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Efficient GPU implementation of the linearly interpolated bounce-back boundary condition

机译:线性内插反弹边界条件的高效GPU实现

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Interpolated bounce-back boundary conditions for the lattice Boltzmann method (LBM) make the accurate representation of complex geometries possible. In the present work, we describe an implementation of a linearly interpolated bounce-back (LIBB) boundary condition for graphics processing units (GPUs). To validate our code, we simulated the flow past a sphere in a square channel. At low Reynolds numbers, results are in good agreement with experimental data. Moreover, we give an estimate of the critical Reynolds number for transition from steady to periodic flow. Performance recorded on a single node server with eight GPU based computing devices ranged up to 2.63×10~9 fluid node updates per second. Comparison with a simple bounce-back version of the solver shows that the impact of LIBB on performance is fairly low.
机译:晶格玻尔兹曼方法(LBM)的插值反弹边界条件使精确表示复杂几何成为可能。在当前的工作中,我们描述了图形处理单元(GPU)的线性内插反弹(LIBB)边界条件的实现。为了验证我们的代码,我们模拟了通过方形通道中的球体的流动。在低雷诺数下,结果与实验数据吻合良好。此外,我们给出了从稳态到周期性流动过渡的临界雷诺数的估计。在具有八个基于GPU的计算设备的单节点服务器上记录的性能,每秒高达2.63×10〜9个流体节点更新。与简单的反弹器版本的比较器比较表明,LIBB对性能的影响相当低。

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