The objective of the present investigation is to study the vortex-induced vibrations （VIV） for flow past a circular cylinder. The turbulent flow is simulated by using a 2-D standard k-ε model incorporating the finite volume method （FVM） and the Semi-Implicit Method for the Pressure Linked Equations （SIMPLE） algorithm on non-orthogonal boundary-fitted collocated grids. The wall boundaries are approximated with wall functions. In the numerical cases, the turbulent wake patterns are studied by plotting the streamlines and the turbulent kinetic energy contours. The pressure distributions are investigated. Analyses of the vortex-induced force coefficients and the structural vibrations are carried out. The variations of the Strouhal number with the Reynolds number and of the vortex-induced force coefficients with the reduced velocity are obtained. The results show that this numerical approach is feasible and efficient in investigating the VIV problem for a circular cylinder.
A new damage-locating method for bridges subjected to a moving load is presented, and a new ‘moving load dam- age-locating indicator’ (MLDI) is introduced. A vehicle is modeled as a moving load, the bridge is simplified as an Euler-Bernoulli beam, and the damage is simulated by a reduction of stiffness properties of the elements. The curvature and MLDI values at each node of the baseline model (undamaged) and the damage model are computed respectively. Then the damage or damages can be located from a sudden change of the MLDI value. The feasibility and effectiveness of the proposed method are validated by nu- merical simulation. The results indicate that the method is effective, being able to not only locate a single damage accurately, but also locate multiple damages in simply-supported bridges, including multiple damages in continuous bridges. The results also indicate that the MLDI can accurately locate damages under 5% measurement noise.
LIU FushunLI HuajunYU GuangmingZHANG YantaoWANG WeiyingSUN Wanqing
In the present work, damage detection for offshore platforms is divided into three steps. Firstly, the located direction of the damaged member is detemfined by the pmbabilistic neural network with input of the change rate of normalized medal frequency. Secondly, the profile and layer of the damaged member is also determined by the pmbabilistic neural network with input of the normalized damage-signal index. Finally, the damage extent is determined by the back propagation neural networks with input of the squared change rate of modal frequency. So the size of the network and the training time can be reduced greatly. All these networks are trained with simulated data obtained from the finite element model of an experiment model. Then these trained neural networks are examined with data obtained from impulse tests on the experiment model. The experiment results show that the trained neural networks are able to detect the damaged member with reasonable accuracy.
