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NASA NTRS2008

Efficient Kriging via Fast Matrix-Vector Products

Interpolating scattered data points is a problem of wide ranging interest. Ordinary kriging is an optimal scattered data estimator, widely used in geosciences and remote sensing. A generalized version of this technique, called cokriging, can be used for image fusion of remotely sensed data. However, it is computationally very expensive for large data sets. We demonstrate the time efficiency and accuracy of approximating ordinary kriging through the use of fast matrixvector products combined with iterative methods. We used methods based on the fast Multipole methods and nearest neighbor searching techniques for implementations of the fast matrix-vector products.

Memarsadeghi, Nargess↗
NASA NTRS2008

Novel Species of Non-Spore-Forming Bacteria

While cataloging cultivatable microbes from the airborne biological diversity of the atmosphere of the Regenerative Enclosed life-support Module Simulator (REMS) system at Marshall Space Flight Center, two strains that belong to one novel bacterial species were isolated. Based on 16S rRNA gene sequencing and the unique morphology and the taxonomic characteristics of these strains, it is shown that they belong to the family Intrasporangiaceae, related to the genus Tetrasphaera, with phylogenetic distances from any validly described species of the genus Tetrasphaera ranging from 96.71 to 97.76 percent. The fatty acid profile supported the affiliation of these novel strains to the genus Tetrasphaera except for the presence of higher concentrations of octadecenoic acid (C18:0) and cis-9-octadecenoic acid (C18:1), which discriminates these strains from other valid species. In addition, DNA-DNA hybridization studies indicate that these strains belong to a novel species that could be readily distinguished from its nearest neighbor, Tetrasphaera japonica AMC 5116T, with less than 20 percent DNA relatedness. Physiological and biochemical tests show few phenotypic dissimilarities, but genotypic analysis allowed the differentiation of these gelatin-liquefying strains from previously reported strains. The name Tetrasphaera remsis sp. Nov. is proposed with the type strain 3-M5-R-4(sup T) (=ATCC BAA-1496(sup T)=CIP 109413(sup T). The cells are Gram-positive, nonmotile, cocci, in tetrad arrangement and clusters. Spore formation is not observed. No species of Tetrashpaera has ever been isolated from airborne samples. Previous discoveries have come from soil and activated sludge samples. As other species of this genus have demonstrated enhanced biological phosphorus removal activity, further tests are required to determine if this newly discovered species would have bioremediation applications.

Briegel, Ariane↗
NASA NTRS2008

Spiking Neurons for Analysis of Patterns

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Huntsberger, Terrance↗
NASA NTRS2010

Comparison of Node-Centered and Cell-Centered Unstructured Finite-Volume Discretizations: Inviscid Fluxes

Cell-centered and node-centered approaches have been compared for unstructured finite-volume discretization of inviscid fluxes. The grids range from regular grids to irregular grids, including mixed-element grids and grids with random perturbations of nodes. Accuracy, complexity, and convergence rates of defect-correction iterations are studied for eight nominally second-order accurate schemes: two node-centered schemes with weighted and unweighted least-squares (LSQ) methods for gradient reconstruction and six cell-centered schemes two node-averaging with and without clipping and four schemes that employ different stencils for LSQ gradient reconstruction. The cell-centered nearest-neighbor (CC-NN) scheme has the lowest complexity; a version of the scheme that involves smart augmentation of the LSQ stencil (CC-SA) has only marginal complexity increase. All other schemes have larger complexity; complexity of node-centered (NC) schemes are somewhat lower than complexity of cell-centered node-averaging (CC-NA) and full-augmentation (CC-FA) schemes. On highly anisotropic grids typical of those encountered in grid adaptation, discretization errors of five of the six cell-centered schemes converge with second order on all tested grids; the CC-NA scheme with clipping degrades solution accuracy to first order. The NC schemes converge with second order on regular and/or triangular grids and with first order on perturbed quadrilaterals and mixed-element grids. All schemes may produce large relative errors in gradient reconstruction on grids with perturbed nodes. Defect-correction iterations for schemes employing weighted least-square gradient reconstruction diverge on perturbed stretched grids. Overall, the CC-NN and CC-SA schemes offer the best options of the lowest complexity and secondorder discretization errors. On anisotropic grids over a curved body typical of turbulent flow simulations, the discretization errors converge with second order and are small for the CC-NN, CC-SA, and CC-FA schemes on all grids and for NC schemes on triangular grids; the discretization errors of the CC-NA scheme without clipping do not converge on irregular grids. Accurate gradient reconstruction can be achieved by introducing a local approximate mapping; without approximate mapping, only the NC scheme with weighted LSQ method provides accurate gradients. Defect correction iterations for the CC-NA scheme without clipping diverge; for the NC scheme with weighted LSQ method, the iterations either diverge or converge very slowly. The best option in curved geometries is the CC-SA scheme that offers low complexity, second-order discretization errors, and fast convergence.

Diskin, Boris↗
NASA NTRS2010

A New Lunar Globe as Seen by the Moon Mineralogy Mapper: Image Coverage Spectral Dimensionality and Statistical Anomalies

The Moon Mineralogy Mapper (M3), a NASA Discovery Mission of Opportunity, was launched October 22, 2008 from Shriharikota in India on board the Indian ISRO Chandrayaan- 1 spacecraft for a nominal two-year mission in a 100-km polar lunar orbit. M3 is a high-fidelity imaging spectrometer with 260 spectral bands in Target Mode and 85 spectral bands in a reduced-resolution Global Mode. Target Mode pixel sizes are nominally 70 meters and Global pixels (binned 2 by 2) are 140 meters, from the planned 100-km orbit. The mission was cut short, just before halfway, in August, 2009 when the spacecraft ceased operations. Despite the abbreviated mission and numerous technical and scientific challenges during the flight, M3 was able to cover more than 95% of the Moon in Global Mode. These data, presented and analyzed here as a global whole, are revolutionizing our understanding of the Moon. Already, numerous discoveries relating to volatiles and unexpected mineralogy have been published [1], [2], [3]. The rich spectral and spatial information content of the M3 data indicates that many more discoveries and an improved understanding of the mineralogy, geology, photometry, thermal regime and volatile status of our nearest neighbor are forthcoming from these data. Sadly, only minimal high-resolution Target Mode images were acquired, as these were to be the focus of the second half of the mission. This abstract gives the reader a global overview of all the M3 data that were collected and an introduction to their rich spectral character and complexity. We employ a Principal Components statistical method to assess the underlying dimensionality of the Moon as a whole, as seen by M3, and to identify numerous areas that are low-probability targets and thus of potential interest to selenologists.

Boardman, J. W.↗
NASA NTRS2009

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

EXPLICIT PHYSICS ALGORITHMS↗
NASA NTRS2010

Comparison of Node-Centered and Cell-Centered Unstructured Finite-Volume Discretizations: Viscous Fluxes

Discretization of the viscous terms in current finite-volume unstructured-grid schemes are compared using node-centered and cell-centered approaches in two dimensions. Accuracy and complexity are studied for four nominally second-order accurate schemes: a node-centered scheme and three cell-centered schemes - a node-averaging scheme and two schemes with nearest-neighbor and adaptive compact stencils for least-square face gradient reconstruction. The grids considered range from structured (regular) grids to irregular grids composed of arbitrary mixtures of triangles and quadrilaterals, including random perturbations of the grid points to bring out the worst possible behavior of the solution. Two classes of tests are considered. The first class of tests involves smooth manufactured solutions on both isotropic and highly anisotropic grids with discontinuous metrics, typical of those encountered in grid adaptation. The second class concerns solutions and grids varying strongly anisotropically over a curved body, typical of those encountered in high-Reynolds number turbulent flow simulations. Tests from the first class indicate the face least-square methods, the node-averaging method without clipping, and the node-centered method demonstrate second-order convergence of discretization errors with very similar accuracies per degree of freedom. The tests of the second class are more discriminating. The node-centered scheme is always second order with an accuracy and complexity in linearization comparable to the best of the cell-centered schemes. In comparison, the cell-centered node-averaging schemes may degenerate on mixed grids, have a higher complexity in linearization, and can fail to converge to the exact solution when clipping of the node-averaged values is used. The cell-centered schemes using least-square face gradient reconstruction have more compact stencils with a complexity similar to that of the node-centered scheme. For simulations on highly anisotropic curved grids, the least-square methods have to be amended either by introducing a local mapping based on a distance function commonly available in practical schemes or modifying the scheme stencil to reflect the direction of strong coupling. The major conclusion is that accuracies of the node centered and the best cell-centered schemes are comparable at equivalent number of degrees of freedom.

Diskin, Boris↗
NASA NTRS2011

Efficient Kriging Algorithms

More efficient versions of an interpolation method, called kriging, have been introduced in order to reduce its traditionally high computational cost. Written in C++, these approaches were tested on both synthetic and real data. Kriging is a best unbiased linear estimator and suitable for interpolation of scattered data points. Kriging has long been used in the geostatistic and mining communities, but is now being researched for use in the image fusion of remotely sensed data. This allows a combination of data from various locations to be used to fill in any missing data from any single location. To arrive at the faster algorithms, sparse SYMMLQ iterative solver, covariance tapering, Fast Multipole Methods (FMM), and nearest neighbor searching techniques were used. These implementations were used when the coefficient matrix in the linear system is symmetric, but not necessarily positive-definite.

Memarsadeghi, Nargess↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗
NASA NTRS2011

Volatile Analyzer for Lunar Polar Missions

One of the major questions remaining for the future exploration of the Moon by humans concerns the presence of volatiles on our nearest neighbor in space. Observational studies, and investigations involving returned lunar samples and using robotic spacecraft infer the existence of volatile compounds particularly water [1]. It seems very likely that a volatile component will be concentrated at the poles in circumstances where low-temperatures exist to provide cryogenic traps. However, the full inventory of species, their concentration and their origin and sources are unknown. Of particular importance is whether abundances are sufficient to act as a resource of consumables for future lunar expeditions especially if a long-term base involving humans is to be established. To address some of these issues requires a lander designed specifically for operation at a high-lunar latitude. A vital part of the payload needs to be a volatile analyzer such as the Gas Analysis Package specifically designed for identification quantification of volatile substances and collecting information which will allow the origin of these volatiles to be identified [1]. The equipment included, particularly the gas analyzer, must be capable of operation in the extreme environmental conditions to be encountered. No accurate information yet exists regarding volatile concentration even for sites closer to the lunar equator (because of contamination). In this respect it will be important to understand (and thus limit) contamination of the lunar surface by extraneous material contributed from a variety of sources. The only data for the concentrations of volatiles at the poles comes from orbiting spacecraft and whilst the levels at high latitudes may be greater than at the equator, the volatile analyzer package under consideration will be designed to operate at the highest specifications possible and in a way that does not compromise the data.

Gibons, Everett K.↗
NASA NTRS2011

Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

Restrepo, Carolina↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗
NASA NTRS2005

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

Li, Winston↗
NASA NTRS2011

Biological Technologies for Life Beyond Low Earth Orbit (BT4LBLEO): Study Introductions and Synopsis

The study will address the following mission concerns: -Extended human presence in the environments of deep space as well as the Moon and Mars will require a solid biological understanding of the integrated effects of diminished gravity, enhanced radiation, and transit- and destination-specific variables from the sub-cellular to the whole organism level. -Biological and associated technologies for biological and robotic precursor missions to realize future objectives for space colonization. -Surfaces, gravity levels, radiation environments, and atmospheres of these nearest neighbors are radically different in chemical and geological make-up from those on our home planet, and all of these contributory effects must be considered.

Hines, John W.↗
NASA NTRS2012

Baby Solar System

What did our solar system look like in its infancy,...... when the planets were forming? We cannot travel back in time to take an image of the early solar system, but in principle we can have the next best thing: images of infant planetary systems around Sun-like stars with ages of 1 to 5 million years, the time we think it took for the giant planets to form. Infant exoplanetary systems are critically important because they can help us understand how our solar system fits within the context of planet formation in general. More than 80% of stars are born with gas- and dust-rich disks, and thus have the potential to form planets. Through many methods we have identified more than 760 planetary systems around middle-aged stars like the Sun, but many of these have architectures that look nothing like our solar system. Young planetary systems are important missing links between various endpoints and may help us understand how and when these differences emerge. Well-known star-forming regions in Taurus, Scorpius. and Orion contain stars that could have infant planetary systems. But these stars are much more distant than our nearest neighbors such as Alpha Centauri or Sirius, making it extremely challenging to produce clear images of systems that can reveal signs of recent planet formation, let alone reveal the planets themselves. Recently, a star with the unassuming name LkCa 15 may have given us our first detailed "baby picture" of a young planetary system similar to our solar system. Located about 450 light-years away in the Taurus starforming region. LkCa 15 has a mass comparable to the Sun (0.97 solar mass) and an age of l to 5 million years, comparable to the time at which Saturn and perhaps Jupiter formed. The star is surrounded by a gas-rich disk similar in structure to the one in our solar system from which the planets formed. With new technologies and observing strategies, we have confirmed suspicions that LkCa 15's disk harbors a young planetary system.

Currie, Thayne↗
NASA NTRS2011

Multiangle Implementation of Atmospheric Correction (MAIAC):: Radiative Transfer Basis and Look-up Tables - 1

This paper describes a radiative transfer basis of the algorithm MAIAC which performs simultaneous retrievals of atmospheric aerosol and bidirectional surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). The retrievals are based on an accurate semianalytical solution for the top-of-atmosphere reflectance expressed as an explicit function of three parameters of the Ross-Thick Li-Sparse model of surface bidirectional reflectance. This solution depends on certain functions of atmospheric properties and geometry which are precomputed in the look-up table (LUT). This paper further considers correction of the LUT functions for variations of surface pressure/height and of atmospheric water vapor, which is a common task in the operational remote sensing. It introduces a new analytical method for the water vapor correction of the multiple ]scattering path radiance. It also summarizes the few basic principles that provide a high efficiency and accuracy of the LUT ]based radiative transfer for the aerosol/surface retrievals and optimize the size of LUT. For example, the single-scattering path radiance is calculated analytically for a given surface pressure and atmospheric water vapor. The same is true for the direct surface-reflected radiance, which along with the single-scattering path radiance largely defines the angular dependence of measurements. For these calculations, the aerosol phase functions and kernels of the surface bidirectional reflectance model are precalculated at a high angular resolution. The other radiative transfer functions depend rather smoothly on angles because of multiple scattering and can be calculated at coarser angular resolution to reduce the LUT size. At the same time, this resolution should be high enough to use the nearest neighbor geometry angles to avoid costly three ]dimensional interpolation. The pressure correction is implemented via linear interpolation between two LUTs computed for the standard and reduced pressure levels. A linear mixture and a modified linear mixture methods are used to represent different aerosol types in the aerosol/surface retrievals from several base models of the fine and coarse aerosol fractions. In summary, the developed LUT algorithm allows fast high-accuracy simulations of the outgoing radiance with full variability of the atmospheric and surface bidirectional reflectance properties for the aerosol/surface remote sensing.

Lyapustin, Alexei↗
NASA NTRS2012

Introduction to Special Section on Results of the Lunar Reconnaissance Orbiter Mission

Since 2009 the Lunar Reconnaissance Orbiter (LRO) has made comprehensive measurements of the Moon and its environment. The seven LRO instruments use a variety of primarily remote sensing techniques to obtain a unique set of observations. The analyses of the LRO data sets have overturned previous beliefs and deepened our appreciation of the complex nature of our nearest neighbor. This introduction to the special section describes the LRO mission and summarizes some of the science results in the papers that follow.

Vondrak, Richard R.↗
NASA NTRS2014

Three-Dimensional Imaging and Numerical Reconstruction of Graphite/Epoxy Composite Microstructure Based on Ultra-High Resolution X-Ray Computed Tomography

A combined experimental and computational study aimed at high-resolution 3D imaging, visualization, and numerical reconstruction of fiber-reinforced polymer microstructures at the fiber length scale is presented. To this end, a sample of graphite/epoxy composite was imaged at sub-micron resolution using a 3D X-ray computed tomography microscope. Next, a novel segmentation algorithm was developed, based on concepts adopted from computer vision and multi-target tracking, to detect and estimate, with high accuracy, the position of individual fibers in a volume of the imaged composite. In the current implementation, the segmentation algorithm was based on Global Nearest Neighbor data-association architecture, a Kalman filter estimator, and several novel algorithms for virtualfiber stitching, smoothing, and overlap removal. The segmentation algorithm was used on a sub-volume of the imaged composite, detecting 508 individual fibers. The segmentation data were qualitatively compared to the tomographic data, demonstrating high accuracy of the numerical reconstruction. Moreover, the data were used to quantify a) the relative distribution of individual-fiber cross sections within the imaged sub-volume, and b) the local fiber misorientation relative to the global fiber axis. Finally, the segmentation data were converted using commercially available finite element (FE) software to generate a detailed FE mesh of the composite volume. The methodology described herein demonstrates the feasibility of realizing an FE-based, virtual-testing framework for graphite/fiber composites at the constituent level.

Czabaj, M. W.↗