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Herkese Açık·8 üye

Gta Iv Patch 1.1 3 Crack Indirl REPACK

the experimental setup is shown in figure 6. the cantilever is excited by a step force and the displacement of each pzt patch is detected. the voltage response at each pzt patch is measured and then simulated using the model. for the first two cases, the mass and stiffness of the beam are considered unknown and are to be identified. the mass of the cantilever is assumed to be known a priori and the stiffness parameters are the unknowns to be identified for the first two cases.

Gta Iv Patch 1.1 3 Crack Indirl

in this study, as the second case, pzt patch is bonded on both sides of the beam as shown in figure 8. the first and second natural frequency of modes of vibration are 845.31 and 1271.69 hz respectively. rayleigh damping with the modal damping ratio of 5% is used for the first two modes of vibration. the beam is excited by providing an impulse force of 5 n at node 6 over a time of 0.01s in the time step of 0.001.

this study is applied to the beam as shown in figure 10 and the host material (aluminium) is taken from the literature [ 30 ]. in this study, two pzt patches are bonded on either side of the beam and the beam is divided into five eulerbernoulli beam elements. the flexural rigidity (ei) of each element is 1.738 nm1

this study proposes a new approach of si using the output voltage from the one-dimensional model pzt patch. it is based on non-classical optimization algorithm, particle swarm optimization (pso) [ 17 ], to identify the crack location and depth simultaneously with high accuracy in a quick and effective manner. pso algorithm has been used for si in the literature only in the recent years, e.g. [ 13, 14, 15, 16 ] which is not suitable for si because of the nature of the problem. the novelty of this approach is that the optimization algorithm is not a classically defined heuristic method. pso algorithm is evolved according to a real-valued vector space, the particles are controlled by velocity vector and position vector. each particle represents a potential solution to the optimization problem. every particle has a position vector and a velocity vector. the position vector is the candidate solution and the velocity vector is the optimization parameter.


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