Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

• Published in: Complexity (New York, N.Y.) Vol. 2017; no. 2017; pp. 1 - 15
• Main Authors: Zhao, Zhicao, Zhao, Fuliang, Mao, Zhaoyong, Tian, Wenlong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Analysis; Artificial neural networks; Autonomous underwater vehicles; Back propagation networks; Computational fluid dynamics; Computer simulation; Design of experiments; Design optimization; Drag reduction; Energy; Engineering; Fluid dynamics; Genetic algorithms; Mathematical optimization; Methods; Minimum drag; Neural networks; Parameters; Remote submersibles; Simulation; Turbulence models; Vehicles; United States
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2017/5769794

This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a back-propagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that (1) the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and (2) the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV.