Accurate and Fast Electrostatic Analysis Using Mesh Smoothing and Geometric Multi-Grid for Numerical Human Body Model
Accurate and Fast Electrostatic Analysis Using Mesh Smoothing and Geometric Multi-Grid for Numerical Human Body Model
复制标题
使用网格平滑和几何多重网格对数值人体模型进行准确快速的静电分析
DOI：
10.1109/cefc55061.2022.9940714
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
A. Takei
中科院分区：
文献类型：
--
作者：
M. Nomura;A. Takei
在使用由体素构建的数值人体模型的静电分析中，电场强度在不同材料的阶梯边界附近被高估（称为阶梯误差）。为了减少这些阶梯误差，网格平滑使用移动立方体和拉普拉斯平滑的开发和应用到一部分的数值人体模型。此外，为了减少计算时间的增加，静电分析，由于这种网格平滑，几何多重网格（GMG）方法处理非结构化的格子。结果表明，这种网格光顺方法减少了阶梯误差，并且这种GMG占用的计算时间为一般线性代数求解器的1/16。此外，最重要的发现是，精确的静电分析网格平滑导致的GMG的迭代次数减少。
In an electrostatic analysis using a numerical human body model constructed with voxels, the electric field strength is overestimated near staircase boundaries of different materials (called staircasing errors). To reduce these staircasing errors, mesh smoothing using marching cubes and Laplacian smoothing is developed and applied to a part of the numerical human body model. In addition, to reduce the increase in computation time for electrostatic analysis due to this mesh smoothing, the Geometric Multi-Grid (GMG) method dealing with unstructured lattices is applied. As a result, this mesh smoothing method reduces the stair casing errors and this GMG takes up to 1/16 of the computation time of a general linear algebra solver. Furthermore, the most important finding is that the accurate electrostatic analysis by mesh smoothing leads to a reduction in the iteration count of the GMG.