SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
During the past few years considerable insight was gained into the QUEST algorithm both as a maximum likelihood estimator and as a Kalman filter/smoother for systems devoid of dynamical noise. The new algorithms and software are described and analytical comparisons are made with the more conventional attitude Kalman filter. It is also described how they may be accommodated to noisy dynamical systems.
This paper illustrates the use of Krylov vectors and Lanczos vectors for reduced-order modeling in structural dynamics and for control of flexible structures. Krylov vectors and Lanczos vectors are defined and illustrated, and several applications that have been under study at The University of Texas at Austin are reviewed: model reduction for undamped structural dynamics systems, component mode synthesis using Krylov vectors, model reduction of damped structural dynamics systems, and one-sided and two-sided unsymmetric block-Lanczos model-reduction algorithms.
Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.
Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.
Haynes 188, a cobalt-based superalloy, will be used to make thermal energy storage (TES) containment canisters for a 2 kW solar dynamic ground test demonstrator (SD GTD). Haynes 188 containment canisters with a high thermal emittance (epsilon) are desired for radiating heat away from local hot spots, improving the heating distribution, which will in turn improve canister service life. In addition to needing a high emittance, the surface needs to be durable in an elevated temperature, high vacuum environment for an extended time period. Thirty-five Haynes 188 samples were exposed to 14 different types of surface modification techniques for emittance and vacuum heat treatment (VHT) durability enhancement evaluation. Optical properties were obtained for the modified surfaces. Emittance enhanced samples were exposed to VHT for up to 2692 hours at 827 C and less than or equal to 10(exp -6) torr with integral thermal cycling. Optical properties were taken intermittently during exposure, and after final VHT exposure. The various surface modification treatments increased the emittance of pristine Haynes 188 from 0.11 up to 0.86. Seven different surface modification techniques were found to provide surfaces which met the SD GTD receiver VHT durability requirement. Of the 7 surface treatments, 2 were found to display excellent VHT durability: an alumina based (AB) coating and a zirconia based coating. The alumina based coating was chosen for the epsilon enhancement surface modification technique for the SD GTD receiver. Details of the performance and vacuum heat treatment durability of this coating and other Haynes 188 emittance surface modification techniques are discussed. Technology from this program will lead to successful demonstration of solar dynamic power for space applications, and has potential for application in other systems requiring high emittance surfaces.
An analysis is presented for multistage multimesh gear transmission systems. The analysis predicts the overall system dynamics and the transmissibility to the gear box or the enclosed structure. The modal synthesis approach of the analysis treats the uncoupled lateral/torsional model characteristics of each stage or component independently. The vibration signature analysis evaluates the global dynamics coupling in the system. The method synthesizes the interaction of each modal component or stage with the nonlinear gear mesh dynamics and the modal support geometry characteristics. The analysis simulates transient and steady state vibration events to determine the resulting torque variations, speeds, changes, rotor imbalances, and support gear box motion excitations. A vibration signature analysis examines the overall dynamic characteristics of the system, and the individual model component responses. The gear box vibration analysis also examines the spectral characteristics of the support system.
An analysis is presented for multistage multimesh gear transmission systems. The analysis predicts the overall system dynamics and the transmissibility to the gear box or the enclosed structure. The modal synthesis approach of the analysis treats the uncoupled lateral/torsional model characteristics of each stage or component independently. The vibration signature analysis evaluates the global dynamics coupling in the system. The method synthesizes the interaction of each modal component or stage with the nonlinear gear mesh dynamics and the modal support geometry characteristics. The analysis simulates transient and steady state vibration events to determine the resulting torque variations, speeds, changes, rotor imbalances, and support gear box motion excitations. A vibration signature analysis examines the overall dynamic characteristics of the system, and the individual model component responses. The gear box vibration analysis also examines the spectral characteristics of the support system.
The Dynamic Radioisotope Power System (DRPS) Testbed is a configurable laboratory test article designed to demonstrate the latest topology of a dynamic radioisotope power system generator. It utilizes an array of Stirling convertors arranged around a centrally located heat source that is radiantly coupled to the convertor hot ends. The Testbed is designed to dissipate all the waste heat through the housing and is outfitted with means for auxiliary cooling methods as well. During the past three years, the team at NASA’s Glenn Research Center (GRC) has been designing and analyzing the various aspects of the DRPS Testbed. Currently, the parts for the Testbed are being manufactured, and the team is focusing on developing the test stand, supporting hardware, and assembly and operation procedures in preparation for the anticipated first operation of the DRPS Testbed in summer of 2022.
An operator interactive video system for the measurement of roentgen angiographically outlined structures is described. Left ventricular volume and three-dimensional shapes are calculated from up to 200 pairs of diameters measured from ventriculograms at the rate of 60 pairs of biplane images per second. The accuracy and reproducibility of volumes calculated by the system were established by analysis of roentgenograms of inanimate objects of known volume and by comparison of left ventricular stroke volumes calculated by the system with the stroke volumes calculated by an indicator-dilution technique and an aortic root electromagnetic flowmeter. Computer-generated display of the large amounts of data obtained by the videometry system is described.
A control algorithm that can locally stabilize a specific class of multi-input multi-output nonautonomous nonlinear dynamical systems while satisfying individual input constraints is developed. The proposed Lyapunov-based state-feedback control law inherently accounts for the actuator amplitude saturation limits without the need for computationally expensive real-time optimization techniques. In addition to the control law, a formal definition for the local “controllable region” within which the controller can asymptotically drive the system states to the origin and satisfy the input saturation limits is also presented. The nonautonomous nature of the system dynamics implies that the “controllable region” continuously evolves with time. Therefore, a sufficient condition to maintain the system states within the “controllable region” is proposed in this work to make practical implementation feasible. The effectiveness of the controller is tested for a specific control problem arising in tokamaks, which are toroidal devices that use strong magnetic fields to confine a plasma (hot ionized gas). Here, the primary emphasis of tokamak research is to regulate the plasma properties around predetermined values to achieve stable plasma confinement. Nonlinear simulations show that the proposed controller can achieve the desired plasma control objectives in a DIII-D tokamak scenario.
A simulation toolbox has been developed to model the electrical portions of Electrified Aircraft Propulsion (EAP) systems, including self-heating of electrical systems. Dynamic models of EAP systems often include turbomachinery, an electrical power system, and a thermal management system. These portions of the propulsion system can operate on timescales covering six orders of magnitude, which poses a challenging modeling task. Assumptions and approximations are made in an effort to capture the dynamics relevant to engine, electrical system, and thermal system interactions, while still allowing for fast simulations. These assumptions and approximations enable the electrical component models to run at the time scale of the turbomachinery, neglecting the very high frequency electrical dynamics, while demonstrating representative behavior of the end-to-end system. Thus the models execute in an acceptable time frame for what would otherwise be an extremely stiff simulation. The resulting simulations of EAP systems are appropriate for system-level control design and analysis. The objective of this paper is to present an overview of the Electrical Modeling and Thermal Analysis Toolbox (EMTAT). It will describe some of the approximations made to accommodate rapid execution, and some of the design tools that allow matching the model to component specifications. This paper will also provide some examples of EAP system modeling using EMTAT.
A simulation toolbox has been developed to model the electrical portions of Electrified Aircraft Propulsion (EAP) systems, including self-heating of electrical systems. Dynamic models of EAP systems often include turbomachinery, an electrical power system, and a thermal management system. These portions of the propulsion system can operate on timescales covering six orders of magnitude, which poses a challenging modeling task. Assumptions and approximations are made in an effort to capture the dynamics relevant to engine, electrical system, and thermal system interactions, while still allowing for fast simulations. These assumptions and approximations enable the electrical component models to run at the time scale of the turbomachinery, neglecting the very high frequency electrical dynamics, while demonstrating representative behavior of the end-to-end system. Thus the models execute in an acceptable time frame for what would otherwise be an extremely stiff simulation. The resulting simulations of EAP systems are appropriate for system-level control design and analysis. The objective of this paper is to present an overview of the Electrical Modeling and Thermal Analysis Toolbox (EMTAT). It will describe some of the approximations made to accommodate rapid execution, and some of the design tools that allow matching the model to component specifications. This paper will also provide some examples of EAP system modeling using EMTAT.
A simulation toolbox has been developed to model the electrical portions of Electrified Aircraft Propulsion (EAP) systems, including self-heating of electrical systems. Dynamic models of EAP systems often include turbomachinery, an electrical power system, and a thermal management system. These portions of the propulsion system can operate on timescales covering six orders of magnitude, which poses a challenging modeling task. Assumptions and approximations are made in an effort to capture the dynamics relevant to engine, electrical system, and thermal system interactions, while still allowing for fast simulations. These assumptions and approximations enable the electrical component models to run at the time scale of the turbomachinery, neglecting the very high frequency electrical dynamics, while demonstrating representative behavior of the end-to-end system. Thus the models execute in an acceptable time frame for what would otherwise be an extremely stiff simulation. The resulting simulations of EAP systems are appropriate for system-level control design and analysis. The objective of this User's Guide is to present an overview of the usage of the Electrical Modeling and Thermal Analysis Toolbox (EMTAT). It will describe some of the approximations made to accommodate rapid execution, and some of the design tools that allow matching the model to component specifications. This paper will describe the basic usage of the software toolbox, and also provide an example of EAP system modeling using EMTAT.
Multistage model predictive control (MPC) provides a robust control strategy for dynamic systems with uncertainties and a setpoint tracking objective. Moreover, extending MPC to minimize an economic cost instead of tracking a pre-calculated optimal setpoint improves controller performance. This paper presents a novel multistage economic nonlinear model predictive control (E-NMPC) framework for dynamic systems operating under uncertainty, with specific application to natural gas transmission networks. A key innovation lies in the integration of cyclic steady-state (CSS) constraints within the multistage MPC formulation, enabling the controller to manage periodic operating conditions commonly observed in energy systems. A Lyapunov-based descent condition is enforced to ensure robust stability of the controller. The multistage economic MPC framework is validated on two gas pipeline case studies, where it successfully minimizes net energy consumption, respects operational constraints under uncertain demand profiles, and guides the network to optimal cyclic operation. The Lyapunov function remains bounded in both case studies, validating the robust stability of multistage E-NMPC.
Operator theoretic methods in dynamical system have been dominated by the use of Koopman operators and their continuous time counterparts, such as Koopman Generators and Liouville Operators. The advantage gained from their use primarily stems from the ability to extract subspaces and eigenfunctions within a space of observables that are invariant with respect to the Koopman operator over that space. When this occurs, a dynamic mode decomposition of the systems state provides a linear model for the dynamical system. Not all Koopman operators have eigenfunctions that may be exploited in this manner. However, the framework can still be leveraged for approximations using other operators. In this setting, we present a different operator for the study of dynamical systems, the weighted composition operator. These operators are compact for a wide range of dynamics and spaces, and through their interactions with occupation kernels and vector valued kernels, they admit an estimation of the underlying dynamics. Here, this manuscript presents a new algorithm for the data driven study of dynamical systems from data, and also provides two numerical experiments where convergence is achieved as a proof of concept.
G-DYN is a multi-body dynamic simulation software engine that automatically assembles and integrates equations of motion for arbitrarily connected multibody dynamic systems. The algorithm behind G-DYN is based on a primal-dual formulation of the dynamics that captures the position and velocity vectors (primal variables) of each body and the interaction forces (dual variables) between bodies, which are particularly useful for control and estimation analysis and synthesis. It also takes full advantage of the spare matrix structure resulting from the system dynamics to numerically integrate the equations of motion efficiently. Furthermore, the dynamic model for each body can easily be replaced without re-deriving the overall equations of motion, and the assembly of the equations of motion is done automatically. G-DYN proved an essential software tool in the simulation of spacecraft systems used for small celestial body surface sampling, specifically in simulating touch-and-go (TAG) maneuvers of a robotic sampling system from a comet and asteroid. It is used extensively in validating mission concepts for small body sample return, such as Comet Odyssey and Galahad New Frontiers proposals.
For many systems, automatic fault diagnosis is critical to ensuring safe and efficient operation. Fault isolation is performed by analyzing measured signals from the system, and reasoning over the system behavior to determine which faults have occurred, based on models of predicted faulty behavior. For dynamic systems, reasoning may be performed using qualitative analysis of the differences between measured signals and their predicted values, in which observations take the form of qualitative symbols. Such an approach is quick to isolate faults, but depends critically on correct generation of the qualitative symbols from the signals. In this paper, we develop an approach to qualitative event-based fault isolation for dynamic systems that is robust to incorrect qualitative observations. Observations are treated as uncertain, where multiple interpretations of an observation, each with its own probability, are considered. By interpreting observed symbols in a probabilistic manner, the approach degrades gracefully as the number of incorrectly-generated symbols increases. The approach is demonstrated on an electrical power system testbed, and experiments using real data obtained from the hardware demonstrate the improved fault isolation performance in the presence of incorrect symbol generation.
This paper discusses the motivation and goals of a research project which addresses the problems and issues of operator training in complex engineering sytems. The research proposes a tutor/aid paradigm for the design of an intelligent tutoring system (ITS) that evolves from a tutor to an operator's assistant for supervisory control of complex dynamic systems. Characteristics of an intelligent tutoring/aiding system are identified with respect to the representation of domain knowledge, the tutor's pedagogical structure, and the student knowledge representation. The research represents a first step in the design of an intelligent complex dynamic systems.