The purpose of this paper is to analyze a mixed method for linear elasticity eigenvalue problem, which approximates numerically the stress, displacement, and rotation, by piecewise $(k+1)$, $k$ and $(k+1)$-th degree polynomial functions ($k\geq 1$), respectively. The numerical eigenfunction of stress is symmetric. By the discrete $H^1$-stability of numerical displacement, we prove an $O(h^{k+2})$ approximation to the $L^{2}$-orthogonal projection of the eigenspace of exact displacement for the eigenvalue problem, with proper regularity assumption. Thus via postprocessing, we obtain a better approximation to the eigenspace of exact displacement for the eigenproblem than conventional methods. We also prove that numerical approximation to the eigenfunction of stress is locking free with respect to Poisson ratio. We introduce a hybridization to reduce the mixed method to a condensed eigenproblem and prove an $O(h^2)$ initial approximation (independent of the inverse of the elasticity operator) of the eigenvalue for the nonlinear eigenproblem by using the discrete $H^1$-stability of numerical displacement, while only an $O(h)$ approximation can be obtained if we use the traditional inf-sup condition. Finally, we report some numerical experiments.
翻译:本文的目的是分析一种线性弹性本征值问题的混合方法,该方法通过分段$(k+1)$,$k$和$(k+1)$次多项式函数($k\geq 1$)数值逼近应力、位移和旋转。数值应力模态是对称的。通过数值位移的离散$H^1$稳定性,我们证明了在适当的正则性假设下,数值位移的$L^{2}$正交投影与精确位移本征空间的误差是$O(h^{k+2})$级别的。因此,通过后处理,我们获得了比传统方法更好的对精确位移本征问题的解的逼近。我们还证明了数值应力本征函数相对于泊松比的锁定性不明显。我们引入了混合化技术,将混合方法化简为一种凝聚的本征问题,并通过使用数值位移的离散$H^1$稳定性证明了非线性本征问题的初始误差为$O(h^2)$(独立于弹性算子的逆),而传统的inf-sup条件只能得到$O(h)$的逼近结果。最后,我们报告了一些数值实验。