Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 624-627.doi: 10.11896/jsjkx.191100154

• Interdiscipline & Application • Previous Articles     Next Articles

Fast Calculation Method of Aircraft Component Strength Check Based on ICCG

XU Xin-peng1, HU Bin-xing2   

  1. 1 Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China
    2 Aerospace System Engineering Shanghai,Shanghai 201109,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:XU Xin-peng,M.S.His main research interests include flight dynamics,missile guidance and control system.
    HU Bin-xing,Ph.D.His main research interests include flight dynamics,software framework,parallel programming and applications.

Abstract: With the requirement of fast diagnosis for reusable aircraft structures,GPU is used as the coprocessor to solve the sparse linear equations with high parallelization and high memory bandwidth.In view of the most time-consuming solution ofli-near equations,the incomplete Cholesky conjugate gradient method is used to verify computing efficiency using wing as an example.The acceleration ratio of GTX1060 graphics card is about 25 times higher than that of E3 1230V5.The results show that the ICCG algorithm based on CUDA can satisfy the relevant diagnostic calculation of the finite element model of aircraft with order less than 60 000.

Key words: CUDA, Sparse matrix, Conjugate gradient method, Incomplete Cholesky

CLC Number: 

  • TP319
[1] LI J W.Modeling,Design and Analysis of Large Strap-onLaunch Vehicle's Attitude Control System[D].Graduate School of National University of Defense Technology,Changsha,Hunan,P.R.China. April,2011 .
[2] 杨超,许赟,谢长川.高超声速飞行器气动弹性力学研究综述[J].航空学报,2010,31(1):1-11.
[3] 张希彬,宗群,曾凡琳.考虑气动—推进—弹性耦合的高超声速飞行器面向控制建模与分析[J].宇航学报,2014,35(5):528-536.
[4] RAVISHANKAR B,HAFTKA R,SANKAR B.Homogenization of Integrated Thermal Protection System with Rigid Insulation Bars[C]//52nd AIAA Structures,Structural Dynamics and Materials Conference.Denver,Colorado,2012.
[5] 彭小波.可重复使用新型航天飞行器结构设计[M].北京:中国宇航出版社,2015.
[6] 周树荃,梁维泰,邓绍忠.计算方法丛书——有限元结构分析并行计算[M].北京:科学出版社,1999.
[7] 董廷星,李新亮,李森,等.GPU上计算流体力学的加速[J].计算机系统应用,2011,20(01):104-109.
[8] NGUYEN T D,SCHMIDT B,ZHENG Z,et al.Efficient andAccurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting[J].IEEE/ACM Trans Comput Biol Bioinform,2015,12(5):1060-1073.
[9] 郑经纬,安雪晖,黄绵松.基于CUDA的大规模稀疏矩阵的PCG算法优化[J].清华大学学报(自然科学版),2014,54(8):1006-1012.
[10] ZELEWSKI A K,ZIENIUK E,KAPTURCZAK M.Accelera-tion of integration in parametric integral equations system using CUDA[J].Computers and Structures,2015,152:113-124.
[11] BARTEZZAGHI A,CREMONESI M,PAROLINI N,et al.An explicit dynamics GPU structural solver for thin shell finite elements[J].Computers & Structures,2015,154:29-40.
[12] LACERDA SILVA G R,De MEDEIROS R R,JAIMES B R A,et al.CUDA-Based Parallelization of Power Iteration Clustering for Large Datasets[J].IEEE Access,2017,5:27263-27271.
[13] 谷同祥,安恒斌,刘兴平.迭代方法和预处理技术(上册)[M].北京:科学出版社,2015.
[14] COOK S.CUDA Programming:A Developer's Guide to Parallel Computing with GPUs[M].340 Pine Street,Sixth Floor,San Francisco,CA:Morgan Kaufmann Publishers Inc.,2012.
[15] KAMEARI A.Improvement of ICCG Convergence for Thin Ele-ments in Magnetic Field Analyses Using the Finite-Element Method[J].IEEE Transactions on Magnetics,2008,44(6):1178-1181.
[16] VOLKOV V,BARBIERI D,HOGG J,et al.CUBLAS Library User Guide,v10.1th ed[OL].Santa Clara,CA:NVIDIA,2018.https://docs.nvidia.c-om/pdf/CUBLAS_Library.pdf.
[17] CHANG L W,VALERO-LARA P,MARTÍNEZ-PÉREZ I.CUSPARSE Library,v10.1th ed[OL].Santa Clara,CA:2018.
[18] BELL N,GARLAND M.Efficient Sparse Matrix-Vector Multiplication on CUDA,NVR-2008-004[R].Santa Clara,CA:NVIDIA,2008.
[19] LIN S,XIE Z.A Jacobi_PCG solver for sparse linear systems on multi-GPU cluster[J].The Journal of Supercomputing,2017,73(1):433-454.
[20] SANDERS J,KANDROT E.CUDA By Example An Introduction To General-Purpose GPU Programming[M].Addison-Wesley Professional,2010:38-46.
[21] GEORGE A V,MANOJ S,GUPTE S R,et al.Thrust++:Extending Thrust Framework for Better Abstraction and Performance[C]//2017 IEEE 24th International Conference on High Performance Computing (HIPC).IEEE,2017.
[22] REAÑO C,SILLA F.On the support of inter-node P2P GPUmemory copies in rCUDA[J].Journal of Parallel and Distributed Computing,2019,127(5):28-43.
[23] FANG Y,CHEN Q.A real-time and reliable dynamic migration model for concurrent taskflow in a GPU cluster[J].Cluster Computing,2019,22(2):585-599.
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