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An Unsteady Entropy Adjoint Approach for Adaptive Solution of the Shallow-Water Equations

机译:浅水方程组自适应解的非稳态熵伴随方法

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This paper presents a novel approach to solution-based adaptation for unsteady discretizations of symmetrizable conservation laws. This approach is based on an extension of the entropy adjoint approach, which was previously introduced for steady-state simulations. Key to the approach is the interpretation of symmetrizing entropy variables as adjoint solutions for an output that states the entropy balance in the space-time computational domain. This relationship is shown for general first-order conservation laws, and it is applied to the case of the shallow water equations. Specifically, the entropy variable weighted residual is used to drive an adaptive indicator that targets regions of the space-time domain where spurious entropy generation is greatest. The error estimation and adaptation strategies are the same as those prescribed by output-based theory, with the advantage that no separate adjoint solution is required. Results for the unsteady shallow water equations in both one and two spatial dimensions show that the adaptive indicator performs better than uniform refinement and as well as or better than an unweighted residual indicator.
机译:本文提出了一种新的方法,用于基于解决方案的自适应对称守恒律非稳态离散化。该方法基于熵伴随方法的扩展,该方法是先前为稳态仿真引入的。该方法的关键是将对称变量解释为输出的伴随解,该输出在时空计算域中表示熵平衡。对于一般的一阶守恒定律显示了这种关系,并将其应用于浅水方程的情况。具体而言,熵变量加权残差用于驱动自适应指标,该指标以空时域的伪熵产生最大的区域为目标。误差估计和自适应策略与基于输出的理论所规定的相同,其优点是不需要单独的伴随解决方案。在一个和两个空间维上的非稳态浅水方程的结果表明,自适应指标的表现优于均匀细化,也优于非加权残差指标。

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