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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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Comparison of Kalman filter and optimal smoother estimates of spacecraft attitude

Given a valid system model and adequate observability, a Kalman filter will converge toward the true system state with error statistics given by the estimated error covariance matrix. The errors generally do not continue to decrease. Rather, a balance is reached between the gain of information from new measurements and the loss of information during propagation. The errors can be further reduced, however, by a second pass through the data with an optimal smoother. This algorithm obtains the optimally weighted average of forward and backward propagating Kalman filters. It roughly halves the error covariance by including future as well as past measurements in each estimate. This paper investigates whether such benefits actually accrue in the application of an optimal smoother to spacecraft attitude determination. Tests are performed both with actual spacecraft data from the Extreme Ultraviolet Explorer (EUVE) and with simulated data for which the true state vector and noise statistics are exactly known.

Sedlak, J.↗
DOE OSTI2020

Leak detection in a subcritical boiler

Thermal power plants experience cycling duty leading to the fatigue of the boiler and heat exchanger tubes. As a result, tube failures occur frequently in coal fired fleets leading to forced outages. Furthermore, because the tube leaks have been the major source of unwanted shutdowns and the number of outages is increasing, present work focuses on the detection and isolation of the leak in a subcritical boiler based upon the process data from a commercial coalfired power plant. The mass balance equation around the steam drum was analyzed using timeseries data collected from a 300 MW power plant. The ratio of the feed water mass flow rate to the steam mass flow rate was defined as a key parameter for detecting leaks. The difference in slope between the feedwater and steam mass flow rate during the normal and faulty operations was established as the upper control limit for real time monitoring. To reduce false alarm rates that arise when raw signal is directly compared against the threshold due to common process fluctuations, an optimal filter was derived for smoothing. It was found that the optimal filter reacted much more quickly to process changes than an exponential moving average filter, around 8 h earlier on average. Occurrence of relatively high false alarm rates even in the filtered responses was related to the cycling of the boiler from the base load condition. Variable threshold was established to keep false alarm rates to the minimum while maintaining the leak detection rate. Finally, the leak was located at the economizer and this could readily be isolated by investigating the magnitude of the mass flow rates ratio and the temperature at the economizer outlet.

42 ENGINEERING↗
NASA NTRS1992

Interactive Spectral Analysis and Computation (ISAAC)

Isaac is a task in the NSO external package for IRAF. A descendant of a FORTRAN program written to analyze data from a Fourier transform spectrometer, the current implementation has been generalized sufficiently to make it useful for general spectral analysis and other one dimensional data analysis tasks. The user interface for Isaac is implemented as an interpreted mini-language containing a powerful, programmable vector calculator. Built-in commands provide much of the functionality needed to produce accurate line lists from input spectra. These built-in functions include automated spectral line finding, least squares fitting of Voigt profiles to spectral lines including equality constraints, various filters including an optimal filter construction tool, continuum fitting, and various I/O functions.

Lytle, D. M.↗
NASA NTRS1999

Behavior of Filters and Smoothers for Strongly Nonlinear Dynamics

The Kalman filter is the optimal filter in the presence of known gaussian error statistics and linear dynamics. Filter extension to nonlinear dynamics is non trivial in the sense of appropriately representing high order moments of the statistics. Monte Carlo, ensemble-based, methods have been advocated as the methodology for representing high order moments without any questionable closure assumptions. Investigation along these lines has been conducted for highly idealized dynamics such as the strongly nonlinear Lorenz model as well as more realistic models of the means and atmosphere. A few relevant issues in this context are related to the necessary number of ensemble members to properly represent the error statistics and, the necessary modifications in the usual filter situations to allow for correct update of the ensemble members. The ensemble technique has also been applied to the problem of smoothing for which similar questions apply. Ensemble smoother examples, however, seem to be quite puzzling in that results state estimates are worse than for their filter analogue. In this study, we use concepts in probability theory to revisit the ensemble methodology for filtering and smoothing in data assimilation. We use the Lorenz model to test and compare the behavior of a variety of implementations of ensemble filters. We also implement ensemble smoothers that are able to perform better than their filter counterparts. A discussion of feasibility of these techniques to large data assimilation problems will be given at the time of the conference.

Zhu, Yanqui↗
NASA NTRS1999

The Behavior of Filters and Smoothers for Strongly Nonlinear Dynamics

The Kalman filter is the optimal filter in the presence of known Gaussian error statistics and linear dynamics. Filter extension to nonlinear dynamics is non trivial in the sense of appropriately representing high order moments of the statistics. Monte Carlo, ensemble-based, methods have been advocated as the methodology for representing high order moments without any questionable closure assumptions (e.g., Miller 1994). Investigation along these lines has been conducted for highly idealized dynamics such as the strongly nonlinear Lorenz (1963) model as well as more realistic models of the oceans (Evensen and van Leeuwen 1996) and atmosphere (Houtekamer and Mitchell 1998). A few relevant issues in this context are related to the necessary number of ensemble members to properly represent the error statistics and, the necessary modifications in the usual filter equations to allow for correct update of the ensemble members (Burgers 1998). The ensemble technique has also been applied to the problem of smoothing for which similar questions apply. Ensemble smoother examples, however, seem to quite puzzling in that results of state estimate are worse than for their filter analogue (Evensen 1997). In this study, we use concepts in probability theory to revisit the ensemble methodology for filtering and smoothing in data assimilation. We use Lorenz (1963) model to test and compare the behavior of a variety implementations of ensemble filters. We also implement ensemble smoothers that are able to perform better than their filter counterparts. A discussion of feasibility of these techniques to large data assimilation problems will be given at the time of the conference.

Zhu, Yanqiu↗
NASA NTRSDate not supplied

Characterization of the Eugene Stansbery-Meterclass Autonomous Telescope on Ascension Island

In a focused effort to meet full operational capability for NASA’s Eugene Stansbery Meter Class Autonomous Telescope (ES-MCAT), a thorough system characterization analysis was completed. NASA’s Orbital Debris Program Office (ODPO)utilizes ES-MCAT as the primary sensor for characterizing the geosynchronous orbit (GEO)environment to acquire photometric data of small, faint debris objects in or near GEO. ES-MCAT is located on Ascension Island in the middle of the Atlantic Ocean at nearly 8° South latitude and 15° West longitude. This location provides dark skies suited for faint object observations but is also continuously subject to a harsh environment exposed to volcanic ash and salt spray. To better assess the overall system performance of the optical instrument, a historical assessment of the system’s performance was conducted. This analysis investigated all systematic and optical operational data to determine the overall performance parameters for ES-MCAT.A complete optical system throughput calculation was performed to determine the optimal filter for observing orbital debris in GEO orbits. The responses of each optical component to the solar spectrum, with atmospheric absorption, were multiplied and integrated to give ES-MCAT’s total system response for various filters. With the highest flux values, the Sloan Digital Sky Survey (SDSS) r’ and g’ were determined to be the optimal filters for ES-MCAT observations. Further analysis with known GEO debris objects enabled the selection of the r’ filter for characterization of the GEO debris population. A detailed overview of the optical system throughput, data reduction, photometric and astrometric data, and other system characteristics that define ES-MCAT will be discussed in the subsequent paper.

C. Cruz↗
NASA NTRSDate not supplied

Characterization of the Eugene Stansbery-Meter Class Autonomous Telescope on Ascension Island

In a focused effort to meet full operational capability for NASA’s Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT), a thorough system characterization analysis was completed. NASA’s Orbital Debris Program Office (ODPO) utilizes ES-MCAT as the primary sensor for characterizing the geosynchronous Earth orbit (GEO) environment to acquire photometric data of small, faint debris objects in or near GEO. ES-MCAT is located on Ascension Island in the middle of the Atlantic Ocean at nearly 8° South latitude and 14° West longitude. This location provides dark skies suited for faint object observations but is also continuously subject to a harsh environment exposed to volcanic ash and salt spray. To better assess the overall system performance of the optical instrument, a historical assessment of the system’s performance was conducted. This analysis investigated all systematic and optical operational data to determine the overall performance parameters for ES-MCAT. A complete optical system throughput calculation was performed to determine the optimal filter for observing orbital debris in GEO orbits. The responses of each optical component to the solar spectrum, with atmospheric absorption, were multiplied and integrated to give ES-MCAT’s total system response for various filters. With the highest flux values, the Sloan Digital Sky Survey (SDSS) r′ and g′ were determined to be the optimal filters for ES-MCAT observations. Further analysis with known GEO debris objects enabled the selection of the r′ filter for characterization of the GEO debris population. A detailed overview of the optical system throughput, data reduction, photometric and astrometric data, and other system characteristics that define ES-MCAT are discussed.

Corbin L Cruz↗
NASA NTRS2017

High Count-Rate Study of Two TES X-Ray Microcalorimeters With Different Transition Temperatures

We have developed transition-edge sensor (TES) microcalorimeter arrays with high count-rate capability and high energy resolution to carry out x-ray imaging spectroscopy observations of various astronomical sources and the Sun. We have studied the dependence of the energy resolution and throughput (fraction of processed pulses) on the count rate for such microcalorimeters with two different transition temperatures T(sub c). Devices with both transition temperatures were fabricated within a single microcalorimeter array directly on top of a solid substrate where the thermal conductance of the microcalorimeter is dependent upon the thermal boundary resistance between the TES sensor and the dielectric substrate beneath. Because the thermal boundary resistance is highly temperature dependent, the two types of device with different T(sub c)(sup s) had very different thermal decay times, approximately one order of magnitude different. In our earlier report, we achieved energy resolutions of 1.6 and 2.eV at 6 keV from lower and higher T(sub c) devices, respectively, using a standard analysis method based on optimal filtering in the low flux limit. We have now measured the same devices at elevated x-ray fluxes ranging from 50 Hz to 1000 Hz per pixel. In the high flux limit, however, the standard optimal filtering scheme nearly breaks down because of x-ray pile-up. To achieve the highest possible energy resolution for a fixed throughput, we have developed an analysis scheme based on the socalled event grade method. Using the new analysis scheme, we achieved 5.0 eV FWHM with 96 Percent throughput for 6 keV x-rays of 1025 Hz per pixel with the higher T(sub c) (faster) device, and 5.8 eV FWHM with 97 Percent throughput with the lower T(sub c) (slower) device at 722 Hz.

Lee, Sang-Jun↗
NASA NTRS2020

Required Filtering to Optimize Use of TOS

This presentation was originally given to ATD-2 industry partners at the Surface CDM (Collaborative Decision Making) Team (SCT) & Flow Evaluation Team (FET) meeting at NTX on 2/20/2020. The briefing presented lessons learned from Stormy 19, an overview of how terminal restrictions are handled by the ATD-2 system, and additional Trajectory Option Sets (TOS) route filtering capabilities planned for Stormy 20.

ATD-2↗
NASA NTRS2016

Performance Assessment of Different Pulse Reconstruction Algorithms for the ATHENA X-Ray Integral Field Unit

The X-ray Integral Field Unit (X-IFU) microcalorimeter, on-board Athena, with its focal plane comprising 3840 Transition Edge Sensors (TESs) operating at 90 mK, will provide unprecedented spectral-imaging capability in the 0.2-12 keV energy range. It will rely on the on-board digital processing of current pulses induced by the heat deposited in the TES absorber, as to recover the energy of each individual events. Assessing the capabilities of the pulse reconstruction is required to understand the overall scientific performance of the X-IFU, notably in terms of energy resolution degradation with both increasing energies and count rates. Using synthetic data streams generated by the X-IFU End-to-End simulator, we present here a comprehensive benchmark of various pulse reconstruction techniques, ranging from standard optimal filtering to more advanced algorithms based on noise covariance matrices. Beside deriving the spectral resolution achieved by the different algorithms, a first assessment of the computing power and ground calibration needs is presented. Overall, all methods show similar performances, with the reconstruction based on noise covariance matrices showing the best improvement with respect to the standard optimal filtering technique. Due to prohibitive calibration needs, this method might however not be applicable to the X-IFU and the best compromise currently appears to be the so-called resistance space analysis which also features very promising high count rate capabilities.

microcalorimeters↗
NASA NTRS2015

Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis

The traditional method of applying a digital optimal filter to measure X-ray pulses from transition-edge sensor (TES) devices does not achieve the best energy resolution when the signals have a highly non-linear response to energy, or the noise is non-stationary during the pulse. We present an implementation of a method to analyze X-ray data from TESs, which is based upon principal component analysis (PCA). Our method separates the X-ray signal pulse into orthogonal components that have the largest variance. We typically recover pulse height, arrival time, differences in pulse shape, and the variation of pulse height with detector temperature. These components can then be combined to form a representation of pulse energy. An added value of this method is that by reporting information on more descriptive parameters (as opposed to a single number representing energy), we generate a much more complete picture of the pulse received. Here we report on progress in developing this technique for future implementation on X-ray telescopes. We used an 55Fe source to characterize Mo/Au TESs. On the same dataset, the PCA method recovers a spectral resolution that is better by a factor of two than achievable with digital optimal filters.

Low-temperature detectors A[center dot] Transition↗
NASA NTRS1972

The use of linear programming techniques to design optimal digital filters for pulse shaping and channel equalization

A time domain technique is developed to design finite-duration impulse response digital filters using linear programming. Two related applications of this technique in data transmission systems are considered. The first is the design of pulse shaping digital filters to generate or detect signaling waveforms transmitted over bandlimited channels that are assumed to have ideal low pass or bandpass characteristics. The second is the design of digital filters to be used as preset equalizers in cascade with channels that have known impulse response characteristics. Example designs are presented which illustrate that excellent waveforms can be generated with frequency-sampling filters and the ease with which digital transversal filters can be designed for preset equalization.

Houts, R. C.↗
NASA NTRS1980

Geomagnetic field modeling by optimal recursive filtering

Data sets selected for mini-batches and the software modifications required for processing these sets are described. Initial analysis was performed on minibatch field model recovery. Studies are being performed to examine the convergence of the solutions and the maximum expansion order the data will support in the constant and secular terms.

Source record↗
NASA NTRS1980

Geomagnetic field modeling by optimal recursive filtering

Five individual 5 year mini-batch geomagnetic models were generated and two computer programs were developed to process the models. The first program computes statistics (mean sigma, weighted sigma) on the changes in the first derivatives (linear terms) of the spherical harmonic coefficients between mini-batches. The program ran successfully. The statistics are intended for use in computing the state noise matrix required in the information filter. The second program is the information filter. Most subroutines used in the filter were tested, but the coefficient statistics must be analyzed before the filter is run.

Source record↗
NASA NTRS1981

Optimal spatial filtering and transfer function for SAR ocean wave spectra

The impulse response of the SAR system is not a delta function and the spectra represent the product of the underlying image spectrum with the transform of the impulse response which must be removed. A digitally computed spectrum of SEASAT imagery of the Atlantic Ocean east of Cape Hatteras was smoothed with a 5 x 5 convolution filter and the trend was sampled in a direction normal to the predominant wave direction. This yielded a transform of a noise-like process. The smoothed value of this trend is the transform of the impulse response. This trend is fit with either a second- or fourth-order polynomial which is then used to correct the entire spectrum. A 16 x 16 smoothing of the spectrum shows the presence of two distinct swells. Correction of the effects of speckle is effected by the subtraction of a bias from the spectrum.

Beal, R. C.↗
NASA NTRS1981

Optimal spatial filtering and transfer function for SAR ocean wave spectra

The Seasat Synthetic Aperture Radar (SAR) has proved to be an instrument of great utility in the sensing of ocean conditions on a global scale. An analysis of oceanographic and atmospheric aspects of Seasat data has shown that the features observed in the imagery are linked to ocean phenomena such as storm sources and their resulting swell systems. However, there remains one central problem which has not been satisfactorily solved to date. This problem is related to the accurate measurement of wind-generated ocean wave spectra. Investigations addressing this problem are currently being conducted. The problem has two parts, including the accurate measurement of the image spectra and the inference of actual surface wave spectra from these measurements. A description is presented of the progress made towards solving the first part of the problem, taking into account a digital rather than optical computation of the image transforms.

Goldfinger, A. D.↗
NASA NTRS1981

Geomagnetic modeling by optimal recursive filtering

The results of a preliminary study to determine the feasibility of using Kalman filter techniques for geomagnetic field modeling are given. Specifically, five separate field models were computed using observatory annual means, satellite, survey and airborne data for the years 1950 to 1976. Each of the individual field models used approximately five years of data. These five models were combined using a recursive information filter (a Kalman filter written in terms of information matrices rather than covariance matrices.) The resulting estimate of the geomagnetic field and its secular variation was propogated four years past the data to the time of the MAGSAT data. The accuracy with which this field model matched the MAGSAT data was evaluated by comparisons with predictions from other pre-MAGSAT field models. The field estimate obtained by recursive estimation was found to be superior to all other models.

Gibbs, B. P.↗