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Fast Grid-Based Fluid Dynamics Simulation with Conservation of Momentum and Kinetic Energy on GPU

机译:在GPU上基于动量和动能守恒的基于网格的快速流体动力学仿真

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Since the computation of fluid animation is often too heavy to run in real-time simulation, we propose a fast grid-based method with parallel acceleration. In order to reduce the cost of computation keeping a balance between fluid stability and diversity, we consider the Navier-Stokes equation on the grid structure with momentum conservation, and introduce the kinetic energy for collision handling and boundary condition. Our algorithm avoids the mass loss during the energy transfer, and can be applied to the two-way coupling with a solid body. Importantly, we propose to use the forward-tracing-based motion and design for parallel computing on Graphics Processing Unit (GPU). In particular, these experiments illustrate the benefits of our method, both in conserving fluid density and momentum. They show that our method is suitable to solve the energy transfer when object interaction is considered during fluid simulation.
机译:由于流体动画的计算通常过于繁琐而无法在实时仿真中运行,因此我们提出了一种具有并行加速的基于网格的快速方法。为了降低在流体稳定性和多样性之间保持平衡的计算成本,我们考虑了具有动量守恒的网格结构上的Navier-Stokes方程,并介绍了用于碰撞处理和边界条件的动能。我们的算法避免了能量转移过程中的质量损失,可以应用于与固体的双向耦合。重要的是,我们建议将基于前向跟踪的运动和设计用于图形处理单元(GPU)上的并行计算。特别是,这些实验说明了我们方法在保持流体密度和动量方面的优势。他们表明,当在流体模拟过程中考虑对象交互作用时,我们的方法适用于解决能量传递。

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