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Results for “modeling and filtering”

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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

A study of forecast growth with a barotropic model of the atmosphere

A barotropic model of the atmosphere was used to test various sources of forecast error. These errors are classified as truncation error, physical error, or initial error. It was shown that growth patterns due to each category differ significantly. Initial errors were shown not to grow in a barotropic model contrary to reports of other studies which indicate that they basically do grow. Also, random initial errors were shown to decrease due to the filtering effect of the model itself. Results seem to indicate that instabilities are required for error growth, be they barotropic or baroclinic, and that random errors are not representative of true initial conditions.

Halberstam, I. M.↗
NASA NTRS2013

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Data Assimilation↗
NASA NTRS1985

Testing of a failure accommodation system on a highly flexible grid

This paper presents a scheme for on-line failure detection in systems with appreciable structural dynamics. The design is suboptimal because of extensive computational requirements of optimal schemes. To accomplish failure detection, the innovations sequence of a finite order Kalman filter is examined. Because of the heavy dependence of the system on the zero-mean character of the innovations sequence of the filter much attention has been given to the design and evaluation of the Kalman filters used. The filter designs are based on modal models of the structural dynamics. Two modal models were considered, one based on an analytic finite element model and the other based on empirically derived frequency and damping. Experiments using a grid structure are presented which illustrate operation and performance of the filter designs based on these models. The general character of the results presented is that appreciable errors exist in the filter design based on the finite element model. Substantial improvement results if the design model is modified to include empirically derived frequency and damping.

Montgomery, R. C.↗
NASA NTRS2013

PROcess Based Diagnostics PROBE

Many of the aspects of the climate system that are of the greatest interest (e.g., the sensitivity of the system to external forcings) are emergent properties that arise via the complex interplay between disparate processes. This is also true for climate models most diagnostics are not a function of an isolated portion of source code, but rather are affected by multiple components and procedures. Thus any model-observation mismatch is hard to attribute to any specific piece of code or imperfection in a specific model assumption. An alternative approach is to identify diagnostics that are more closely tied to specific processes -- implying that if a mismatch is found, it should be much easier to identify and address specific algorithmic choices that will improve the simulation. However, this approach requires looking at model output and observational data in a more sophisticated way than the more traditional production of monthly or annual mean quantities. The data must instead be filtered in time and space for examples of the specific process being targeted.We are developing a data analysis environment called PROcess-Based Explorer (PROBE) that seeks to enable efficient and systematic computation of process-based diagnostics on very large sets of data. In this environment, investigators can define arbitrarily complex filters and then seamlessly perform computations in parallel on the filtered output from their model. The same analysis can be performed on additional related data sets (e.g., reanalyses) thereby enabling routine comparisons between model and observational data. PROBE also incorporates workflow technology to automatically update computed diagnostics for subsequent executions of a model. In this presentation, we will discuss the design and current status of PROBE as well as share results from some preliminary use cases.

PROBE↗
NASA NTRSDate not supplied

Evaluating the Utility of Pressure Scanners for Unsteady Pressure Measurements in Wind Tunnel Characterization of the Space Launch System

In many aerodynamic measurement applications, the ability to make time-accurate measurements with pressure scanners provides practical advantages over making the same measurements directly using flush-mounted transducers. The pneumatic distortion due to the tubing between the sensing module and surface port limits the usefulness of such measurements to averaged, steady data only. This issue has restricted the application of pressure scanners for unsteady pressure measurements in wind tunnel and flight-testing applications. The Wiener-filtered inverse system response model has been proven to be effective in reconstructing complex unsteady pressure signals acquired remotely using pressure scanners. In this work, the feasibility of using pressure scanning systems for time-resolved pressure measurements in the NASA Langley 14- by 22-Foot Subsonic Wind Tunnel was evaluated. Results demonstrated good agreement between the reference transducers and reconstructed pressure scanner measurements up to frequencies of approximately 500 Hz.

Unsteady pressure↗
DOE OSTI2024

a priori uncertainty quantification of reacting turbulence closure models using Bayesian neural networks

While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulations (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the model chosen. Increased adoption of such models requires reliable uncertainty estimates both in the data-informed and out-of-distribution regimes. Here, in this work, we employ Bayesian neural networks (BNNs) to capture both epistemic and aleatoric uncertainties in a reacting flow model. In particular, we model the filtered progress variable scalar dissipation rate which plays a key role in the dynamics of turbulent premixed flames. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. We also propose a method for the incorporation of out-of-distribution information in a BNN, which can be used for out-of-distribution query detection. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

Design of a nonlinear adaptive filter for suppression of shuttle pilot-induced oscillation tendencies

Analysis of a longitudinal pilot-induced oscillation (PIO) experienced just prior to touchdown on the final flight of the space shuttle's approach landing tests indicated that the source of the problem was a combination of poor basic handling qualities aggravated by time delays through the digital flight control computer and rate limiting of the elevator actuators due to high pilot gain. A nonlinear PIO suppression (PIOS) filter was designed and developed to alleviate the vehicle's PIO tendencies by reducing the gain in the command path. From analytical and simulator studies it was shown that the PIOS filter, in an adaptive fashion, can attenuate the command path gain without adding phase lag to the system. With the pitch attitude loop of a simulated shuttle model closed, the PIOS filter increased the gain margin by a factor of about two.

Smith, J. W.↗
NASA NTRS1986

A 25-kW Series-Resonant Power Converter

Prototype exhibited efficiency of 93.9 percent. 25-kW resonant dc/dc power converter designed, developed, fabricated, and tested, using Westinghouse D7ST transistors as high-power switches. D7ST transistor characterized for use as switch in series-resonant converters, and refined base-drive circuit developed. Technical base includes advanced switching magnetic, and filter components, mathematical circuit models, control philosophies, and switch-drive strategies. Power-system benefits such as lower losses when used for high-voltage distribution, and reduced magnetics and filter mass realized.

Frye, R. J.↗
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.↗
NASA NTRS2013

The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction.

LAI↗
DOE OSTI2020

Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network

Developing accurate, efficient, and robust closure models is essential in the construction of reduced order models (ROMs) for realistic nonlinear systems, which generally require drastic ROM mode truncations. We propose a deep residual neural network (ResNet) closure learning framework for ROMs of nonlinear systems. The novel ResNet-ROM framework consists of two steps: (i) In the first step, we use ROM projection to filter the given nonlinear system and construct a spatially filtered ROM. This filtered ROM is low-dimensional, but is not closed. (ii) In the second step, we use ResNet to close the filtered ROM, i.e., to model the interaction between the resolved and unresolved ROM modes. We emphasize that in the new ResNet-ROM framework, data is used only to complement classical physical modeling (i.e., only in the closure modeling component), not to completely replace it. We also note that the new ResNet-ROM is built on general ideas of spatial filtering and deep learning and is independent of (restrictive) phenomenological arguments, e.g., of eddy viscosity type. The numerical experiments for the 1D Burgers equation show that the ResNet-ROM is significantly more accurate than the standard projection ROM. The new ResNet-ROM is also more accurate and significantly more efficient than other modern ROM closure models.

Xie, Xuping↗
DOE OSTI2023

Generalized filtered lifting line theory for arbitrary chord lengths and application to wind turbine blades

Abstract The filtered lifting line theory is an analytical approach used to solve the equations of flow subjected to body forces with a Gaussian distribution, such as used in the actuator line model. In the original formulation, the changes in chord length along the blade were assumed to be small. This assumption can lead to errors in the induced velocities predicted by the theory compared to full solutions of the equations. In this work, we revisit the original derivation and provide a more general formulation that can account for significant changes in chord along the blade. The revised formulation can be applied to wings with significant changes in chord along the span, such as wind turbine blades.

17 WIND ENERGY↗
NASA NTRS1989

Application of optimal control theory to the design of the NASA/JPL 70-meter antenna servos

The application of Linear Quadratic Gaussian (LQG) techniques to the design of the 70-m axis servos is described. Linear quadratic optimal control and Kalman filter theory are reviewed, and model development and verification are discussed. Families of optimal controller and Kalman filter gain vectors were generated by varying weight parameters. Performance specifications were used to select final gain vectors.

Alvarez, L. S.↗
NASA NTRS1991

Study of geopotential error models used in orbit determination error analysis

The uncertainty in the geopotential model is currently one of the major error sources in the orbit determination of low-altitude Earth-orbiting spacecraft. The results of an investigation of different geopotential error models and modeling approaches currently used for operational orbit error analysis support at the Goddard Space Flight Center (GSFC) are presented, with emphasis placed on sequential orbit error analysis using a Kalman filtering algorithm. Several geopotential models, known as the Goddard Earth Models (GEMs), were developed and used at GSFC for orbit determination. The errors in the geopotential models arise from the truncation errors that result from the omission of higher order terms (omission errors) and the errors in the spherical harmonic coefficients themselves (commission errors). At GSFC, two error modeling approaches were operationally used to analyze the effects of geopotential uncertainties on the accuracy of spacecraft orbit determination - the lumped error modeling and uncorrelated error modeling. The lumped error modeling approach computes the orbit determination errors on the basis of either the calibrated standard deviations of a geopotential model's coefficients or the weighted difference between two independently derived geopotential models. The uncorrelated error modeling approach treats the errors in the individual spherical harmonic components as uncorrelated error sources and computes the aggregate effect using a combination of individual coefficient effects. This study assesses the reasonableness of the two error modeling approaches in terms of global error distribution characteristics and orbit error analysis results. Specifically, this study presents the global distribution of geopotential acceleration errors for several gravity error models and assesses the orbit determination errors resulting from these error models for three types of spacecraft - the Gamma Ray Observatory, the Ocean Topography Experiment, and the Cosmic Background Explorer.

Yee, C.↗
NASA NTRS2009

Aircraft Flight Envelope Determination using Upset Detection and Physical Modeling Methods

The development of flight control systems to enhance aircraft safety during periods of vehicle impairment or degraded operations has been the focus of extensive work in recent years. Conditions adversely affecting aircraft flight operations and safety may result from a number of causes, including environmental disturbances, degraded flight operations, and aerodynamic upsets. To enhance the effectiveness of adaptive and envelope limiting controls systems, it is desirable to examine methods for identifying the occurrence of anomalous conditions and for assessing the impact of these conditions on the aircraft operational limits. This paper describes initial work performed toward this end, examining the use of fault detection methods applied to the aircraft for aerodynamic performance degradation identification and model-based methods for envelope prediction. Results are presented in which a model-based fault detection filter is applied to the identification of aircraft control surface and stall departure failures/upsets. This application is supported by a distributed loading aerodynamics formulation for the flight dynamics system reference model. Extensions for estimating the flight envelope due to generalized aerodynamic performance degradation are also described.

Keller, Jeffrey D.↗
NASA NTRS1995

A Game Theoretic Fault Detection Filter

The fault detection process is modelled as a disturbance attenuation problem. The solution to this problem is found via differential game theory, leading to an H(sub infinity) filter which bounds the transmission of all exogenous signals save the fault to be detected. For a general class of linear systems which includes some time-varying systems, it is shown that this transmission bound can be taken to zero by simultaneously bringing the sensor noise weighting to zero. Thus, in the limit, a complete transmission block can he achieved, making the game filter into a fault detection filter. When we specialize this result to time-invariant system, it is found that the detection filter attained in the limit is identical to the well known Beard-Jones Fault Detection Filter. That is, all fault inputs other than the one to be detected (the "nuisance faults") are restricted to an invariant subspace which is unobservable to a projection on the output. For time-invariant systems, it is also shown that in the limit, the order of the state-space and the game filter can be reduced by factoring out the invariant subspace. The result is a lower dimensional filter which can observe only the fault to be detected. A reduced-order filter can also he generated for time-varying systems, though the computational overhead may be intensive. An example given at the end of the paper demonstrates the effectiveness of the filter as a tool for fault detection and identification.

Chung, Walter H.↗
NASA NTRS1990

Integrated system identification and modal state estimation for control of flexible space structures

A novel approach of integrated system identification and modal state estimation is proposed for control of linear dynamical systems including flexible space structures. There are four steps involved in this approach. First, the relation between a stochastic state space model of a dynamical system and the coefficients of its autoregressive model with exogenous input is derived. Second, an adaptive least-squares transversal predictor is used to estimate the coefficients of the model. Third, a state space model and a steady state Kalman filter gain of the dynamical system are then identified from the coefficients of the model by using the eigensystem realization algorithm. Fourth, a modal state estimator is constructed using the modal parameters of the identified model. On-line implementation of this algorithm can continually improve the modal parameters and the filter gain. It can also gradually update the system model when the system characteristics are slowly changing. A numerical example is used to illustrate the feasibility of the new approach.

Chen, Chung-Wen↗
NASA NTRS1983

Lagged average forecasting, an alternative to Monte Carlo forecasting

A 'lagged average forecast' (LAF) model is developed for stochastic dynamic weather forecasting and used for predictions in comparison with the results of a Monte Carlo forecast (MCF). The technique involves the calculation of sample statistics from an ensemble of forecasts, with each ensemble member being an ordinary dynamical forecast (ODF). Initial conditions at a time lagging the start of the forecast period are used, with varying amounts of time for the lags. Forcing by asymmetric Newtonian heating of the lower layer is used in a two-layer, f-plane, highly truncated spectral model in a test forecasting run. Both the LAF and MCF are found to be more accurate than the ODF due to ensemble averaging with the MCF and the LAF. When a regression filter is introduced, all models become more accurate, with the LAF model giving the best results. The possibility of generating monthly or seasonal forecasts with the LAF is discussed.

Hoffman, R. N.↗