Lars Radtke, Michele Torre, Thomas J.R. Hughes, Alexander Düster, Giancarlo Sangalli, Alessandro Reali
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The results show that B-spline discretization with a consistent mass matrix are more accurate than those based on Lagrange shape functions, which holds true in the boundary-fitted as well as in the immersed setting. On the other hand, Lagrange shape functions are more robust with respect to standard lumping techniques, which cannot be directly applied for B-splines without loss of accuracy. In general, we observe that none of the standard lumping schemes yields optimal results for B-splines, even in the boundary-fitted setting. For the immersed setting, also Lagrange shape functions show a drop in accuracy which depends on the position of the boundary that cuts the element. Several remedies are considered in order to overcome these issues, including interpolatory B-spline bases as well as eigenvalue stabilization methods. 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引用次数: 0
摘要
我们研究了用于双曲问题离散化的不同形状函数的行为。特别是,我们考虑了经典的拉格朗日多项式和 B 样条函数。研究重点是这些函数作为空间离散化方法与显式时间行进方案相结合的性能。在这方面,主要关注的是对临界时间步长施加限制的最大特征值,以及产生对角质量矩阵的合适凑整技术。根据特征值和特征向量的收敛性,以渐进方式评估离散化方法的精度。此外,还根据全谱对全局精度进行了研究。结果表明,具有一致质量矩阵的 B-样条离散法比基于拉格朗日形状函数的离散法更精确,这在边界拟合和浸入环境中都是正确的。另一方面,拉格朗日形状函数相对于标准叠加技术更为稳健,而标准叠加技术不能直接应用于 B-样条曲线而不损失精度。总的来说,我们发现即使在边界拟合的情况下,也没有一种标准的叠加方案能为 B-样条曲线带来最佳结果。对于浸没设置,拉格朗日形状函数也显示出精度下降,这取决于切割元素的边界位置。为了克服这些问题,我们考虑了几种补救方法,包括插值 B-样条曲线基以及特征值稳定方法。虽然使用这些补救方法可以提高精度和稳定性,但我们通过研究得出结论,如何设计一种离散化方法,在沉浸环境中结合对角质量矩阵和高精度实现大临界时间步长,仍然是一个未决问题。我们注意到,这些考虑因素主要与线性结构动力学应用有关,例如结构声学。在汽车碰撞动力学等非线性问题中,其他考虑因素则占主导地位。以一维弹塑性棒材撞击刚性墙壁为例进行说明。
An analysis of high order FEM and IGA for explicit dynamics: Mass lumping and immersed boundaries
We investigate the behavior of different shape functions for the discretization of hyperbolic problems. In particular, we consider classical Lagrange polynomials and B-splines. The studies focus on the performance of the these functions as a spatial discretization approach combined with an explicit time marching scheme. In this regard, a major concern is the maximum eigenvalue that imposes restrictions on the critical time step size and suitable lumping techniques that yield a diagonal mass matrix. The accuracy of the discretization methods is assessed in an asymptotic manner in terms of the convergence of eigenvalues and eigenvectors. Further, the global accuracy is investigated in terms of the full spectrum. The results show that B-spline discretization with a consistent mass matrix are more accurate than those based on Lagrange shape functions, which holds true in the boundary-fitted as well as in the immersed setting. On the other hand, Lagrange shape functions are more robust with respect to standard lumping techniques, which cannot be directly applied for B-splines without loss of accuracy. In general, we observe that none of the standard lumping schemes yields optimal results for B-splines, even in the boundary-fitted setting. For the immersed setting, also Lagrange shape functions show a drop in accuracy which depends on the position of the boundary that cuts the element. Several remedies are considered in order to overcome these issues, including interpolatory B-spline bases as well as eigenvalue stabilization methods. While accuracy and stability can be improved using these remedies, we conclude from our study that it is still an open question, how to design a discretization method that achieves large critical time step sizes in combination with a diagonal mass matrix and high accuracy in the immersed setting. We note that these considerations primarily relate to linear structural dynamics applications, such as for example, structural acoustics. In nonlinear problems, such as automotive crash dynamics, other considerations predominate. An example of a one-dimensional elastic-plastic bar impacting a rigid wall is illustrative.
期刊介绍:
The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems.
The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.