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SPH truncation error in estimating a 3D derivative

机译:估计3D导数时的SPH截断错误

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Following the procedure proposed in Quinlan et al. (Int. J. Numer. Meth. Engng. 2006; 66:2064-2085) for a 1D generic derivative, a 3D formulation of the Smoothed Particle Hydrodynamics (SPH) truncation error (εT) has been derived and validated. We have then underlined the differences between traditional SPH simulations, which are not consistent, and estimations using renormalization, a first-order consistency technique. The consistency order is here defined as the highest degree of a generic polynomial function, which can be exactly reproduced by an SPH approximation. Under the homogeneous conditions assumed in our analyses renormalization generally reduces the relative truncation error by 1 or 2 orders of magnitude, both at inner points and boundary locations. Due to renormalization the error tends to a lowest constant value as the kernel support size (h) goes to zero, while in general with no consistency the error behaves like 1/h. In contrast to formulations without any consistency estimations, using renormalization there is a weak dependence of the error on the absolute value of the displacement of the particles from their volume barycentre (δ). In addition, for simulations with renormalization, the best choice for the kernel function seems to be the closest to Dirac's delta, while for the ones with no consistency, the preferences are altered. Furthermore, we observe that renormalization reduces the number of neighbors that are necessary to obtain a discretization error that is negligible with respect to the integral error.
机译:按照Quinlan等人提出的程序。 (Int。J. Numer。Meth。Engng。2006; 66:2064-2085)对于一维通用衍生物,已推导并验证了平滑粒子流体动力学(SPH)截断误差(εT)的3D公式。然后,我们强调了不一致的传统SPH仿真与使用一阶一致性技术重归一化进行的估计之间的差异。一致性顺序在此定义为通用多项式函数的最高次数,可以通过SPH近似准确地将其再现。在我们的分析假设的均匀条件下,重新归一化通常在内部点和边界位置将相对截断误差减小1或2个数量级。由于重新归一化,当内核支持大小(h)变为零时,该错误趋向于最低的恒定值,而通常在没有一致性的情况下,该错误的行为类似于1 / h。与没有任何一致性估计的公式相比,使用重新归一化时,误差对粒子相对于其体积重心(δ)的位移的绝对值的依赖性很小。此外,对于具有重归一化的仿真,内核函数的最佳选择似乎是最接近Dirac的增量,而对于那些不一致的仿真,偏好会发生变化。此外,我们观察到,重新归一化减少了获得离散误差所需的邻居数量,该离散误差相对于积分误差可忽略不计。

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