Abstract
In order to reduce the total resistance of a hull, an optimization framework for the bulbous bow optimization was presented. The total resistance in calm water was selected as the objective function, and the overset mesh technique was used for mesh generation. RANS method was used to calculate the total resistance of the hull. In order to improve the efficiency and smoothness of the geometric reconstruction, the arbitrary shape deformation (ASD) technique was introduced to change the shape of the bulbous bow. To improve the global search ability of the particle swarm optimization (PSO) algorithm, an improved particle swarm optimization (IPSO) algorithm was proposed to set up the optimization model. After a series of optimization analyses, the optimal hull form was found. It can be concluded that the simulation based design framework built in this paper is a promising method for bulbous bow optimization.
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References
Gui, L., Longo, J. and Stern, F., 2001a. Biases of PIV measurement of turbulent flow and the masked correlation-based interrogation algorithm, Experiments in Fluids, 30(1), 27–35.
Gui, L., Longo, J. and Stern, F., 2001b. Towing tank PIV measurement system. data and uncertainty assessment for DTMB model 5512, Experiments in Fluids, 31(3), 336–346.
Hirt, C.W. and Nichols, B.D., 1981. Volume of fluid (VOF) method for the dynamics of free boundaries, Journal of Computational Physics, 39(1), 201–225.
Hou, Y.H., Hu, Y.L., Wang, W.Q., Guo, C.Y. and Wang, C., 2012. Ship type selection based on group decision-making with multi-objects, Journal of Shanghai Jiaotong University, 46(3), 385–389. (in Chinese)
Hou, Y.H., Liang, X., Jiang, X.J., 2016. Application of uncertainty optimization based on interval programming in ship hull SBD optimal design, Journal of Huazhong University of Science and Technology (Nature Science Edition), 44(6), 72–77. (in Chinese)
Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, IEEE, Perth, WA, Australia, 1942–1948.
Li, G.Y., Guo, C. and Li, Y.X., 2014. Fractional-order control of USV course based on improved PSO algorithm, Systems Engineering and Electronics, 36(6), 1146–1151. (in Chinese)
Li, S.Z., 2012. Research on Hull form Design Optimization Based on SBD Technique, Ph.D. Thesis, China Ship Scientific Research Center, Beijing. (in Chinese)
Liu, W.B., Liu, B.G., Liu, Z.Z. and Cui, S.D., 2009. Parameter identification of creep constitutive model of rock based on modified PSO algorithm, Journal of Beijing Jiaotong University, 33(4), 140–143. (in Chinese)
Longo, J. and Stern, F., 2005. Uncertainty assessment for towing tank tests with example for surface combatant DTMB model 5415, Journal of Ship Research, 49(1), 55–68.
Luo, J., Wu, Z.Q., 2009. VAR optimization in wind power system based on improved PSO, Insulating Materials, 42(2), 67–70. (in Chinese)
CD-adapco, 2014. User Guide STAR-CCM+, Version 9.0.2.
Wang, D.Y. and Liu, Y.A., 2009. Studies on turning angle to avoid collision between ships with PSO arithmetic, Computer Engineering and Design, 30(14), 3380–3382. (in Chinese)
Acknowledgements
The study in this paper was carried out as a part of the research project: Research on Hull Form Optimization Design of Minimum Resistance Which was based on the Rankine Source Method and The Optimization Research of the Ship Hydrodynamic Performance based on SBD technique.
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Foundation item: The study was financially supported by the National Natural Science Foundation of China (Grant No. 51009087), and the National Science Foundation of Shanghai (Grant No. 14ZR1419500).
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Zhang, Sl., Zhang, Bj., Tezdogan, T. et al. Research on bulbous bow optimization based on the improved PSO algorithm. China Ocean Eng 31, 487–494 (2017). https://doi.org/10.1007/s13344-017-0055-9
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DOI: https://doi.org/10.1007/s13344-017-0055-9