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Results for “dynamic state estimation”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

An extended Kalman filter for spinning spacecraft attitude estimation

An extended Kalman filter for real-time ground attitude estimation of a gyro-less spinning spacecraft was developed and tested. The filter state vector includes the angular momentum direction, phase angle, inertial nutation angle, and inertial and body nutation rates. The filter solves for the nutating three-axis attitude and accounts for effects due to principle axes offset from the body axes. The attitude is propagated using the kinematics of a rigid body symmetric about the principle spin axis; disturbance torques are assumed to be small. Filter updates consist only of the measured angles between celestial objects (Sun, Earth, etc.) and the nominal spin axis, and the times these angles were measured. Both simulated data and real data from the Dynamics Explorer -A (DE-A) spacecraft were used to test the filter; the results are presented. Convergence was achieved rapidly from a wide range of a priori state estimates, and sub-degree accuracy was attained. Systematic errors affecting the solution accuracy are discussed, as are the results of an attempt to solve for sensor measurement angle biases in the state vector.

Baker, David F.↗
DOE OSTI2025

A dynamic likelihood approach to filtering transport processes: advection-diffusion dynamics

A Bayesian data assimilation scheme is formulated for advection-dominated advective and diffusive evolutionary problems, based upon the Dynamic Likelihood (DLF) approach to filtering. The DLF was developed specifically for hyperbolic problems –waves–, and in this paper, it is extended via a split step formulation, to handle advection-diffusion problems. In the dynamic likelihood approach, observations and their statistics are used to propagate probabilities along characteristics, evolving the likelihood in time. The estimate posterior thus inherits phase information. For advection-diffusion the advective part of the time evolution is handled on the basis of observations alone, while the diffusive part is informed through the model as well as observations. We expect, and indeed show here, that in advection-dominated problems, the DLF approach produces better estimates than other assimilation approaches, particularly when the observations are sparse and have low uncertainty. The added computational expense of the method is cubic in the total number of observations over time, which is on the same order of magnitude as a standard Kalman filter and can be mitigated by bounding the number of forward propagated observations, discarding the least informative data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Anomalous entropy-driven kinetics of dislocation nucleation

The kinetics of dislocation reactions, such as dislocation multiplication, controls the plastic deformation in crystals beyond their elastic limit, therefore critical mechanisms in a number of applications in materials science. We present a series of large-scale molecular dynamics simulations that shows that one such type of reactions, the nucleation of dislocation at free surfaces, exhibit unconventional kinetics, including unexpectedly large nucleation rates under compression, very strong entropic stabilization under tension, as well as strong non-Arrhenius behavior. These unusual kinetics are quantitatively rationalized using a variational transition state theory approach coupled with an efficient numerical scheme for the estimation of vibrational entropy changes. These results highlight the need for a variational treatment of the kinetics to quantitatively capture dislocation reaction kinetics, especially at low-to-moderate strains where large deformations are required to activate reactions. These observations suggest possible explanations to previously observed unconventional deformation kinetics in both molecular dynamics simulations and experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Hourly dynamic line ratings for existing transmission across the contiguous United States (preliminary results) [Slides]

The thermal rating of a transmission line depends on the rate at which heat generated by current flow through the line can be dissipated into the environment by convective and radiative cooling, keeping the conductor below a maximum operating temperature. Static or seasonal line ratings assume nearly-worst-case weather conditions (high ambient air temperature, high solar irradiance, and low wind speed), resulting in a low risk of "overrating" a line (i.e., allowing more current to flow than the line is designed for) but a high fraction of time when the line is "underrated" (i.e., able to flow more current while staying below the maximum temperature). Dynamic transmission line ratings (DLR) use real-time measurements of weather conditions, line sag, or line temperature to determine the thermal rating of transmission lines at hourly or higher time resolution, allowing for increased ratings during some (cold, windy, and/or dark) periods and correcting for overrating during other (hot, calm, and/or sunny) periods. Many studies have explored the benefits of DLR, but typically for small collections of transmission lines under a limited sample of weather conditions, leaving open the question of how widely applicable the benefits of DLR might be over diverse geographies and weather conditions. Here, we combine geospatial data on tens of thousands of transmission line routes across the contiguous United States with high-resolution hourly historical weather data for 2007-2013 to estimate the impact of DLR and other weather-adjusted rating systems. We compare static and dynamic line ratings to intermediate ratings adjusted by ambient temperature, day/night irradiance, clear-sky irradiance, and/or measured irradiance, illustrating the benefits of incorporating progressively more detailed weather data into the calculated line ratings. Moving from nighttime-adjusted ratings to clear-sky-adjusted ratings provides more benefit than moving from clear-sky-adjusted ratings to measured-irradiance-adjusted ratings. Depending on the assumed static windspeed for irradiance-adjusted-ratings, DLR either corrects for overrated hours or provides substantially increased ratings in the majority of hours on the majority of lines.

14 SOLAR ENERGY↗
NASA NTRS1978

Time delay occultation data of the Helios spacecraft for probing the electron density distribution in the solar corona

S-band time delay measurements were collected from the spacecraft Helios A and B during three solar occultations in 1975/76 within heliocentric distances of about 3 and 215 earth radius in terms of range, Doppler frequency shift, and electron content. Characteristic features of measurement and data processing are described. Typical data sets are discussed to probe the electron density distribution near the sun (west and east limb as well) including the outer and extended corona. Steady-state and dynamical aspects of the solar corona are presented and compared with earth-bound-K-coronagraph measurements. Using a weighted least squares estimation, parameters of an average coronal electron density profile are derived in a preliminary analysis to yield electron densities at r = 3, 65, 215 earth radius. Transient phenomena are discussed and a velocity of propagation v is nearly equal to 900 km/s is determined for plasma ejecta from a solar flare observed during an extraordinary set of Helios B electron content measurements.

Edenhofer, P.↗
NASA NTRS2003

NASA IKONOS Multispectral Radiometric Calibration and 3-Year Temporal Stability Assessment

Radiometric calibration of commercial imaging satellite products is required to ensure that science and application communities can place confidence in the imagery they use and can fully understand its properties. Inaccurate radiometric calibrations can lead to erroneous decisions and invalid conclusions and can limit intercomparisons with other system. In addition, the user community has little or no insight into the design and operation of commercial sensors or into the methods involved in generating commercial products. To address this calibration need, the NASA Stennis Space Center (SSC) Earth Science Applications (ESA) Directorate established a commercial satellite imaging radiometric calibration team consisting of three independent groups: NASA, SSC,ESA, the University of Arizona Remote Sensing Group, and South Dacota State University. Each group determined the absolute radiometric calibration coefficients of the Space Imaging IKONOS 4-band, 4 m multispectral product covering the visible through near-infrared spectral region. For a three year period beginning in 2000, each team employed some variant of a reflectance-based vicarious calibration approach, requiring ground-based measurements coincident with IKONOS image acquisitions and radiative transfer calculations. Several study sites throughout the United States were employed that covered nearly the entire dynamic range of the IKONOS sensor. IKONOS at-sensor radiance values were compared to those estimated by each independent group to determine the IKONOS sensor's radiometric accuracy and stability. Over 10 individual vicariously determined at-sensor radiance estimates were used each year. When combined, these estimates provided a high-precision radiometric gain calibration coefficient. No significant calibration offset was observed. The results of this evaluation provide the scientific community with an independent assessment of the IKONOS sensor's absolute calibration and temporal stability over the 3-year period. While the techniques and method described in this paper reflect those developed at the NASA SSC, the results of the entire team are included.

Pagnutti, Mary↗
NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗
NASA NTRS2006

Radiometric Characterization Results for the IKONOS, Quickbird, and OrbView-3 Sensor

Radiometric calibration of commercial imaging satellite products is required to ensure that science and application communities better understand commercial imaging satellite properties. Inaccurate radiometric calibrations can lead to erroneous decisions and invalid conclusions and can limit intercomparisons with other systems. To address this calibration need, the NASA Applied Sciences Directorate (ASD) at Stennis Space Center established a commercial satellite imaging radiometric calibration team consisting of three independent groups: NASA ASD, the University of Arizona Remote Sensing Group, and South Dakota State University. Each group independently determined the absolute radiometric calibration coefficients of available high-spatial-resolution commercial 4-band multispectral products, in the visible though near-infrared spectrum, from GeoEye(tradeMark) (formerly SpaceImaging(Registered TradeMark)) IKONOS, DigitalGlobe(Regitered TradeMark) QuickBird, and GeoEye (formerly ORBIMAGE(Registered TradeMark) OrbView. Each team member employed some variant of reflectance-based vicarious calibration approach, requiring ground-based measurements coincident with image acquisitions and radiative transfer calculations. Several study sites throughout the United States that covered a significant portion of the sensor's dynamic range were employed. Satellite at-sensor radiance values were compared to those estimated by each independent team member to evaluate the sensor's radiometric accuracy. The combined results of this evaluation provide the user community with an independent assessment of these sensors' absolute calibration values.

Holekamp, Kara↗
NASA NTRS2013

Wind Turbine Contingency Control Through Generator De-Rating

Maximizing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. In that context, systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage to the turbine. Advanced contingency control is one way to enable autonomous decision-making by providing the mechanism to enable safe and efficient turbine operation. The work reported herein explores the integration of condition monitoring of wind turbines with contingency control to balance the trade-offs between maintaining system health and energy capture. The contingency control involves de-rating the generator operating point to achieve reduced loads on the wind turbine. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.

condition monitoring↗
NASA NTRS2002

Experimental Feedback Control of Flow Induced Cavity Tones

An experimental study of the application of discrete-time, linear quadratic control design methods to the cavity tone problem is described. State space models of the dynamics from a synthetic jet actuator at the leading edge of the cavity to two pressure sensors in the cavity were computed from experimental data. Variations in model order, control order, control bandwidth, and properties of a Kalman state estimator were studied. Feedback control reduced the levels of multiple cavity tones at Mach 0.275, 0.35, and 0.45. Closed loop performance was often limited by excitation of sidebands of cavity tones, and creation of new tones in the spectrum. State space models were useful for explaining some of these limitations, but were not able to account for non-linear dynamics, such as interactions between tones at different frequencies.

Cabell, Randolph H.↗
NASA NTRS1992

Dynamically stratified Monte Carlo forecasting

A new method for performing Monte Carlo forecasts is introduced. The method, called dynamic stratification, selects initial perturbations based on a stratification of the error distribution. A simple implementation is presented in which the error distribution used for the stratification is estimated from a linear model derived from a large ensemble of 12-h forecasts with the full dynamic model. The stratification thus obtained is used to choose a small subsample of initial states with which to perform the dynamical Monte Carlo forecasts. Several test cases are studied using a simple two-level general circulation model with uncertain initial conditions. It is found that the method provides substantial reductions in the sampling error of the forecast mean and variance when compared to the more traditional approach of choosing the initial perturbations at random. The degree of improvement, however, is sensitive to the nature of the initial error distribution and to the base state. In practice the method may be viable only if the computational burden involved in obtaining an adequate estimate of the error distribution is shared with the data-assimilation procedure.

Schubert, Siegfried↗
NASA NTRS1997

Application of the Two-Step Filter to Process Ranging Measurements for Relative Navigation in an Elliptical Orbit

This estimator breaks a nonlinear estimation problem into a set of over determined 'first step' states which are linear in the observations and 'second step' states which are ultimately the states of interest. Linear estimation methods are applied to filter the observations and produce the optimal first step state estimate. The 'second step' states are obtained through iterative nonlinear parameter estimation considering the first step states as observations. It has been shown that this process exactly minimizes the least squares cost function for static problems and provides a better solution than the iterated extended Kalman filter (EKF) for dynamic problems. The two step filter is applied in this paper to process range and range rate measurements between the two spacecraft. Details of the application of the two step estimator to this problem will be given, highlighting the use of a test for ill-conditioned covariance estimates that can result from the first order covariance propagation. A comparison will be made between the performance of the two step filter and the IEKF.

Garrison, James L.↗
DOE OSTI2024

Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real-time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision-making to effectively reduce Scope 2 water usage and emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Correlation techniques to determine model form in robust nonlinear system realization/identification

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Stry, Greselda I.↗
NASA NTRS1972

A biased filter for linear discrete dynamic systems.

A recursive estimator, the ridge filter, was developed for the linear discrete dynamic estimation problem. Theorems were established to show that the ridge filter can be, on the average, closer to the expected value of the system state than the Kalman filter. On the other hand, Kalman filter, on the average, is closer to the instantaneous system state than the ridge filter. The ridge filter has been formulated in such a way that the computational features of the Kalman filter are preserved.

Chang, J. W.↗
NASA NTRS1977

Time delay occultation data of the Helios spacecrafts and preliminary analysis for probing the solar corona

S-band time delay measurements were collected from the spacecrafts Helios A and B during three solar occultations in 1975/76 within heliocentric distances of about 3 and 215 solar radii in terms of range, Doppler frequency shift, and electron content. A description is given concerning some characteristic features of the methods of measurement and data processing. Typical data sets are discussed to probe the electron density distribution near the sun (west and east limb as well) including the outer and extended corona. Steady-state and dynamical aspects of the solar corona are presented and compared with earth-bound K-coronagraph measurements. Using a weighted least squares estimation 3 parameters of an average coronal electron density profile are derived in a preliminary analysis to yield electron densities of about 130 billion; 100 million; 7 million/cu m at r?3; 65; 215 solar radii. Transient phenomena are discussed and a velocity of propagation v approximately 900 km/s for plasma ejecta from a solar flare is determined from an extraordinary set of Helios B electron content measurements on April 30/May 1, 1976.

Edenhofer, P.↗
NASA NTRS1985

Self-Tuning Adaptive-Controller Using Online Frequency Identification

A real time adaptive controller was designed and tested successfully on a fourth order laboratory dynamic system which features very low structural damping and a noncolocated actuator sensor pair. The controller, implemented in a digital minicomputer, consists of a state estimator, a set of state feedback gains, and a frequency locked loop (FLL) for real time parameter identification. The FLL can detect the closed loop natural frequency of the system being controlled, calculate the mismatch between a plant parameter and its counterpart in the state estimator, and correct the estimator parameter in real time. The adaptation algorithm can correct the controller error and stabilize the system for more than 50% variation in the plant natural frequency, compared with a 10% stability margin in frequency variation for a fixed gain controller having the same performance at the nominal plant condition. After it has locked to the correct plant frequency, the adaptive controller works as well as the fixed gain controller does when there is no parameter mismatch. The very rapid convergence of this adaptive system is demonstrated experimentally, and can also be proven with simple root locus methods.

Chiang, W. W.↗