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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

An experimental study of nonlinear dynamic system identification

A technique for robust identification of nonlinear dynamic systems is developed and illustrated using both simulations and analog experiments. The technique is based on the Minimum Model Error 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 of the current work is the ability to identify nonlinear dynamic systems without prior assumptions regarding the form of the nonlinearities, in constrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. 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.↗
DOE OSTI2018

Discretization Procedure for Linear Dynamical Systems

We describe in detail a method of construction of a discrete linear dynamical model of a controllable object in the recurrent Cauchy form corresponding to an initial continuous dynamical system. We describe discretization algorithms for free and forced motion of a linear continuous dynamical system and propose methods of construction of the state, control, and perturbation transition matrices.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Stabilizing feedback control for dynamical systems with bounded uncertainty

The theories of differential games and generalized dynamic systems are used to deduce stabilizing controllers for quasi-linear systems. Attention is given to a class of dynamic systems subject to parameter and input uncertainty whose values range in a given compact set. Using a worst case design philosophy, a feedback control is derived that assures uniform asymptotic (Liapunov) stability of the origin under all admissible uncertainties.

Gutman, S.↗
NASA NTRS1987

A qualitative model of human interaction with complex dynamic systems

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Hess, Ronald A.↗
NASA NTRS1987

A Qualitative Model of Human Interaction with Complex Dynamic Systems

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Hess, Ronald A.↗
NASA NTRS1982

Application of control theory to dynamic systems simulation

The application of control theory is applied to dynamic systems simulation. Theory and methodology applicable to controlled ecological life support systems are considered. Spatial effects on system stability, design of control systems with uncertain parameters, and an interactive computing language (PARASOL-II) designed for dynamic system simulation, report quality graphics, data acquisition, and simple real time control are discussed.

Auslander, D. M.↗
DOE OSTI2014

Sequential Monte Carlo sampling in hidden Markov models of nonlinear dynamical systems

We investigate the issue of which state functionals can have their uncertainty estimated efficiently in dynamical systems with uncertainty. Because of the high dimensionality and complexity of the problem, sequential Monte Carlo (SMC) methods are used. We investigate SMC methods where the proposal distribution is computed by maximum likelihood or by a linearization approach. We prove that the variance of the SMC method is bounded linearly in the number of time steps when the proposal distribution is truncated normal distribution. We also show that for a moderate large number of steps the error produced by approximation of dynamical systems linearly accumulates on the condition that the logarithm of the density function of noise is Lipschitz continuous. This finding is significant because the uncertainty in many dynamical systems, in particular, in chemical engineering systems, can be assumed to have this nature. We demonstrate our findings for a simple test case from chemical engineering. The theoretical findings provide a foundation for the parallel software SISTOS.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Analysis of dynamic system response to product random processes

The response of dynamic systems to the product of two independent Gaussian random processes is developed by use of the Fokker-Planck and associated moment equations. The development is applied to the amplitude modulated process which is used to model atmospheric turbulence in aeronautical applications. The exact solution for the system response is compared with the solution obtained by the quasi-steady approximation which omits the dynamic properties of the random amplitude modulation. The quasi-steady approximation is valid as a limiting case of the exact solution for the dynamic response of linear systems to amplitude modulated processes. In the nonlimiting case the quasi-steady approximation can be invalid for dynamic systems with low damping.

Sidwell, K.↗
DOE OSTI2020

Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ 1 -regularization

We develop a new data-driven paradigm for efficient model reduction of a broad class of nonlinear dynamical systems. Our model reduction method directly enables the interpretation of key components of the dynamical system, unlike traditional projection-based model reduction methods that focus on reducing computational complexity more than interpretability. Our method is not application specific and is simple to implement on nonlinear dynamical systems arising from a variety of different fields. It requires minimal parameterization using a single parameter to trade-off between model complexity and estimation error. We use a data-driven paradigm to formulate model reduction as an efficient convex optimization problem that scales polynomially in the original size of the complex system, enabling systems with as many as thousands of components to be reduced in a matter of minutes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A swarm intelligence-based approach to anomaly detection of dynamic systems

Here we propose a novel Swarm Intelligence-based approach for anomaly detection of dynamic systems. Specifically, we consider observation processes for dynamic systems from a foraging perspective, and ant foraging behavior-based search techniques are applied to the discovery of the ‘intrinsic communication topology’ of systems. The key idea exploited here is that systems can be viewed as ‘communication networks’ where interconnected elements communicate with one another through physical phenomena and the elements ‘process’ the communications through their dynamics; this defines the ‘intrinsic communication topology’ of the system. The behaviors of individual elements can be at least partially observed in the dynamics of other communicating system elements and, hence, condition changes will be reflected in changes in the intrinsic communications. Anomaly detection is then performed by observing the changes in the intrinsic communication topology by tracking graph similarity measures and using change point detection to determine when statistically significant changes have occurred. The performance of the proposed approach is investigated in simulation where we consider a system composed of multiple networks, each with different topologies and connection strengths, and where the individual networks comprising the system can be switched at arbitrary times. The proposed methodology is applied to this system to detect the switches between the networks and to discover the communication topologies associated with each of the network configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Koopman Operator Methods for Global Phase Space Exploration of Equivariant Dynamical Systems

In this paper, we develop the Koopman operator theory for dynamical systems with symmetry. In particular, we investigate how the Koopman operator and eigenfunctions behave under the action of the symmetry group of the underlying dynamical system. Further, exploring the underlying symmetry, we propose an algorithm to construct a global Koopman operator from local Koopman operators. In particular, we show, by exploiting the symmetry, data from all the invariant sets are not required for constructing the global Koopman operator; that is, local knowledge of the system is enough to infer the global dynamics.

Sinha, Subhrajit↗
DOE OSTI2015

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the control of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Design an optimum safety policy for personnel safety management - A system dynamic approach

Personnel safety management (PSM) ensures that employee's work conditions are healthy and safe by various proactive and reactive approaches. Nowadays it is a complex phenomenon because of increasing dynamic nature of organisations which results in an increase of accidents. An important part of accident prevention is to understand the existing system properly and make safety strategies for that system. System dynamics modelling appears to be an appropriate methodology to explore and make strategy for PSM. Many system dynamics models of industrial systems have been built entirely for specific host firms. This thesis illustrates an alternative approach. The generic system dynamics model of Personnel safety management was developed and tested in a host firm. The model was undergone various structural, behavioural and policy tests. The utility and effectiveness of model was further explored through modelling a safety scenario. In order to create effective safety policy under resource constraint, DOE (Design of experiment) was used. DOE uses classic designs, namely, fractional factorials and central composite designs. It used to make second order regression equation which serve as an objective function. That function was optimized under budget constraint and optimum value used for safety policy which shown greatest improvement in overall PSM. The outcome of this research indicates that personnel safety management model has the capability for acting as instruction tool to improve understanding of safety management and also as an aid to policy making.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1989

Robust control synthesis for uncertain dynamical systems

This paper presents robust control synthesis techniques for uncertain dynamical systems subject to structured parameter perturbation. Both QFT (quantitative feedback theory) and H-infinity control synthesis techniques are investigated. Although most H-infinity-related control techniques are not concerned with the structured parameter perturbation, a new way of incorporating the parameter uncertainty in the robust H-infinity control design is presented. A generic model of uncertain dynamical systems is used to illustrate the design methodologies investigated in this paper. It is shown that, for a certain noncolocated structural control problem, use of both techniques results in nonminimum phase compensation.

Byun, Kuk-Whan↗
DOE OSTI2020

Data Driven Online Learning of Power System Dynamics

With the advancement of sensing and communication in power networks, high-frequency real-time data from a power network can be used as a resource to develop better monitoring capabilities. In this work, a systematic approach based on data-driven operator theoretic methods involving Koopman operator is proposed for the online identification of power system dynamics. In particular, a new algorithm is provided, which unlike any previously existing algorithms, updates the Koopman operator iteratively as new data points are acquired. The proposed algorithm has three advantages: a) allows for real-time monitoring of the power system dynamics b) linear power system dynamics (this linear system is usually in a higher dimensional feature space and is not same as linearization of the underlying nonlinear dynamics) and c) computationally fast and less intensive when compared to the popular Extended Dynamic Mode Decomposition (EDMD) algorithm. The efficiency of the proposed algorithm is illustrated on an IEEE 9 bus system using synthetic data from the nonlinear model and on IEEE 39 bus system using synthetic data from the linearized model.

Sinha, Subhrajit↗
NASA NTRS2005

Development of a Stirling System Dynamic Model With Enhanced Thermodynamics

The Stirling Convertor System Dynamic Model developed at NASA Glenn Research Center is a software model developed from first principles that includes the mechanical and mounting dynamics, the thermodynamics, the linear alternator, and the controller of a free-piston Stirling power convertor, along with the end user load. As such it represents the first detailed modeling tool for fully integrated Stirling convertor-based power systems. The thermodynamics of the model were originally a form of the isothermal Stirling cycle. In some situations it may be desirable to improve the accuracy of the Stirling cycle portion of the model. An option under consideration is to enhance the SDM thermodynamics by coupling the model with Gedeon Associates Sage simulation code. The result will be a model that gives a more accurate prediction of the performance and dynamics of the free-piston Stirling convertor. A method of integrating the Sage simulation code with the System Dynamic Model is described. Results of SDM and Sage simulation are compared to test data. Model parameter estimation and model validation are discussed.

Regan, Timothy F.↗
DOE OSTI2022

Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems

The flow-driven spectral chaos (FSC) is a recently developed method for tracking and quantifying uncertainties in the long-time response of stochastic dynamical systems using the spectral approach. The method uses a novel concept called enriched stochastic flow maps as a means to construct an evolving finite-dimensional random function space that is both accurate and computationally efficient in time. In this paper, we present a multi-element version of the FSC method (the ME-FSC method for short) to tackle (mainly) those dynamical systems that are inherently discontinuous over the probability space. In ME-FSC, the random domain is partitioned into several elements, and then the problem is solved separately on each random element using the FSC method. Subsequently, results are aggregated to compute the probability moments of interest using the law of total probability. To demonstrate the effectiveness of the ME-FSC method in dealing with discontinuities and long-time integration of stochastic dynamical systems, four representative numerical examples are presented in this paper, including the Van-der-Pol oscillator problem and the Kraichnan-Orszag three-mode problem. Results show that the ME-FSC method is capable of solving problems that have strong nonlinear dependencies over the probability space, both reliably and at low computational cost.

97 MATHEMATICS AND COMPUTING↗