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首页> 外文期刊>Journal of Computing and Information Science in Engineering >Large Scale Finite Element Analysis Via Assembly-Free Deflated Conjugate Gradient
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Large Scale Finite Element Analysis Via Assembly-Free Deflated Conjugate Gradient

机译:通过无装配放气共轭梯度进行大规模有限元分析

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Large-scale finite element analysis (FEA) with millions of degrees of freedom (DOF) is becoming commonplace in solid mechanics. The primary computational bottleneck in such problems is the solution of large linear systems of equations. In this paper, we propose an assembly-free version of the deflated conjugate gradient (DCG)for solving such equations, where neither the stiffness matrix nor the deflation matrix is assembled. While assembly-free FEA is a well-known concept, the novelty pursued in this paper is the use of assembly-free deflation. The resulting implementation is particularly well suited for large-scale problems and can be easily ported to multicore central processing unit (CPU) and graphics-programmable unit (GPU) architectures. For demonstration, we show that one can solve a 50 × 10~6 degree of freedom system on a single GPU card, equipped with 3 GB of memory. The second contribution is an extension of the "rigid-body agglomeration" concept used in DCG to a "curvature-sensitive agglomeration." The latter exploits classic plate and beam theories for efficient deflation of highly ill-conditioned problems arising from thin structures.
机译:具有数百万个自由度(DOF)的大规模有限元分析(FEA)在固体力学中正变得司空见惯。这些问题的主要计算瓶颈是大型线性方程组的解决方案。在本文中,我们提出了用于求解此类方程的无组装形式的放气共轭梯度(DCG),其中既没有组装刚度矩阵也没有组装放气矩阵。尽管无装配FEA是一个众所周知的概念,但本文追求的新颖之处在于无装配放气的使用。最终的实现特别适合于大规模问题,并且可以轻松移植到多核中央处理器(CPU)和图形可编程单元(GPU)架构中。为了演示,我们展示了一个可以在配备3 GB内存的单个GPU卡上解决50×10〜6自由度系统的问题。第二个贡献是将DCG中使用的“刚体凝聚”概念扩展为“对曲率敏感的凝聚”。后者利用经典的板和梁理论有效地缩小了由薄结构引起的病态严重的问题。

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