hallway and in one experiment outdoors, accuracy was higher for objects in the neighborhood of eye level. We consider these results in the light of two hypotheses. One proposes that observers localize the horizon as a reference for judging relative size, and the other proposes that observers perceive the general neighborhood of the horizon and then employ a height-in-visual-field heuristic. The finding that relative size judgments are best around the horizon implies that information that is independent of distance perception is used in perceiving size.

NASA Center ARC↗

NASA NTRS2007

A Comparison of Satellite-Based Multilayered Cloud Detection Methods

Both techniques show skill in detecting multilayered clouds, but they disagree more than 50% of the time. BTD method tends to detect more ML clouds than CO2 method and has slightly higher detection accuracy. CO2 method might be better for minimizing false positives, but further study is needed. Neither method as been optimized for GOES data. BTD technique developed on AVHRR, better BTD signals & resolution. CO2 developed on MODIS, better resolution & 4 CO2 channels. Many additional comparisons with ARSCL data will be used to optimize both techniques. A combined technique will be examined using MODIS & Meteosat-8 data. After optimization, the techniques will be implemented in the ARM operational satellite cloud processing.

Minnis, Patrick↗

NASA NTRS2008

Reconfigurable Processing for Satellite On-Board Automatic Cloud Cover Assessment (ACCA)

Clouds have a critical role in many studies such as weather- and climate-related investigations. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. In addition, sending cloudy data to ground stations can result in an inefficient utilization of the communication bandwidth. This requires satellite on-board cloud detection capability to mask out cloudy pixels from further processing. Remote sensing satellite missions have always required smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the-art reconfigurable platforms, SRC-6. It will be shown that our work provides higher detection accuracy and over one order of magnitude improvement in performance when compared to previously reported investigations.

El-Araby, Esam↗

NASA NTRS2010

Trace Gas Measurements on Mars and Earth Using Optical Parametric Generation

Trace gases and their isotopic ratios in planetary atmospheres offer important but subtle clues as to the origins of a planet's atmosphere, hydrology, geology, and potential for biology. An orbiting laser remote sensing instrument is capable of measuring trace gases on a global scale with unprecedented accuracy, and higher spatial resolution that can be obtained by passive instruments. We have developed an active sensing instrument for the remote measurement of trace gases in planetary atmospheres (including Earth). The technique uses widely tunable, seeded optical parametric generation (OPG) to measure methane, CO2, water vapor, and other trace gases in the near and mid-infrared spectral regions. Methane is a strong greenhouse gas on Earth and it is also a potential biogenic marker on Mars and other planets. Methane in the Earth's atmosphere survives for a shorter time than CO2 but its impact on climate change can be larger than CO2. Methane levels have remained relatively constant over the last decade around 1.78 parts per million (ppm) but recent observations indicate that methane levels may be on the rise. Increasing methane concentrations may trigger a positive feedback loop and a subsequent runaway greenhouse effect, where increasing temperatures result in increasing methane levels. The NRC Decadal Survey recognized the importance of global observations of greenhouse gases and called for simultaneous CH4, CO, and CO2 measurements but also underlined the technological limitations for these observations. For Mars, methane measurements are of great interest because of its potential as a strong biogenic marker. A remote sensing instrument that can measure day and night over all seasons and latitudes can identify and localize sources of biogenic gas plumes produced by subsurface chemistry or biology, and aid in the search for extra-terrestrial life. It can identify the dynamics of methane generation over time and latitude and identify future lander mission sites for more detailed in-situ analysis. In this paper we report on remote sensing measurements of methane using a high peak power, widely tunable optical parametric generator (OPG) operating at 3.3 micron and 1.65 micron. The OPG is pumped by a passively q-switched single frequency laser (3ns, 5KHz, 50uJ) and seeded by a diode laser. The spectral width of both signal and idler of seeded OPG is nearly Fourier transform limited. The output of seeded OPG is single frequency with high spectral purity and is widely tunable. Both 1650 nm and 3300 nm can be generated with a conversion efficiency of more than 30%. We have demonstrated detection of methane at 3274 nm and 1650 nm in a cell and also performed open path atmospheric measurements of methane at the same wavelengths. Finally, we were able to demonstrate simultaneous detection of methane at 3270.4 nm and CO2 at 1578.2 nm. In this paper we will discuss the OPG performance and atmospheric open path measurement results.

Numata, Kenji↗

NASA NTRS2006

PixelLearn

PixelLearn is an integrated user-interface computer program for classifying pixels in scientific images. Heretofore, training a machine-learning algorithm to classify pixels in images has been tedious and difficult. PixelLearn provides a graphical user interface that makes it faster and more intuitive, leading to more interactive exploration of image data sets. PixelLearn also provides image-enhancement controls to make it easier to see subtle details in images. PixelLearn opens images or sets of images in a variety of common scientific file formats and enables the user to interact with several supervised or unsupervised machine-learning pixel-classifying algorithms while the user continues to browse through the images. The machinelearning algorithms in PixelLearn use advanced clustering and classification methods that enable accuracy much higher than is achievable by most other software previously available for this purpose. PixelLearn is written in portable C++ and runs natively on computers running Linux, Windows, or Mac OS X.

Mazzoni, Dominic↗

NASA NTRS2012

Parametric Study of Decay of Homogeneous Isotropic Turbulence Using Large Eddy Simulation

Numerical simulations of decaying homogeneous isotropic turbulence are performed with both low-order and high-order spatial discretization schemes. The turbulent Mach and Reynolds numbers for the simulations are 0.2 and 250, respectively. For the low-order schemes we use either second-order central or third-order upwind biased differencing. For higher order approximations we apply weighted essentially non-oscillatory (WENO) schemes, both with linear and nonlinear weights. There are two objectives in this preliminary effort to investigate possible schemes for large eddy simulation (LES). One is to explore the capability of a widely used low-order computational fluid dynamics (CFD) code to perform LES computations. The other is to determine the effect of higher order accuracy (fifth, seventh, and ninth order) achieved with high-order upwind biased WENO-based schemes. Turbulence statistics, such as kinetic energy, dissipation, and skewness, along with the energy spectra from simulations of the decaying turbulence problem are used to assess and compare the various numerical schemes. In addition, results from the best performing schemes are compared with those from a spectral scheme. The effects of grid density, ranging from 32 cubed to 192 cubed, on the computations are also examined. The fifth-order WENO-based scheme is found to be too dissipative, especially on the coarser grids. However, with the seventh-order and ninth-order WENO-based schemes we observe a significant improvement in accuracy relative to the lower order LES schemes, as revealed by the computed peak in the energy dissipation and by the energy spectrum.

Swanson, R. C.↗

NASA NTRS2014

Signal Processing Methods for Removing the Effects of Whole Body Vibration upon Speech

Humans may be exposed to whole-body vibration in environments where clear speech communications are crucial, particularly during the launch phases of space flight and in high-performance aircraft. Prior research has shown that high levels of vibration cause a decrease in speech intelligibility. However, the effects of whole-body vibration upon speech are not well understood, and no attempt has been made to restore speech distorted by whole-body vibration. In this paper, a model for speech under whole-body vibration is proposed and a method to remove its effect is described. The method described reduces the perceptual effects of vibration, yields higher ASR accuracy scores, and may significantly improve intelligibility. Possible applications include incorporation within communication systems to improve radio-communication systems in environments such a spaceflight, aviation, or off-road vehicle operations.

human response to vibration↗

NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.

Khlopenkov, Konstantin V.↗

NASA NTRS2019

An Initial Exploration of Improved Numerics within the Guidelines of the Negative Spalart-Allmaras Turbulence Model

A simple modification to the negative Spalart-Allmaras turbulence model is suggested so that when the turbulence working variable, ~v, is negative, diagonal dominance is increased, as is the tendency for the time-advancement scheme to push e toward positive values. Owing to the fact that the modification is only active when ~v is less than zero, the physical model is left unchanged. Using the proposed modification with a strong implicit solver based on Newton's method, convergence rates can be somewhat improved, with typical reductions in iterations and computer time on the order of 15-50%. The benefits are realized primarily when second- or higher-order accuracy is used for discretizing the convective terms in the turbulence model because of large overshoots that can occur with these schemes at the edges of boundary layers and wakes. For flowfields with few regions of negative ~v, or on very fine meshes where ~v is always greater than zero, little or no benefits should be expected.

Anderson, William K.↗

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 NTRSDate not supplied

Supervised Machine Learning Approach for Classifying Earth Science Publications

The data collections archived and distributed by the GES DISC NASA data center are widely utilized for various Earth Science studies. As these collections are created, many research works are published regarding these collections' algorithms, their validation, and their applications. As NASA data centers collect these publications for public use, it is helpful to categorize them based on how they relate to their associated datasets. Specifically, whether the publication linked to the GES DISC dataset is using it for applicational research, describing the algorithm used for the dataset creation, validating the dataset, or providing a general overview of the data collection. Currently, this process requires simple manual labeling, and as such, it may be possible to solve via automation. To approach this problem, machine learning classifiers were developed to predict a publication's category. Manually labeled publications were used as the training data for the supervised machine learning algorithms, specifically Random Forest and Multinomial Naïve Bayes. After balancing the dataset and implementing the Multinomial Naïve Bayes algorithm, the classification accuracy achieved was substantially higher than the baseline accuracy, thus significantly improving the efficiency of publication labeling.

Rohan Dayal↗

NASA NTRS1992

Improved accuracy for finite element structural analysis via a new integrated force method

A comparative study was carried out to determine the accuracy of finite element analyses based on the stiffness method, a mixed method, and the new integrated force and dual integrated force methods. The numerical results were obtained with the following software: MSC/NASTRAN and ASKA for the stiffness method; an MHOST implementation method for the mixed method; and GIFT for the integrated force methods. The results indicate that on an overall basis, the stiffness and mixed methods present some limitations. The stiffness method generally requires a large number of elements in the model to achieve acceptable accuracy. The MHOST method tends to achieve a higher degree of accuracy for course models than does the stiffness method implemented by MSC/NASTRAN and ASKA. The two integrated force methods, which bestow simultaneous emphasis on stress equilibrium and strain compatibility, yield accurate solutions with fewer elements in a model. The full potential of these new integrated force methods remains largely unexploited, and they hold the promise of spawning new finite element structural analysis tools.

Patnaik, Surya N.↗

NASA NTRS1992

Improved accuracy for finite element structural analysis via an integrated force method

Patnaik, S. N.↗

NASA NTRS1975

Data resolution versus forestry classification and modeling

This paper examines the effects on timber stand computer classification accuracies caused by changes in the resolution of remotely sensed multispectral data. This investigation is valuable, especially for determining optimal sensor and platform designs. Theoretical justification and experimental verification support the finding that classification accuracies for low resolution data could be better than the accuracies for data with higher resolution. The increase in accuracy is constructed as due to the reduction of scene inhomogeneity at lower resolution. The computer classification scheme was a maximum likelihood classifier.

Kan, E. P.↗

NASA NTRS1993

Satellite Altimeter Measurements of Surface Wind

In the present work, geophysical model functions (GMFs) for altimeter wind speed measurements are described. These GMFs account for an effect of the actual degree of sea development on the radar cross section by utilizing information on the significant wave height. The wind speed measurement accuracy achieved (the overall bias 0.1 m/s and the rms error about 1.6 m/s) is higher that the accuracy of commonly employed GMFs while the wave-age-related trend is reduced to a geophysically insignificant level.

Greysukh, Alexander↗

NASA NTRS1993

Satellite altimeter measurements of surface wind

Recent analyses of wind speed measurements by the Geosat altimeter showed that the radar cross section is affected by oceanographic factors, particularly by the degree of sea development, which are not directly accounted for in the geophysical model functions (GMF). In the present work, two new GMFs which account for the effects of the actual degree of sea development are proposed. Along with the radar cross section, these models use significant wave height information. One particular version is recommended for applications in oceanographic and climate studies where wind speed (or wind stress) data have to be binned (i.e., averaged over time and/or space intervals). The accuracy of this GMF (overall bias of 0.1 m/s and rms error of about 1.6 m/s) is higher than the accuracy of commonly employed GMFs, while the wave-age-related trend is reduced to a geophysically insignificant level. Finally, the wind speed histograms for the collocated data set are derived and compared with the ground truth data as well as with the histograms yielded by presently known GMFs. It is also shown that the accuracy of altimeter measurements could be increased even further if some additional information on the wave field were available from independent sources (e.g., the dominant wavelength from synthetic aperture radar images).

Glazman, Roman E.↗

NASA NTRS1986

Fatigue and fracture overview

The accomplishments achieved under the isotropic creep-fatigue crack initiation life prediction program are summarized. A sizeable creep-fatigue crack initiation data base was generated on the nickel-base superalloy, B-1900. Companion constitutive modeling programs have also generated extensive data bases on the same heat of material. The crack initiation results have formed the basis of a new approach to creep-fatigue life prediction. The term Cyclic Damage Accumulation (CDA) was coined for the method, which was evaluated under isothermal, uniaxial conditions. Stringent laboratory verification experiments were used to test the accuracy of the method. Considering the quite limited material property data needed to evaluate the constants in the approach, the prediction accuracy is acceptable. At the expense of the larger data base required, Lewis developed total strain- strainrange partitioning method (TS-SRP) is capable of a higher degree of accuracy.

Halford, Gary R.↗

NASA NTRS2016

Dynamic Filtering Improves Attentional State Prediction with fNIRS

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Harrivel, Angela R.↗