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Higher order Galerkin-collocation time discretization with Nitsche's method for the Navier-Stokes equations

机译:与Nirsche的Navier-Stokes方程的方法更高阶Galerkin-Conlocation时间离散化

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We propose and study numerically the implicit approximation in time of the Navier-Stokes equations by a Galerkin-collocation method in time combined with inf-sup stable finite element methods in space. The conceptual basis of the Galerkin-collocation approach is the establishment of a direct connection between the Galerkin method and the classical collocation methods, with the perspective of achieving the accuracy of the former with reduced computational costs in terms of less complex algebraic systems of the latter. Regularity of higher order in time of the discrete solution is ensured further. As an additional ingredient, we employ Nitsche's method to impose all boundary conditions in weak form with the perspective that evolving domains become feasible in the future. We carefully compare the performance properties of the Galerkin-collocation approach with a standard continuous Galerkin-Petrov method using piecewise linear polynomials in time, that is algebraically equivalent to the popular Crank-Nicholson scheme. The condition number of the arising linear systems after Newton linearization as well as the reliable approximation of the drag and lift coefficient for laminar flow around a cylinder (DFG flow benchmark with Re = 100; cf. (Turek and Schafer, 1996)) are investigated. The superiority of the Galerkin-collocation approach over the linear in time, continuous Galerkin-Petrov method is demonstrated therein.
机译:我们在数量上提出并研究了Navier-Stokes方程的时间内隐式近似,通过在空间中与INF-SUP稳定的有限元方法相结合。 Galerkin-Collocation方法的概念依据是在Galerkin方法和经典配件方法之间建立直接连接,以实现前者的准确性,从而减少后者的复杂代数系统的计算成本降低。进一步确保了离散解决方案的时间更高阶的规律性。作为额外的成分,我们采用了NITSCHE的方法,以便在未来发展域变得可行的角度来强加弱形的边界条件。我们仔细比较了使用分段线性多项式的标准连续Galerkin-Petrov方法的Galerkin-Collocation方法的性能特性,这些方法及时是代数相当于流行的曲柄 - 尼科尔森方案。牛顿线性化之后产生的线性系统的条件数以及气缸周围的层流的阻力系数的可靠逼近(DFG流量基准与RE = 100; CF.(Turek和Schafer,1996))进行了调查。在线性地,连续的Galerkin-Petrov方法在线性时,Galerkin-Collocation方法的优势在其中。

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