Parallel Computing Study on Finite Element Modal Analysis over Ten-million Degrees of Freedom
FAN Xuan-hua1 XIAO Shi-Fu1 CHEN Pu2
1. Institute of systems engineering, CAEP, Mianyang 621900, China;
2. Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100871, China;
Abstract:In the development of large equipments, the demand of large-scale finite element modal analysis is very strong due to its important significance in realizing the systemic analysis of entire structures. Based on the three predominant algorithms (i.e. implicitly restarted Arnoldi method, Krylov-Schur method and Jacobi-Davidson method ) and the PANDA framework, a large-scale parallel computing system for modal analysis is established. As a typical application, the solution system is applied to the main structure of Shenguang and a parallel modal analysis with over ten-million degrees of freedom and thousands of CPU processors is achieved. The adaptability and parallel scalability of the three algorithms are discussed according to the numerical example. Results show that the parallel solution system can solve the modal analysis problems over ten millions degrees of freedom within one hour and the parallel performance is very favorable.
范宣华1 肖世富1 陈璞2. 千万自由度量级有限元模态分析并行计算研究[J]. 振动与冲击, 2015, 34(17): 77-82.
FAN Xuan-hua1 XIAO Shi-Fu1 CHEN Pu2. Parallel Computing Study on Finite Element Modal Analysis over Ten-million Degrees of Freedom. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(17): 77-82.
[1] Y. Saad. Numerical Methods for Large Eigenvalue Problems. Halsted Press, Div. of John Wiley &Sons, Inc., New York, 1992.
[2] 尹家聪. 模态分析的自动多重子结构法与动力重分析研究. 博士学位论文,北京大学,2012.
YIN Jia-Cong. Automated Multi-Level Substructuring for Modal Analysis and Research on Dynamical Re-Analysis. Ph.D. dissertation, Peking University, 2012.
[3] Z. Bai, J. Demmel, J. Dongarra, A. Ruhe, and H. van der Vorst, editors. Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. SIAM, Philadelphia, 2000.
[4] D. S. Watkins. The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods. SIAM, Philadelphia, 2007.
[5] W. E.Arnoldi. The Principle of minimized iterations in the solution of the matrix eigenvalue problem. Quart. Appl. Math., 1951,(9):17~29.
[6] D. C. Sorensen. Implicit application of polynomial filters in a k-step Arnoldi method. SIAM J. Matrix Anal. Appl., 1992, (13):357~385.
[7] Lehoucq, R. B., D. C. Sorensen, and C. Yang. ARPACK Users’ Guide: Solution of Large-Scale Eigenvalue Problems by Implicitly Restarted Arnoldi Methods. SIAM, Philadelphia, PA,1998.
[8] G.W. Stewart. A Krylov-Schur algorithm for large eigenproblems. SIAM J. Matrix Anal. Appl., 2001,23(3): 601~614.
[9] K. Meerbergen, A. Spence and D. Roose. Shift-Invert and Cayley transforms for detection of rightmost eigenvalues of nonsymmetric matrices. BIT Numerical Mathematics, 1994, (34)3: 409~423.
[10] E. R. Davidson. The iterative calculation of a few of the lowest eigenvalues and corresponding eigenvectors of large real symmetric matrices. J. Comp. Phy., 1975, (17): 87~94.
[11] M. Crouzeix, B. Philippe, and M. Sadkane. The Davidson method. SIAM J. Sci. Comput., 1994,(15):62~67.
[12] G. L. G. Sleijpen and H. A. van der Vorst. A Jacobi-Davidson iteration method for linear eigenvalue problems. SIAM J. Matrix Anal. Appl., 1996, 17(2):401~425.
[13] G. M. Shi, R. Wu and K. Y. Wang. Discussion about the design for mesh data structure within the parallel framework. 9th World Congress on Computational Mechanics and 4th Asian Pacific Congress on Computational Mechanics, Sydney, Australia, 2010.
[14] 范宣华. 基于Panda框架的大规模有限元模态分析并行计算及应用. 博士学位论文, 北京大学, 2013.
FAN Xuan-Hua. Parallel Computation and Applications of Large-scale Finite Element Modal Analysis Based on Panda Framework. Ph.D. dissertation, Peking University, 2013.
[15] G. Karypis and V. Kumar. Metis - a software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. Technical Report, University of Minnesota, Minneapolis, 1998.
[16] S.Balay, K. Buschelman, and V. Eijkhout et al.. PETSc Users Manual. Technical Report ANL-95/11 - Revision 3.0.0, Argonne National Laboratory, 2008.
[17] V. Hernandez, J. Roman, A. Tomas, and V. Vidal. SLEPc: a scalable and flexible toolkit for the solution of eigenvalue problems. ACM Trans. Math. Softw., 2005, 31(3):351~362.
[18] P. R. Amestoy, I. S. Duff, J.-Y. L’Excellent and J. Koster. MUMPS: A general purpose distributed memory sparse solver. Lecture Notes in Computer Science, 2001, (1947):121~131.