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DNS of hydrodynamically interacting droplets in turbulent clouds: Parallel implementation and scalability analysis using 2D domain decomposition

机译:湍流云中水动力相互作用液滴的DNS:使用2D域分解的并行实现和可伸缩性分析

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The study of turbulent collision of cloud droplets requires simultaneous considerations of the transport by background air turbulence (i.e., geometric collision rate) and influence of droplet disturbance flows (i.e., collision efficiency). In recent years, this multiscale problem has been addressed through a hybrid direct numerical simulation (HDNS) approach (Ayala et al., 2007). This approach, while currently is the only viable tool to quantify the effects of air turbulence on collision statistics, is computationally expensive. In order to extend the HDNS approach to higher flow Reynolds numbers, here we developed a highly scalable implementation of the approach using 2D domain decomposition. The scalability of the parallel implementation was studied using several parallel computers, at 512~3 and 1024~3 grid resolutions with O(10~6)-O(10~7) droplets. It was found that the execution time scaled with number of processors almost linearly until it saturates and deteriorates due to communication latency issues. To better understand the scalability, we developed a complexity analysis by partitioning the execution tasks into computation, communication, and data copy. Using this complexity analysis, we were able to predict the scalability performance of our parallel code. Furthermore, the theory was used to estimate the maximum number of processors below which the approximately linear scalability is sustained. We theoretically showed that we could efficiently solved problems of up to 8192~3 with O(100,000) processors. The complexity analysis revealed that the pseudo-spectral simulation of background turbulent flow for a dilute droplet suspension typical of cloud conditions typically takes about 80% of the total execution time, except when the droplets are small (less than 5 μmin a flow with energy dissipation rate of 400 cm~2/s~3 and liquid water content of 1 g/m~3), for which case the particle-particle hydrodynamic interactions become the bottleneck. The complexity analysis was also used to explore some alternative methods to handle FFT calculations within the flow simulation and to advance droplets less than 5 μmin radius, for better computational efficiency. Finally, preliminary results are reported to shed light on the Reynolds number-dependence of collision kernel of non-interacting droplets.
机译:对云滴的湍流碰撞的研究需要同时考虑背景空气湍流的传输(即几何碰撞率)和液滴扰动流的影响(即碰撞效率)。近年来,这种多尺度问题已通过混合直接数值模拟(HDNS)方法得到解决(Ayala等,2007)。这种方法虽然目前是量化空气湍流对碰撞统计数据影响的唯一可行工具,但在计算上却很昂贵。为了将HDNS方法扩展到更高流量的雷诺数,在这里我们使用2D域分解开发了该方法的高度可扩展的实现。使用几台并行计算机研究了并行实现的可伸缩性,这些计算机在512〜3和1024〜3的网格分辨率下具有O(10〜6)-O(10〜7)小滴。发现执行时间几乎与处理器数量成线性比例,直到由于通信延迟问题而饱和并恶化为止。为了更好地理解可伸缩性,我们通过将执行任务划分为计算,通信和数据复制来开发了复杂性分析。使用这种复杂性分析,我们能够预测并行代码的可伸缩性性能。此外,该理论用于估计处理器的最大数量,在此数量以下,可以维持近似线性的可伸缩性。从理论上讲,使用O(100,000)处理器可以有效地解决多达8192〜3个问题。复杂性分析表明,对于云条件下典型的稀薄液滴悬浮液,本底湍流的伪谱模拟通常要花费总执行时间的80%,除非液滴很小(小于5μmin的能量耗散流)流速为400 cm〜2 / s〜3和液体水含量为1 g / m〜3)时,颗粒间的流体动力相互作用成为瓶颈。复杂度分析还用于探索一些替代方法来处理流模拟中的FFT计算,并推进半径小于5μmin的液滴,以提高计算效率。最后,据报道初步结果揭示了非相互作用液滴碰撞核的雷诺数依赖性。

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