Trajectory Design for GENESIS Using a Dynamical System Approach
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Necessary and sufficient conditions on coefficients of real nonnegative polynomial pi/omega/, obtaining algebraic criterion for stability, optimality and passivity of dynamic linear systems
This paper considers verterbrate gravity receptors as dynamic sensors. That is, it is hypothesized that gravity is a constant force to which an acceleration-sensing system would readily adapt. Premises are considered in light of the presence of kinocilia on hair cells of vertebrate gravity sensors; differences in loading of the sensors among species; and of possible reduction in loading by inclusion of much organic material in otoconia. Moreover, organic-inorganic interfaces may confer a piezoelectric property upon otoconia, which increase the sensitivity of the sensory system to small accelerations. Comparisons with man-made accelerometers are briefly taken up.
A summary of the propulsion system modeling under NASA's High Speed Project (HSP) AeroPropulsoServoElasticity (APSE) task is provided with a focus on the propulsion system for the lowboom supersonic configuration developed by Lockheed Martin and referred to as the N+2 configuration. This summary includes details on the effort to date to develop computational models for the various propulsion system components. The objective of this paper is to summarize the model development effort in this task, while providing more detail in the modeling areas that have not been previously published. The purpose of the propulsion system modeling and the overall APSE effort is to develop an integrated dynamic vehicle model to conduct appropriate unsteady analysis of supersonic vehicle performance. This integrated APSE system model concept includes the propulsion system model, and the vehicle structural-aerodynamics model. The development to date of such a preliminary integrated model will also be summarized in this report.
The generator design incorporates an induction generator for application where a utility line is present and a synchronous generator for standalone applications. A combination of feed forward and feedback control is used to achieve synchronous speed prior to connecting the generator to the load, and to control the power level once the generator is connected. The dynamics of the drive train affect several aspects of the system operation. These were analyzed to arrive at the required shaft stiffness. The rotor parameters that affect the stability of the feedback control loop vary considerably over the wind speed range encountered. Therefore, the controller gain was made a function of wind speed in order to maintain consistent operation over the whole wind speed range. The velocity requirement for the pitch control mechanism is related to the nature of the wind gusts to be encountered, the dynamics of the system, and the acceptable power fluctuations and generator dropout rate. A model was developed that allows the probable dropout rate to be determined from a statistical model of wind gusts and the various system parameters, including the acceptable power fluctuation.
The statistical behavior of nonlinear systems near stable.
The purpose of this study was to develop a process to convert input signals from one facility into another by reflecting geometric and environmental settings. The Dynamic Energy Transport and Integration Laboratory (DETAIL) is one facility in development that aims to emulate the daily interactions among power production industry systems and be capable of receiving real-time data from those systems as inputs. To convert signals and ensure that the temporal sequences and magnitudes reflect the laboratory settings, the ability to scale and project data is essential. To demonstrate this ability, Dynamical System Scaling (a methodology that enables systems to scale and project or extrapolate datasets to desired environments while conserving the observed transient behavior based on first principles) was applied to DETAIL’s thermocline thermal storage system in the Thermal Energy Distribution System. The thermocline system was successfully scaled and a test case was conducted to generate a doubly accelerated energy charge and discharge in reference to past experimental data from the facility. The accelerated data were determined as able to conserve the amount of energy stored and the associated test boundary conditions were charge line maximum temperature, charge line velocity, and thermocline maximum temperature at 354 °C, 0.458 m/s, and 418 °C, respectively. The research results represented a case that required signals to be accelerated without altering the stored energy.
The world’s electric power grids have evolved over the last 120 years from a single power line to today’s large networks. The evolution will continue at an accelerated rate with extensive smart grid development worldwide. Here for example, the U.S. government has set a goal of reaching 100 percent carbon pollution-free electricity by 2035, while the Department of Energy (DOE) shared a goal to deploy 30 GW and 110 GW of offshore wind by 2030 and 2050, respectively. To meet such ambitious goals, in the years to come, a significant percentage of electricity will come from intermittent renewable sources, electric vehicles (EVs), and be supplied to a vast number of loads that will actively respond to grid conditions and incentive signals. This development is largely driven by environmental and economic factors, such as reducing carbon emissions and saving electricity cost for consumers. These energy resources lead to new uncertain behaviors and dynamics which the grid has never seen and were not considered in its design. Operating such a dynamic grid with sufficient reliability and efficiency is a monumental challenge.
The Bayesian approach has been used for the dynamic state estimation (DSE) of a power system. However, due to the complexity of noise resources, it is difficult to quantify measurement and process noise using probability density functions (PDFs). To overcome the difficulty, the authors of this paper propose a modified eigen-decomposition-based interval analysis (MEDIA) method, which employs bounds instead of PDFs to quantify the noise, and uses the eigen decomposition method to reduce the negative impact of the overestimation problem. Using the simulation data generated from IEEE 16-machine and IEEE 10-machine systems, it is shown that the proposed MEDIA method can estimate the hard boundaries of dynamic states in real time. Furthermore, comparison with the forward-backward propagation method and the extended set-membership filter also shows that the proposed MEDIA method performs better by providing narrower boundaries in the DSE.
A classical MEMS gyro transforms angular rates into electrical values through Euler's equations of angular rotation. Production models of a MEMS gyroscope will have manufacturing errors in the coefficients of the differential equations. The output signal of a production gyroscope will be corrupted by noise, with a major component of the noise due to the manufacturing errors. As is the case of the components in an analog electronic circuit, one way of controlling the variability of a subsystem is to impose extremely tight control on the manufacturing process so that the coefficient values are within some specified bounds. This can be expensive and may even be impossible as is the case in certain applications of micro-electromechanical (MEMS) sensors. In a recent paper [2], the authors introduced a method for combining the measurements from several nominally equal MEMS gyroscopes using a technique based on a concept from electronic circuit design called dynamic element matching [1]. Because the method in this paper deals with systems rather than elements, it is called a dynamic system matching technique (DSMT). The DSMT generates a single output by randomly switching the outputs of several, nominally identical, MEMS gyros in and out of the switch output. This has the effect of 'spreading the spectrum' of the noise caused by the coefficient errors generated in the manufacture of the individual gyros. A filter can then be used to eliminate that part of the spread spectrum that is outside the pass band of the gyro. A heuristic analysis in that paper argues that the DSMT can be used to control the effects of the random coefficient variations. In a follow-on paper [4], a simulation of a DSMT indicated that the heuristics were consistent. In this paper, analytic expressions of the DSMT noise are developed which confirm that the earlier conclusions are valid. These expressions include the various DSMT design parameters and, therefore, can be used as design tools for DSMT systems.
The dynamics of the Shuttle Tethered Satellite System are influenced by attaching the tether at some point other than the center-of-masses of the Shuttle and the subsatellite. At the Shuttle, the tether attachment is made at the end of a boom deployed out of the payload bay. This attachment noticeably affects retrieval dynamics of the satellite pendulous motion. At the satellite, the tether attachment is assumed to be made on the circumference of the satellite. This attachment greatly affects the attitude motion of the satellite about its own center-of-mass. Computer simulation results are presented showing the effects of the Shuttle boom in a three-dimensional model and the effects of satellite attachment in a planar model.
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The goal of this project was to study the impact of integrating one and two 850-kW wind turbine generators into the eastern power system network of Sumba Island, Indonesia. A model was created for the 20-kV distribution network as it existed in the first quarter of 2015 with a peak load of 5.682 MW. Detailed data were collected for each element of the network. Load flow, short-circuit, and transient analyses were performed using DIgSILENT PowerFactory 15.2.1.
The MPMS mechanism possess two revolute degrees-of-freedom and allows the user to measure the mass, center of gravity, and the inertia tensor of an unknown mass. The dynamics of the Mass Properties Measurement System (MPMS) from the Lagrangian approach to illustrate the dependency of the motion on the unknown parameters.
The objective of this investigation has been to examine the mass and momentum exchange between the atmosphere, oceans, solid Earth, hydrosphere, and cryosphere. The investigation has focused on changes in the Earth's gravity field, its rotation rate, atmospheric and oceanic circulation, global sea level change, ice sheet change, and global ground water circulation observed by contemporary sensors and models. The primary component of the mass exchange is water. The geodetic observables provided by these satellite sensors are used to study the transport of water mass in the hydrological cycle from one component of the Earth to another, and they are also used to evaluate the accuracy of models. As such, the investigation is concerned with the overall global water cycle. This report provides a description of scientific, educational and programmatic activities conducted during the period July 1, 1999 through June 30,2000. Research has continued into measurements of time-varying gravity and its relationship to Earth rotation. Variability of angular momentum and the related excitation of polar motion and Earth rotation have been examined for the atmosphere and oceans at time-scales of weeks to several years. To assess the performance of hydrologic models, we have compared geodetic signals derived from them with those observed by satellites. One key component is the interannual mass variability of the oceans obtained by direct observations from altimetry after removing steric signals. Further studies have been conducted on the steric model to quantify its accuracy at global and basin-scales. The results suggest a significant loss of water mass from the Oceans to the land on time-scales longer than 1-year. These signals are not reproduced in any of the models, which have poorly determined interannual fresh water fluxes. Output from a coupled atmosphere-ocean model testing long-term climate change hypotheses has been compared to simulated errors from the Gravity Recovery and Climate Experiment (GRACE) mission. Results indicate that GRACE will be able to observe seasonal signals at half-wavelengths ranging from 1000 to 10000 km, and may be able to observe secular trends at half- wavelengths of greater than 2000-3000 km for soil moisture and snow depth if they are as large as some of the climate experiments predict.
Open-loop solutions of dynamical optimization problems can be numerically computed usingexisting software packages. The computed time histories of the state and control variables, formultiple sets of end conditions can then be used to train a neural network to 'recognize' the optimal,nonlinear feedback relation between the states and controls of the system. The 'learned' network canthen be used to output an approximate optimal control given a full set (or a partial set) of measuredsystem states. With simple neural networks, we have successfully demonstrated the efficacy of theproposed approach using a minimum-time orbit injection problem. The usefulness and limitations ofthis novel approach on real-life optimal guidance and control problems, with many state and control variables as well as path inequality constraints, remain to be seen.
We have established a general non-equilibrium thermodynamic formalism consistently applicable to both spatially homogeneous and, more importantly, spatially inhomogeneous systems, governed by the Langevin and Fokker-Planck stochastic dynamics with multiple state transition mechanisms, using the potential-flux landscape framework as a bridge connecting stochastic dynamics with non-equilibrium thermodynamics. A set of non-equilibrium thermodynamic equations, quantifying the relations of the non-equilibrium entropy, entropy flow, entropy production, and other thermodynamic quantities, together with their specific expressions, is constructed from a set of dynamical decomposition equations associated with the potential-flux landscape framework. The flux velocity plays a pivotal role on both the dynamic and thermodynamic levels. On the dynamic level, it represents a dynamic force breaking detailed balance, entailing the dynamical decomposition equations. On the thermodynamic level, it represents a thermodynamic force generating entropy production, manifested in the non-equilibrium thermodynamic equations. The Ornstein-Uhlenbeck process and more specific examples, the spatial stochastic neuronal model, in particular, are studied to test and illustrate the general theory. This theoretical framework is particularly suitable to study the non-equilibrium (thermo)dynamics of spatially inhomogeneous systems abundant in nature. This paper is the second of a series.
The increasing complexity of power system has made it difficult to simulate large-scale power system in detail. Therefore, to improve computational efficiency, model reduction techniques has been applied to reduce the external area and focus on the dynamics of the internal area. This paper proposes a measurement-based model reduction method which reduces the external system by replacing external system with ARX equivalents. The modeled transfer function parameters are obtained using system identification approach. Case studies on the NPCC system and EI system are carried out. Results show that the proposed reduction method can effectively retain the dynamic behavior of the study area while mitigating computation burden.