基于变保真度模型的AUV流体动力参数预测

doi:10.3901/JME.2017.18.176

机械工程学报 ›› 2017, Vol. 53 ›› Issue (18): 176-182.doi: 10.3901/JME.2017.18.176

基于变保真度模型的AUV流体动力参数预测

宋保维, 王新晶, 王鹏

西北工业大学航海学院西安 710072

收稿日期:2016-09-08 修回日期:2017-05-20 出版日期:2017-09-20 发布日期:2017-09-20
通讯作者: 王鹏(通信作者),男,1978年出生,博士,教授.主要研究方向为水下航行器总体设计、多学科设计优化、高精度外形设计、减阻降噪等.E-mail:wangpeng305@nwpu.edu.cn
作者简介:宋保维,男,1963年出生,博士,教授,博士研究生导师.主要研究方向为水下航行器总体设计、多学科设计优化、减阻降噪、可靠性等.E-mail:hanghai@nwpu.edu.cn;王新晶,男,1990年出生,博士研究生.主要研究方向为代理优化算法、水下航行器设计优化.E-mail:bj0213@mail.nwpu.edu.cn
基金资助:
国家自然科学基金资助项目（51375389）。

Predictions of AUV's Hydrodynamic Parameters Based on Variable-fidelity Modeling

SONG Baowei, WANG Xinjing, WANG Peng

College of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072

Received:2016-09-08 Revised:2017-05-20 Online:2017-09-20 Published:2017-09-20

摘要/Abstract

摘要： 变保真度模型（Variable-fidelity modeling，VFM）作为一种有效的代理模型方法被广泛用于工程设计，其中低保真度模型（Low-fidelity modeling，LF）用于获取目标模型的整体趋势，高保真度模型（High-fidelity modeling，HF）用于校正低保真度模型的准确度，二者通过桥函数（Bridge function，BF）融合，完成对目标模型的近似。将VFM用于预测自主水下航行器（Autonomous underwater vehicle，AUV）在外界工况（速度和攻角）下的流体动力参数（阻力系数、升力系数、力矩系数），其中高低保真度模型使用径向基函数构建，桥函数使用克里金函数构建，从遗传算法交叉算子产生的候选样本集中选择变保真度模型与高保真度模型误差最大的样本进行更新，最后建立变保真度代理模型。结果显示VFM能够准确描述流体动力参数在设计空间的变化；在相同的计算条件和时间下，VFM比高保真度模型精度高；随着高保真度样本的增加，模型的精度稳步提升。

关键词: AUV流体动力参数, 变保真度模型, 代理模型, 桥函数

Abstract: Variable fidelity modeling (VFM) which is regarded as an effective surrogate model is widely used in engineering design. In VFM, low-fidelity modeling's (LF) role is to capture the trend of objectives while the high-fidelity modeling's (HF) role is to revise the accuracy of LF. LF and HF are fused through bridge function (BF) and then finish the approximation for true model. This paper adopts VFM to predict autonomous underwater vehicle's (AUV) hydrodynamic parameters (drag, lift and torque coefficients) on certain velocity and angle of attack, in which LF, HF are built using RBF and BF is built using Kriging. Crossover operator of genetic algorithm produces candidate sample set and the sample with maximum error between VFM and HF will be chosen as newly added sample. Finally, VFM is established with all samples. The results show that compared to HF, VFM can describe precisely the change of hydrodynamic parameters in design space. Under same computing condition and time, VFM can gain better precision than HF and with the increases of HF samples in VFM, the accuracy of VFM becomes higher.

Key words: bridge function, hydrodynamic parameters of AUV, surrogate model, variable-fidelity modeling

宋保维, 王新晶, 王鹏. 基于变保真度模型的AUV流体动力参数预测[J]. 机械工程学报, 2017, 53(18): 176-182.

SONG Baowei, WANG Xinjing, WANG Peng. Predictions of AUV's Hydrodynamic Parameters Based on Variable-fidelity Modeling[J]. Journal of Mechanical Engineering, 2017, 53(18): 176-182.

参考文献

[1] TANG C, GEE K, LAWRENCE S. Generation of aerodynamic data using a design of experiment and data fusion approach[C]//43rd AIAA Aerospace Sciences Meeting and Exhibit, 2005:1137.
[2] ALEXANDROV N M, DENNIS J E, LEWIS R M, et al. A trust-region framework for managing the use of approximation models in optimization[J]. Structural and Multidisciplinary Optimization, 1998, 15(1):16-23.
[3] GANO S E, RENAUD J E, MARTIN J D, et al. Update strategies for kriging models used in variable fidelity optimization[J]. Structural and Multidisciplinary Optimization, 2006, 32(4):287-298.
[4] 兰志林, 周家波.无人水下航行器发展[J].国防科技, 2008, 29(2):11-15. LAN Zhilin, ZHOU Jiabo. An overview of development of unmanned underwater vehicles[J]. National Defense Science and Technology, 2008, 29(2):11-15.
[5] 宋保维, 潘光, 毛昭勇.水下航行器现代设计理论与方法[M].西安:西北工业大学出版社, 2004. SONG Baowei, PAN Guang, MAO Zhaoyong. Modern design theory and Methodology of underwater vehicle[M]. Xi'an:Northwestern Polytechnic University Press, 2004.
[6] HAN Z H, GÖRTZ S, HAIN R. A variable-fidelity modeling method for aero-loads prediction[M]//New Results in Numerical and Experimental Fluid Mechanics VII. Springer Berlin Heidelberg, 2010:17-25.
[7] XIONG Y, CHEN W, TSUI K L. A new variable-fidelity optimization framework based on model fusion and objective-oriented sequential sampling[J]. Journal of Mechanical Design, 2008, 130(11):111401-11401-9.
[8] HAN Z H, ZIMMERMANN R, GöRTZ S. A new cokriging method for variable-fidelity surrogate modeling of aerodynamic data[C]//48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, FL, 2010:1225.
[9] ZHENG J, SHAO X, GAO L, et al. A hybrid variable-fidelity global approximation modeling method combing tuned radial basis function base and kriging correction[J]. Journal of Engineering Design, 2013, 24(8):604-622.
[10] DONG H, SONG B, WANG P, et al. Multi-fidelity information fusion based on prediction of kriging[J]. Structural and Multidisciplinary Optimization, 2015, 51(6):1267-1280.
[11] 陈小前, 姚雯, 魏月兴, 等. 飞行器多学科设计优化理论的工程应[J]. 国防科技大学学报, 2011, 33(5):1-8. CHEN Xiaoqian, YAO Wen, WEI Yuexing, et al. Engineering application on multidisciplinary design optimization of flight vehicles[J]. Journal of National University of Defense Technology, 2011, 33(5):1-8.
[12] FANG H, RAIS-ROHANI M, LIU Z, et al. A comparative study of meta-modeling methods for multiobjective crashworthiness optimization[J]. Computers & Structures, 2005, 83(25):2121-2136.
[13] SIMPSON T W, POPLINSKI J D, KOCH P N, et al. Meta-models for computer-based engineering design:Survey and recommendations[J]. Engineering with Computers, 2001, 17(2):129-150.
[14] 龙腾, 刘建, WANG G G, 等. 基于计算试验设计与代理模型的飞行器近似优化策略探讨[J]. 机械工程学报, 2016, 52(14):79-105. LONG Teng, LIU Jian, WANG G G, et al. Discuss on approximate optimization strategies using design of computer experiments and metamodels for flight vehicle design[J]. Journal of Mechanical Engineering, 2016, 52(14):79-105.
[15] SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments[J]. Statistical Science, 1989:409-423.
[16] DYN N, LEVIN D, RIPPA S. Numerical procedures for surface fitting of scattered data by Radial Basis Functions[J]. SIAM Journal on Science and statistical Computing, 1986, 7(2):639-659.
[17] 张宇文. 鱼雷总体设计原理与方法[M]. 西安:西北工业大学出版社, 1998. ZHANG Yuwen. Torpedo general design principles and methods[M]. Xi'an:Northwestern Polytechnic University Press, 1998.

基于变保真度模型的AUV流体动力参数预测

Predictions of AUV's Hydrodynamic Parameters Based on Variable-fidelity Modeling

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 14

编辑推荐

Metrics

本文评价

[1]	武宇飞, 龙腾, 史人赫, WANG G Gary. 针对高维优化问题的快速追峰采样方法[J]. 机械工程学报, 2019, 55(3): 138-146.
[2]	彭海军, 施博洋, 王昕炜, 谢小辉, 孙立宁. 考虑区间不确定性的双摆吊车运动轨迹规划[J]. 机械工程学报, 2019, 55(2): 204-213.
[3]	孙清超, 黄庆涛, 孙志勇, 齐艳华, 孙伟. 基于梯度虚拟材料的栓接结合部连接参数表征[J]. 机械工程学报, 2018, 54(11): 102-109.
[4]	龙腾, 刘建, 陈余军, 史人赫, 袁斌, 刘莉. 基于约束EGO的对地观测卫星多学科设计优化[J]. 机械工程学报, 2018, 54(10): 133-142.
[5]	曾锋, 周金柱. 集成最小化置信下限和信赖域的动态代理模型优化策略[J]. 机械工程学报, 2017, 53(13): 170-178.
[6]	龙腾, 李学亮, 黄波, 蒋孟龙. 基于自适应代理模型的翼型气动隐身多目标优化^*[J]. 机械工程学报, 2016, 52(22): 101-111.
[7]	龙腾, 刘建, WANGGGary, 刘莉, 史人赫, 郭晓松. 基于计算试验设计与代理模型的飞行器近似优化策略探讨^*[J]. 机械工程学报, 2016, 52(14): 79-105.
[8]	洪煌杰, 王红岩, 李建阳, 芮强, 张芳. 空投装备气囊缓冲系统评价方法[J]. 机械工程学报, 2015, 51(4): 148-154.
[9]	张扬, 张维刚, 马桃, 唐婷. 基于全局敏感性分析和动态代理模型的复杂非线性系统优化设计方法[J]. 机械工程学报, 2015, 51(4): 126-131.
[10]	龙腾;郭晓松;彭磊;刘莉. 基于信赖域的动态径向基函数代理模型优化策略[J]. , 2014, 50(7): 184-190.
[11]	熊芬芬. 稳健优化设计中代理模型不确定性的研究[J]. , 2014, 50(19): 136-143.
[12]	伊建军;李云鹏;陈飙松;张盛. 结构耐撞性优化中径向基函数散布常数选取方法[J]. , 2014, 50(17): 171-178.
[13]	彭磊;刘莉;龙腾. 基于动态径向基函数代理模型的优化策略[J]. , 2011, 47(7): 164-170.
[14]	谷海涛;林扬;胡志强;俞建成. 基于代理模型的水下滑翔机机翼设计优化方法[J]. , 2009, 45(12): 7-14.