Simulation of large-scale dynamic systems
The classes of large-scale dynamic systems are discussed in terms of modeling with emphasis on air transportation system modeling.
Roberts, L.↗
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Results for “dynamic system”
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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Multiloop Manual Control of Dynamic Systems
Human interaction with a simple, multiloop dynamic system in which the human's activity was systematically varied by changing the levels of automation was studied. The control loop structure resulting from the task definition parallels that for any multiloop manual control system, is considered a sterotype. Simple models of the human in the task, and upon extending a technique for describing the manner in which the human subjectively quantifies his opinion of task difficulty were developed. A man in the loop simulation which provides data to support and direct the analytical effort is presented.
Hess, R. A.↗
Structural dynamics system model reduction
Loads analysis for structural dynamic systems is usually performed by finite element models. Because of the complexity of the structural system, the model contains large number of degree-of-freedom. The large model is necessary since details of the stress, loads and responses due to mission environments are computed. However, a simplified model is needed for other tasks such as pre-test analysis for modal testing, and control-structural interaction studies. A systematic method of model reduction for modal test analysis is presented. Perhaps it will be of some help in developing a simplified model for the control studies.
Chen, J. C.↗
Constraint elimination in dynamical systems
Large space structures (LSSs) and other dynamical systems of current interest are often extremely complex assemblies of rigid and flexible bodies subjected to kinematical constraints. A formulation is presented for the governing equations of constrained multibody systems via the application of singular value decomposition (SVD). The resulting equations of motion are shown to be of minimum dimension.
Singh, R. P.↗
Building a Prospective LCA Framework to Analyze Emerging Technologies in a Dynamic System Context
NREL's Lifecycle Analysis Integration into Opensource Numerical models (LiAISON) framework computes temporally explicit life cycle impacts and resource uses for specific technologies (foreground) in a dynamic system context (background). LiAISON computes results for a series of environmental mid-points enabling an analysis of prospective tradeoffs of emerging technologies toward 2100. This prospective feature is of critical importance when analyzing present-day emerging technologies whose large-scale impacts during deployment phases will occur in different, future system contexts. LiAISON systematically accounts for dynamic system changes by applying an integrated background of the future energy-economy-land-climate system, generated by exogenous integrated assessment models (IAMs). Using IAM scenarios, LiAISON generates a time-series of life cycle inventory (LCI) databases, which are then used to calculate the impacts per functional unit per time step. This expands current practice of using static, future system assumptions, e.g., a specific grid-mix each year. Further, IAM scenarios are provided in a standardized format of shared-socioeconomic pathways (SSP) and representative concentration pathways (RCP) combinations. These are coherent, regularly, published, and peer-reviewed scenario combinations that establish a reproducible and standardized societal and climate mitigation futures context. They are comparable across IAMs and expand the system boundary of the traditional LCA by including dimensions such as societal and behavioral changes. We apply the framework to assess two emerging Power-to-Hydrogen processes, high temperature electrolysis using solid oxide fuel cell (HT-SOE) and polymer electrolyte membrane electrolysis (PEME). We compare the technologies to a baseline Hydrogen production process via steam methane reforming. Despite the decarbonized electricity systems' beneficial effects on the PtH2 processes' carbon intensities, we find environmental tradeoffs, which require technology improvements via learning-by-doing to be alleviated. Future work via ongoing collaborations will focus on linking the framework to other energy-economy-land-climate models and open-source life cycle inventory databases.
ENERGY PLANNING, POLICY, AND ECONOMY↗
Dynamic Systems Analysis for Turbine Based Aero Propulsion Systems
The aircraft engine design process seeks to optimize the overall system-level performance, weight, and cost for a given concept. Steady-state simulations and data are used to identify trade-offs that should be balanced to optimize the system in a process known as systems analysis. These systems analysis simulations and data may not adequately capture the true performance trade-offs that exist during transient operation. Dynamic systems analysis provides the capability for assessing the dynamic tradeoffs at an earlier stage of the engine design process. The dynamic systems analysis concept, developed tools, and potential benefit are presented in this paper. To provide this capability, the Tool for Turbine Engine Closed-loop Transient Analysis (TTECTrA) was developed to provide the user with an estimate of the closed-loop performance (response time) and operability (high pressure compressor surge margin) for a given engine design and set of control design requirements. TTECTrA along with engine deterioration information, can be used to develop a more generic relationship between performance and operability that can impact the engine design constraints and potentially lead to a more efficient engine.
dynamic analysis↗
A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model
Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper and simpler ways to describe the dynamics of these systems but are inexact and introduce additional approximation errors. In order to overcome the computational difficulties of the full complex models, on one hand, and the limitations of surrogate models, on the other, this work proposes a new accelerated time-stepping strategy that combines information from both. This approach is based on the multirate infinitesimal general-structure additive Runge--Kutta framework. The inexpensive surrogate model is integrated with a small time step to guide the solution trajectory, and the full model is treated with a large time step to occasionally correct for the surrogate model error and ensure convergence. Here, we provide a theoretical error analysis, and several numerical experiments, to show that this approach can be significantly more efficient than using only the full or only the surrogate model for the integration.
Surrogate models↗
Accurate data-driven surrogates of dynamical systems for forward propagation of uncertainty
Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter-to-solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. Here, this work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data-driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC-over-dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full-field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.
42 ENGINEERING↗
Interaction of feel system and flight control system dynamics on lateral flying qualities
An investigation of feel system and flight control system dynamics on lateral flying qualities was conducted using the variable stability USAF NT-33 aircraft. Experimental variations in feel system natural frequency, force-deflection gradient, control system command architecture type, flight control system filter frequency, and control system delay were made. The experiment data include pilot ratings using the Cooper-Harper (1969) rating scale, pilot comments, and tracking performance statistic. Three test pilots served as evaluators. The data indicate that as the feel system natural frequency is reduced lateral flying qualities degrade. At the slowest feel system frequency, the closed-loop response becomes nonlinear with a 'bobweight' effect apparent in the feel system. Feel system influences were essentially independent of the control system architecture. The flying qualities influence due to the feel system was different than when the identical dynamic systenm was used as a flight control system element.
Bailey, Randall E.↗
Robust integrated neurocontroller for complex dynamic systems
The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system; (2) Development of a robust neurocontroller system, and; (3) Integration of above systems to create a robust Integration Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.
Zein-Sabbato, S.↗
Robust Integrated Neurocontroller for Complex Dynamic Systems
The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are: adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as: sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system, (2) Development of a robust neurocontroller system, and 3. Integration of above systems to create a Robust Integrated Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.
Zein-Sabatto, S.↗
Robustified time-optimal control of uncertain structural dynamic systems
A new approach for computing open-loop time-optimal control inputs for uncertain linear dynamical systems is developed. In particular, the single-axis, rest-to-rest maneuvering problem of flexible spacecraft in the presence of uncertainty in model parameters is considered. Robustified time-optimal control inputs are obtained by solving a parameter optimization problem subject to robustness constraints. A simple dynamical system with a rigid-body mode and one flexible mode is used to illustrate the concept.
Liu, Qiang↗
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems
In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F) and Koopman operators. Here, the uncertainty can be characterized in terms of the moments of a probability density function. We demonstrate how the P-F and Koopman operators are used for propagating the moments. Time-series data is used for the finite-dimensional approximation of the linear operators, thereby enabling data-driven approach for moment propagation. Simulation results are presented to demonstrate the effectiveness of the proposed method.
42 ENGINEERING↗
Guidance of Nonlinear Nonminimum-Phase Dynamic Systems
The research work has advanced the inversion-based guidance theory for: systems with non-hyperbolic internal dynamics; systems with parameter jumps; and systems where a redesign of the output trajectory is desired. A technique to achieve output tracking for nonminimum phase linear systems with non-hyperbolic and near non-hyperbolic internal dynamics was developed. This approach integrated stable inversion techniques, that achieve exact-tracking, with approximation techniques, that modify the internal dynamics to achieve desirable performance. Such modification of the internal dynamics was used (a) to remove non-hyperbolicity which is an obstruction to applying stable inversion techniques and (b) to reduce large preactuation times needed to apply stable inversion for near non-hyperbolic cases. The method was applied to an example helicopter hover control problem with near non-hyperbolic internal dynamics for illustrating the trade-off between exact tracking and reduction of preactuation time. Future work will extend these results to guidance of nonlinear non-hyperbolic systems. The exact output tracking problem for systems with parameter jumps was considered. Necessary and sufficient conditions were derived for the elimination of switching-introduced output transient. While previous works had studied this problem by developing a regulator that maintains exact tracking through parameter jumps (switches), such techniques are, however, only applicable to minimum-phase systems. In contrast, our approach is also applicable to nonminimum-phase systems and leads to bounded but possibly non-causal solutions. In addition, for the case when the reference trajectories are generated by an exosystem, we developed an exact-tracking controller which could be written in a feedback form. As in standard regulator theory, we also obtained a linear map from the states of the exosystem to the desired system state, which was defined via a matrix differential equation.
Devasia, Santosh↗
Coherent structures and dynamical systems
Any flow of a viscous fluid has a finite number of degrees of freedom, and can therefore be seen as a dynamical system. A coherent structure can be thought of as a lower dimensional manifold in whose neighborhood the dynamical system spends a substantial fraction of its time. If such a manifold exists, and if its dimensionality is substantially lower that that of the full flow, it is conceivable that the flow could be described in terms of the reduced set of degrees of freedom, and that such a description would be simpler than one in which the existence of structure was not recognized. Several examples are briefly summarized.
Jimenez, Javier↗
A dynamical systems analysis of the kinematics of time-periodic vortex shedding past a circular cylinder
Computer flow simulation aided by dynamical systems analysis is used to investigate the kinematics of time-periodic vortex shedding past a two-dimensional circular cylinder in the context of the following general questions: (1) Is a dynamical systems viewpoint useful in the understanding of this and similar problems involving time-periodic shedding behind bluff bodies; and (2) Is it indeed possible, by adopting such a point of view, to complement previous analyses or to understand kinematical aspects of the vortex shedding process that somehow remained hidden in previous approaches. We argue that the answers to these questions are positive. Results are described.
Ottino, Julio M.↗
An approach to the identification and control of linear dynamic systems with unknown parameters
Identification and control of linear dynamic systems with unknown parameters
CONTROL↗
Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems
This Git repository contains codes for the 'Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems' paper that will be published in 2023 SIGKDD. This work uses physical-informed prior (PiP) information to learn and predict fluid dynamics in a reduced mesh space. We propose a two-stage graph-based model for fluid velocity field reconstruction and prediction. In the first stage, we learn a subgraph autoencoder to summarize the information in a mesh-reduced space using physical-informed priors. In the second stage, we learn a dynamics predictor to predict subgraph evolution. We demonstrate the effectiveness of our model on two fluid flow datasets: lid-driven cavity flow data and cylinder flow data. This code is also applicable for any other dynamic systems with corresponding data.
Lei, Bo↗