Structure Optimization of a Vibration Suppression Device for Underwater Moored Platforms Using CFD and Neural Network

Abstract

We only consider the underwater mooring platform (UMP) and the plate moving in the transverse direction, and the plate can be relative to the UMP free rotation. In the case of constant flow rate (U=1 m/s), the effect of different dimensionless plate length (Lp/D) and damping value (c) on the UMP was studied. We get the sample data point set by computational fluid dynamics (CFD) simulation with changing the dimensionless plate length (Lp/D=0.3, 0.5, 0.75, 1.0, 1.25, 1.5) and damping value (c=50, 75, 100, 125, 175, 250, 300 (N × s/m)). The optimal value of the vibration suppression rate is obtained by backpropagation (BP) neural network and genetic algorithm. The optimal vibration suppression rate is Py=0.9878 and the corresponding variable value is Lp/D=1.0342, c=57.9631 (N × s/m). In order to verify the accuracy of the optimization, we perform the CFD numerical simulation with the optimized parameters and compare the theoretical optimization results with the CFD simulation result. The absolute error between CFD simulation and optimal Py is only 0.0037. Finally, we compare the results of CFD simulation based on optimal parameter with the bare UMP and analyze their dimensionless amplitude, wake structure, and lift coefficient. It is shown that BP neural network and generic algorithm are effective.