SEARCH · Engineering Papers

Results for “algorithms and data structure”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29

All sources NASA NTRS DOE OSTI

NASA NTRS1985

Estimation of dynamic rotor loads for the rotor systems research aircraft: Methodology development and validation

The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission systm from the fuselage. A mathematical model relating applied rotor loads and inertial loads of the rotor/transmission system to the load cell response is required to allow the load cells to be used to estimate rotor loads from flight data. Such a model is derived analytically by applying a force and moment balance to the isolated rotor/transmission system. The model is tested by comparing its estimated values of applied rotor loads with measured values obtained from a ground based shake test. Discrepancies in the comparison are used to isolate sources of unmodeled external loads. Once the structure of the mathematical model has been validated by comparison with experimental data, the parameters must be identified. Since the parameters may vary with flight condition it is desirable to identify the parameters directly from the flight data. A Maximum Likelihood identification algorithm is derived for this purpose and tested using a computer simulation of load cell data. The identification is found to converge within 10 samples. The rapid convergence facilitates tracking of time varying parameters of the load cell model in flight.

Duval, R. W.↗

NASA NTRS1987

State-space formulation of multi-shaker modal analysis

As modal testing techniques have improved in recent years, the demands placed on the accuracy of modal test results have increased. This paper describes a new multi-input, multi-output modal parameter estimation algorithm suitable for the identification of reduced-order models of structures. The method described is a frequency-domain method based on excitation and response spectra. The principal features of the algorithm are: (1) applicability to general linear, time-invariant systems, (2) direct use of multiple input and multiple response data, (3) identification of reduced-order models with minimum user interaction through the use of two automatic model-reduction techniques, (4) identification of a consistent set of modal parameters through the use of numerically stable least-squares techniques, and (5) complete user documentation.

Craig, Roy R., Jr.↗

NASA NTRS1991

Sensor placement for on-orbit modal testing

A systematic procedure of placing accelerometers for the on-orbit modal identification of large flexible space structures is addressed. Target modes for modal testing are selected by examining the modal cost of each mode. Assuming that a time-domain modal identification algorithm such as Eigensystem Realization Algorithm is employed to identify the modes from measured time response data, the sensors are placed to ensure the recovery of the target modes. As an application example of the procedure, an accelerometer placement study for the Space Station Freedom On-Orbit Modal Identification Experiment is presented.

Lim, Tae W.↗

NASA NTRS1990

Rain and rain system structure from SSM/I measurements

Algorithms that are currently being developed for the Tropical Rainfall Measuring Mission for the retrieval of the surface rainfall and of the rain-system vertical structure from passive microwave measurements are described. The utility of the procedure is illustrated using data from the Special Sensor Microwave/Imager. The results, which are still of preliminary nature, show the advantages of incorporating the rain-system vertical structure into the precipitation retrievals.

Kummerow, Christian↗

NASA NTRS1994

Spectral representation of high-frequency Space Shuttle data

High frequency Space Shuttle liftoff data are treated by autoregressive (AR) and autoregressive-moving-average (ARMA) digital algorithms. These algorithms provide useful information on the spectral densities of the data. Further, they yield spectral models, which lend themeselves to incorporation into the concept of the random response spectrum. This concept yields a reasonably smooth power spectrum for the design of structural and mechanical systems when the available data bank is limited. Due to the nonstationary of the liftoff event, the pertinent data are split into three slices. Each of the slices is associated with a rather distinguished phase of the liftoff event, in which stationarity can be expected. The presented results are preliminary in nature; they aim to call attention to the availability of the discussed concepts and to the need to augment the Space Shuttle data bank as more flights are completed.

Spanos, P. D.↗

NASA NTRS1998

Simulation of Long Lived Tracers Using an Improved Empirically-Based Two-Dimensional Model Transport Algorithm

We have developed a new empirically-based transport algorithm for use in our GSFC two-dimensional transport and chemistry assessment model. The new algorithm contains planetary wave statistics, and parameterizations to account for the effects due to gravity waves and equatorial Kelvin waves. We will present an overview of the new algorithm, and show various model-data comparisons of long-lived tracers as part of the model validation. We will also show how the new algorithm gives substantially better agreement with observations compared to our previous model transport. The new model captures much of the qualitative structure and seasonal variability observed methane, water vapor, and total ozone. These include: isolation of the tropics and winter polar vortex, the well mixed surf-zone region of the winter sub-tropics and mid-latitudes, and the propagation of seasonal signals in the tropical lower stratosphere. Model simulations of carbon-14 and strontium-90 compare fairly well with observations in reproducing the peak in mixing ratio at 20-25 km, and the decrease with altitude in mixing ratio above 25 km. We also ran time dependent simulations of SF6 from which the model mean age of air values were derived. The oldest air (5.5 to 6 years) occurred in the high latitude upper stratosphere during fall and early winter of both hemispheres, and in the southern hemisphere lower stratosphere during late winter and early spring. The latitudinal gradient of the mean ages also compare well with ER-2 aircraft observations in the lower stratosphere.

Fleming, Eric L.↗

NASA NTRS2001

Observed and Simulated Radiative and Microphysical Properties of Tropical Convective Storms

Increases in the ice content, albedo and cloud cover of tropical convective storms in a warmer climate produce a large negative contribution to cloud feedback in the GISS GCM. Unfortunately, the physics of convective upward water transport, detrainment, and ice sedimentation, and the relationship of microphysical to radiative properties, are all quite uncertain. We apply a clustering algorithm to TRMM satellite microwave rainfall retrievals to identify contiguous deep precipitating storms throughout the tropics. Each storm is characterized according to its size, albedo, OLR, rain rate, microphysical structure, and presence/absence of lightning. A similar analysis is applied to ISCCP data during the TOGA/COARE experiment to identify optically thick deep cloud systems and relate them to large-scale environmental conditions just before storm onset. We examine the statistics of these storms to understand the relative climatic roles of small and large storms and the factors that regulate convective storm size and albedo. The results are compared to GISS GCM simulated statistics of tropical convective storms to identify areas of agreement and disagreement.

DelGenio, Anthony D.↗

NASA NTRS2000

Filtering in Hybrid Dynamic Bayesian Networks

We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).

Andersen, Morten Nonboe↗

NASA NTRS2008

Focused Metabolite Profiling for Dissecting Cellular and Molecular Processes of Living Organisms in Space Environments

Regulatory control in biological systems is exerted at all levels within the central dogma of biology. Metabolites are the end products of all cellular regulatory processes and reflect the ultimate outcome of potential changes suggested by genomics and proteomics caused by an environmental stimulus or genetic modification. Following on the heels of genomics, transcriptomics, and proteomics, metabolomics has become an inevitable part of complete-system biology because none of the lower "-omics" alone provide direct information about how changes in mRNA or protein are coupled to changes in biological function. The challenges are much greater than those encountered in genomics because of the greater number of metabolites and the greater diversity of their chemical structures and properties. To meet these challenges, much developmental work is needed, including (1) methodologies for unbiased extraction of metabolites and subsequent quantification, (2) algorithms for systematic identification of metabolites, (3) expertise and competency in handling a large amount of information (data set), and (4) integration of metabolomics with other "omics" and data mining (implication of the information). This article reviews the project accomplishments.

Source record↗

NASA NTRS2009

A Survey of New Trends in Symbolic Execution for Software Testing and Analysis

Symbolic execution is a well-known program analysis technique which represents values of program inputs with symbolic values instead of concrete (initialized) data and executes the program by manipulating program expressions involving the symbolic values. Symbolic execution has been proposed over three decades ago but recently it has found renewed interest in the research community, due in part to the progress in decision procedures, availability of powerful computers and new algorithmic developments. We provide a survey of some of the new research trends in symbolic execution, with particular emphasis on applications to test generation and program analysis. We first describe an approach that handles complex programming constructs such as input data structures, arrays, as well as multi-threading. We follow with a discussion of abstraction techniques that can be used to limit the (possibly infinite) number of symbolic configurations that need to be analyzed for the symbolic execution of looping programs. Furthermore, we describe recent hybrid techniques that combine concrete and symbolic execution to overcome some of the inherent limitations of symbolic execution, such as handling native code or availability of decision procedures for the application domain. Finally, we give a short survey of interesting new applications, such as predictive testing, invariant inference, program repair, analysis of parallel numerical programs and differential symbolic execution.

Pasareanu, Corina S.↗

NASA NTRS2018

Data Mining for Vortices on the Earth's Magnetosphere - Algorithm Application for Detection and Analysis

Unsteady processes in the solar wind– magnetosphere interaction, such as vortices developed at the magnetopause boundary by the Kelvin–Helmholtz instability, may contribute to the process of mass, momentum and energy transfer into the Earth’s magnetosphere. The research described in this paper validates an algorithm to automatically detect and characterize vortices based on velocity data from simulations. The vortex identification algorithm (VIA) systematically searches the 3-D velocity fields to identify critical points where the magnitude of the velocity vector vanishes. The velocity gradient tensor is computed and its invariants are used to assess vortex structure in the flow field. We use the Community Coordinated Modeling Center (CCMC) Runs on Request capability to create a series of model runs initialized from the conditions observed by the Cluster mission in the Hwang et al. (2011) analysis of Kelvin–Helmholtz vortices observed during southward interplanetary magnetic field (IMF) conditions. We analyze further the properties of the vortices found in the runs, including the velocity changes within their motion across the magnetosheath. We also demonstrate the potential of our tool to identify and characterize other transient features (e.g., flux transfer events, FTEs) with vortical internal structures. We find that the vortices are associated with flows on the magnetosheath side of the magnetopause that reach speeds greater than the solar wind speed at the bow shock.

Collado-Vega, Yaireska M.↗

NASA NTRS2015

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Segments↗

NASA NTRS2023

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Yang Zhang↗

NASA NTRS1975

Transfer-function-parameter estimation from frequency response data: A FORTRAN program

A FORTRAN computer program designed to fit a linear transfer function model to given frequency response magnitude and phase data is presented. A conjugate gradient search is used that minimizes the integral of the absolute value of the error squared between the model and the data. The search is constrained to insure model stability. A scaling of the model parameters by their own magnitude aids search convergence. Efficient computer algorithms result in a small and fast program suitable for a minicomputer. A sample problem with different model structures and parameter estimates is reported.

Seidel, R. C.↗

NASA NTRS2018

Food Quality and Phytoplankton Community Composition in San Francisco Bay using Imaging Spectroscopy Data from the California HyspIRI Airborne Campaign

The San Francisco Bay (SFB) is the largest estuary on the west coast of the United States. It is an important transition zone between marine, freshwater, and inland terrestrial watersheds. The SFB is an important region for the cycling of nutrients and pollutants and it supports nurseries of ecologically and commercially important fisheries, including some threatened species. Phytoplankton community structure influences food web dynamics, and the taxonomy of the phytoplankton may be more important in determining primary food quality than environmental factors. As such, estimating food quality from phytoplankton community composition can be a robust tool to understand trophic transfer of energy. Recent work explores phytoplankton food quality in SFB through the use of microscopy and phytoplankton chemotaxonomy to evaluate how changes in phytoplankton composition may have influenced the recent trophic collapse of pelagic fishes in the northern part of the SFB. The objective of this study is to determine if the approach can also be applied to imaging spectroscopy data in order to quantify phytoplankton food quality from space. Imaging spectroscopy data of SFB from the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) was collected during the Hyperspectral Infrared (HyspIRI) Airborne Campaign in California (2013 2015) and used in this study. Estimates of ocean chlorophyll and phytoplankton community structure were determined using standard ocean chlorophyll algorithms and the PHYtoplankton Detection with Optics (PHYDOTax) algorithms. These were validated using in situ observations of phytoplankton composition using microscopic cell counts and phytoplankton chemotaxonomy from the US Geological Surveys ship surveys of the SFB. The findings from this study may inform the use of future high spectral resolution satellite sensors with the spatial resolution appropriate for coastal systems (e.g., HyspIRI) to assess food quality from space.

HyspIRI↗

NASA NTRS2018

ICESat-2 Photon Classification: Finding Signal Photons in the ATL03 Geolocated Photon Data Product

ICESat-2 carries NASA's next-generation laser altimeter, ATLAS, (Advanced Topographic Laser Altimeter System), designed to measure changes in ice sheet height, sea ice freeboard, and vegetation canopy height. ATLAS contains a photon-counting lidar which transmits green (532-nm) pulses at 10kHz. Each pulse is split into 3 pairs of beams (one strong and one weak). Approximately 1014 photons per pulse travel from ATLAS through the atmosphere to reflect off the Earth's surface. Some return back into the ATLAS telescope where they are recorded. Photons from sunlight and instrument noise at the same wavelength are also recorded. The flight software time tags all photons within a 500m to 6 km range window and generates histograms. Using the histograms, it selects a telemetry window which varies from 20m over flat surfaces to hundreds of meters over rougher terrain. ATL03 contains the time, height (relative to the WGS-84 ellipsoid), latitude and longitude of every photon within the telemetry window. The basic challenge is to determine which of these photons were reflected off the surface. We have developed an algorithm that identifies these signal photons and assigns a confidence level (low, medium, or high) to each signal photon based on the signal to noise ratio. We present an overview of the signal identification algorithm and show the results on actual ICESat-2 data over ice sheet, sea ice, vegetated, and water surfaces. Higher level ATLAS products work with aggregations of the photons in order to determine the ellipsoidal height of the Earth, canopy height and structure, and other quantities of geophysical interest.

Remote Sensing↗

NASA NTRS2021

Understanding the Global Three-Dimensional Distribution of Precipitation Mean Particle Size with the Global Precipitation Measurement Mission

This study addresses the global distribution of precipitation mean particle size using data from the Global Precipitation Measurement (GPM) mission. The mass-weighted mean diameter Dm is a characteristic parameter of the precipitation particle size distribution (PSD), estimated from the GPM Combined Radar–Radiometer Algorithm (CORRA) using data from GPM’s dual-frequency precipitation radar and microwave imager. We examine Dm in individual precipitation systems in different climate regimes and investigate a 6-yr (2014–20) global climatology within 70°N–70°S. The vertical structure of Dm is demonstrated with cases of deep convection, frontal rain and snow, and stratocumulus light rain. The Dm values, detectable by GPM, range from ~0.7 mm in stratocumulus precipitation to >3.5 mm in the ice layers of intense convection. Within the constraint of the 12-dBZ detectability threshold, the smallest annual mean Dm (~0.8 mm) are found in the eastern oceans, and the largest values (~2 mm) occur above the melting levels in convection over land in summer. The standard deviation of the annual mean is generally <0.45 mm below 6 km. Climate regimes are characterized with Dm annual/seasonal variations, its convective/stratiform components, and vertical variabilities (2–10 km). The U.S. Central Plains and Argentina are associated with the largest Dm in a deep layer. Tropical Africa has larger Dm and standard deviation than Amazon. Large convective Dm occurs at high latitudes of Eurasia and North America in summer; the Southern Hemisphere high latitudes have shallower systems with smaller Dm. Oceanic storm tracks in both hemispheres have relatively large Dm, particularly for convective Dm in winter. Relatively small Dm occurs over tropical oceans, including ITCZ, requiring further investigation.

Mei Han↗

NASA NTRS1976

Architecture and data processing alternatives for the TSE computer. Volume 3: Execution of a parallel counting algorithm using array logic (Tse) devices

A parallel algorithm for counting the number of logic-l elements in a binary array or image developed during preliminary investigation of the Tse concept is described. The counting algorithm is implemented using a basic combinational structure. Modifications which improve the efficiency of the basic structure are also presented. A programmable Tse computer structure is proposed, along with a hardware control unit, Tse instruction set, and software program for execution of the counting algorithm. Finally, a comparison is made between the different structures in terms of their more important characteristics.

Metcalfe, A. G.↗