Iterative and Parallel Solution Strategies for PC based Large Scale, Finite Element Analyses

Author(s):  
C. Kasbergen ◽  
A. Scarpas ◽  
J. Blaauwendraad
1986 ◽  
Vol 24 (4) ◽  
pp. 625-635 ◽  
Author(s):  
R.D. VanLuchene ◽  
R.H. Lee ◽  
V.J. Meyers

2014 ◽  
Vol 693 ◽  
pp. 171-176
Author(s):  
Milan Sága ◽  
Peter Pecháč ◽  
Lenka Jakubovičová

The paper presents fundamental principles and application of the large-scale truss structure PKP25-20i optimal design based on a multi-criteria optimization algorithm. The multi-objective function contains conditions for deformation, stability and cumulative damage obtained by finite element analyses. The whole process was implemented and realized in special Matlab’s procedures and FEM software Cosmos/M.


1983 ◽  
Vol 105 (3) ◽  
pp. 234-240 ◽  
Author(s):  
M. B. Smith ◽  
P. D. Pattillo

The purpose of this paper is to report the results of finite element analyses of the collapse of perforated casing. In the study, both inline (0-deg phasing) and staggered (90-deg phasing) patterns are considered. The primary intent of the study is to illustrate the severe loss of casing cross-sectional integrity accompanying extrusion of a ductile formation into the wellbore. It is shown that, for reasonable perforation densities, the primary effect of the perforation pattern is not strength reduction of the cross section, but definition of a nonuniform loading pattern resulting from formation production. In this regard, in the presence of large-scale formation failure in the near wellbore region, it is crucial to the integrity of the casing that a solids control technique be employed.


2014 ◽  
Vol 701-702 ◽  
pp. 207-213
Author(s):  
Qing Hu Zhang ◽  
Dong Wang ◽  
Ya Peng Jiang ◽  
Jun Quan Chen

We present a parallel solution based on CUDA for accelerating the computation for solving large-scale Finite Element equations in electrical and magnetic field. JCG is used for solving equations and corresponding kernel function is designed for spMV. A computation speed test for solving FE equations is taken on NVIDIA Tesla K20c GPU hardware platform, the result proves that the method of kernel can reach 17.1 times faster than the solution using CPU, however it cannot ensure the advantage with CPU if we only use the lib functions on GPU to solve equations.


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