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NASA NTRS1990

Machine learning of fault characteristics from rocket engine simulation data

Transformation of data into knowledge through conceptual induction has been the focus of our research described in this paper. We have developed a Machine Learning System (MLS) to analyze the rocket engine simulation data. MLS can provide to its users fault analysis, characteristics, and conceptual descriptions of faults, and the relationships of attributes and sensors. All the results are critically important in identifying faults.

Ke, Min↗

NASA NTRS2010

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

Masunaga, Hirohiko↗

NASA NTRS2010

ICESat-2 Simulated Data from Airborne Altimetery

Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2015 and will carry onboard the Advanced Topographic Laser Altimeter System (ATLAS), which represents a new approach to spaceborne determination of surface elevations. Specifically, the current ATLAS design is for a micropulse, multibeam, photon-counting laser altimeter with lower energy, a shorter pulse width, and a higher repetition rate relative to the Geoscience Laser Altimeter (GLAS), the instrument that was onboard ICESat. Given the new and untested technology associated with ATLAS, airborne altimetry data is necessary (1) to test the proposed ATLAS instrument geometry, (2) to validate instrument models, and (3) to assess the atmospheric effects on multibeam altimeters. We present an overview of the airborne instruments and datasets intended to address the ATLAS instrument concept, including data collected over Greenland (July 2009) using an airborne SBIR prototype 100 channel, photon-counting, terrain mapping altimeter, which addresses the first of these 3 scientific concerns. Additionally, we present the plan for further simulator data collection over vegetated and ice covered regions using Multiple Altimeter Beam Experimental Lidar (MABEL), intended to address the latter two scientific concerns. As the ICESAT-2 project is in the design phase, the particular configuration of the ATLAS instrument may change. However, we expect this work to be relevant as long as ATLAS pursues a photon-counting approach.

Brunt, Kelly M.↗

NASA NTRSDate not supplied

Comparing the Electrical Modeling and Thermal Analysis Toolbox Simulation Data to Electrified Aircraft Propulsion Test Hardware Data

The Electrical Modeling and Thermal Analysis Toolbox (EMTAT), a National Aeronautics and Space Administration (NASA)-developed Simulink™ model block library of electrical components developed with the goal of facilitating system-level control design and analysis, has two levels of fidelity depending on the test needs. The Physics Based model blocks are designed to use physical component characteristics to more accurately model real hardware, including power losses, efficiency, thermal effects, electromagnetic losses, voltage drops and current requirements. These blocks model electrical dynamics that occur at the millisecond turbomachinery time scale which allows for faster-than-real-time electrified turbomachinery simulations. A model was developed to mirror the Hybrid Propulsion Emulation Rig (HyPER) hardware, a laboratory focused on Electrified Aircraft Propulsion (EAP) hardware tests. The present hardware setup supports Turbine Electrified Energy Management (TEEM) testing. The outputs of the model were compared to the results of several tests on the HyPER laboratory motor-generator setup with the goal of matching the steady state simulation data to steady state hardware data within 5% of full scale. The objective of this paper is to present the background, setup, testing and results of this comparison. It will describe some of the adjustments that were necessary to match the system hardware, as well as next steps in verification and validation.

Hyper↗

NASA NTRS1992

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Mitra, Sunanda↗

NASA NTRS1984

Feature selection and the information content of Thematic Mapper simulator data for forest structural assessment

An assessment is made of the information content of Thematic Mapper Simulator (TMS) data for the case of a forested region, in order to determine the sensitivity of such data to forest crown closure and tree size class. Principal components analysis and Monte Carlo simulation indicated that channels 4, 7, 5 and 3 were optimal for four-channel forest structure analysis. As the number of channels supplied to the Monte Carlo feature selection routine increased, classification accuracy increased. The greatest sensitivity to the forest structural parameters, which included succession within clearcuts as well as crown closure and size class, was obtained from the 7-channel TMS data.

Spanner, M. A.↗

NASA NTRS1982

An evaluation of thematic mapper simulator data for mapping forest cover

Computer-aided analysis techniques applied to Thematic Mapper Simulator (TMS) data were evaluated for the purpose of mapping forest cover types. Classification results obtained using a supervised set of training statistics and various combinations of three and four channel subsets of the seven available TMS channels are compared for the L2 (Minimum Euclidean Distance), GML (Gaussian Maximum Likelihood), and SECHO (Supervised Extraction and Classification of Homogeneous Objects) classification algorithms. SECHO performed significantly better than either of the two per-point classifiers for the untransformed data. Overall classification results of the Karhunen-Loeve transformation increased for the L2 algorithm, but decreased for both the GML and SECHO algorithms.

Dean, M. E.↗

NASA NTRS1983

Feature selection and information content of thematic mapper simulator data for a forested environment

Feature selection and the information content of Thematic Mapper Simulator (TMS) data are investigated for a forested region in northern Idaho. The optimal TMS channels for forest structural characteristics are determined, and the capability of TMS data to describe the structural variability within a forest stand is evaluated. The comparative performance of TMS and MSS data to discriminate forest structural factors using per-pixel maximum likelihood classification is examined, and four optimal TMS channels are classified in order to ascertain if the full complement of TM channels provide higher accuracies than the four optimal ones.

Spanner, M. A.↗

NASA NTRS1983

Automated vegetation classification using Thematic Mapper Simulation data

The present investigation is concerned with the results of a study of Thematic Mapper Simulation (TMS) data. One of the objectives of the study was related to an evaluation of the usefulness of the Thematic Mapper's (TM) improved spatial resolution and spectral coverage. The study was undertaken as part of a preparation for the efficient incorporation of Landsat 4 data into ongoing technology development in remote sensing. The study included an application of automated Landsat vegetation classification technology to TMS data. Results of comparing TMS data to Multispectral Scanner (MSS) data were found to indicate that all field definition, crop type discrimination, and subsequent proportion estimation may be greatly increased with the availability of TM data.

Nedelman, K. S.↗

NASA NTRS1984

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

Morrissey, L. A.↗

NASA NTRS1982

Analysis of thematic mapper simulator data collected over eastern North Dakota

The results of the analysis of aircraft-acquired thematic mapper simulator (TMS) data, collected to investigate the utility of thematic mapper data in crop area and land cover estimates, are discussed. Results of the analysis indicate that the seven-channel TMS data are capable of delineating the 13 crop types included in the study to an overall pixel classification accuracy of 80.97% correct, with relative efficiencies for four crop types examined between 1.62 and 26.61. Both supervised and unsupervised spectral signature development techniques were evaluated. The unsupervised methods proved to be inferior (based on analysis of variance) for the majority of crop types considered. Given the ground truth data set used for spectral signature development as well as evaluation of performance, it is possible to demonstrate which signature development technique would produce the highest percent correct classification for each crop type.

Anderson, J. E.↗

NASA NTRS1982

Analysis of thematic mapper simulator data acquired during winter season over Pearl River, Mississippi, test site

Digital processed aircraft-acquired thematic mapping simulator (TMS) data collected during the winter season over a forested site in southern Mississippi are presented to investigate the utility of TMS data for use in forest inventories and monitoring. Analyses indicated that TMS data are capable of delineating the mixed forest land cover type to an accuracy of 92.5 % correct. The accuracies associated with river bottom forest and pine forest were 95.5 and 91.5 % correct. The accuracies associated with river bottom forest and pine forest were 95.5 and 91.5 % correct, respectively. The figures reflect the performance for products produced using the best subset of channels for each forest cover type. It was found that the choice of channels (subsets) has a significant effect on the accuracy of classification produced, and that the same channels are not the most desirable for all three forest types studied. Both supervised and unsupervised spectral signature development techniques are evaluated; the unsupervised methods proved unacceptable for the three forest types considered.

Anderson, J. E.↗

NASA NTRS1984

Improved classification of small-scale urban watersheds using thematic mapper simulator data

The utility of Landsat MSS classification methods in the case of small, highly urbanized hydrological basins containing complex land-use patterns is limited, and is plagued by misclassifications due to the spectral response similarity of many dissimilar surfaces. Landsat MSS data for the Conley Creek basin near Atlanta, Georgia, have been compared to thematic mapper simulator (TMS) data obtained on the same day by aircraft. The TMS data were able to alleviate many of the recurring patterns associated with MSS data, through bandwidth optimization, an increase of the number of spectral bands to seven, and an improvement of ground resolution to 30 m. The TMS is thereby able to detect small water bodies, powerline rights-of-way, and even individual buildings.

Owe, M.↗

NASA NTRS2003

Securing Sensitive Flight and Engine Simulation Data Using Smart Card Technology

NASA Glenn Research Center has developed a smart card prototype capable of encrypting and decrypting disk files required to run a distributed aerospace propulsion simulation. Triple Data Encryption Standard (3DES) encryption is used to secure the sensitive intellectual property on disk pre, during, and post simulation execution. The prototype operates as a secure system and maintains its authorized state by safely storing and permanently retaining the encryption keys only on the smart card. The prototype is capable of authenticating a single smart card user and includes pre simulation and post simulation tools for analysis and training purposes. The prototype's design is highly generic and can be used to protect any sensitive disk files with growth capability to urn multiple simulations. The NASA computer engineer developed the prototype on an interoperable programming environment to enable porting to other Numerical Propulsion System Simulation (NPSS) capable operating system environments.

Blaser, Tammy M.↗

NASA NTRS1990

Low Reynolds number kappa-epsilon modeling with the aid of direct simulation data

The constant C(sub mu) and the near-wall damping function f(sub mu) in the eddy-viscosity relation of the kappa-epsilon model are evaluated from direct numerical simulation (DNS) data for developed channel and boundary layer flow at two Reynolds numbers each. Various existing f(sub mu) model functions are compared with the DNS data, and a new function is fitted to the high-Reynolds-number channel flow data. The epsilon-budget is computed for the fully developed channel flow. The relative magnitude of the terms in the epsilon-equation is analyzed with the aid of scaling arguments, and the parameter governing this magnitude is established. Models for the sum of all source and sink terms in the epsilon-equation are tested against the DNS data, and an improved model is proposed.

Rodi, W.↗

NASA NTRS1993

Low Reynolds number k-epsilon modelling with the aid of direct simulation data

The constant C sub mu and the near-wall damping function f sub mu in the eddy-viscosity relation of the k-epsilon model are evaluated from direct numerical simulation (DNS) data for developed channel and boundary layer flow at two Reynolds numbers each. Various existing f sub mu model functions are compared with the DNS data, and a new function is fitted to the high-Reynolds-number channel flow data. The epsilon-budget is computed for the fully developed channel flow. The relative magnitude of the terms in the epsilon-equation is analyzed with the aid of scaling arguments, and the parameter governing this magnitude is established. Models for the sum of all source and sink terms in the epsilon-equation are tested against the DNS data, and an improved model is proposed.

Rodi, W.↗

NASA NTRS1992

Determining the transferability of flight simulator data

This paper presented a method for collecting and graphically correlating subjective ratings and objective flight test data. The method enables flight-simulation engineers to enhance the simulator characterization of rotor craft flight in order to achieve maximum transferability of simulator experience.

Green, David↗

NASA NTRS1993

Neural networks analysis on SSME vibration simulation data

The neural networks method is applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME to supplement the statistical method utilized in the prototype system. The investigation of neural networks analysis is conducted using SSME vibration data from a NASA developed numerical simulator. The limited application of neural networks to the HPFTP has also shown the effectiveness in diagnosing the anomalies of turbopump vibrations.

Lo, Ching F.↗