基于代理模型的水下航行体头型优化设计方法研究
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摘要
文章采用Kriging代理模型技术针对水下航行体头型优化设计开展研究。采用CFD方法分析了不同头型水下航行体的流体动力性能,将性能参数作为初始样本建立代理模型;根据测试样本点的预测均方误差选择加点策略更新代理模型,提高代理模型预测精度;采用非劣分类遗传算法对阻力和表面压力开展多目标优化,寻找近似最优解。结果表明:基于代理模型技术的水下航行体头型多目标优化设计方法可以有效提高设计效率,获得具有良好水动特性的航行体头型。
Based on Kriging surrogate model, a configuration optimization design method of underwater vehicle was studied. A set of different forehead configurations were chosen as original sample points to construct the surrogate model. The hydrodynamic forces are calculated by CFD analysis tool. To improve the attainable precision, sampling strategy was used according to the MSE(mean square error) of test points. After that, NSGA-Ⅱ optimization method was implemented under given condition to find the approximate optimal solution considering minimum drag coefficient and maximum pressure coefficient. The results show that the multi-object optimization design method based on the surrogate model gives a set of Pareto optimal solutions. Therefore it can improve the configuration design efficiency of underwater vehicle, and find the vehicle configuration with good hydrodynamic performance.
Based on Kriging surrogate model, a configuration optimization design method of underwater vehicle was studied. A set of different forehead configurations were chosen as original sample points to construct the surrogate model. The hydrodynamic forces are calculated by CFD analysis tool. To improve the attainable precision, sampling strategy was used according to the MSE(mean square error) of test points. After that, NSGA-Ⅱ optimization method was implemented under given condition to find the approximate optimal solution considering minimum drag coefficient and maximum pressure coefficient. The results show that the multi-object optimization design method based on the surrogate model gives a set of Pareto optimal solutions. Therefore it can improve the configuration design efficiency of underwater vehicle, and find the vehicle configuration with good hydrodynamic performance.
引文
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[2]王鹏,宋保维,刘旭琳,余德海,姜军.鱼雷外形多学科设计优化与仿真[J].系统仿真学报,2008,20(7):1915-1918.Wang Peng,Song Baowei,Liu Xulin,Yu Dehai,Jiang Jun.Multidisciplinary design optimization and simulation validation for shape design of torpedo[J].Journal of System Simulation,2008,20(7):1915-1918.
[3]马洋,王丹丹,杨涛,丰志伟,张青斌.基于代理模型的运载器头罩外形优化设计[J].航空动力学报,2014,29(1):192-198.Ma Yang,Wang Dandan,Yang Tao,Feng Zhiwei,Zhang Qinbin.Optimization design of shroud configuration of transporter based on surrogate model[J].Journal of Aerospace Power,2014,29(1):192-198.
[4]温庆国,宋保维,王鹏,王司令.改进的Kriging近似方法及其在鱼雷外形优化中的应用[J].上海交通大学学报,2013,47(4):613-618.Wen Qingguo,Song Baowei,Wang Peng,Wang Siling.An improved Kriging approximation and its application to shape design optimization of torpedo[J].Journal of Shanghai Jiao Tong University,2013,47(4):613-618.
[5]姚拴宝,郭迪龙,杨国伟.基于径向基函数网格变形的高速列车头型优化[J].力学学报,2013,45(6):982-986.Yao Shuanbao,Guo Dilong,Yang Guowei.Aerodynamic optimization of high-speed train based on RBF mesh deformation[J].Chinese Journal of Theoretical and Applied Mechanics,2013,45(6):982-986.
[6]Shawn E.Gano,Harold Kim,Don E.Brown II.Comparison of three surrogate modeling techniques:datascape,Kriging,and Second order regression[C].11th AIAA/ISSMO Multidisciplinary Analysis Conference,6-8 Sep,2006.AIAA-2006-7048.
[7]Grosso A.Finding maximum Latin hypercube designs by iterated local search heuristics[J].J Oper Res,2009,197(2):541-547.
[8]王献孚.空化泡和超空化泡流动理论及应用[M].北京:国防工业出版社,2009:3.
[9]Pratab A,Deb K.A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-II[R].Kan-GAL report 200001,Indian Institute of Technology,Kanpur,India,2000.
[10]赖宇阳.Isight参数优化理论与实例详解[M].北京:北京航空航天大学出版社,2012:264.Lai Yuyang.Isight parameter optimization theory and example explanation[M].Beijing:Beijing University of Aeronautics and Astronautics Press,2012:264.