计算机科学 ›› 2017, Vol. 44 ›› Issue (3): 48-50.doi: 10.11896/j.issn.1002-137X.2017.03.012

• 2015全国高性能计算学术年会 • 上一篇    下一篇

并行计算水下大尺度弹性壳体的低频声散射

张建民,安俊英,慈国庆,王宁   

  1. 中国科学院声学研究所北海研究站 青岛266023;中国海洋大学信息科学与工程学院 青岛266100,中国科学院声学研究所北海研究站 青岛266023,中国科学院声学研究所北海研究站 青岛266023,中国海洋大学信息科学与工程学院 青岛266100
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受山东省超级计算科技专项:水中大尺度复杂结构弹性体目标声特性的并行计算技术研究,国家自然科学基金项目(11304344),山东省自然科学基金项目(ZR2013AQ026)资助

Parallel Calculation of Acoustic Scattering from Underwater Large Scale Elastic Shell at Low Frequency

ZHANG Jian-min, AN Jun-ying, CI Guo-qing and WANG Ning   

  • Online:2018-11-13 Published:2018-11-13

摘要: 有限元与边界元耦合模型是研究水下弹性壳体目标低频声散射常用的数值方法。应用该模型计算大尺度弹性目标的声散射时需要大量的计算时间与存储空间,采用并行数值的方式可以解决这一问题。首先并行计算生成有限元矩阵和边界元矩阵,然后应用并行化的广义极小残差(GMRES)迭代算法求解大型非对称线性方程组。详细叙述了并行GMRES(m)迭代算法的执行过程,并以球壳的声散射计算为例分析了迭代步数对算法收敛情况的影响。最后计算了Benchmark目标模型的低频散射声场,分析了其收发分置散射目标强度以及表面声场的分布。

关键词: 声散射,有限元,边界元,GMRES(m)算法,并行计算

Abstract: The coupling model of finite element method (FEM) and boundary element method (BEM) is effective to calculate the acoustic scattering from submerged elastic shell at low frequency.Applying this model,the calculation of acoustic scattering from large scale elastic object may consume massive computing time and memory space.In this paper,the parallel computing technology was used when implementing the calculation.At first,the matrices of FEM and BEM are generated through parallel computing,secondly the paralleled generalized minimum residual method (GMRES) is used to solve the large nonsymmetric linear equations which generated by the coupling of FEM and BEM.The paralleled GMRES(m) iterative algorithm is detailedly described and the convergence of the algorithm is analyzed by setting different iterative steps when calculating sound scattering from spherical shell.At last,the scattering of Benchmark model at low frequency is calculated,the bistatic target strength (TS) and the surface field of the Benchmark model are analyzed.

Key words: Acoustic scattering,FEM,BEM,GMRES(m) algorithm,Paralleled computing

[1] HAN J,KAMBER M.数据挖掘:概念与技术(第2版)[M].范明,孟小峰,译.北京:机械工业出版社,2007.
[2] LAI Y X,LIU J P,YANG G X.K-Means Clustering Analysis Based on Genetic Algorithm[J].Computer Engineering,2008,34(20):200-202.(in Chinese) 赖玉霞,刘建平,杨国兴.基于遗传算法的K均值聚类分析[J].计算机工程,2008,4(20):200-202.
[3] TANG Z X.K-means Clustering Algorithm Based on Improved Genetic Algorithm[J].Journal of Chengdu University (Nature Science Edition),2011,30(2):162-164.(in Chinese) 唐朝霞.一种改进的基于遗传算法的K均值聚类算法[J].成都大学学报(自然科学版),2011,0(2):162-164.
[4] LI J B,YANG L,HUA B.Research on parallel K-Medoids algorithm on multi-core platform[J].Application Research of Computers,2011,28(2):498-500.(in Chinese) 李静滨,杨柳,华蓓.基于多核平台并行K-Medoids算法研究[J].计算机应用研究,2011,8(2):498-500.
[5] LI J B,YANG L,CHEN N J.An improved parallel K-Medoids algorithm based on MapReduce[J].Journal of Guangxi University (Nature Science Edition),2014(2):341-345.(in Chinese) 李静滨,杨柳,陈宁江.基于MapReduce的改进K-Medoids并行算法[J].广西大学学报(自然科学版),2014(2):341-345.
[6] ZHANG X P,GONG K L,ZHAO G C.Parallel K-Medoids algorithm based on MapReduce[J].Journal of Computer Application,2013,33(4):1023-1025.(in Chinese) 张雪萍,龚康莉,赵广才.基于MapReduce的K-Medoids并行算法[J].计算机应用,2013,3(4):1023-1025.
[7] JIANG Y B,ZHANG J M.Parallel K-Medoids Clustering Algorithm Based on Hadoop[C]∥IEEE Beijing Section.Proceedings of 2014 IEEE 5th International Conference on Software Engineering and Service Science.IEEE Beijing Section,2014:4.
[8] ZHU Y,WANG F,SHAN X,et al.K-medoids clustering based on MapReduce and optimal search of medoids[C]∥2014 9th International Conference on Computer Science & Education (ICCSE).IEEE,2014:573-577.
[9] SRINIVASULU D L,REDDY A V,AKULA V S G,et al.Improving the Scalability and Efficiency of K-Medoids By Map Reduce [J].International Journal of Engineering and Applied Sciences (IJEAS),2015(4):88-90.
[10] ALBA E,TROYA J M.A survey of parallel distributed genetic algorithms [J].Complexity,1999,4(4):31-52.
[11] GUO T C,MU C D.The Parallel Drifts of Genetic Algorithms[J].System Engineering-Theory & Practice,2002,22(2):15-23,41.(in Chinese) 郭彤城,慕春棣,并行遗传算法的新进展[J].系统工程理论与实践,2002,2(2):15-23,41.
[12] WANG X L,LI Q.Application and Research on Parallel Genetic Algorithm[J].Microcomputer Information,2007,23(9):205-206.(in Chinese) 王小良,李强.并行遗传算法研究及其应用[J].微计算机信息,2007,3(9):205-206.
[13] JOHAR F M,AZMIN F A,SUAIDI M K,et al.A review of genetic algorithms and parallel genetic algorithms on Graphics Processing Unit (GPU)[C]∥2013 IEEE International Confe-rence on Control System,Computing and Engineering (ICCSCE).IEEE,2013:264-269.
[14] FERRUCCI F,SALZA P,KECHADI M T,et al.A Parallel Genetic Algorithms Framework based on Hadoop MapReduce[C]∥The ACM Symposium.ACM,2015:1664-1667 .
[15] Jin C,Vecchiola C,Buyya R.Mrpga:an extension of mapreduce for parallelizing genetic algorithms[C]∥ IEEE Fourth International Conference on EScience,2008 EScience’08.IEEE,2008:214-221.
[16] FERRUCCI F,KECHADI M,SALZA P,et al.A Framework for Genetic Algorithms Based on Hadoop[J].arXiv preprint arXiv:1312.0086,3.
[17] ZHANG X,WANG J,WU F,et al.Genetic K-Medoids Spatial Clustering with Obstacles Constraints[C]∥2006 3rd International IEEE Conference on Intelligent Systems.IEEE,2006:826-831.
[18] SHENG W,LIU X.A genetic k-medoids clustering algorithm[J].Journal of Heuristics,2006,12(6):447-466.
[19] DEAN J,GHEMAWAT S.MapReduce:Simplified Data Pro-cessing on Large Clusters[C]∥ Conference on Symposium on Operting Systems Design & Implementation.2004:107-113.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!