Flexible rotors are characterized by inherent uncertainties affecting the parameters that influence the dynamic responses of the system. In this context, the handling of variability in rotor dynamics is a natural and necessary extension of the modeling capability of the existing techniques of deterministic analysis. Among the various methods used to model uncertainties, the stochastic finite element method has received major attention, as it is well adapted for applications involving complex engineering systems of industrial interest. In the present contribution, the stochastic finite element method applied to a flexible rotor system, with random parameters modeled as random fields is presented. The uncertainties are modeled as homogeneous Gaussian stochastic fields and are discretized according to the spectral method by using Karhunen-Loève expansions. The modeling procedure is confined to the frequency and time domain analyses, in which the envelopes of frequency response functions, the Campbell's diagram and the orbits of the stochastic flexible rotor system are generated. Also, Monte Carlo simulation method combined with the Latin Hypercube sampling is used as stochastic solver. After the presentation of the underlying theoretical formulations, numerical applications of moderate complexity are presented and discussed aiming at demonstrating the main features of the stochastic modeling procedure of flexible rotor systems.

This paper is dedicated to the analyses of the effect of uncertain parameters on the dynamic behavior of a flexible rotor containing two rigid discs and supported by two fluid film bearings. A stochastic method has been extensively used to model uncertain parameters, i.e., the so-called Monte Carlo simulation. However, in the present contribution, the inherent uncertainties of the bearings' parameters (i.e. the oil viscosity as a function of the oil temperature, and the radial clearance) are modeled by using a fuzzy dynamic analysis. This alternative methodology seems to be more appropriated when the stochastic process that models the uncertainties is unknown. The analysis procedure is confined to the time domain, being generated by the envelopes of the rotor orbits and the unbalance responses obtained from a run-down operating condition. The hydrodynamic supporting forces are determined by considering a nonlinear model, which is based on the solution of the dimensionless Reynolds' equation for cylindrical and short journal bearings. This numerical study illustrates the versatility and convenience of the mentioned fuzzy approach for uncertainty analysis. The results from the stochastic analysis are also presented for comparison purposes.

Parallel mechanisms are unavoidably subjected to uncertainties. These uncertainties produce a small variation of link lengths and joints position associated with the clearances. Therefore, this contribution aims at analyzing the effect of these uncertainties on the kinematic performance of the mechanism by examining the kinematic performance atlases. Initially, the complete kinematic model of the mechanism is formulated by considering the uncertainties thar are included in the modeled. The kinematic performance with the uncertainties is computed by using the Monte Carlo method. Then, the kinematic performance atlases based on workspace size and kinematic dexterity are analyzed including the uncertainties. Finally, the kinematic accuracy is evaluated for different link lengths in order to show the correspondent relationship with the kinematic performance atlases.

Despite the good accuracy of finite element (FE) models to represent the dynamic behaviour of mechanical systems, practical applications show significant discrepancies between analytical predictions and experimental results, which are mostly due to uncertainties on the geometry configuration, imprecise material parameters and vague boundary conditions. Thereby, different approaches have been proposed to solve the inverse problems associated with the updating of FE models. Among them, the techniques based on minimization processes have shown to be some of the most promising ones. In this paper, a self-adaptive heuristic optimization method, namely the self-adaptive differential evolution (SADE), is evaluated. Differently from the canonical differential evolution (DE) algorithm, the SADE strategy is able to update dynamically the required parameters such as population size, crossover parameter and perturbation rate. This is done by considering a defined convergence rate on the evolution process of the algorithm in order to reduce the number of evaluations of the objective function. For illustration purposes, the SADE strategy is applied to the solution of typical mathematical functions. Additionally, the strategy is equally used to update the FE model of a rotating machine composed by a horizontal flexible shaft, two rigid discs and two unsymmetrical bearings. For comparison purposes, the canonical DE is also used. The results indicate that the SADE algorithm is a recommended technique for dealing with this kind of inverse problem.

This paper proposes a semiactive vibration control technique dedicated to a rotating machine passing by its critical speed during the transient rotation, by using a Smart Spring Mechanism (SSM). SSM is a patented concept that, using an indirect piezoelectric (PZT) stack actuation, changes the stiffness characteristics of one or more rotating machine bearings to suppress high vibration amplitudes. A Genetic Algorithm (GA) optimization technique is used to determine the best design of the SSM parameters with respect to performance indexes associated with the control efficiency. Additionally, the concept of ecologically correct systems is incorporated to this work including the PZT stack energy consumption in the indexes considered for the optimization process. Simulation carried out on Finite Element Method (FEM) model suggested the feasibility of the SSM for vibration attenuation of rotors for different operating conditions and demonstrated the possibility of incorporating SSM devices to develop high-performance ecologic control systems.

The present work is dedicated to active modal control applied to flexible rotors. The effectiveness of the corresponding techniques for controlling a flexible rotor is tested numerically and experimentally. Two different approaches are used to determine the appropriate controllers. The first uses the linear quadratic regulator and the second approach is the fuzzy modal control. This paper is focused on the electromagnetic actuator, which in this case is part of a hybrid bearing. Due to numerical reasons it was necessary to reduce the size of the model of the rotating system so that the design of the controllers and estimator could be performed. The role of the Kalman estimator in the present contribution is to estimate the modal states of the system and to determine the displacement of the rotor at the position of the hybrid bearing. Finally, numerical and experimental results demonstrate the success of the methodology conveyed.

Some active vibration control methods are based on mathematical models. In these cases, parameter variations play an important role in the system performance. As it is not possible to know in advance the precise values for all parameters of the mechanical system, a possible alternative is to design robust controllers that take into account the uncertainties. In this context, this work presents a vibration active control technique devoted to rotating machinery by incorporating electromagnetic actuators, which considers uncertainties in the parameters of the system. the gains of the electromagnetic actuator are determined by using linear matrix inequalities, which consist in a powerful tool for the cases in which parameter uncertainties are taken into account. In addition, Kalman estimators are employed to deduce the modal states of the system. The model of the rotating system is obtained by using the finite element method and the potentiality of the methodology for applications in engineering was investigated through experimental tests.

The components of flexible rotors are subjected to uncertainties. The main sources of uncertainties include the variation of mechanical properties. This contribution aims at analyzing the dynamics of flexible rotors under uncertain parameters modeled as fuzzy and fuzzy random variables. The uncertainty analysis encompasses the modeling of uncertain parameters and the numerical simulation of the corresponding flexible rotor model by using an approach based on fuzzy dynamic analysis. The numerical simulation is accomplished by mapping the fuzzy parameters of the deterministic flexible rotor model. Thereby, the flexible rotor is modeled by using both the Fuzzy Finite Element Method and the Fuzzy Stochastic Finite Element Method. Numerical simulations illustrate the methodology conveyed in terms of orbits and frequency response functions subject to uncertain parameters.