Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/5769794 |
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| _version_ | 1832558105417744384 |
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| author | Wenlong Tian Zhaoyong Mao Fuliang Zhao Zhicao Zhao |
| author_facet | Wenlong Tian Zhaoyong Mao Fuliang Zhao Zhicao Zhao |
| author_sort | Wenlong Tian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-72ec5848428f4014937a7c6ad8abffa5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-72ec5848428f4014937a7c6ad8abffa52025-02-03T01:33:17ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/57697945769794Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network MethodWenlong Tian0Zhaoyong Mao1Fuliang Zhao2Zhicao Zhao3School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaXi’an Institute of Applied Optics, Xian 710065, ChinaThis 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.http://dx.doi.org/10.1155/2017/5769794 |
| spellingShingle | Wenlong Tian Zhaoyong Mao Fuliang Zhao Zhicao Zhao Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method Complexity |
| title | Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method |
| title_full | Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method |
| title_fullStr | Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method |
| title_full_unstemmed | Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method |
| title_short | Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method |
| title_sort | layout optimization of two autonomous underwater vehicles for drag reduction with a combined cfd and neural network method |
| url | http://dx.doi.org/10.1155/2017/5769794 |
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