翼身融合自主式水下航行器的多泡结构耐压舱分步优化设计
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摘要
针对翼身融合自主式水下航行器的扁平机身结构,提出了一种多泡结构耐压舱,它具有很强的抗压能力,并且充分地利用了机身空间。由径向基函数(radial basis function,RBF)代理模型和Kriging代理模型组成了精确度更高的混合代理模型,采用候选点采样和局部最优采样2种加点策略,对多泡结构耐压舱进行外形和结构的分步优化设计。以最大排水体积为优化目标、外形约束为约束条件,对多泡结构耐压舱进行外形的优化;选取最小结构质量作为优化目标、最大等效应力和屈曲系数为约束条件,对多泡结构耐压舱进行结构的优化。使用基于有限元方法的分析软件ANSYS对模型的强度和稳定性进行了分析。
In this paper,multi-bubble pressure cabin is proposed for the flat fuselage of blended-wing-body( BWB)autonomous underwater vehicle( AUV). It has strong compressive capacity and makes full use of the fuselage space.Radial basis function surrogate model and Kriging surrogate model are used to construct mixture surrogate model for higher accuracy. Two infill sampling methods are adopted: the candidate point sampling and the local optimal sampling. Multi-step optimization of multi-bubble pressure cabin is carried out including shape optimization and structure optimization. To optimize shape,the maximum displacement is selected as the objective function and the shape constraint is chosen as the constraint condition. The minimum structural quality is selected as the objective function,the maximum equivalent stress and bulking factor are chosen as the constraint condition to optimize structure. Finite element method( FEM) analysis is carried out to study the strength and stability performance of multi-bubble pressure cabin using the commercial computational structural mechanics code ANSYS.
In this paper,multi-bubble pressure cabin is proposed for the flat fuselage of blended-wing-body( BWB)autonomous underwater vehicle( AUV). It has strong compressive capacity and makes full use of the fuselage space.Radial basis function surrogate model and Kriging surrogate model are used to construct mixture surrogate model for higher accuracy. Two infill sampling methods are adopted: the candidate point sampling and the local optimal sampling. Multi-step optimization of multi-bubble pressure cabin is carried out including shape optimization and structure optimization. To optimize shape,the maximum displacement is selected as the objective function and the shape constraint is chosen as the constraint condition. The minimum structural quality is selected as the objective function,the maximum equivalent stress and bulking factor are chosen as the constraint condition to optimize structure. Finite element method( FEM) analysis is carried out to study the strength and stability performance of multi-bubble pressure cabin using the commercial computational structural mechanics code ANSYS.
引文
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[12]赵秋月,左万利,田中生,等.一种基于改进D-S证据理论的信任关系强度评估方法研究[J].计算机学报,2014,37(4):873-883Zhao Qiuyue,Zuo Wanli,Tian Zhongsheng,et al.A Method for Assessment of Trust Relationship Strength Based on the Improved D-S Evidence Theory[J].Chinses Journal of Computers,2014,37(4):873-883(in Chinese)
[13]Regis R G,Shoemaker C A.A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions[J].Informs Journal on Computing,2007,19(4):497-509
[2]Javaid M Y,Ovinis M,Nagarajan T,et al.Underwater Gliders:A Review[C]∥MATEC Web of Conferences,2014,13:02020
[3]靖海宏.潜水器耐压壳结构稳定性分析与研究[D].武汉:武汉理工大学,2011Jing Haihong.Analysis and Study on Structural Stability of Submarine Pressure Hull[D].Wuhan,Wuhan University of Technology,2011(in Chinese)
[4]Annaratone D.Pressure vessel design[M].Heidelberg,Springer,2007
[5]Liebeck R H.Design of the Blended Wing Body Subsonic Transport[J].Journal of Aircraft,2004,41(1):10-25
[6]Mukhopadhyay V,Sobieszczanski-Sobieski J,Kosaka I,et al.Analysis,Design,and Optimization of Noncylindrical Fuselage for Blended-Wing-Body Vehicle[J].Journal of Aircraft,2004,41(4):925-930
[7]JMM Geuskens F J,Bergsma O K,Koussios S,et al.Analysis of Conformable Pressure Vessels:Introducing the Multi-Bubble[J].AIAA Journal,2011,49(8):1683-1692
[8]Forrester A I J,Keane A J.Recent Advances in Surrogate-Based Optimization[J].Progress in Aerospace Sciences,2009,45(1):50-79
[9]解欢,杨岳,童林军,等.基于混合代理模型的高速轨道车辆悬挂参数多目标优化[J].铁道科学与工程学报,2016,13(10):2056-2063Xie Huan,Yang Yue,Tong Linjun,et al.Multi-objective Optimization of the Suspension Parameters for High Speed Rail Vehicle Based on a Hybrid Surrogate Model[J].Journal of Railway Science and Engineering,2016,13(10):2056-2063(in Chinese)
[10]李召,王大志,时统宇,等.基于熵权与混合代理模型的永磁驱动器的优化设计[J].电机与控制学报,2016,20(6):102-108Li Zhao,Wang Dazhi,Shi Tongyu,et al.Optimization Design for Permanent Magnet Drive Based on Entropy-Weight and Hybrid Surrogate Model[J].Electric Machines and Control,2016,20(6):102-108(in Chinese)
[11]Cawley G C.Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs[C]∥International Joint Conference on Neural Networks,2006:1661-1668
[12]赵秋月,左万利,田中生,等.一种基于改进D-S证据理论的信任关系强度评估方法研究[J].计算机学报,2014,37(4):873-883Zhao Qiuyue,Zuo Wanli,Tian Zhongsheng,et al.A Method for Assessment of Trust Relationship Strength Based on the Improved D-S Evidence Theory[J].Chinses Journal of Computers,2014,37(4):873-883(in Chinese)
[13]Regis R G,Shoemaker C A.A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions[J].Informs Journal on Computing,2007,19(4):497-509
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