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Parallel Adaptive Mesh Refinement for Capturing Front Displacements: Application to Thermal EOR Processes

机译:用于捕获前置位移的并行自适应网格细化:应用于热EOR过程的应用

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This paper presents a parallel adaptive mesh refinement scheme, that allows the achievement of an optimal accuracy by using dynamic mesh adaptation. The mesh refinement is close to the high error of temperature field. Periodic adaptive refinement is performed such as the refined zones describe accurately the temperature front displacement. This is based on the definition of an error estimator and the search of the optimal mesh that minimizes the error estimator under the constraint of a given number of cells . The main bottleneck of adaptive mesh refinement in a parallel context is the load unbalance between processes due to refinement around local physical phenomena. This paper presents also, a parallel mesh adaptation algorithm able to deal with partitioned and distributed meshes. Unbalance detection is also taken into account by using a load balancing algorithm capable to improve effectively the performance of the simulation. All AMR algorithms are integrated as a part of Arcane [4], a IFPEN-CEA parallel object oriented framework. Application of this dynamic meshing approach in simulation models with steam injection demonstrates that efficient dynamic meshing can be implemented in a general purpose simulator to provide sufficient physical and spatial details in meaningful field or pattern models for EOR.
机译:本文介绍了一个并行自适应网格细化方案,它可以通过使用动态网格自适应来实现最佳精度。网格细化接近温度场的高误差。进行周期性自适应细化,例如精确描述温度前置位的精确区域。这基于误差估计器的定义和搜索最佳网格,其在给定数量的小区的约束下最小化误差估计器。并行上下文中的自适应网格细化的主要瓶颈是由于局部物理现象周围的细化而导致的过程之间的负载不平衡。此纸张也呈现了能够处理分区和分布式网格的并行网格自适应算法。还通过使用能够有效地提高模拟性能的负载平衡算法来考虑不平衡检测。所有AMR算法都集成为术略术的一部分[4],一个IFPEN-CEA并行对象面向框架。这种动态网格化方法在蒸汽喷射模拟模型中的应用演示了高效的动态网格可以在通用模拟器中实现,以提供有意义的字段或eOR模式模型中的足够的物理和空间细节。

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