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A new class of accurate, mesh-free hydrodynamic simulation methods

机译:新型的精确无网格流体动力学模拟方法

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摘要

We present two new Lagrangian methods for hydrodynamics, in a systematic comparison with moving-mesh, smoothed particle hydrodynamics (SPH), and stationary (non-moving) grid methods. The new methods are designed to simultaneously capture advantages of both SPH and grid-based/adaptive mesh refinement (AMR) schemes. They are based on a kernel discretization of the volume coupled to a high-order matrix gradient estimator and a Riemann solver acting over the volume ‘overlap’. We implement and test a parallel, second-order version of the method with self-gravity and cosmological integration, in the code gizmo:1 this maintains exact mass, energy and momentum conservation; exhibits superior angular momentum conservation compared to all other methods we study; does not require ‘artificial diffusion’ terms; and allows the fluid elements to move with the flow, so resolution is automatically adaptive. We consider a large suite of test problems, and find that on all problems the new methods appear competitive with moving-mesh schemes, with some advantages (particularly in angular momentum conservation), at the cost of enhanced noise. The new methods have many advantages versus SPH: proper convergence, good capturing of fluid-mixing instabilities, dramatically reduced ‘particle noise’ and numerical viscosity, more accurate sub-sonic flow evolution, and sharp shock-capturing. Advantages versus non-moving meshes include: automatic adaptivity, dramatically reduced advection errors and numerical overmixing, velocity-independent errors, accurate coupling to gravity, good angular momentum conservation and elimination of ‘grid alignment’ effects. We can, for example, follow hundreds of orbits of gaseous discs, while AMR and SPH methods break down in a few orbits. However, fixed meshes minimize ‘grid noise’. These differences are important for a range of astrophysical problems.
机译:在与运动网格,平滑粒子流体动力学(SPH)和固定(非运动)网格方法进行系统比较的基础上,我们提出了两种新的拉格朗日流体动力学方法。新方法旨在同时捕获SPH和基于网格/自适应网格细化(AMR)方案的优点。它们基于与高阶矩阵梯度估计器和作用于体积“重叠”的Riemann求解器耦合的体积的内核离散化。我们在代码gizmo中实现并测试了具有自重和宇宙学集成的并行,二阶方法的并行版本:1可以保持精确的质量,能量和动量守恒;与我们研究的所有其他方法相比,具有更好的角动量守恒性;不需要“人为传播”字词;并允许流体元素随流量移动,因此分辨率会自动调整。我们考虑了大量的测试问题,发现在所有问题上,新方法似乎都与移动网格方案具有竞争优势,并且具有某些优势(尤其是在角动量守恒方面),但代价是噪声增加。与SPH相比,这些新方法具有许多优势:正确收敛,良好地捕获流体混合不稳定性,显着降低“粒子噪声”和数值粘度,更精确的亚音速流演化以及清晰的震动捕获。与不移动的网格相比,优点包括:自动适应性,对流误差和数值过度混合的显着减少,与速度无关的误差,与重力的精确耦合,良好的角动量守恒以及消除了“网格对齐”效应。例如,我们可以跟踪气态圆盘的数百个轨道,而AMR和SPH方法分解为几个轨道。但是,固定网格可将“网格噪声”降至最低。这些差异对于一系列天体物理学问题很重要。

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    Hopkins Philip F.;

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  • 年度 2015
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