990 resultados para Linear Series


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The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

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The recent years have seen the appearance of innovative system for acoustic and vibration attenuation, most of them integrating new actuator technologies. In this sense, the study of algorithms for active vibrations control in rotating machinery became an area of enormous interest, mainly due to countless demands of an optimal performance of mechanical systems in aircraft, aerospace and automotive structures. In this way, this paper presents an approach that is numerically verified for active vibration control in a rotor using Active Magnetic Bearings (AMB). The control design in a discrete state-space formulation is carried out through feedback technique and Linear Matrix Inequalities (LMI) approach. LMI is useful for system with uncertainties. The AMB uses electromagnetic forces to support a rotor without mechanical contact. By monitoring the position of the shaft and changing the dynamics of the system accordingly, the AMB keeps the rotor in a desired position. This unique feature has broadened for the applications of AMB and now they can be considered not only as a main support bearing in a machine but also as dampers for vibration control and force actuators. © 2009 Society for Experimental Mechanics Inc.

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This paper is mainly devoted to the study of the limit cycles that can bifurcate from a linear center using a piecewise linear perturbation in two zones. We consider the case when the two zones are separated by a straight line Σ and the singular point of the unperturbed system is in Σ. It is proved that the maximum number of limit cycles that can appear up to a seventh order perturbation is three. Moreover this upper bound is reached. This result confirms that these systems have more limit cycles than it was expected. Finally, center and isochronicity problems are also studied in systems which include a first order perturbation. For the latter systems it is also proved that, when the period function, defined in the period annulus of the center, is not monotone, then it has at most one critical period. Moreover this upper bound is also reached.

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Structural damage identification is basically a nonlinear phenomenon; however, nonlinear procedures are not used currently in practical applications due to the complexity and difficulty for implementation of such techniques. Therefore, the development of techniques that consider the nonlinear behavior of structures for damage detection is a research of major importance since nonlinear dynamical effects can be erroneously treated as damage in the structure by classical metrics. This paper proposes the discrete-time Volterra series for modeling the nonlinear convolution between the input and output signals in a benchmark nonlinear system. The prediction error of the model in an unknown structural condition is compared with the values of the reference structure in healthy condition for evaluating the method of damage detection. Since the Volterra series separate the response of the system in linear and nonlinear contributions, these indexes are used to show the importance of considering the nonlinear behavior of the structure. The paper concludes pointing out the main advantages and drawbacks of this damage detection methodology. © (2013) Trans Tech Publications.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Engenharia Mecânica - FEIS

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We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.

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The linearity assumption in the structural dynamics analysis is a severe practical limitation. Further, in the investigation of mechanisms presented in fighter aircrafts, as for instance aeroelastic nonlinearity, friction or gaps in wing-load-payload mounting interfaces, is mandatory to use a nonlinear analysis technique. Among different approaches that can be used to this matter, the Volterra theory is an interesting strategy, since it is a generalization of the linear convolution. It represents the response of a nonlinear system as a sum of linear and nonlinear components. Thus, this paper aims to use the discrete-time version of Volterra series expanded with Kautz filters to characterize the nonlinear dynamics of a F-16 aircraft. To illustrate the approach, it is identified and characterized a non-parametric model using the data obtained during a ground vibration test performed in a F-16 wing-to-payload mounting interfaces. Several amplitude inputs applied in two shakers are used to show softening nonlinearities presented in the acceleration data. The results obtained in the analysis have shown the capability of the Volterra series to give some insight about the nonlinear dynamics of the F-16 mounting interfaces. The biggest advantage of this approach is to separate the linear and nonlinear contributions through the multiple convolutions through the Volterra kernels.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Engenharia Mecânica - FEIS