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An adaptive moving finite element method for steady low Mach number compressible combustion problems

机译:一种自适应移动有限元方法,用于稳定低马赫数可压缩燃烧问题

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摘要

This work surveys an r-adaptive moving mesh finite element method for the numerical solution of premixed laminar flame problems. Since the model of chemically reacting flow involves many different modes with diverse length scales, the computation of such a problem is often extremely time-consuming. Importantly, to capture the significant characteristics of the flame structure when using detailed chemistry, a much more stringent requirement on the spatial resolution of the interior layers of some intermediate species is necessary. Here, we propose a moving mesh method in which the mesh is obtained from the solution of so-called moving mesh partial differential equations. Such equations result from the variational formulation of a minimization problem for a given target functional that characterizes the inherent difficulty in the numerical approximation of the underlying physical equations. Adaptive mesh movement has emerged as an area of intense research in mesh adaptation in the last decade. With this approach, points are only allowed to be shifted in space leaving the topology of the grid unchanged. In contrast to methods with local refinement, data structure hence is unchanged and load balancing is not an issue as grid points remain on the processor where they are. We will demonstrate the high potential of moving mesh methods for effectively optimizing the distribution of grid points to reach the required resolution for chemically reacting flows with extremely thin boundary layers.
机译:该工作调查R-Adaptive Math Mesh有限元方法,用于预混合层状火焰问题的数值解。由于化学反应流的模型涉及许多具有不同长度尺度的不同模式,因此这种问题的计算通常非常耗时。重要的是,在使用详细化学时捕获火焰结构的显着特征,需要对一些中间物种的内部层的空间分辨率进行更严格的要求。这里,我们提出了一种移动的网格方法,其中网格是从所谓的移动网格偏微分方程的解决方案获得的。这种等式由对给定目标功能的最小化问题的变分制作者产生了表征底层物理方程的数值近似的固有难度的特征。自适应网格运动已成为过去十年来对网格适应的激烈研究领域。通过这种方法,仅允许点在空间中移位,使网格的拓扑保持不变。与本地改进的方法相比,数据结构因此不变,负载平衡不是问题,因为网格点保留在它们所在的处理器上。我们将展示移动网格方法的高潜力,以便有效地优化网格点的分布,以达到具有极薄边界层的化学反应流动的所需分辨率。

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