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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

Shameldin, Ramez Ahmed↗
NASA NTRS2010

Design and Demonstration of a Miniature Lidar System for Rover Applications

A basic small and portable lidar system for rover applications has been designed. It uses a 20 Hz Nd:YAG pulsed laser, a 4-inch diameter telescope receiver, a custom-built power distribution unit (PDU), and a custom-built 532 nm photomultiplier tube (PMT) to measure the lidar signal. The receiving optics have been designed, but not constructed yet. LabVIEW and MATLAB programs have also been written to control the system, acquire data, and analyze data. The proposed system design, along with some measurements, is described. Future work to be completed is also discussed.

Robinson, Benjamin↗
NASA NTRS2009

Monitoring Invasive Aquatic Vegetation in Lake Okeechobee, Florida, Using NDVI Derived from Modis Data

Lake Okeechobee, located in southern Florida, encompasses approximately 1,700 sq km and is a vital part of the Lake Okeechobee and Everglades ecosystem. Major cyanobacterial blooms have been documented in Lake Okeechobee since the 1970s and have continued to plague the ecosystem. Similarly, hydrilla, water hyacinth, and water lettuce have been documented in the lake and continue to threaten the ecosystem by their rapid growth. This study examines invasive aquatic vegetation occurrence through the use of the Normalized Difference Vegetation Index (NDVI) calculated on MOD09 surface reflectance imagery. Occurrence during 2008 was analyzed using the Time Series Product Tool (TSPT), a MATLAB-based program developed at John C. Stennis Space Center. This project tracked spatial and temporal variability of cyanobacterial blooms, and overgrowth of water lettuce, water hyacinth, and hydrilla. In addition, this study presents an application of Moderate Resolution Imaging Spectroradiometer (MODIS) data to assist in water quality management.

Woods, Kate↗
NASA NTRS2010

Public Risk Assessment Program

The Public Entry Risk Assessment (PERA) program addresses risk to the public from shuttle or other spacecraft re-entry trajectories. Managing public risk to acceptable levels is a major component of safe spacecraft operation. PERA is given scenario inputs of vehicle trajectory, probability of failure along that trajectory, the resulting debris characteristics, and field size and distribution, and returns risk metrics that quantify the individual and collective risk posed by that scenario. Due to the large volume of data required to perform such a risk analysis, PERA was designed to streamline the analysis process by using innovative mathematical analysis of the risk assessment equations. Real-time analysis in the event of a shuttle contingency operation, such as damage to the Orbiter, is possible because PERA allows for a change to the probability of failure models, therefore providing a much quicker estimation of public risk. PERA also provides the ability to generate movie files showing how the entry risk changes as the entry develops. PERA was designed to streamline the computation of the enormous amounts of data needed for this type of risk assessment by using an average distribution of debris on the ground, rather than pinpointing the impact point of every piece of debris. This has reduced the amount of computational time significantly without reducing the accuracy of the results. PERA was written in MATLAB; a compiled version can run from a DOS or UNIX prompt.

Mendeck, Gavin↗
NASA NTRS2010

Particle Swarm Optimization Toolbox

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Grant, Michael J.↗
NASA NTRS2010

Sonic Booms on Big Structures (SonicBOBS) Phase I Database; NASA Dryden Sensors

This DVD contains 13 channels of microphone and up to 22 channels of pressure transducer data collected in September, 2009 around several buildings located at Edwards Air Force Base. These data were recorded by NASA Dryden. Not included are data taken by NASA Langley and Gulfstream. Each day's data is in a separate folder and each pass is in a file beginning with "SonicBOBS_" (for microphone data) or "SonicBOBSBB_" (for BADS and BASS data) followed by the month, day, year as two digits each, followed by the hour, minute, sec after midnight GMT. The filename time given is for the END time of the raw recording file. In the case of the microphone data, this time may be several minutes after the sonic boom, and is according to the PC's uncalibrated clock. The Matlab data files have the actual time as provided by a GPS-based IRIG-B signal recorded concurrently with the data. Microphone data is given for 5 seconds prior to 20 seconds after the sonic boom. BADS and BASS data is given for the full recording, 6 seconds for the BADS and 10 seconds for the BASS. As an example of the naming convention, file "SonicBOBS_091209154618.mat" is from September 12, 2009 at 15:46:18 GMT. Note that data taken on September 12, 2009 prior to 01:00:00 GMT was of the Space Shuttle Discovery (a sonic boom of opportunity), which was on September 11, 2009 in local Pacific Daylight Time.

Haering, Edward A., Jr.↗
NASA NTRS2010

Optical Imaging and Radiometric Modeling and Simulation

OPTOOL software is a general-purpose optical systems analysis tool that was developed to offer a solution to problems associated with computational programs written for the James Webb Space Telescope optical system. It integrates existing routines into coherent processes, and provides a structure with reusable capabilities that allow additional processes to be quickly developed and integrated. It has an extensive graphical user interface, which makes the tool more intuitive and friendly. OPTOOL is implemented using MATLAB with a Fourier optics-based approach for point spread function (PSF) calculations. It features parametric and Monte Carlo simulation capabilities, and uses a direct integration calculation to permit high spatial sampling of the PSF. Exit pupil optical path difference (OPD) maps can be generated using combinations of Zernike polynomials or shaped power spectral densities. The graphical user interface allows rapid creation of arbitrary pupil geometries, and entry of all other modeling parameters to support basic imaging and radiometric analyses. OPTOOL provides the capability to generate wavefront-error (WFE) maps for arbitrary grid sizes. These maps are 2D arrays containing digital sampled versions of functions ranging from Zernike polynomials to combination of sinusoidal wave functions in 2D, to functions generated from a spatial frequency power spectral distribution (PSD). It also can generate optical transfer functions (OTFs), which are incorporated into the PSF calculation. The user can specify radiometrics for the target and sky background, and key performance parameters for the instrument s focal plane array (FPA). This radiometric and detector model setup is fairly extensive, and includes parameters such as zodiacal background, thermal emission noise, read noise, and dark current. The setup also includes target spectral energy distribution as a function of wavelength for polychromatic sources, detector pixel size, and the FPA s charge diffusion modulation transfer function (MTF).

Ha, Kong Q.↗
NASA NTRS2010

AutoGNC Testbed

A simulation testbed architecture was developed and implemented for the integration, test, and development of a TRL-6 flight software set called Auto- GNC. The AutoGNC software will combine the TRL-9 Deep Impact AutoNAV flight software suite, the TRL-9 Virtual Machine Language (VML) executive, and the TRL-3 G-REX guidance, estimation, and control algorithms. The Auto- GNC testbed was architected to provide software interface connections among the AutoNAV and VML flight code written in C, the G-REX algorithms in MATLAB and C, stand-alone image rendering algorithms in C, and other Fortran algorithms, such as the OBIRON landmark tracking suite. The testbed architecture incorporates software components for propagating a high-fidelity truth model of the environment and the spacecraft dynamics, along with the flight software components for onboard guidance, navigation, and control (GN&C). The interface allows for the rapid integration and testing of new algorithms prior to development of the C code for implementation in flight software. This testbed is designed to test autonomous spacecraft proximity operations around small celestial bodies, moons, or other spacecraft. The software is baselined for upcoming comet and asteroid sample return missions. This architecture and testbed will provide a direct improvement upon the onboard flight software utilized for missions such as Deep Impact, Stardust, and Deep Space 1.

Carson, John M., III↗
NASA NTRS2010

Incorporation of SemiSpan SuperSonic Transport (S4T) Aeroservoelastic Models into SAREC-ASV Simulation

The Simulink-based Simulation Architecture for Evaluating Controls for Aerospace Vehicles (SAREC-ASV) was modified to incorporate linear models representing aeroservoelastic characteristics of the SemiSpan SuperSonic Transport (S4T) wind-tunnel model. The S4T planform is for a Technology Concept Aircraft (TCA) design from the 1990s. The model has three control surfaces and is instrumented with accelerometers and strain gauges. Control laws developed for wind-tunnel testing for Ride Quality Enhancement, Gust Load Alleviation, and Flutter Suppression System functions were implemented in the simulation. The simulation models open- and closed-loop response to turbulence and to control excitation. It provides time histories for closed-loop stable conditions above the open-loop flutter boundary. The simulation is useful for assessing the potential impact of closed-loop control rate and position saturation. It also provides a means to assess fidelity of system identification procedures by providing time histories for a known plant model, with and without unmeasured turbulence as a disturbance. Sets of linear models representing different Mach number and dynamic pressure conditions were implemented as MATLAB Linear Time Invariant (LTI) objects. Configuration changes were implemented by selecting which LTI object to use in a Simulink template block. A limited comparison of simulation versus wind-tunnel results is shown.

Christhilf, David M.↗
NASA NTRS2010

Enhanced Modeling of First-Order Plant Equations of Motion for Aeroelastic and Aeroservoelastic Applications

A methodology is described for generating first-order plant equations of motion for aeroelastic and aeroservoelastic applications. The description begins with the process of generating data files representing specialized mode-shapes, such as rigid-body and control surface modes, using both PATRAN and NASTRAN analysis. NASTRAN executes the 146 solution sequence using numerous Direct Matrix Abstraction Program (DMAP) calls to import the mode-shape files and to perform the aeroelastic response analysis. The aeroelastic response analysis calculates and extracts structural frequencies, generalized masses, frequency-dependent generalized aerodynamic force (GAF) coefficients, sensor deflections and load coefficients data as text-formatted data files. The data files are then re-sequenced and re-formatted using a custom written FORTRAN program. The text-formatted data files are stored and coefficients for s-plane equations are fitted to the frequency-dependent GAF coefficients using two Interactions of Structures, Aerodynamics and Controls (ISAC) programs. With tabular files from stored data created by ISAC, MATLAB generates the first-order aeroservoelastic plant equations of motion. These equations include control-surface actuator, turbulence, sensor and load modeling. Altitude varying root-locus plot and PSD plot results for a model of the F-18 aircraft are presented to demonstrate the capability.

Pototzky, Anthony S.↗
NASA NTRS2010

SCDU Testbed Automated In-Situ Alignment, Data Acquisition and Analysis

In the course of fulfilling its mandate, the Spectral Calibration Development Unit (SCDU) testbed for SIM-Lite produces copious amounts of raw data. To effectively spend time attempting to understand the science driving the data, the team devised computerized automations to limit the time spent bringing the testbed to a healthy state and commanding it, and instead focus on analyzing the processed results. We developed a multi-layered scripting language that emphasized the scientific experiments we conducted, which drastically shortened our experiment scripts, improved their readability, and all-but-eliminated testbed operator errors. In addition to scientific experiment functions, we also developed a set of automated alignments that bring the testbed up to a well-aligned state with little more than the push of a button. These scripts were written in the scripting language, and in Matlab via an interface library, allowing all members of the team to augment the existing scripting language with complex analysis scripts. To keep track of these results, we created an easily-parseable state log in which we logged both the state of the testbed and relevant metadata. Finally, we designed a distributed processing system that allowed us to farm lengthy analyses to a collection of client computers which reported their results in a central log. Since these logs were parseable, we wrote query scripts that gave us an effortless way to compare results collected under different conditions. This paper serves as a case-study, detailing the motivating requirements for the decisions we made and explaining the implementation process.

Automation↗
NASA NTRS2010

NAIF Toolkit - Extended

The Navigation Ancillary Infor ma tion Facility (NAIF) at JPL, acting under the direction of NASA s Office of Space Science, has built a data system named SPICE (Spacecraft Planet Instrument Cmatrix Events) to assist scientists in planning and interpreting scientific observations (see figure). SPICE provides geometric and some other ancillary information needed to recover the full value of science instrument data, including correlation of individual instrument data sets with data from other instruments on the same or other spacecraft. This data system is used to produce space mission observation geometry data sets known as SPICE kernels. It is also used to read SPICE kernels and to compute derived quantities such as positions, orientations, lighting angles, etc. The SPICE toolkit consists of a subroutine/ function library, executable programs (both large applications and simple utilities that focus on kernel management), and simple examples of using SPICE toolkit subroutines. This software is very accurate, thoroughly tested, and portable to all computers. It is extremely stable and reusable on all missions. Since the previous version, three significant capabilities have been added: Interactive Data Language (IDL) interface, MATLAB interface, and a geometric event finder subsystem.

Acton, Charles H., Jr.↗
NASA NTRS2010

Phenological Parameters Estimation Tool

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE

McKellip, Rodney D.↗
NASA NTRS2010

Observing System Simulation Experiment (OSSE) for the HyspIRI Spectrometer Mission

The OSSE software provides an integrated end-to-end environment to simulate an Earth observing system by iteratively running a distributed modeling workflow based on the HyspIRI Mission, including atmospheric radiative transfer, surface albedo effects, detection, and retrieval for agile exploration of the mission design space. The software enables an Observing System Simulation Experiment (OSSE) and can be used for design trade space exploration of science return for proposed instruments by modeling the whole ground truth, sensing, and retrieval chain and to assess retrieval accuracy for a particular instrument and algorithm design. The OSSE in fra struc ture is extensible to future National Research Council (NRC) Decadal Survey concept missions where integrated modeling can improve the fidelity of coupled science and engineering analyses for systematic analysis and science return studies. This software has a distributed architecture that gives it a distinct advantage over other similar efforts. The workflow modeling components are typically legacy computer programs implemented in a variety of programming languages, including MATLAB, Excel, and FORTRAN. Integration of these diverse components is difficult and time-consuming. In order to hide this complexity, each modeling component is wrapped as a Web Service, and each component is able to pass analysis parameterizations, such as reflectance or radiance spectra, on to the next component downstream in the service workflow chain. In this way, the interface to each modeling component becomes uniform and the entire end-to-end workflow can be run using any existing or custom workflow processing engine. The architecture lets users extend workflows as new modeling components become available, chain together the components using any existing or custom workflow processing engine, and distribute them across any Internet-accessible Web Service endpoints. The workflow components can be hosted on any Internet-accessible machine. This has the advantages that the computations can be distributed to make best use of the available computing resources, and each workflow component can be hosted and maintained by their respective domain experts.

Turmon, Michael J.↗
NASA NTRS2010

Closed-Loop Simulation Study of the Ares I Upper Stage Thrust Vector Control Subsystem for Nominal and Failure Scenarios

As a replacement to the current Shuttle, the Ares I rocket and Orion crew module are currently under development by the National Aeronautics and Space Administration (NASA). This new launch vehicle is segmented into major elements, one of which is the Upper Stage (US). The US is further broken down into subsystems, one of which is the Thrust Vector Control (TVC) subsystem which gimbals the US rocket nozzle. Nominal and off-nominal simulations for the US TVC subsystem are needed in order to support the development of software used for control systems and diagnostics. In addition, a clear and complete understanding of the effect of off-nominal conditions on the vehicle flight dynamics is desired. To achieve these goals, a simulation of the US TVC subsystem combined with the Ares I vehicle as developed. This closed-loop dynamic model was created using Matlab s Simulink and a modified version of a vehicle simulation, MAVERIC, which is currently used in the Ares I project and was developed by the Marshall Space Flight Center (MSFC). For this report, the effects on the flight trajectory of the Ares I vehicle are investigated after failures are injected into the US TVC subsystem. The comparisons of the off-nominal conditions observed in the US TVC subsystem with those of the Ares I vehicle flight dynamics are of particular interest.

Chicatelli, Amy↗
NASA NTRS2010

Software Development to Assist in the Processing and Analysis of Data Obtained Using Fiber Bragg Grating Interrogation Systems

A fiber Bragg grating is a portion of a core of a fiber optic stand that has been treated to affect the way light travels through the strand. Light within a certain narrow range of wavelengths will be reflected along the fiber by the grating, while light outside that range will pass through the grating mostly undisturbed. Since the range of wavelengths that can penetrate the grating depends on the grating itself as well as temperature and mechanical strain, fiber Bragg gratings can be used as temperature and strain sensors. This capability, along with the light-weight nature of the fiber optic strands in which the gratings reside, make fiber optic sensors an ideal candidate for flight testing and monitoring in which temperature and wing strain are factors. A team of NASA Dryden engineers has been working to advance the fiber optic sensor technology since the mid 1990 s. The team has been able to improve the dependability and sample rate of fiber optic sensor systems, making them more suitable for real-time wing shape and strain monitoring and capable of rivaling traditional strain gauge sensors in accuracy. The sensor system was recently tested on the Ikhana unmanned aircraft and will be used on the Global Observer unmanned aircraft. Since a fiber Bragg grating sensor can be placed every halfinch on each optic fiber, and since fibers of approximately 40 feet in length each are to be used on the Global Observer, each of these fibers will have approximately 1,000 sensors. A total of 32 fibers are to be placed on the Global Observer aircraft, to be sampled at a rate of about 50 Hz, meaning about 1.6 million data points will be taken every second. The fiber optic sensors system is capable of producing massive amounts of potentially useful data; however, methods to capture, record, and analyze all of this data in a way that makes the information useful to flight test engineers are currently limited. The purpose of this project is to research the availability of software capable of processing massive amounts of data in both real-time and post-flight settings, and to produce software segments that can be integrated to assist in the task as well. The selected software must be able to: (1) process massive amounts of data (up to 4GB) at a speed useful in a real-time settings (small fractions of a second); (2) process data in post-flight settings to allow test reproduction or further data analysis, inclusive; (3) produce, or make easier to produce, three-dimensional plots/graphs to make the data accessible to flight test engineers; and (4) be customized to allow users to use their own processing formulas or functions and display the data in formats they prefer. Several software programs were evaluated to determine their utility in completing the research objectives. These programs include: OriginLab, Graphis, 3D Grapher, Visualization Sciences Group (VSG) Avizo Wind, Interactive Analysis and Display System (IADS), SigmaPlot, and MATLAB.

Hicks, Rebecca↗
NASA NTRS2010

A High-Fidelity Simulation of a Generic Commercial Aircraft Engine and Controller

A new high-fidelity simulation of a generic 40,000 lb thrust class commercial turbofan engine with a representative controller, known as CMAPSS40k, has been developed. Based on dynamic flight test data of a highly instrumented engine and previous engine simulations developed at NASA Glenn Research Center, this non-proprietary simulation was created especially for use in the development of new engine control strategies. C-MAPSS40k is a highly detailed, component-level engine model written in MATLAB/Simulink (The MathWorks, Inc.). Because the model is built in Simulink, users have the ability to use any of the MATLAB tools for analysis and control system design. The engine components are modeled in C-code, which is then compiled to allow faster-than-real-time execution. The engine controller is based on common industry architecture and techniques to produce realistic closed-loop transient responses while ensuring that no safety or operability limits are violated. A significant feature not found in other non-proprietary models is the inclusion of transient stall margin debits. These debits provide an accurate accounting of the compressor surge margin, which is critical in the design of an engine controller. This paper discusses the development, characteristics, and capabilities of the C-MAPSS40k simulation

May, Ryan D.↗
NASA NTRS2010

The Kepler Science Operations Center Pipeline Framework Extensions

The Kepler Science Operations Center (SOC) is responsible for several aspects of the Kepler Mission, including managing targets, generating on-board data compression tables, monitoring photometer health and status, processing the science data, and exporting the pipeline products to the mission archive. We describe how the generic pipeline framework software developed for Kepler is extended to achieve these goals, including pipeline configurations for processing science data and other support roles, and custom unit of work generators that control how the Kepler data are partitioned and distributed across the computing cluster. We describe the interface between the Java software that manages the retrieval and storage of the data for a given unit of work and the MATLAB algorithms that process these data. The data for each unit of work are packaged into a single file that contains everything needed by the science algorithms, allowing these files to be used to debug and evolve the algorithms offline.

Klaus, Todd C.↗