为解决目标雷达波隐身优化计算过程中资源消耗大的问题,利用代理模型模拟电磁散射特性计算的过程,降低电磁散射计算所需要的次数,优化电磁散射计算效率。针对某典型舰船进气格栅的设计方案,利用Kriging代理模型方法模拟其RCS (radar cross section)随几何变量的变化关系,并利用自适应遗传算法对代理模型的计算消耗进行优化。经优化计算,获得了进气格栅在典型雷达波参数下最优隐身方案,与初始方案相比RCS均值降低了约42%,通过计算结果验证了通过利用Kriging代理模型具备提高舰船设备RCS优化效率的可行性。
In order to solve the problem of large consumption of target radar wave stealth optimization calculation, surrogate model was used to reduce the calculation of electromagnetic scattering characteristics by reducing the number of calculations for electromagnetic scattering and improving the efficiency of optimization calculation. For a typical ship intake grille scheme, the change relationship between RCS (radar cross section) and geometric variables was simulated by Kriging surrogate model, and the surrogate model was optimized by adaptive genetic algorithm. The optimal stealth scheme of the intake grille under typical radar wave parameters was obtained, and the RCS mean value was reduced by 42% compared with the initial scheme. The feasibility of using the Kriging agent model to improve the optimization efficiency of ship equipment RCS is verified.
2022,44(9): 133-136 收稿日期:2021-08-11
DOI:10.3404/j.issn.1672-7649.2022.09.027
分类号:U662.3
作者简介:杜晓佳(1988-),男,博士,工程师,研究方向为舰船综合隐身技术
参考文献:
[1] 杜晓佳, 丁凡. 舰船进气格栅隐身性分析及灵敏度计算[J]. 中国舰船研究, 2019, 14(6): 81–87
DU X j, DING F. Stealth analysis and sensitivity calculation of naval air-intake grille[J]. Chinese Journal of Ship Research, 2019, 14(6): 81–87
[2] MUKHOPADHYAY T, CHAJRABORTY S, DEYS. A critical assessment of Kriging model variants for high-fidelity uncertainty quantification in dynamics of composite shells[J]. Archives of Computational Methods in Engineering, 2017, 24(3): 495–518
[3] JEONG S, MURAYAMA M, YAMAMOTO K. Efficient optimization design method using Kriging model[J]. Journal of Aircraft, 2005, 42(2): 413–420
[4] 罗文俊, 王德禹. 基于兴趣子域动态代理模型的船舶结构可靠性优化[J]. 中国舰船研究, 2021: 1–12
LUO W j, WANG D y. Reliability-based optimization of ship structure based on interest subdomain dynamic approximation model[J]. Chinese Journal of Ship Research, 2021: 1–12
[5] 王刚成, 马宁, 顾解忡. 基于Kriging代理模型的船舶水动力性能多目标快速协同优化[J]. 上海交通大学学报, 2018, 52(6): 666–673
WANG GC, MA N, GU X c. Fast collaborative multi-objective optimization for hydrodynamic based on Kriging surrogate model[J]. Journal of Shanghai Jiaotong University, 2018, 52(6): 666–673
[6] 张晓东, 权晓波, 王占莹. 代理模型在水下航行体空泡压力预示的应用研究[J]. 船舶力学, 2018, 22(1): 12–21
ZHANG X d, QUAN X b, WANG Z y. Research on the prediction method of unsteady cavity pressure development of underwater vehicle based on surrogate model[J]. Journal of Ship Mechanics, 2018, 22(1): 12–21
[7] QIN S, ZHANG Y, ZHOU Y L. Dynamic model updating for bridge structures using the Kriging model and pso algorithm ensemble with higher vibration modes[J]. Sensors, 2018, 18(6): 1879
[8] 韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报, 2016, 37(11): 3197–3225
HAN Z H. Kriging surrogate model and its application to design optimization: A review of recent progress[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(11): 3197–3225. (in Chinese)
[9] 宋磊, 王建, 杨卓懿. Kriging模型在潜器型线优化设计中的应用研究[J]. 船舶力学, 2013, 17(Z1): 8–13
SONG L, WANG J, YANG Z Y. Research on shape optimization design of submersible based on Kriging model[J]. Journal of Ship Mechanics, 2013, 17(Z1): 8–13. (in Chinese)
[10] SONG J, CHE W W. Fast multipole method solution using parametric geometry[J]. Microw. opt. Technology. Lett, 2010, 7(16): 760–765