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首页> 外文期刊>Mathematics of computation >ADAPTIVE REGULARIZATION, LINEARIZATION, AND DISCRETIZATION AND A POSTERIORI ERROR CONTROL FOR THE TWO-PHASE STEFAN PROBLEM
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ADAPTIVE REGULARIZATION, LINEARIZATION, AND DISCRETIZATION AND A POSTERIORI ERROR CONTROL FOR THE TWO-PHASE STEFAN PROBLEM

机译:两阶段Stefan问题的自适应调节,线性化和离散化以及正误差控制

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摘要

We consider in this paper the time-dependent two-phase Stefan problem and derive a posteriori error estimates and adaptive strategies for its conforming spatial and backward Euler temporal discretizations. Regularization of the enthalpy-temperature function and iterative linearization of the arising systems of nonlinear algebraic equations are considered. Our estimators yield a guaranteed and fully computable upper bound on the dual norm of the residual, as well as on the L-2(L-2) error of the temperature and the L-2(H-1) error of the enthalpy. Moreover, they allow us to distinguish the space, time, regularization, and linearization error components. An adaptive algorithm is proposed, which ensures computational savings through the online choice of a sufficient regularization parameter, a stopping criterion for the linearization iterations, local space mesh refinement, time step adjustment, and equilibration of the spatial and temporal errors. We also prove the efficiency of our estimate. Our analysis is quite general and is not focused on a specific choice of the space discretization and of the linearization. As an example, we apply it to the vertex-centered finite volume (finite element with mass lumping and quadrature) and Newton methods. Numerical results illustrate the effectiveness of our estimates and the performance of the adaptive algorithm.
机译:在本文中,我们考虑了时间相关的两阶段Stefan问题,并推导了后验误差估计和自适应策略,以适应其空间和后向Euler时间离散化。考虑了焓-温度函数的正则化和非线性代数方程组的迭代线性化。我们的估计器在残差的对偶范数以及温度的L-2(L-2)误差和焓的L-2(H-1)误差上产生有保证的且可完全计算的上限。此外,它们使我们能够区分空间,时间,正则化和线性化误差分量。提出了一种自适应算法,该算法通过在线选择足够的正则化参数,线性化迭代的停止准则,局部空间网格细化,时间步长调整以及空间和时间误差的平衡来确保节省计算量。我们还证明了我们估算的效率。我们的分析是很笼统的,没有集中在空间离散化和线性化的特定选择上。例如,我们将其应用于以顶点为中心的有限体积(具有质量集总和正交的有限元)和牛顿法。数值结果说明了我们的估计的有效性和自适应算法的性能。

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