Browse > Article
http://dx.doi.org/10.1016/j.ijnaoe.2016.05.007

Optimal design of hydraulic support landing platform for a four-rotor dish-shaped UUV using particle swarm optimization  

Zhang, Bao-Shou (School of Marine Science and Technology, Northwestern Polytechnical University)
Song, Bao-Wei (School of Marine Science and Technology, Northwestern Polytechnical University)
Jiang, Jun (School of Marine Science and Technology, Northwestern Polytechnical University)
Mao, Zhao-Yong (School of Marine Science and Technology, Northwestern Polytechnical University)
Publication Information
International Journal of Naval Architecture and Ocean Engineering / v.8, no.5, 2016 , pp. 475-486 More about this Journal
Abstract
Four-rotor dish-shaped unmanned underwater vehicles (FRDS UUVs) are new type underwater vehicles. The main goal of this paper is to develop a quick method to optimize the design of hydraulic support landing platform for the new UUV. In this paper, the geometry configuration and instability type of the platform are defined. Computational investigations are carried out to study the hydrodynamic performance of the landing platform using the Computational Fluid Dynamics (CFD) method. Then, the response surface model of the optimization objective is established. The intelligent particle swarm optimization (PSO) is applied to finding the optimal solution. The result demonstrates that the stability of landing platform is significantly improved with the global objective index increasing from 1.045 to 1.158 (10.86% higher) after the optimization process.
Keywords
Four-rotor dish-shaped UUV; Hydraulic support; Particle swarm optimization; Response surface model; Computational fluid dynamics;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Agrawal, R.K., Bawane, N.G., 2015. Multi-objective PSO based adaption of neural network topology for pixel classification in satellite imagery. Appl. Soft Comput. J. 28, 217-225.   DOI
2 Andrews, P.S., 2006. An investigation into mutation operators for particle swarm optimization. IEEE Congr. Evol. Comput. 1044-1051.
3 Angeline, P.J., 1998. Using selection to improve particle swarm optimization. IEEE Int. Conf. Comput. 84-89.
4 Cerveira, Frederico, Fonseca, Nuno, Pascoal, Ricardo, 2013. Mooring system influence on the efficiency of wave energy converters. Int. J. Mar. Energy 3-4, 65-81.   DOI
5 Chan, K., Dillon, T.S., Chang, E., 2013. An intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems. IEEE Trans. Ind. Electron 60, 4714-4725.   DOI
6 Chen, P., 2015. Two-level hierarchical approach to unit commitment using expert system and elite PSO. IEEE Trans. Power Syst. 27, 780-789.
7 Chen, H.N., Zhu, Y.L., Hu, K.Y., Ku, T., 2008. Global optimization based on hierarchical convolutions model. IEEE Congr. Evol. Comput. 1497-1504.
8 Eberhart, R., Kennedy, J., 1995. A New Optimizer Using Particle Swarm Theory. International Symposium on MICRO Machine and Human Science, Nagoya, Japan, pp. 39-43.
9 Feng, Z., Allen, R., 2004. Evaluation of the effects of the communication cable on the dynamics of an underwater flight vehicle. Ocean Eng. 31, 1019-1035.   DOI
10 Hu, Fengjun, Wu, Fan, 2010. Diploid hybrid particle swarm optimization with differential evolution for open vehicle routing problem. Eighth World Congr. Automatic Control Artif. Intell. 20, 2692-2697.
11 Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization, vol. 4. IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942-1948.
12 Huston, R.L., Kamman, J.W., 1982. Validation of finite segment cable models. Comput. Struct. 15 (6), 653-660.   DOI
13 Jin, N., Rahmat-Samii, Y., 2010. Hybrid real-binary particle swarm optimization (HPSO) in engineering electromagnetics. IEEE Trans. Ant. Prop. 58, 3786-3794.   DOI
14 Juang, C.F., 2004. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. Syst. Man Cybern. B Cybern 34, 997-1006.   DOI
15 Liang, J.J., Qu, B.Y., Suganthan, P.N., et al., 2012. Dynamic multi-swarm particle swarm optimization for multi-objective optimization problems. IEEE Congr. Evol. Comput. 22 (10), 1-8.
16 Wang, Li-Zhong, Guo, Zhen, Yuan, Feng, 2010. Three-dimensional interaction between anchor line and seabed. Appl. Ocean Res. 32, 404-413.   DOI
17 Sun, Chunya, Song, Baowei, Wang, Peng, 2015. Parametric geometric model and shape optimization of an underwater glider with blended-wing-body. Int. J. Nav. Archit. Ocean Eng. 7, 995-1006.   DOI
18 Song, Baowei, Zhu, Xinyao, San, Zhixiong, et al., 2012. Hydrodynamic characteristics and stability analysis of UUV (Unmanned Underwater Vehicle) parking on the seabed. J. Northwest. Polytech. Univ. 30 (1), 94-101.
19 Song, Bao-wei, Zhang, Bao-shou, Jiang, Jun, et al., 2016. Estimation of equation of motion of four-rotor dish-shaped AUVand simulation research on its hydrodynamic characteristics. Acta Armamentarii 37 (2), 299-306.