当前位置: X-MOL 学术ACM Trans. Graph. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Optimized Refinement for Spatially Adaptive SPH
ACM Transactions on Graphics  ( IF 6.2 ) Pub Date : 2021-01-06 , DOI: 10.1145/3363555
Rene Winchenbach 1 , Andreas Kolb 1
Affiliation  

In this article, we propose an improved refinement process for the simulation of incompressible low-viscosity turbulent flows using Smoothed Particle Hydrodynamics, under adaptive volume ratios of up to 1 : 1, 000, 000. We derive a discretized objective function, which allows us to generate ideal refinement patterns for any kernel function and any number of particles a priori without requiring intuitive initial user-input. We also demonstrate how this objective function can be optimized online to further improve the refinement process during simulations by utilizing a gradient descent and a modified evolutionary optimization. Our investigation reveals an inherent residual refinement error term, which we smooth out using improved and novel methods. Our improved adaptive method is able to simulate adaptive volume ratios of 1 : 1, 000, 000 and higher, even under highly turbulent flows, only being limited by memory consumption. In general, we achieve more than an order of magnitude greater adaptive volume ratios than prior work.

中文翻译:

空间自适应 SPH 的优化细化

在本文中,我们提出了一种改进的细化过程,用于使用平滑粒子流体动力学模拟不可压缩的低粘度湍流,自适应体积比高达 1 : 1, 000, 000。我们推导出一个离散的目标函数,它允许我们为任何核函数和任何数量的粒子生成理想的细化模式先验无需直观的初始用户输入。我们还演示了如何通过利用梯度下降和改进的进化优化在线优化这个目标函数,以进一步改进模拟过程中的细化过程。我们的调查揭示了一个固有的剩余细化误差项,我们使用改进和新颖的方法对其进行了平滑处理。我们改进的自适应方法能够模拟 1:1、000、000 和更高的自适应体积比,即使在高度湍流的情况下,也仅受内存消耗的限制。一般来说,我们实现的自适应体积比比以前的工作高一个数量级以上。
更新日期:2021-01-06
down
wechat
bug