arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2301.01622physics.flu-dyncond-mat.softphysics.comp-ph
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轻量级格子玻尔兹曼方法

Lightweight Lattice Boltzmann

Adriano Tiribocchi, Andrea Montessori, Giorgio Amati, Massimo Bernaschi, Fabio Bonaccorso, Sergio Orlandini, Sauro Succi, Marco Lauricella

AI总结:

本文提出一种GPU加速的轻量级格子玻尔兹曼方法,通过从流体力学变量重构分布函数以降低内存和数据访问成本,并通过液滴碰撞等基准测试验证其在软物质模拟中的有效性。

摘要:

本文介绍了一种GPU加速的格子玻尔兹曼方法版本,用于高效模拟软材料。与标准方法不同,该方法从可用的流体力学变量(密度、动量和压力张量)重构分布函数,而无需存储完整的离散粒子群集合。该方案表现出令人满意的数值稳定性、显著降低的内存需求以及数据访问成本。本文讨论了一系列与软物质相关的基准测试,例如流体液滴的碰撞,以验证该方法。这些结果对于未来百亿亿次(exascale)计算机上软物质系统的高性能模拟可能具有特别的意义。

英文摘要:

A GPU-accelerated version of the lattice Boltzmann method for efficient simulation of soft materials is introduced. Unlike standard approaches, this method reconstructs the distribution functions from available hydrodynamic variables (density, momentum, and pressure tensor) without storing the full set of discrete populations. This scheme shows satisfactory numerical stability, significantly lower memory requirements, and data access cost. A series of benchmark tests of relevance to soft matter, such as collisions of fluid droplets, is discussed to validate the method. The results can be of particular interest for high-performance simulations of soft matter systems on future exascale computers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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