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Complexity
Volume 2017, Article ID 5392539, 21 pages
https://doi.org/10.1155/2017/5392539
Research Article

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

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Correspondence should be addressed to Zhaoyong Mao; nc.ude.upwn@gnoyoahzoam

Received 8 July 2017; Accepted 13 September 2017; Published 11 December 2017

Academic Editor: Junpei Zhong

Copyright © 2017 Zhaoyong Mao and Fuliang Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 ( m/s), the effect of different dimensionless plate length () and damping value () on the UMP was studied. We get the sample data point set by computational fluid dynamics (CFD) simulation with changing the dimensionless plate length (, 0.5, 0.75, 1.0, 1.25, 1.5) and damping value (, 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 and the corresponding variable value is ,  (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 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.