论文标题
通过高阶不连续的Galerkin方法对脑门力学的数值建模
Numerical Modelling of the Brain Poromechanics by High-Order Discontinuous Galerkin Methods
论文作者
论文摘要
我们介绍并分析了一种不连续的盖尔金方法,用于在动态公式中的多项式毛弹性理论(MPET)方程的数值建模。 MPET模型可以全面描述大脑中考虑多种尺度流体的功能变化。关于空间离散化,我们在多边形和多面体网格上采用了高阶不连续的盖尔金方法,我们得出了稳定性和先验误差估计。时间离散化是基于动量方程的Newmark $β$ -METHOD与压力方程的$θ$ -Method之间的耦合。提出了一些验证数值测试后,我们使用脑切片的几何形状的团聚晶粒进行了收敛分析。最后,我们在从磁共振图像重建的三维患者特异性大脑中提出了模拟。本文提出的模型可以被视为对大脑灌注建模的初步尝试。
We introduce and analyze a discontinuous Galerkin method for the numerical modelling of the equations of Multiple-Network Poroelastic Theory (MPET) in the dynamic formulation. The MPET model can comprehensively describe functional changes in the brain considering multiple scales of fluids. Concerning the spatial discretization, we employ a high-order discontinuous Galerkin method on polygonal and polyhedral grids and we derive stability and a priori error estimates. The temporal discretization is based on a coupling between a Newmark $β$-method for the momentum equation and a $θ$-method for the pressure equations. After the presentation of some verification numerical tests, we perform a convergence analysis using an agglomerated mesh of a geometry of a brain slice. Finally we present a simulation in a three dimensional patient-specific brain reconstructed from magnetic resonance images. The model presented in this paper can be regarded as a preliminary attempt to model the perfusion in the brain.