A systematic methodology is applied for performing parametric identification and health monitoring in the suspension substructures of complex vehicle models. The equations of motion are derived by applying a finite element method. As a consequence, they involve quite a large number of degrees-of-freedom (DOF). In addition, they include strongly nonlinear terms. In particular, the main nonlinearities arise due to the function of the suspension dampers and springs. Moreover, the action of the bushings connecting the suspension subsystems to the vehicle body is also strongly nonlinear. Since the resulting number of DOF is large, an appropriate coordinate condensation technique is applied first. This drastically reduces the dimension of the original system and allows the application of a statisticcal system identification methodology, which is effective for dynamical systems with relatively small dimension, in order to perform parametric identification and fault detection studies in the suspension subsystems of an example vehicle model. In the second part of this study, the methodology developed is applied and yields numerical results related to parametric identification and fault detection in the suspensions of the vehicle model examined. The results are found to be sufficiently accurate even in the presence of considerable measurement noise and model errors.
In the present study, dynamic response of single-cylinder reciprocating engines is investigated. The models examined take into account the flexibility of the engine mechanism associated with either its connecting rod or its supporting bearings. In addition, both the driving and the resisting moments are expressed as functions of the crankshaft motion. This leads to dynamic models with equations of motion appearing in a strongly nonlinear form. These equations are then solved numerically, by employing methodologies of both the time and the frequency domain. In particular, these methodologies include determination of transient response by direct integration or direct determination of complete branches of steady state response. The first set of numerical results refers to engine mechanisms with a flexible connecting rod. After dealing briefly with the special case of constant crank angular velocity, which can be investigated more easily and provides valuable insight into some aspects of the system dynamics, the emphasis is shifted to the general case of non-ideal forcing. Next, numerical results are presented for engine models with flexible bearings. Initially, mechanisms with rigid members supported by bearings involving linear anisotropic or isotropic properties are considered. Finally, similar results are also presented for hydrodynamic bearings, whose behavior is governed by the classical finite-length impedance theory. In all cases, the attention is focused on investigating the influence of the system parameters on its dynamics.
A systematic methodology is presented for investigating long term ride dynamics of large order vehicle models in a computationally efficient way. First, the equations of motion for each of the main structural components of the vehicle are set up by applying the finite element method. As a consequence of the geometric complexity of these components, the number of the resulting equations is so high that the classical coordinate reduction methodologies become numerically ineffective to apply. In addition, the composite model possesses strongly nonlinear characteristics. However, the method applied overcomes some of these difficulties by imposing a multi-level substructuring procedure, based on the sparsity pattern of the stiffness matrix. In this way, the number of the equations of motion of the complete system is substantially reduced. Subsequently, this allows the application of appropriate numerical methodologies for predicting response spectra of the nonlinear models to periodic road excitation. Results obtained by direct integration of the equations of motion are also presented. Where possible, the accuracy and validity of the applied methodology is verified by comparison with results obtained for the original models.
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