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NASA NTRS2018

An Investigation of Parallel Programming Techniques Applied to Monte Carlo Simulations for Post-Flight Reconstruction of Spacecraft Trajectory

Parallelizing software to execute on multi-core central processing units (CPUs) and graphics processing units (GPUs) can be challenging. For some fields outside of Computer Science, this transition comes with new issues. For example, memory limitations can require modifications to code not initially developed to run on GPUs. This work applies the Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) directive-based parallelization strategies on a Monte Carlo simulation approach for trajectory reconstruction enabling it to run on multi-core CPUs and GPUs. Large matrix operations are the most common use of GPUs, which are not present in this algorithm; however, the natural parallelism of independent trajectories in Monte Carlo simulations is exploited. Benchmarking data are presented comparing execution times of the software for single-thread CPUs, multi-thread CPUs with OpenMP, and multi-thread GPUs using OpenACC. These data were collected using nodes with Intel® Xeon® E5-2670 (Sandy Bridge) CPUs enhanced with NVIDIA® Tesla® K40 GPUs on the Pleiades Supercomputer cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC) and a local Intel® Xeon Phi™ node at NASA Langley Research Center (LaRC).

Williams, R. Anthony↗

NASA NTRS1991

An X-ray image of the violent interstellar medium in 30 Doradus

A detailed analysis of the X-ray emission from the largest H II region complex in the Local Group, 30 Dor, is presented. Applying a new maximum entropy deconvolution algorithm to the Einstein Observatory data, reveals striking correlations among the X-ray, radio, and optical morphologies of the region, with X-ray-emitting bubbles filling cavities surrounded by H-alpha shells and coextensive diffuse X-ray and radio continuum emission from throughout the region. The total X-ray luminosity in the 0.16-3.5 keV band from an area within 160 pc of the central cluster R136 is about 2 x 10 to the 37th ergs/sec.

Wang, Q.↗

NASA NTRS2008

Hail Size Distribution Mapping

A 3-D weather radar visualization software program was developed and implemented as part of an experimental Launch Pad 39 Hail Monitor System. 3DRadPlot, a radar plotting program, is one of several software modules that form building blocks of the hail data processing and analysis system (the complete software processing system under development). The spatial and temporal mapping algorithms were originally developed through research at the University of Central Florida, funded by NASA s Tropical Rainfall Measurement Mission (TRMM), where the goal was to merge National Weather Service (NWS) Next-Generation Weather Radar (NEXRAD) volume reflectivity data with drop size distribution data acquired from a cluster of raindrop disdrometers. In this current work, we adapted these algorithms to process data from a cluster of hail disdrometers positioned around Launch Pads 39A or 39B, along with the corresponding NWS radar data. Radar data from all NWS NEXRAD sites is archived at the National Climatic Data Center (NCDC). That data can be readily accessed at . 3DRadPlot plots Level III reflectivity data at four scan elevations (this software is available at Open Channel Software, ). By using spatial and temporal interpolation/extrapolation based on hydrometeor fall dynamics, we can merge the hail disdrometer array data coupled with local Weather Surveillance Radar-1988, Doppler (WSR-88D) radial velocity and reflectivity data into a 4-D (3-D space and time) picture of hail size distributions. Hail flux maps can then be generated and used for damage prediction and assessment over specific surfaces corresponding to structures within the disdrometer array volume. Immediately following a hail storm, specific damage areas and degree of damage can be identified for inspection crews.

Source record↗

NASA NTRS1985

Extracting sea ice data from satellite SAR imagery

With the prospect operational satellite SAR's by the end of the decade, there is a clear need to develop automated algorithms for the extraction of geophysical data about sea ice from high resolution radar imagery. To this end, techniques were developed for distinguishing ice from open water and for resolving the details of deformation within areas 100 km square imaged by SEASAT SAR. The classification of ice and open water is based on the creation of two bands of lower resolution image data: local average brightness, and the local variance of brightness. In the space of these two variables, ice and open water are separated into two distinct clusters. The deformation is found on a 3.4 km mesh by local cross-correlations of the brightness. Comparison with manually determined deformation shows room for improvement in regions of high deformation by using smaller areas for cross-correlation. Th concentration and deformation data are used together to determine localized regions of the scene where open water is produced or lost.

Fily, M.↗

NASA NTRS2000

Message Passing vs. Shared Address Space on a Cluster of SMPs

The convergence of scalable computer architectures using clusters of PCs (or PC-SMPs) with commodity networking has become an attractive platform for high end scientific computing. Currently, message-passing and shared address space (SAS) are the two leading programming paradigms for these systems. Message-passing has been standardized with MPI, and is the most common and mature programming approach. However message-passing code development can be extremely difficult, especially for irregular structured computations. SAS offers substantial ease of programming, but may suffer from performance limitations due to poor spatial locality, and high protocol overhead. In this paper, we compare the performance of and programming effort, required for six applications under both programming models on a 32 CPU PC-SMP cluster. Our application suite consists of codes that typically do not exhibit high efficiency under shared memory programming. due to their high communication to computation ratios and complex communication patterns. Results indicate that SAS can achieve about half the parallel efficiency of MPI for most of our applications: however, on certain classes of problems SAS performance is competitive with MPI. We also present new algorithms for improving the PC cluster performance of MPI collective operations.

Shan, Hongzhang↗

NASA NTRS1989

Image segmentation by iterative parallel region growing and splitting

The spatially constrained clustering (SCC) iterative parallel region-growing technique is applied to image analysis. The SCC algorithm is implemented on the massively parallel processor at NASA Goddard. Most previous region-growing approaches have the drawback that the segmentation produced depends on the order in which portions of the image are processed. The ideal solution to this problem (merging only the single most similar pair of spatially adjacent regions in the image in each iteration) becomes impractical except for very small images, even on a massively parallel computer. The SCC algorithm overcomes these problems by performing, in parallel, the best merge within each of a set of local, possibly overlapping, subimages. A region-splitting stage is also incorporated into the algorithm, but experiments show that region splitting generally does not improve segmentation results. The SCC algorithm has been tested on various imagery data, and test results for a Landsat TM image are summarized.

Tilton, James C.↗

NASA NTRS1983

Parallel architectures for iterative methods on adaptive, block structured grids

A parallel computer architecture well suited to the solution of partial differential equations in complicated geometries is proposed. Algorithms for partial differential equations contain a great deal of parallelism. But this parallelism can be difficult to exploit, particularly on complex problems. One approach to extraction of this parallelism is the use of special purpose architectures tuned to a given problem class. The architecture proposed here is tuned to boundary value problems on complex domains. An adaptive elliptic algorithm which maps effectively onto the proposed architecture is considered in detail. Two levels of parallelism are exploited by the proposed architecture. First, by making use of the freedom one has in grid generation, one can construct grids which are locally regular, permitting a one to one mapping of grids to systolic style processor arrays, at least over small regions. All local parallelism can be extracted by this approach. Second, though there may be a regular global structure to the grids constructed, there will be parallelism at this level. One approach to finding and exploiting this parallelism is to use an architecture having a number of processor clusters connected by a switching network. The use of such a network creates a highly flexible architecture which automatically configures to the problem being solved.

Gannon, D.↗

NASA NTRS1990

Potential, velocity, and density fields from redshift-distance samples: Application - Cosmography within 6000 kilometers per second

A potential flow reconstruction algorithm has been applied to the real universe to reconstruct the three-dimensional potential, velocity, and mass density fields smoothed on large scales. The results are shown as maps of these fields, revealing the three-dimensional structure within 6000 km/s distance from the Local Group. The dominant structure is an extended deep potential well in the Hydra-Centaurus region, stretching across the Galactic plane toward Pavo, broadly confirming the Great Attractor (GA) model of Lynden-Bell et al. (1988). The Local Supercluster appears to be an extended ridge on the near flank of the GA, proceeding through the Virgo Southern Extension to the Virgo and Ursa Major clusters. The Virgo cluster and the Local Group are both falling toward the bottom of the GA potential well with peculiar velocities of 658 + or - 121 km/s and 565 + or - 125 km/s, respectively.

Bertschinger, Edmund↗

NASA NTRS2001

Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks

Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that expected hitting time is a rational function - i.e. a ratio of two polynomials - of the probability of applying an additive search operator. Experimentally, we report on synthetically generated BNs as well as BNs from applications, and compare SGSs performance to that of Hugin, which performs BN inference by compilation to and propagation in clique trees. On synthetic networks, SGS speeds up computation by approximately two orders of magnitude compared to Hugin. In application networks, our approach is highly competitive in Bayesian networks with a high degree of determinism. In addition to showing that stochastic local search can be competitive with clique tree clustering, our empirical results provide an improved understanding of the circumstances under which portfolio-based SLS outperforms clique tree clustering and vice versa.

Mengshoel, Ole J.↗

NASA NTRS2002

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 kin or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed- shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗

NASA NTRS2013

ISSM: Ice Sheet System Model

In order to have the capability to use satellite data from its own missions to inform future sea-level rise projections, JPL needed a full-fledged ice-sheet/iceshelf flow model, capable of modeling the mass balance of Antarctica and Greenland into the near future. ISSM was developed with such a goal in mind, as a massively parallelized, multi-purpose finite-element framework dedicated to ice-sheet modeling. ISSM features unstructured meshes (Tria in 2D, and Penta in 3D) along with corresponding finite elements for both types of meshes. Each finite element can carry out diagnostic, prognostic, transient, thermal 3D, surface, and bed slope simulations. Anisotropic meshing enables adaptation of meshes to a certain metric, and the 2D Shelfy-Stream, 3D Blatter/Pattyn, and 3D Full-Stokes formulations capture the bulk of the ice-flow physics. These elements can be coupled together, based on the Arlequin method, so that on a large scale model such as Antarctica, each type of finite element is used in the most efficient manner. For each finite element referenced above, ISSM implements an adjoint. This adjoint can be used to carry out model inversions of unknown model parameters, typically ice rheology and basal drag at the ice/bedrock interface, using a metric such as the observed InSAR surface velocity. This data assimilation capability is crucial to allow spinning up of ice flow models using available satellite data. ISSM relies on the PETSc library for its vectors, matrices, and solvers. This allows ISSM to run efficiently on any parallel platform, whether shared or distrib- ISSM: Ice Sheet System Model NASA's Jet Propulsion Laboratory, Pasadena, California uted. It can run on the largest clusters, and is fully scalable. This allows ISSM to tackle models the size of continents. ISSM is embedded into MATLAB and Python, both open scientific platforms. This improves its outreach within the science community. It is entirely written in C/C++, which gives it flexibility in its design, and the power/speed that C/C++ allows. ISSM is svn (subversion) hosted, on a JPL repository, to facilitate its development and maintenance. ISSM can also model propagation of rifts using contact mechanics and mesh splitting, and can interface to the Dakota software. To carry out sensitivity analysis, mesh partitioning algorithms are available, based on the Scotch, Chaco, and Metis partitioners that ensure equal area mesh partitions can be done, which are then usable for sampling and local reliability methods.

Larour, Eric↗

NASA NTRS2002

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 km or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed-shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗

NASA NTRS2006

Diurnal Variability of Vertical Structure from a TRMM Passive Microwave "Virtual Radar" Retrieval

Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).

Boccippio, Dennis J.↗

NASA NTRS2015

A Data Exploration Tool for Large Sets of Spectra

We present an exploration tool for very large spectrum data sets such as the SDSS (Sloan Digital Sky Survey), LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope), and 4MOST (4-meter Multi-Object Spectroscopic Telescope) data sets. The tool works in two stages: the first uses batch processing and the second runs interactively. The latter employs the NASA hyperwall, a configuration of 128 workstation displays (8 by 16 array) controlled by a parallelized software suite running on NASA's Pleiades supercomputer. The stellar subset of the Sloan Digital Sky Survey, DR10, was chosen to show how the our tool may be used. In stage one, SDSS files for 569,740 stars are processed through our data pipeline. The pipeline fits each spectrum using an iterative continuum algorithm, distinguishing emission from absorption and handling molecular absorption bands correctly. It then measures 1659 discrete atomic and molecular spectral features that were carefully preselected based on their likelihood of being visible at some spectral type. The depths relative to the local continuum at each feature wavelength are determined for each spectrum: these depths, the local S/N (signal to noise ratio) level, and DR10-supplied variables such as magnitudes, colors, positions, and radial velocities are the basic measured quantities used on the hyperwall. In stage two, each hyperwall panel is used to display a 2-D scatter plot showing the depth of feature A vs the depth of feature B for all of the stars. A and B change from panel to panel. The relationships between the various (A,B) strengths and any distinctive clustering are immediately apparent when examining and inter-comparing the different panels on the hyperwall. The interactive software allows the user to select the stars in any interesting region of any 2-D plot on the hyperwall, immediately rendering the same stars on all the other 2-D plots in a unique color. The process may be repeated multiple times, each selection displaying a distinctive color on all the plots. At any time, the spectra of the selected stars may be examined in detail on a connected workstation display. We illustrate how our approach allows us to quickly isolate and examine such interesting stellar subsets as EMP (Extremely Metal‐Poor) stars, CV (Cataclymic Variable) stars and C (Carbon)-rich stars.

Data Exploration↗

NASA NTRS2002

Identification of characteristic oligonucleotides in the bacterial 16S ribosomal RNA sequence dataset

MOTIVATION: The phylogenetic structure of the bacterial world has been intensively studied by comparing sequences of 16S ribosomal RNA (16S rRNA). This database of sequences is now widely used to design probes for the detection of specific bacteria or groups of bacteria one at a time. The success of such methods reflects the fact that there are local sequence segments that are highly characteristic of particular organisms or groups of organisms. It is not clear, however, the extent to which such signature sequences exist in the 16S rRNA dataset. A better understanding of the numbers and distribution of highly informative oligonucleotide sequences may facilitate the design of hybridization arrays that can characterize the phylogenetic position of an unknown organism or serve as the basis for the development of novel approaches for use in bacterial identification. RESULTS: A computer-based algorithm that characterizes the extent to which any individual oligonucleotide sequence in 16S rRNA is characteristic of any particular bacterial grouping was developed. A measure of signature quality, Q(s), was formulated and subsequently calculated for every individual oligonucleotide sequence in the size range of 5-11 nucleotides and for 15mers with reference to each cluster and subcluster in a 929 organism representative phylogenetic tree. Subsequently, the perfect signature sequences were compared to the full set of 7322 sequences to see how common false positives were. The work completed here establishes beyond any doubt that highly characteristic oligonucleotides exist in the bacterial 16S rRNA sequence dataset in large numbers. Over 16,000 15mers were identified that might be useful as signatures. Signature oligonucleotides are available for over 80% of the nodes in the representative tree.

NASA Discipline Life Sciences Technologies↗

NASA NTRS1990

An optimal sensing strategy of a proximity sensor system for recognition and localization of polyhedral objects

An algorithm is presented for the recognition and localization of thre-dimensional polyhedral objects based on an optical proximity sensor system capable of measuring the depth and orientation of a local area of an object surface. Emphasis is given to the determination of an optimal sensor trajectory or an optimal probing, for efficient discrimination among all the possible interpretations. The determination of an optimal sensor trajectory for the next probing consists of the selection of optimal beam orientations based on the surface normal vector distribution of the multiple interpretation image (MII) and the selection of an optimal probing plane by projecting the MII onto the projection plane perpendicular to a selected beam orientation and deriving the optimal path on the projection plane. The selection of optimal beam orientation and probing plane is based on the measure of discrimination power of a cluster of surfaces of an MII. Simulation results are shown.

Lee, Sukhan↗

NASA NTRS2023

Multiyear Dry Periods in Southern Africa

Characteristics and physical features related to low precipitation across many years in Southern Africa that lead to societal disruptions are diagnosed using observed analyses and an ensemble of historical coupled climate model simulations during 1921 to 2014. Four regions are evaluated, as identified through a hierarchical clustering algorithm applied to the Standardized Precipitation Index (SPI) during the October–April precipitation season. Although dryness spanning many October–April occurs periodically in each region, they seldom occur simultaneously, consistent with largely insignificant SPI cross-correlations between them. However, characteristics relevant to low precipitation across many years are generalizable between the four regions, including the serial persistence of October–April precipitation, the likelihood of consecutive dry October–April, and the likelihood of dry October–April in temporal extents of up to 10 consecutive such 7-month seasons. Systematic precipitation persistence is not a feature in any of the four Southern Africa regions, as serial correlations of October–April SPI are not statistically significant at any time lags. It follows that there is an exponential-folding decay in the likelihood of consecutive October–April for various SPI thresholds and that there is a large spread in the likelihood of low October–April SPI across many years. In terms of physical features, low October–April SPI in each Southern Africa region is closely related to local atmospheric circulations; however, they are not as closely related to sea surface temperatures (SSTs). These results suggest that dryness spanning many years is determined primarily by persistent local circulations related to atmospheric variability and to a lesser extent variability related to SST anomalies, including the El Niño–Southern Oscillation.

subtropical Indian Ocean dipole↗

NASA NTRS2002

Fatigue Crack and Porosity Measurement in Composite Materials by Thermographic and Ultrasonic Methods

Many nondestructive methods exist for the detection of localized material anomalies in an otherwise good composite structure. The problem arises when the material system as a whole has degraded during service or was improperly manufactured. Porosity and intra-ply microcracking are two such conditions that in unlined composite pressure vessels can be very troublesome to detect and when linked through the thickness can be critical to mission success. These leak paths may lead to loss of pressure/propellant, increased risk of explosion and possible cryo-pumping. Research sought nondestructive methods for quantifying porosity and microcracking in composite tankage. Both thermographic and resonance ultrasound methods have been utilized with artificial neural network and statistical approaches to analyze the data. Resonant ultrasound spectroscopy provides measurements, which are sensitive to fine details in the materials character, such as micro-cracking and porosity. Here, the higher frequency (shorter wavelength) components of the signal train provide more significant interaction with the defects causing the spectral characteristics to shift toward lower amplitudes at the higher frequencies. As the density of the defects increases more interactions occur and more drastic amplitude changes are observed. From a thermal perspective, the higher the defect density the lower the through thickness thermal diffusivity will be. Utilizing a point heat source, and thermographically recording the heat profile with time, diffusivity calculations can be made which in turn can be related to the relative quality of the material. Preliminary experiments to verify the measurable effect on the resonance spectrum of the ultrasonic data to detect microcracking and for porosity detection thermographically are presented. Methods involving supervised and unsupervised artificial neural networks as well as other clustering algorithms are developed for signal identification.

Walker, James L.↗