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Symmetric smoothed particle hydrodynamics (SSPH) method and its application to elastic problems

机译:对称光滑粒子流体动力学(SSPH)方法及其在弹性问题中的应用

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摘要

We discuss the symmetric smoothed particle hydrodynamics (SSPH) method for generating basis functions for a meshless method. It admits a larger class of kernel functions than some other methods, including the smoothed particle hydrodynamics (SPH), the modified smoothed particle hydrodynamics (MSPH), the reproducing kernel particle method (RKPM), and the moving least squares (MLS) methods. For finding kernel estimates of derivatives of a function, the SSPH method does not use derivatives of the kernel function while other methods do, instead the SSPH method uses basis functions different from those employed to approximate the function. It is shown that the SSPH method and the RKPM give the same value of the kernel estimate of a function but give different values of kernel estimates of derivatives of the function. Results computed for a sine function defined on a one-dimensional domain reveal that the L 2, the H 1 and the H 2 error norms of the kernel estimates of a function computed with the SSPH method are less than those found with the MSPH method. Whereas the L 2 and the H 2 norms of the error in the estimates computed with the SSPH method are less than those with the RKPM, the H 1 norm of the error in the RKPM estimate is slightly less than that found with the SSPH method. The error norms for a sample problem computed with six kernel functions show that their rates of convergence with an increase in the number of uniformly distributed particles are the same and their magnitudes are determined by two coefficients related to the decay rate of the kernel function. The revised super Gauss function has the smallest error norm and is recommended as a kernel function in the SSPH method. We use the revised super Gauss kernel function to find the displacement field in a linear elastic rectangular plate with a circular hole at its centroid and subjected to tensile loads on two opposite edges. Results given by the SSPH and the MSPH methods agree very well with the analytic solution of the problem. However, results computed with the SSPH method have smaller error norms than those obtained from the MSPH method indicating that the former will give a better solution than the latter. The SSPH method is also applied to study wave propagation in a linear elastic bar.
机译:我们讨论了用于生成无网格方法基函数的对称平滑粒子流体动力学(SSPH)方法。与其他一些方法相比,它允许使用更多种类的核函数,包括平滑粒子流体动力学(SPH),改进的平滑粒子流体动力学(MSPH),再生核心粒子方法(RKPM)和移动最小二乘(MLS)方法。为了找到函数的导数的核估计,SSPH方法不使用核函数的导数,而其他方法则使用,而是使用不同于近似函数的基本函数。结果表明,SSPH方法和RKPM给出的函数核估计值相同,但是给出函数导数的核估计值不同。针对在一维域上定义的正弦函数的计算结果表明,L 2 ,H 1 和H 2 的误差范数使用SSPH方法计算的函数的内核估计值小于使用MSPH方法计算的函数的内核估计值。 SSPH方法计算出的估计误差的L 2 和H 2 范数小于RKPM的误差范式,而H 1 RKPM估计中的错误范数略小于SSPH方法中发现的范数。用六个核函数计算的样本问题的误差范数表明,随着均匀分布粒子数量的增加,它们的收敛速率相同,并且其大小由与核函数衰减率相关的两个系数确定。修改后的超级高斯函数具有最小的误差范数,在SSPH方法中建议将其作为内核函数使用。我们使用修正的超级高斯核函数在线性弹性矩形板中找到位移场,该矩形板的质心处有一个圆孔,并且在两个相对的边缘上承受拉伸载荷。 SSPH和MSPH方法给出的结果与问题的解析解非常吻合。但是,与从MSPH方法获得的结果相比,使用SSPH方法计算的结果具有更小的误差范数,这表明前者将提供比后者更好的解决方案。 SSPH方法还用于研究线性弹性棒中的波传播。

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