Vortex-induced vibration fatigue damage prediction method for flexible cylinders based on RBF neural network

Abstract

Vortex-induced vibration (VIV) is an important factor resulting in fatigue failure of cylindrical structures in offshore engineering applications (such as risers and cables). The traditional empirical model for VIV prediction often yields unsatisfactory results due to its dependence on the selection of empirical coefficients. Thus, establishing a general approach to VIV damage prediction based on artificial neural networks and engineering experience is of great significance to engineering guidance. In this paper, a VIV damage prediction model of a flexible cylinder was established using a radial basis function neural network (RBFNN) and model test data. Compared with back propagation neural network (BPNN), genetic algorithm-back propagation neural network (GA-BPNN), and generalized regression neural network (GRNN), the RBFNN exhibited excellent performance with high accuracy and the shortest training time in predicting the VIV damage of flexible cylinders. In addition, the VIV fatigue of flexible cylinders fitted with passive control devices (control rods and helical strakes) was predicted. Using three and four control rods, the VIV reduction effect was observed to be better when the attack angle θ was in the range of 30°–45° and 15°–30°, respectively. Further, regarding helical strakes, the damage values of the cylinder were significantly reduced; however, the VIV suppression effect was significantly affected by the yaw angles. The VIV damage prediction model of this paper is expected to serve as important engineering guidance for the preliminary design, installation arrangement, operation, and maintenance of flexible cylindrical structures in offshore engineering applications.