In this work, CFD computations showing the dynamic response of a long flexible cylinder subject to a stepped current immersed in the wake of another cylinder are presented. These two cylinders are placed upstream in tandem configuration, where the flexible cylinder is excited by vortex shedding mechanisms. This work completes from the computational point of view, the research started 2 years ago with experiments conducted at the E.T.S.I. Navales towing tank of the Technical University of Madrid. The flexible cylinder studied is 3 m long having an external diameter of 16 mm. A combination of two codes that simulate the fluid-structure interaction phenomenon was used to obtain the velocity and pressure fields and also to measure the deformation of the cylinder at the same points where the strain gauges where placed during the experiment. This code communicates a finite volume (FV) software that solves the Navier-Stokes equations and reports the shear and pressure fields on the flexible cylinder to a second finite element (FEM) code that is able to compute stresses and deformations. Deformations are reported back to the first fluid solver in order to compute the next time step. In the experiments, only the 65% length of the cylinders were under the water surface, consequently a VOF technique was used to simulate the free surface separation between air and water. The numerical stability of these two combined codes is one of the most delicate aspects of the simulation. Taking into account that the upstream cylinder was orders of magnitude more rigid than the downstream one, we considered the upstream cylinder as stationary and consequently having no role during the FEM calculation. Boundary conditions for the flexible cylinder where such that they should imitate the universal joints used in the experiments. The fundamental natural frequencies of oscillation were monitored and compared to the towing tank experiments.
We discuss an original approach to multidimensional non-stationary time series classification based on dynamic patterns analysis. The main problem in time series classification is construction of appropriate feature space. The success of classification dramatically depends on the quality of the feature space chosen. To construct this space we develop the method for extraction of dynamic patterns that are the most specific for the time series of each class. This problem is formulated as an optimization problem and the genetic algorithms are used to resolve it. The simulation results are given for the real electroencephalogram signals that are used in the braincomputer interfaces.
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