软材料动态大变形分析的光滑总拉格朗日材料点法

IF 2.9 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Shun Zhang, Cunliang Pan, Zhijie Zhu, Wei Sun, Hongfei Ye, Yonggang Zheng
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引用次数: 0

摘要

本文提出了一种光滑全拉格朗日材料点法(STLMPM),可以有效地模拟近不可压缩软质材料中涉及大变形的动态问题。该方法采用全拉格朗日物质点法(TLMPM)对控制方程进行空间离散,并采用显式时间积分格式对控制方程进行时间离散。为了解决物质点法在物理域边界附近计算精度下降的问题,采用高斯核函数进行核校正,建立了粒子与背景网格之间的插值公式。此外,为了减轻由于软质材料几乎不可压缩的性质而引起的体积锁定,在TLMPM的框架内进一步发展了F-bar方法。通过几个有代表性的数值算例验证了所提STLMPM的精度和效率,并将仿真结果与解析解和其他数值方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smoothed Total Lagrangian Material Point Method for Dynamic Large Deformation Analysis of Soft Materials

A smoothed total Lagrangian material point method (STLMPM) is developed in this study to effectively simulate dynamic problems involving large deformation in nearly incompressible soft materials. In this method, the governing equations are spatially discretized within the framework of the total Lagrangian material point method (TLMPM) and temporally discretized using an explicit time integration scheme. To address the issue of decreased computational accuracy of the material point method (MPM) near physical domain boundaries, a Gaussian kernel function with kernel correction is employed to establish the interpolation formulas between particles and the background grid. Furthermore, to mitigate volumetric locking caused by the nearly incompressible nature of soft materials, the F-bar method is further developed within the framework of TLMPM. The accuracy and efficiency of the proposed STLMPM are demonstrated by several representative numerical examples, and the simulation results are compared with analytical solutions and other numerical methods.

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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
276
审稿时长
5.3 months
期刊介绍: The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems. The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.
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