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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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Report presents two new properties of systems characterized by linear state space models (e.g., dynamical systems and associated control systems): estimability and regulability. Provides criteria for reductions of errors and cost functions in control systems. Useful in design of optimal controllers and estimators.
Design of optimal waveforms for optimal estimation or prediction of state of linear dynamical system in presence of Markov and white noise
Optimization and regulation of static and dynamic systems require good estimates of the states of the system model and the parameters of the model in the presence of input and measurement noise. A biased estimator is proposed as an alternative to the familiar best linear unbiased estimator. Depending on the value of a constant, this biased estimator can be made unbiased and is then identical to the best linear unbiased estimator, thus permitting additional freedom of choice in design and application. The sum of squared errors of this bs tmator can be less than the best linear unbiased case. An existence theorem is established and various properties are discussed.
A square root formulation is presented for the discrete combined state parameter estimation problem with linear plant dynamics, Gaussian random disturbances, and constant but uncertain parameters. The estimator is a combination of the classical Kalman filter and a maximum likelihood algorithm which maximizes the parameter log-likelihood function using a first order search routine.
Given the advent of quantum algorithms for a wide array of problems in linear algebra and machine learning, it is important to develop general methods for the simulation of arbitrary (ie non-unitary) operators on quantum hardware. In this talk, we present a novel quantum algorithm based on the quantum singular value transformation (QSVT) to apply an arbitrary operator K to some input state and subsequently estimate the expectation value of some observable. Our construction then immediately yields a route to estimating observables of states undergoing open quantum dynamics, whose effect is captured by a set of non-unitary Kraus operators. Our algorithm succeeds deterministically given the Sz-Nagy dilation, and we provide details on the algorithm's query and gate complexity, numerical verification, and comparisons with prior methods.
A model of the nonlinear dynamics of a deformable maneuvering multibody system is described, whereby elastic deformations are modeled by restoring forces and dissipative forces at point mass appendages. This model is brought into bilinear form. Estimation of deformations occasioned by rapid slewing maneuvers is carried out by a filter based on a globally equivalent linear model of the bilinear dynamics, and is shown to be an improvement over the extended Kalman filter. To further alleviate the computational burden, the estimated deformation state is propagated between observations by a low dimensional operator spline interpolator of bilinear system Volterra series, which is easily implemented.
Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.
Minimax sensitivity criteria used to synthesize filters for estimating state of first order plant subject to dynamic and/or statistical parameters uncertainties
Orbit determination during the post-rendezvous phases of the Comet Rendezvous/Asteroid Flyby mission is described. The orbit determination process is discussed, with emphasis placed on optical imaging of landmarks and Doppler tracking. Rotational dynamics are introduced for the cometary nucleus. State estimation errors are given for spacecraft trajectory prediction and cometary nucleus attitude prediction. Estimation errors are also given for parameters that describe the cometary nucleus such as moments of inertia and gravity harmonics. The orbit determination performance in support of science observations while in orbit about the nucleus is described.
A system identification model that combines generalized-spline function approximation with a nonlinear control system is described. The complete control system contains three main elements: a nonlinear-inverse-dynamic control law that depends on a comprehensive model of the plant, a state estimator whose outputs drive the control law, and a function approximation scheme that models the system dynamics. The system-identification task, which combines an extended Kalman filter with a function approximator modeled as an artificial neural network, is considered. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.
Context. How protoclusters evolved from sparse galaxy overdensities to mature galaxy clusters is still not well understood. In this context, detecting and characterizing the hot intracluster medium (ICM) at high redshifts (z ∼ 2) is key to understanding how the continuous accretion from the filamentary large-scale structure and the mergers along it impact the first phases of cluster formation. Aims. We study the dynamical state and morphology of the z = 1:98 galaxy cluster XLSSC 122 with high-resolution observations (≈5″) of the ICM through the Sunyaev–Zeldovich (SZ) effect. XLSSC 122 is the highest redshift optically confirmed galaxy cluster found in an unbiased, widefield survey. Methods. Via Bayesian forward modeling, we mapped the ICM on scales from the virial radius down to the core of the cluster. To constrain such a broad range of spatial scales, we employed a new technique that jointly forward-models parametric descriptions of the pressure distribution to interferometric ACA and ALMA observations and multiband imaging data from ACT. Results. We detect the SZ effect with 11σ significance in the ALMA+ACA observations and find a flattened inner pressure profile that is consistent with a noncool core classification with a significance of ≥3σ. In contrast to the previous works, we find better agreement between the SZ effect signal and the X-ray emission as well as the cluster member distribution. Further, XLSSC 122 exhibits an excess of SZ flux in the south of the cluster where no X-ray emission is detected. By reconstructing the interferometric observations and modeling in the uv-plane, we obtain a tentative detection of an infalling group or filamentary-like structure in the southeast that is believed to boost and heat up the ICM while the density of the gas is still low. In addition to characterizing the dynamical state of the cluster, we provide an improved SZ mass estimate M500,c = 1.66−0.20+0.23 × 1014 M⊙ . Conclusions. Altogether, the observations indicate that we see XLSSC 122 in a dynamic phase of cluster formation while a large reservoir of gas is already thermalized.
Attitude estimator use observations from different times to reduce the effects of noise. If the vehicle is rotating, the attitude at one time needs to be propagated to that at another time. If the vehicle measures its angular velocity, attitude propagating entails integrating a rotational kinematics equation only. If a measured angular velocity is not available, torques can be computed and an additional rotational dynamics equation integrated to give the angular velocity. Initial conditions for either of these integrations come from the estimation process. Sometimes additional quantities, such as gyro and torque parameters, are also solved for. Although the partial derivatives of attitude with respect to initial attitude and gyro parameters are well known, the corresponding partial derivatives with respect to initial angular velocity and torque parameters are less familiar. They can be derived and computed numerically in a way that is analogous to that used for the initial attitude and gyro parameters. Previous papers have demonstrated the feasibility of using dynamics models for attitude estimation but have not provided details of how each angular velocity and torque parameters can be estimated. This tutorial paper provides some of that detail, notably how to compute the state transition matrix when closed form expressions are not available. It also attempts to put dynamics estimation in perspective by showing the progression from constant to gyro-propagated to dynamics-propagated attitude motion models. Readers not already familiar with attitude estimation will find this paper an introduction to the subject, and attitude specialists may appreciate the collection of heretofore scattered results brought together in a single place.
The F-18 HARV is a modified F-18 Aircraft which is capable of flying in the post-stall regime in order to achieve superagility. The onset of aerodynamic stall, and continued into the post-stall region, is characterized by nonlinearities in the aerodynamic coefficients. These aerodynamic coefficients are not expressed as analytic functions, but rather in the form of tabular data. The nonlinearities in the aerodynamic coefficients yield a nonlinear model of the aircraft's dynamics. Nonlinear system theory has made many advances, but this area is not sufficiently developed to allow its application to this problem, since many of the theorems are existance theorems and that the systems are composed of analytic functions. Thus, the feedback matrices and the state estimators are obtained from linear system theory techniques. It is important, in order to obtain the correct feedback matrices and state estimators, that the linear description of the nonlinear flight dynamics be as accurate as possible. A nonlinear simulation is run under the Advanced Continuous Simulation Language (ACSL). The ACSL simulation uses FORTRAN subroutines to interface to the look-up tables for the aerodynamic data. ACSL has commands to form the linear representation for the system. Other aspects of this investigation are discussed.
The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.
A recursive model-based data fusion algorithm for multi-sensor microburst hazard assessment is described. An analytical microburst model is used to approximate the actual windfield, and a set of 'best' model parameters are estimated from measured winds. The winds corresponding to the best parameter set can then be used to compute alerting factors such as microburst position, extent, and intensity. The estimation algorithm is based on an iterated extended Kalman filter which uses the microburst model parameters as state variables. Microburst state dynamic and process noise parameters are chosen based on measured microburst statistics. The estimation method is applied to data from a time-varying computational simulation of a historical microburst event to demonstrate its capabilities and limitations. Selection of filter parameters and initial conditions is discussed. Computational requirements and datalink bandwidth considerations are also addressed.
A recursive model-based data fusion algorithm for multi-sensor microburst hazard assessment is described. An analytical microburst model is used to approximate the actual windfield, and a set of 'best' model parameters are estimated from measured winds. The winds corresponding to the best parameter set can then be used to compute alerting factors such as microburst position, extent, and intensity. The estimation algorithm is based on an iterated extended Kalman filter which uses the microburst model parameters as state variables. Microburst state dynamic and process noise parameters are chosen based on measured microburst statistics. The estimation method is applied to data from a time-varying computational simulation of a historical microburst event to demonstrate its capabilities and limitations. Selection of filter parameters and initial conditions is discussed. Computational requirements and datalink bandwidth considerations are also addressed.
A parameter and structure estimation technique for multivariable systems is used to obtain a state space representation of open loop dynamics of the space shuttle main engine in alpha-canonical form. The parameterization being used is both minimal and unique. The simplified linear model may be used for fault detection studies and control system design and development.
Test runs in the pilot plants consume significant resources, and therefore, the learning from test runs should be maximized. Test runs conducted in the pilot plants are often steady state. It takes several hours for reaching steady-state in the pilot plants, and thus, the duration of the test runs needs to be long even for collecting few steady-state data points. On the other hand, a large number of measurements can be collected through dynamic test runs in a short span of time. This paper presents a systematic design of dynamic experiments (DoDEs) for identifiability of model parameters, which is achieved by persistently exciting the inputs signals. A pseudorandom binary sequence (PRBS) is designed as the input signal for DoDE due to its efficiency in obtaining sufficient spectral content. However, due to the long sequence size of the PRBS signal, a Schroeder-phase input signal, which is a multisine signal, is also designed. Tests for both types of signals are run in the Pilot Solvent Test Unit (PSTU) at the National Carbon Capture Center in Wilsonville, Alabama. The transient data are used to solve dynamic data reconciliation and parameter estimation problem. The estimated parameters are found to be not only superior to those estimated from using data collected from hundreds of steady-state test runs in a nonreactive (air–water) system, but the parameters could be estimated by using the dynamic data collected for about 24 h from the pilot plant for the MEA-H 2 O–CO 2 system.