Ship appearance optimal design on RCS reduction using response surface method and genetic algorithms

被引：2

作者：

Yang D.-Q. ^{[1
]}

Guo F.-J. ^{[1
]}

机构：

[1] School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University

来源：

Journal of Shanghai Jiaotong University (Science) | 2008年 / 13卷 / 3期

关键词：

Characteristic section design method; Genetic algorithm (GA); Radar cross section (RCS); Response surface method;

D O I：

10.1007/s12204-008-0336-9

中图分类号：

学科分类号：

摘要：

Radar cross section (RCS) reduction technologies are very important in survivability of the military naval vessels. Ship appearance shaping as an effective countermeasure of RCS reduction redirects the scattered energy from one angular region of interest in space to another region of little interest. To decrease the scattering electromagnetic signals from ship scientifically, optimization methods should be introduced in shaping design. Based on the assumption of the characteristic section design method, mathematical formulations for optimal shaping design were established. Because of the computation-intensive analysis and singularity in shaping optimization, the response surface method (RSM) combined genetic algorithm (GA) was proposed. The polynomial response surface method was adopted in model approximation. Then genetic algorithms were employed to solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design, the superiority and effectiveness of proposed design methodology were verified.

引用

页码：336 / 342

页数：6

共 11 条

[1]

George J.L., History of Warships, (1998)

[2]

Kotte E., Shaeffer J., Radar Cross Section, (1993)

[3]

Andersh D., Moore J., Kosanovich S., Xpatch 4: The next generation in high frequency electromagnetic modeling and simulation software, IEEE International Radar Conference, pp. 844-849, (2000)

[4]

van Tonder J.J., Jakobus U., Fast multipole solution of metallic and dielectric scattering problems in FEKO, 2005 IEEE/ACES International Conference on Wireless Communications and Applied Computational Electromagnetics, pp. 513-516, (2005)

[5]

Box G.P., Hunter J.S., Multifactor experimental designs for exploring response surfaces, Annals of Mathematical Statistics, 28, pp. 194-241, (1957)

[6]

Harrison P.N., le Riche R., Haftka R.T., Design of stiffened composite panels by genetic algorithms and response surface approximations, AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Mater. Conf, pp. 58-68, (1995)

[7]

Wang G., Dong Z., Aitcheson P., Adaptive response surface method: A global optimization scheme for approximation-based design problems, Journal of Engineering Optimization, 33, 6, pp. 707-734, (2001)

[8]

Bramantia A., Barbaa P., Farina M., Et al., Combining response surfaces and evolutionary strategies for multi-objective pareto optimization in electromagnetics, International Journal of Applied Electromagnetics and Mechanics, 15, pp. 231-236, (2001)

[9]

Michalewicz Z., Genetic Algorithms +Data Structures=Evolution Programs, (1996)

[10]

Kaelo P., Ali M., Some variants of the controlled random search algorithm for global optimization, Journal of Optimization Theory and Applications, 130, 2, pp. 253-264, (2006)

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