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Enhanced numerical integration scheme based on image-compression techniques: application to fictitious domain methods

机译:基于图像压缩技术的增强的数值集成方案:应用于虚构域方法

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In the present work, we propose a new approach, the so-called compressed adaptive integration scheme (C-AIS), for the computation of the stiffness and mass matrices in fictitious domain methods requiring the integration of discontinuous functions. The novel approach extends the conventional quadtree-decomposition-based adaptive integration scheme (AIS) by an additional step, in which established image-compression techniques are exploited to decrease the number of integration sub-cells. The benefits of the C-AIS are manifold: First, the compression of the sub-cells inevitably leads to significant savings in terms of computational time required by the numerical integration. Second, the compression procedure, which is executed directly after the quadtree-decomposition algorithm, can be easily included in existing codes. Third, if applied to polynomial integrands, the C-AIS yields exactly the same accuracy as the conventional AIS. Finally, the fourth advantage is seen in the fact that the C-AIS can readily be combined with other approaches seeking a reduction of the number of integration points such as the Boolean-FCM. The efficiency of the C-AIS approach is presented in the context of the FCM based on Cartesian meshes applied to problems of linear elastostatics and modal analysis, while it is also a suitable for the quadrature in other fictitious domain approaches, e.g., CutFEM and cgFEM.
机译:在本作工作中,我们提出了一种新的方法,即所谓的压缩自适应积分方案(C-AIS),用于计算虚拟域方法中的刚度和质量矩阵,需要不连续功能集成。该新方法通过附加步骤扩展了基于传统的四分类分解的自适应积分方案(AIS),其中利用建立的图像压缩技术来减少积分子单元的数量。 C-AIS的益处是歧管:首先,在数值集成所需的计算时间方面,子单元的压缩不可避免地导致显着节省。其次,在Quadtree分解算法之后直接执行的压缩过程可以容易地包括在现有代码中。第三,如果施加到多项式积分,C-AIS会产生与传统AIS完全相同的精度。最后,在C-AIS可以容易地与寻求减少诸如BOOLEAN-FCM的集成点数减少的其他方法的事实中,第四个优点。基于笛卡尔网格的FCM的上下文提出了C-AIS方法的效率,其应用于线性弹性物学和模态分析的问题,而在其他虚构的域方法中也是适合于正交的,例如CUTFEM和CGFEM 。

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