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NASA NTRS1999

Kelvin Helmholtz Instability at the Equatorial Magnetotail Boundary: MHD Simulation and Comparison with Geotail Observations

On March 24, 1995 the Geotail spacecraft observed large fluctuations of the magnetic field and plasma properties in the Low Latitude Boundary Layer (LLBL) about 15 R(sub E) tailward of the dusk meridian. Although the magnetospheric and the magnetosheath field were strongly northward, the B(sub z) component showed strong short duration fluctuations in which B(sub z) could even reach negative values. We have used two-dimensional magnetohydrodynamic simulations with magnetospheric and magnetosheath input parameters specifically chosen for this. Geotail event to identify the processes which cause the observed boundary properties. It is shown that these fluctuations can be explained by the Kelvin-Helmholtz instability if the k vector of the instability has a component along the magnetic field direction. The simulation results show many of the characteristic properties of the Geotail observations. In particular, the quasi-periodic strong fluctuations are well explained by satellite crossings through the Kelvin-Helmholtz vortices. It is illustrated how the interior structure of the Kelvin-Helmholtz vortices leads to the rapid fluctuations in the Geotail observations. Our results suggest an average Kelvin-Helmholtz wavelength of about 5 R(sub E) with a vortex size of close to 2 R(sub E) for an average repetition time of 2.5 minutes. The growth time for these waves implies a source region of about 10 to 16 R(sub E) upstream from the location of the Geotail spacecraft (i.e., near the dusk meridian). The results also indicate a considerable mass transport of magnetosheath material into the magnetosphere by magnetic reconnection in the Kelvin-Helmholtz vortices.

Fairfield, Donald H.↗

NASA NTRS2002

Wire Detection Algorithms for Navigation

In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning method) is the need for a very good set of positive and negative examples since the performance depends on the quality of the training set.

Kasturi, Rangachar↗

NASA NTRS2012

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Armstrong, Jeffrey B.↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS1989

An imaging vector magnetograph for the next solar maximum

Researchers describe the conceptual design of a new imaging vector magnetograph currently being constructed at the University of Hawaii. The instrument combines a modest solar telescope with a rotating quarter-wave plate, an acousto-optical tunable prefilter as a blocker for a servo-controlled Fabry-Perot etalon, CCD cameras, and on-line digital image processing. Its high spatial resolution (1/2 arcsec pixel size) over a large field of view (5 by 5 arcmin) will be sufficient to significantly measure, for the first time, the magnetic energy dissipated in major solar flares. Its millisecond tunability and wide spectral range (5000 to 7000 A) enable nearly simultaneous vector magnetic field measurements in the gas-pressure-dominated photosphere and magnetically-dominated chromosphere, as well as effective co-alignment with Solar-A's X ray images. Researchers expect to have the instrument in operation at Mees Solar Observatory (Haleakala) in early 1991. They have chosen to use tunable filters as wavelength-selection elements in order to emphasize the spatial relationships between magnetic field elements, and to permit construction of a compact, efficient instrument. This means that spectral information must be obtained from sequences of images, which can cause line profile distortions due to effects of atmospheric seeing.

Mickey, D. L.↗

NASA NTRS2006

The Role of Aerosols in Cloud Growth, Suppression, and Precipitation: Yoram Kaufman and his Contributions

Aerosol particles are produced in the earth's atmosphere through both natural as well as manmade processes, and contribute profoundly to the (i) formation and characteristics of clouds, (ii) lifetime of clouds, (iii) optical and microphysical properties of clouds, (iv) human health through effects on air quality and the size of particulates as well as vectors for transport of pathogens, (v) climate response and feedbacks, (vi) precipitation, and (vii) harmful algal blooms. Without aerosol particles in the Earth's atmosphere, there would be no fogs, no clouds, ,no mists, and probably no rain, as noted as far back as 1880 by Scottish physicist John Aitken. With the modern development of instrumentation, both groundbased, airborne, and satellite-based, much progress has been made in linkng phenomena and processes together, and putting regional air quality characteristics and hypothesized cloud response into closer scrutiny and linkages. In h s presentation I will summarize the wide ranging contributions that Yoram Kaufman has made in ground-based (AERONET), aircraft field campaigns (such as SCAR-B and TARFOX), and, especially, satellite remote sensing (Landsat, MODIS, POLDER) to shed new light on this broad ranging and interdisciplinary field of cloud-aerosol-precipitation interactions.

King, Michael D.↗

NASA NTRS2014

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle/Multi-Pixel Polarimetric Measurements

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth's atmosphere.

linearization↗

NASA NTRS2014

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

adjoint methods↗

NASA NTRS2020

FOSSIL—Finding Our Cosmic Roots

FOSSIL (Fragments from the Origins of the Solar System and our Interstellar Locale) is a concept to explore the largest solar system object visible to the unaided eye—the zodiacal cloud. The cloud’s many dust particles are each a tiny time capsule from a comet or asteroid. The FOSSIL concept is for the in situ compositional analysis of interplanetary dust particles, and also of the particles passing through the solar system from interstellar space. By measuring the zodiacal and interstellar particles’ velocity vectors and compositions, the approach resolves fundamental questions about the solar system’s origins. The FOSSIL concept is to: (1) Discover whether today’s local interstellar dust matches the composition of the feedstock from which the solar system formed; (2) Determine whether comets’ fine-grained component preserves unprocessed pre-solar dust or shows signs of processing in the early solar system; and (3) Learn whether comets’ and asteroids’ organic material share a common source or formed from distinct reservoirs. The FOSSIL concept is based on the use of a Dust Telescope (DT) with the capability to measure the composition and the velocity vector of dust particles to unambiguously separate interstellar from interplanetary particles, and identify a subset of zodiacal particles that are exclusively cometary. In the DT, particles pass through the trajectory sensor unharmed and impact a target plate, where they are vaporized and partially ionized. The ions are electrostatically focused onto a detector where the time-of-flight mass spectrum is recorded, enabling measurement of the composition of the interstellar solids, detection of cometary minerals altered by high temperatures or exposure to liquid water in the early solar system, and the characterization of the organic materials from the comets and asteroids that are still being delivered to the Earth today. FOSSIL’s objectives crosscut several disciplines with planetary science. Astrophysics interests lie in understanding interstellar solar matter and the only debris disk accessible in situ. Heliophysics interest is in the verification of large-scale heliospheric magnetic field models by the measured effect on the motion of electrically charged interstellar dust. Reporting the makeup of interplanetary dust that ablates in our atmosphere is valuable to Earth sciences. FOSSIL’s DT is scalable and can be accommodated to a variety of mission opportunities, without restriction on launch dates, and for a large number of possible orbits.

Turner, Neal↗

NASA NTRS1985

Effects of Simulated Weightlessness on Mammalian Development. Part 2: Meiotic Maturation of Mouse Oocytes During Clinostat Rotation

In order to understand the role of gravity in basic cellular processes that are important during development, the effects of a simulated microgravity environment on mammalian gametes and early embryos cultured in vitro are examined. A microgravity environment is simulated by use of a clinostat, which essentially reorients cells relative to the gravity vector. Initial studies have focused on assessing the effects of clinostat rotation on the meiotic progression of mouse oocytes. Modifications centered on providing the unique in vitro culture of the clinostat requirements of mammalian oocytes and embryos: 37 C temperature, constant humidity, and a 5% CO2 in air environment. The oocytes are observed under the dissecting microscope for polar body formation and gross morphological appearance. They are then processed for cytogenetic analysis.

Wolgemuth, D. J.↗

NASA NTRS1973

Mars physical parameters as determined from Mariner 9 observations of the natural satellites

Derivation of values for the Mars gravity field, as well as the mass and spin axis direction, by processing combined radio and optical data taken with the Mariner 9 spacecraft. The optical data consists of 62 TV photographs of Phobos and Deimos taken during the Mars orbiting phase of Mariner 9. The radio data consists of apoapsis state vectors obtained from 195 one-revolution fits of the Doppler data. A first-order analytic theory is used to model both the satellites and the spacecraft's motion. However, the shallow resonance condition of Mariner 9's orbit necessitated development of a partial second-order theory to model the long-period perturbations which derive from Mars even-order tesseral harmonics. Use of an analytic theory allows rapid and inexpensive processing of both data types simultaneously. The results are in good agreement with those previously published from Mariner 9 Doppler and landmark data.

Born, G. H.↗

NASA NTRS1977

Interactive image processing for meteorological applications at NASA/Goddard Space Flight Center

The paper reviews the development of the AOIPS (Atmospheric and Oceanographic Information Processing System) system for the interactive manipulation of meteorological satellite images. A block diagram of the system is presented; both software and hardware considerations are examined; and attention is given to the Image 100 processing system and to the interactive terminal. As a example the paper examines the functions of Metpak (the software package) which involve cloud tracking and wind vector generation.

Billingsley, J. B.↗

NASA NTRS1979

Satellite to study earth's magnetic field

The Magnetic Field Satellite (Magsat) designed to measure the near earth magnetic field and crustal anomalies is briefly described. A scalar magnetometer to measure the magnitude of the earth's crustal magnetic field and a vector magnetometer to measure magnetic field direction as well as magnitude are included. The mission and its objectives are summarized along with the data collection and processing system.

Source record↗

NASA NTRS1982

Evidence for quasi-stationary reconnection at the dayside magnetopause

The paper investigates several highly unusual encounters with the earth's magnetopause, that occurred during an approximately 5-hour period on November 22-23, 1979, when the ISEE 1 and 2 were near orbit apogee. A large decrease in the dynamic pressure exerted by the solar wind resulted in an expansion of the magnetosphere to and beyond the apogee of the ISEE 1 and 2 orbit, and the subsolar magnetopause of about 20.4 earth radii is farther than normal in geocentric distance by a factor of about 2. Field rotations varying from about 80 to 120 deg were involved in the transition from the magnetosheath to the magnetosphere, and hodograms of the tangential component of the magnetic field vector suggest that the magnetopause was a rotational discontinuity. These observations indicate that on occasion reconnection at the dayside magnetopause can be a quasi-stationary process.

Gosling, J. T.↗

NASA NTRS1983

Gravitational effects in dendritic growth

The theories of diffusion-controlled dendritic crystallization will be reviewed briefly, along with recently published critical experiments on the kinetics and morphology of dendritic growth in pure substances. The influence of the gravitational body force on dendrite growth kinetics will be shown to be highly dependent on the growth orientation with respect to the gravity vector and on the level of the thermal supercooling. In fact, an abrupt transition occurs at a critical supercooling, above which diffusional transport dominates the growth process and below which convective transport dominates. Our most recent work on binary mixtures shows that dilute solute additions influence the crystallization process indirectly, by altering the interfacial stability, rather than by directly affecting the transport mode. Directions for future studies in this field will also be discussed.

Glicksman, M. E.↗

NASA NTRS1987

Modeling and control of beam-like structures

The most popular finite element codes are based upon appealing theories of convergence of modal frequencies. For example, the popularity of cubic elements for beam-like structures is due to the rapid convergence of modal frequencies and stiffness properties. However, for those problems in which the primary consideration is the accuracy of response of the structure at specified locations it is more important to obtain accuracy in the modal costs than in the modal frequencies. The modal cost represents the contribution of a mode in the norm of the response vector. This paper provides a complete modal cost analysis for beam-like continua. Upper bounds are developed for mode truncation errors in the model reduction process and modal cost analysis dictates which modes to retain in order to reduce the model for control design purposes.

Hu, A.↗

NASA NTRS1989

A parallel finite-difference method for computational aerodynamics

A finite-difference scheme for solving complex three-dimensional aerodynamic flow on parallel-processing supercomputers is presented. The method consists of a basic flow solver with multigrid convergence acceleration, embedded grid refinements, and a zonal equation scheme. Multitasking and vectorization have been incorporated into the algorithm. Results obtained include multiprocessed flow simulations from the Cray X-MP and Cray-2. Speedups as high as 3.3 for the two-dimensional case and 3.5 for segments of the three-dimensional case have been achieved on the Cray-2. The entire solver attained a factor of 2.7 improvement over its unitasked version on the Cray-2. The performance of the parallel algorithm on each machine is analyzed.

Swisshelm, Julie M.↗