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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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NASA NTRS1993

Identification of integrated airframe-propulsion effects on an F-15 aircraft for application to drag minimization

The application of an adaptive real-time measurement-based performance optimization technique is being explored for a future flight research program. The key technical challenge of the approach is parameter identification, which uses a perturbation-search technique to identify changes in performance caused by forced oscillations of the controls. The controls on the NASA F-15 highly integrated digital electronic control (HIDEC) aircraft were perturbed using inlet cowl rotation steps at various subsonic and supersonic flight conditions to determine the effect on aircraft performance. The feasibility of the perturbation-search technique for identifying integrated airframe-propulsion system performance effects was successfully shown through flight experiments and postflight data analysis. Aircraft response and control data were analyzed postflight to identify gradients and to determine the minimum drag point. Changes in longitudinal acceleration as small as 0.004 g were measured, and absolute resolution was estimated to be 0.002 g or approximately 50 lbf of drag. Two techniques for identifying performance gradients were compared: a least-squares estimation algorithm and a modified maximum likelihood estimator algorithm. A complementary filter algorithm was used with the least squares estimator.

Schkolnik, Gerald S.↗

NASA NTRS1993

Identification of integrated airframe: Propulsion effects on an F-15 aircraft for application to drag minimization

Schkolnik, Gerard S.↗

NASA NTRS2002

Distant Cluster Hunting: A Comparison of X-Ray and Optical Cluster Detection Techniques and Catalogs from the ROSAT Optical X-Ray Survey - II

We present and analyze the optical and X-ray catalogs of moderate-redshift cluster candidates from the ROSA TOptical X-Ray Survey, or ROXS. The survey covers the sky area contained in the fields of view of 23 deep archival ROSA T PSPC pointings, 4.8 square degrees. The cross-correlated cluster catalogs were con- structed by comparing two independent catalogs extracted from the optical and X-ray bandpasses, using a matched-filter technique for the optical data and a wavelet technique for the X-ray data. We cross-identified cluster candidates in each catalog. As reported in Paper 1, the matched-filter technique found optical counter- parts for at least 60% (26 out of 43) of the X-ray cluster candidates; the estimated redshifts from the matched filter algorithm agree with at least 7 of 1 1 spectroscopic confirmations (Az 5 0.10). The matched filter technique. with an imaging sensitivity of ml N 23, identified approximately 3 times the number of candidates (155 candidates, 142 with a detection confidence >3 u) found in the X-ray survey of nearly the same area. There are 57 X-ray candidates, 43 of which are unobscured by scattered light or bright stars in the optical images. Twenty-six of these have fairly secure optical counterparts. We find that the matched filter algorithm, when applied to images with galaxy flux sensitivities of mI N 23, is fairly well-matched to discovering z 5 1 clusters detected by wavelets in ROSAT PSPC exposures of 8000-60,000 s. The difference in the spurious fractions between the optical and X-ray (30%) and IO%, respectively) cannot account for the difference in source number. In Paper I, we compared the optical and X-ray cluster luminosity functions and we found that the luminosity functions are consistent if the relationship between X-ray and optical luminosities is steep (Lx o( L&f). Here, in Paper 11, we present the cluster catalogs and a numerical simulation of the ROXS. We also present color-magnitude plots for several of the cluster candidates, and examine the prominence of the red sequence in each. We find that the X-ray clusters in our survey do not all have a prominent red sequence. We conclude that while the red sequence may be a distinct feature in the color-magnitude plots for virialized massive clusters, it may be less distinct in lower mass clusters of galaxies at even moderate redshifts. Multiple, complementary methods of selecting and defining clusters may be essential, particularly at high redshift where all methods start to run into completeness limits, incomplete understanding of physical evolution, and projection effects.

Donahue, Megan↗

NASA NTRS2005

Generic Kalman Filter Software

The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.

Lisano, Michael E., II↗

NASA NTRS1997

Error Analysis System for Spacecraft Navigation Using the Global Positioning System (GPS)

The Flight Dynamics Division (FDD) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) is currently developing improved space-navigation filtering algorithms to use the Global Positioning System (GPS) for autonomous real-time onboard orbit determination. In connection with a GPS technology demonstration on the Small Satellite Technology Initiative (SSTI)/Lewis spacecraft, FDD analysts and programmers have teamed with the GSFC Guidance, Navigation, and Control Branch to develop the GPS Enhanced Orbit Determination Experiment (GEODE) system. The GEODE system consists of a Kalman filter operating as a navigation tool for estimating the position, velocity, and additional states required to accurately navigate the orbiting Lewis spacecraft by using astrodynamic modeling and GPS measurements from the receiver. A parallel effort at the FDD is the development of a GPS Error Analysis System (GEAS) that will be used to analyze and improve navigation filtering algorithms during development phases and during in-flight calibration. For GEAS, the Kalman filter theory is extended to estimate the errors in position, velocity, and other error states of interest. The estimation of errors in physical variables at regular intervals will allow the time, cause, and effect of navigation system weaknesses to be identified. In addition, by modeling a sufficient set of navigation system errors, a system failure that causes an observed error anomaly can be traced and accounted for. The GEAS software is formulated using Object Oriented Design (OOD) techniques implemented in the C++ programming language on a Sun SPARC workstation. The Phase 1 of this effort is the development of a basic system to be used to evaluate navigation algorithms implemented in the GEODE system. This paper presents the GEAS mathematical methodology, systems and operations concepts, and software design and implementation. Results from the use of the basic system to evaluate navigation algorithms implemented on GEODE are also discussed. In addition, recommendations for generalization of GEAS functions and for new techniques to optimize the accuracy and control of the GPS autonomous onboard navigation are presented.

Truong, S. H.↗

NASA NTRS1975

Design and simulation of stratified probability digital receiver with application to the multipath communication

One approach to the problem of simplifying complex nonlinear filtering algorithms is through using stratified probability approximations where the continuous probability density functions of certain random variables are represented by discrete mass approximations. This technique is developed in this paper and used to simplify the filtering algorithms developed for the optimum receiver for signals corrupted by both additive and multiplicative noise.

Deal, J. H.↗

NASA NTRS2011

Principal Component Noise Filtering for NAST-I Radiometric Calibration

The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed- Interferometer (NAST-I) instrument is a high-resolution scanning interferometer that measures emitted thermal radiation between 3.3 and 18 microns. The NAST-I radiometric calibration is achieved using internal blackbody calibration references at ambient and hot temperatures. In this paper, we introduce a refined calibration technique that utilizes a principal component (PC) noise filter to compensate for instrument distortions and artifacts, therefore, further improve the absolute radiometric calibration accuracy. To test the procedure and estimate the PC filter noise performance, we form dependent and independent test samples using odd and even sets of blackbody spectra. To determine the optimal number of eigenvectors, the PC filter algorithm is applied to both dependent and independent blackbody spectra with a varying number of eigenvectors. The optimal number of PCs is selected so that the total root-mean-square (RMS) error is minimized. To estimate the filter noise performance, we examine four different scenarios: apply PC filtering to both dependent and independent datasets, apply PC filtering to dependent calibration data only, apply PC filtering to independent data only, and no PC filters. The independent blackbody radiances are predicted for each case and comparisons are made. The results show significant reduction in noise in the final calibrated radiances with the implementation of the PC filtering algorithm.

Tian, Jialin↗

NASA NTRS1998

Attitude/attitude-rate estimation from GPS differential phase measurements using integrated-rate parameters

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

Oshman, Yaakov↗

NASA NTRS1997

Vector Observation-Aided/Attitude-Rate Estimation Using Global Positioning System Signals

Oshman, Yaakov↗

NASA NTRS2013

Hyperspectral Imager-Tracker

The Hyperspectral Imager-Tracker (HIT) is a technique for visualization and tracking of low-contrast, fast-moving objects. The HIT architecture is based on an innovative and only recently developed concept in imaging optics. This innovative architecture will give the Light Prescriptions Innovators (LPI) HIT the possibility of simultaneously collecting the spectral band images (hyperspectral cube), IR images, and to operate with high-light-gathering power and high magnification for multiple fast- moving objects. Adaptive Spectral Filtering algorithms will efficiently increase the contrast of low-contrast scenes. The most hazardous parts of a space mission are the first stage of a launch and the last 10 kilometers of the landing trajectory. In general, a close watch on spacecraft operation is required at distances up to 70 km. Tracking at such distances is usually associated with the use of radar, but its milliradian angular resolution translates to 100- m spatial resolution at 70-km distance. With sufficient power, radar can track a spacecraft as a whole object, but will not provide detail in the case of an accident, particularly for small debris in the onemeter range, which can only be achieved optically. It will be important to track the debris, which could disintegrate further into more debris, all the way to the ground. Such fragmentation could cause ballistic predictions, based on observations using high-resolution but narrow-field optics for only the first few seconds of the event, to be inaccurate. No optical imager architecture exists to satisfy NASA requirements. The HIT was developed for space vehicle tracking, in-flight inspection, and in the case of an accident, a detailed recording of the event. The system is a combination of five subsystems: (1) a roving fovea telescope with a wide 30 field of regard; (2) narrow, high-resolution fovea field optics; (3) a Coude optics system for telescope output beam stabilization; (4) a hyperspectral-mutispectral imaging assembly; and (5) image analysis software with effective adaptive spectral filtering algorithm for real-time contrast enhancement.

Agurok, Llya↗

DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1974

The real-time acquisition and tracking program for the USNS Vanguard

The computer program for the real-time acquisition and tracking program uses a variety of filtering algorithms including an extended Kalman filter to derive real-time orbit determination (position-velocity state vectors) from shipboard tracking and navigation data. Results from Apollo missions are given to show that orbital parameters can be estimated quickly and accurately using these methods.

Brammer, R. F.↗

NASA NTRS1974

Real-time shipboard orbit determination using Kalman filtering techniques

The real-time tracking and orbit determination program used on board the NASA tracking ship, the USNS Vanguard, is described in this paper. The computer program uses a variety of filtering algorithms, including an extended Kalman filter, to derive real-time orbit determinations (position-velocity state vectors) from shipboard tracking and navigation data. Results from Apollo missions are given to show that orbital parameters can be estimated quickly and accurately using these methods.

Brammer, R. F.↗

NASA NTRS1976

Performance analysis of digital FM demodulators for fading channels in the threshold region

The quasi-optimum digital FM demodulators for fading channels reported earlier (1975) reflected the system performance accurately under high signal to noise ratio conditions. In this paper, the prediction of the system performance for low SNR values is considered. A new set of error variance algorithms is developed from the filter algorithm assuming a Gaussian distribution for the state estimation errors and taking the effect of high frequency terms into consideration. Simulation analysis for an FM system with Rician fading channel shows that these algorithms predict the system performance accurately in the threshold region.

Dharamsi, M. T.↗

NASA NTRS1989

Space shuttle propulsion estimation development verification, volume 1

The results of the Propulsion Estimation Development Verification are summarized. A computer program developed under a previous contract (NAS8-35324) was modified to include improved models for the Solid Rocket Booster (SRB) internal ballistics, the Space Shuttle Main Engine (SSME) power coefficient model, the vehicle dynamics using quaternions, and an improved Kalman filter algorithm based on the U-D factorized algorithm. As additional output, the estimated propulsion performances, for each device are computed with the associated 1-sigma bounds. The outputs of the estimation program are provided in graphical plots. An additional effort was expended to examine the use of the estimation approach to evaluate single engine test data. In addition to the propulsion estimation program PFILTER, a program was developed to produce a best estimate of trajectory (BET). The program LFILTER, also uses the U-D factorized algorithm form of the Kalman filter as in the propulsion estimation program PFILTER. The necessary definitions and equations explaining the Kalman filtering approach for the PFILTER program, the models used for this application for dynamics and measurements, program description, and program operation are presented.

Rogers, Robert M.↗

NASA NTRS1993

Digital Low-Pass Filter Without Phase Shift

Relatively simple low-pass-filtering algorithm suppresses high-frequency noise in digitally sampled signal. Algorithm derived from convolution of signal with causal exponential. Filter implemented by algorithm does not introduce phase shift. Algorithm applies forward convolution in time followed by backward convolution in time, and phase shift in second convolution cancels phase shift in first convolution.

Simon, Wayne E.↗

NASA NTRS2020

Analytical Redundancy Using Kalman Filters for Rocket Engine Sensor Validation

The use of sensor redundancy is crucial in aerospace systems to maintain safe, reliable operation. While hardware redundancy is more common in application, analytical redundancy can provide a viable alternative in systems where the installation of multiple redundant sensors is not viable. To this end, the use of Kalman filters to analytically validate sensor measurements within rocket engines was explored. First, a dynamic model of the RS 25 engine, a derivative of the Space Shuttle Main Engine (SSME), was reduced to a subset of relations, focused around the main combustion chamber pressure. These relations were used within the Kalman filter algorithm to generate an estimate of sensor measurements to be compared with true measurements for data validation purposes. By using a bank of Kalman filters, the residuals between the estimated and true measurements were used to detect and isolate sensor faults. Through fault simulations, the sensor validation performance of this Kalman filter bank design was compared to a hardware redundancy check. Sensor bias and drift faults of various magnitudes were injected into nominal RS 25 engine test data. Results for both approaches show comparable fault detection with most bias faults found nearly instantaneously by both algorithms. Drift fault detection results show certain cases where one algorithm is faster than the other. The key advantage of the Kalman filter algorithm is shown in fault isolation performance where it can isolate faults between two redundant sensors while the hardware redundancy comparisons cannot.

sensors↗

DOE OSTI2023

Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. Furthermore, a series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗