TY - JOUR
T1 - Structure optimization of a vibration suppression device for underwater moored platforms using CFD and neural network
AU - Mao, Zhaoyong
AU - Zhao, Fuliang
N1 - Publisher Copyright:
© 2017 Zhaoyong Mao and Fuliang Zhao.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85042233283&partnerID=8YFLogxK
U2 - 10.1155/2017/5392539
DO - 10.1155/2017/5392539
M3 - 文章
AN - SCOPUS:85042233283
SN - 1076-2787
VL - 2017
JO - Complexity
JF - Complexity
M1 - 5392539
ER -