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Static and dynamic properties of a particle-based algorithm for non-ideal fluids and binary mixtures

机译:基于粒子的非理想流体和二元混合物算法的静态和动态特性

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A recently introduced particle-based model for fluid dynamics with effective excluded volume interactions is analysed in detail. The interactions are modelled by means of stochastic multiparticle collisions which are biased and depend on local velocities and densities. The model is Galilean-invariant; momentum and energy are exactly conserved locally. The isotropy and relaxation to equilibrium are analysed and measured. It is shown how a discrete-time projection operator technique can be used to obtain Green-Kubo relations for the transport coefficients. Because of a large viscosity no long-time tails in the velocity auto-correlation and stress correlation functions were seen. Strongly reduced self-diffusion due to caging and an order/disorder transition are found at high collision frequencies, where clouds consisting of at least four particles form a cubic phase. These structures were analysed by measuring the pair-correlation function above and below the transition. Finally, the algorithm is extended to binary mixtures which phase-separate above a critical collision rate.
机译:详细分析了最近引入的基于粒子的流体动力学模型,该模型具有有效的排除体积相互作用。相互作用是通过随机的多粒子碰撞来建模的,该碰撞是有偏差的,并且取决于局部速度和密度。该模型是伽利略不变的;动量和能量在本地完全守恒。分析和测量了各向同性和平衡松弛。它显示了如何使用离散时间投影算子技术来获得输运系数的Green-Kubo关系。由于粘度较大,因此在速度自相关和应力相关函数中没有出现长时间的拖尾。在高碰撞频率下,由于笼子和有序/无序过渡而导致的自扩散大大降低,其中至少由四个粒子组成的云形成了立方相。通过测量跃迁上方和下方的成对相关函数来分析这些结构。最后,将该算法扩展到在临界碰撞速率以上相分离的二元混合物。

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